Service Level Analysis in Food Retail Supply Chain · no qual os produtos são rececionados, preparados e expedidos, por norma no mesmo dia. Trata-se de um ﬂuxo no qual a melhoria

Faculdade de Engenharia da Universidade do Porto

Service Level Analysisin Food Retail Supply Chain

Maria Inês Ferreira Lima

Masters Dissertation

Supervisor: Prof. Gonçalo Figueira

Mestrado Integrado em Engenharia e Gestão Industrial

June 26, 2017

Abstract

Nowadays the competitiveness in food retail market is increasing. The constant appearanceof new brands, as well as the exponential increase of the amount of products that are available instores results in more complex supply chains.

With the growth of the complexity, is necessary to keep focus on having efficient chains that areable to meet customers demand and expectations. Along the chain there are several intervenientsthat impact the performance of the overall processes hence, it is necessary to monitor them andthe way they can cause impact.

This project aims to map the processes since the product is ordered from the supplier until it isshipped from the warehouses to stores. By mapping these processes it will be possible to identifythe most relevant indicators to measure the chain service level, throughout the different phases.

After identifying the most relevant indicators an analysis will be conducted in order to studythe reasons for the under target values. Posteriorly, the causes are going to be ranked and twovalue trees will be constructed. The creation of this value trees will help to understand where thebiggest improvement opportunities are located.

In the company where the project was developed, there were two types of product flows. Thefirst one did not hold stock in the warehouse and the second one did, these differences impact thecauses for low service levels so the flows were studied separately.

The flow that works without stock, is a type of cross docking, using this kind of technique itis possible to reduce inventory costs and improve the service level. When working in this flowthe warehouse quantity service level is extremely dependent on the supplier service level andfor this reason the best improvement opportunity is giving incentives to the supplier to improveperformance.

When the products are operated in the flow that carries inventory the situation is different.As there is stock, the dependency on the supplier is lower. However, carrying stock representshigher costs for the company in terms of logistics, so the proper management of this resources isfundamental and still can be improved.

This analysis used statistical models in order to validate the results achieve. The regressionmodels constructed showed with relatively high level of confidence that the identified causes areresponsible for low performance. A tool in Excel was constructed in order to help monitoring thedifferent service level indicators and to allow the intervinients in the chain to get to know if theirperformance is improving or not.

The development of this dissertation shows the importance of knowing the entire supply chain,especially when it is a large one, in order to better manage it. A more efficient supply chain willreflect in the improvement of the stores performance, which is the main goal of retail companies.

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Resumo

O retalho alimentar é um mercado cada vez mais competitivo, o aparecimento constante denovas marcas e novas cadeias, assim como o exponencial aumento de produtos disponíveis naslojas, faz com que a cadeia de abastecimento deste tipo de industria seja extremamente complexa.

Com o aumento da complexidade da cadeia é necessário existir um foco constante em mantera sua eficiência de modo a proporcional ao consumidor um serviço adequado às suas expectativas.

Ao longo da cadeia existem vários intervenientes que tem impacto no nível de serviço, peloque é necessário monitoriza-los. O projeto desenvolvido tem como objetivo mapear os níveis deserviço ao longo de toda a cadeia, identificar os indicadores mais relevantes para medir a perfor-mance ao longo do processo e, por fim, mapear as causas que poderão estar na origem de umaperformance inferior ao esperado.

Após serem identificados os vários motivos que influenciam o nível de serviço, desde o fornece-dor, até que o produto é expedido para a loja, foram criadas duas arvores de valor que permitiramdefinir quais as oportunidades de melhoria que trariam maior proveitos se fossem aproveitadas.

Sendo que na empresa em estudo existem produtos que são geridos sem stock no armazéme outros que são geridos com stock, foi necessário estudar as diferenças entre os dois fluxos eadequar os motivos a cada um deles. O fluxo operado sem stock é um fluxo do tipo cross-docking,no qual os produtos são rececionados, preparados e expedidos, por norma no mesmo dia. Trata-se de um fluxo no qual a melhoria da performance do fornecedor será o fator que pode originarmaiores ganhos.

Quando os produtos são operados num fluxo que tem stock no armazém, é nos processos degestão de inventários que está o maior potencial de melhoria. Manter inventário nos centros dedistribuição e nas lojas acarreta custos, que pesam bastante nos custos totais da logística, pelo quea correta gestão destes recursos é fundamental.

Foram utilizados modelos estatísticos com o objetivo de validar os resultados obtidos. Osmodelos de regressão construídos demonstraram, com elevado nível de confiança, que as causasapuradas são responsáveis pela baixa performance. Foi construída uma ferramenta em Excelque permite monitorizar e avaliar melhorias nos indicadores considerados mais importantes nestaanálise.

O desenvolvimento desta dissertação, demonstra que ter conhecimento e controlo sobre osníveis de serviço ao longo de toda a cadeia é fundamental para melhorar o desempenho da mesmae, consequentemente, melhorar os resultados das lojas, que é o principal objetivo deste tipo deempresas.

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Acknowledgements

First of all, I would like to thank Faculty of Engineering of University of Porto and Sonae MCfor providing me this amazing opportunity. Doing my Master Dissertation at Sonae MC was anamazing experience that allowed me to grow as a person and professional.

I would like to thank my tutor at FEUP, Professor Gonçalo Figueira, thank you for all youradvices and guidance during this months.

To Dr. Marcos Hespanhol and Dr. Pedro Corte-Real, my tutors at Sonae, for all the supportand advices they gave me along the way. I also owe a lot to my team, Supply Chain Analytics andPlanning, plus the "LTP boys". Thanks for all the great moments and all the help. Especially RuiNogueira, Claudia Vieites and Paulo Pinho, you made my days at Sonae better.

To my friends, thanks for always being there for me and my natural stupidity. To Rita Fernan-des and Catarina Mendes, thank you for these (almost) five years of friendship, all the late nightsstudying and the group projects. Thank you for your Friendship. To my "Chefinha", Rita Amorim,a huge hug and an even bigger thanks for getting me to smile everyday. You arrived late, but reallystrong.

To my shinning stars, Saudade.To my parents, there are not enough words to express how thankful I am to you. Thanks Mom

and Dad for always supporting me and being by my side during all this years. I would not be herewithout you.

To my little brother, you will always be my baby, but you have already thought me so much. Iwill always be here for you.

Finally, thank you Isaac. Thank you for all your support, for being there for me everyday,every step of the way. You are my safe harbour, my life is better and brighter with you. I hope Iam up to the challenge during your PhD!

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‘What we know is a drop,

what we don’t know is an ocean.”

Sir Isaac Newton

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Contents

1 Introduction 11.1 The Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Project Description and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Document Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Theoretical Framework 52.1 Logistics and Supply Chain Management . . . . . . . . . . . . . . . . . . . . . 52.2 Food Retail Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . 62.3 Service Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4 Inventory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5 Statistic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3 SONAE MC Supply Chain 173.1 Supply Chain Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.2 Supply Chain Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4 Service Level Analysis 294.1 Service Level Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.2 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3 Service Level Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.4 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5 Results and Discussion 395.1 Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395.2 Main Causes Identified . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.3 Value Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.4 Supply Chain Service Level Dashboard . . . . . . . . . . . . . . . . . . . . . . 47

6 Conclusions and Future Work Perspectives 49

A Year Sales 55

B Complete Marketing Structure 57

C Pack Conversion 59

D Pareto Rule 61

E Regression Model 63

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x CONTENTS

Acronyms

BU Business Unit

CD Commercial Direction

DGCA General Direction of Supply Chain

OOS Out Of Stock

PBS Picking By Store

PBL Picking By Line

SKU Stock Keeping Unit

SSL Supplier Service Level

WMS Warehouse Management System

WSL Warehouse Service Level

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xii ACRONYMS

List of Figures

1.1 Sonae Group Constituition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1 Food Supply Chain Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3 Retailers Analysis: [1] Internal Issues; [2] Drivers of Competitive Advantage . . 82.5 Out Of Stocks: [1] Consumer reaction to OOS; [2] OOS main causes . . . . . . . 102.7 Service Level Relation with Stock Quantity . . . . . . . . . . . . . . . . . . . . 122.9 Safety Stock and Cycle Stock . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.1 Sonae MC Marketing Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2 Sonae MC Sales distribution through Commercial Directions and Business Units 183.3 Sonae MC Commercial Divisions and Business Units . . . . . . . . . . . . . . . 183.4 Sonae MC Supply Chain and Logistics Extension . . . . . . . . . . . . . . . . . 193.5 Picking by Store Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.6 Picking by Line Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.7 Boxes Dispatched from Warehouses yearly . . . . . . . . . . . . . . . . . . . . 223.8 Relation between Warehouse Service Level and Supplier Service Level . . . . . . 233.9 Supply Chain Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.10 Porter Value Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.1 Warehouse Quantity and Supplier Service Level . . . . . . . . . . . . . . . . . . 304.2 SKU Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.3 OOS Rate in Fruit & Vegetables Business Unit and OOS Rate in Critical SKUs . 354.4 Service Level Impact throughout the Chain . . . . . . . . . . . . . . . . . . . . 36

5.1 Charts from PBL Regression Model . . . . . . . . . . . . . . . . . . . . . . . . 415.2 Ranked Causes for Low Service Levels in PBL . . . . . . . . . . . . . . . . . . 435.3 Ranked Causes for Low Service Levels in PBS . . . . . . . . . . . . . . . . . . 445.4 Value Tree for PBL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.5 Value Tree for PBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465.6 Dashboard, Page 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.7 Dashboard, Page 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

A.1 Distribution of Sales throughout the Months . . . . . . . . . . . . . . . . . . . . 55

C.1 Sugar Pack Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

D.1 Pareto Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61D.2 Pareto Rule applied to Sonae MC SKUs . . . . . . . . . . . . . . . . . . . . . . 62

E.1 Results of PBL Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . 63

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E.2 Outputs from PBL Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 64E.3 Results of PBS Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . 64E.4 Outputs from PBS Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 65E.5 Residual Plots from PBS Regression Model . . . . . . . . . . . . . . . . . . . . 66E.6 Line Fit Plots from PBS Regression Model . . . . . . . . . . . . . . . . . . . . 67

List of Tables

4.1 Sonae MC Service Level Indicators . . . . . . . . . . . . . . . . . . . . . . . . 294.2 Replenishment Method Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 334.3 Estimation of Replenishment Parameters . . . . . . . . . . . . . . . . . . . . . . 34

5.1 Regression Models Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

B.1 Salt Grocery Marketing Structure . . . . . . . . . . . . . . . . . . . . . . . . . . 57

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xvi LIST OF TABLES

Chapter 1

Introduction

The food sector has changed rapidly in the past decade, forcing retailers to adapt to new

consumer needs. The movement of people to meet work exigencies and migration movements

led to a demand for food products from across the world that retailers have to meet (Dani, 2015).

Nowadays the retail competition is really intense, therefore the main focus needs to be on the

customer. The quality of the service provided to the customer is highly dependent on the global

supply chain service level. As the competition in the retail market gets stronger it becomes more

important to control all the aspects of the chain, creating the need to keep track of upstream and

downstream operations performance (Randall et al., 2011).

The Portuguese PIB in 2015 was e179.504m and, in the same year, Portuguese families spent

e20.786m in food products. The money spent in the retail industry corresponds to 12% of the

national PIB, which shows the importance of this market segment.

Traditional food retailers have faced fierce pressure from alternative channels including ware-

house clubs, supercenters, drug stores, mass retailers and convenience stores, as well as online

retailers and grocery delivery services. As consumers distance themselves from the traditional

supermarket model, grocery retailers attempt to stay competitive by creating more intimate and

innovative shopping experiences tailored to individual shoppers with an emphasis on fresh, or-

ganic and prepared food options.

Since 2008 there have been significant improvements in on-time delivery rates and case fill

rates. Despite the improvement in customer service, the customers are not satisfied yet (Butner,

2010). This project intends to identify the reasons that are behind the customer dissatisfaction.

Having adequate service levels throughout the supply chain and consequently high customer

service, is a way for retailers to achieve competitive advantage. This project arises from the

constant increase of customer demands, which allied to the increasing market competitiveness,

results in a much more tense business process. The project aims to understand how companies can

improve their performance in terms of service in order to improve the overall results.

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2 Introduction

1.1 The Company

Sonae is a multinational company that manages a diversified portfolio of products and ser-

vices in farraginous areas like food and specialized retail, financial services, technology, shopping

centers and telecommunications (SONAE, 2017a).

Sonae was founded in August 1959 as Sociedade Nacional de Estratificados. Being a company

with almost sixty years of life it went through several mutations until becoming one of the largest

Portuguese business groups. In 1985 Sonae opened the doors to the first hypermarket in Portugal,

the Continente store in Matosinhos. Its mission has been since then, creating long-term economic

and social value, taking the benefits of progress and innovation to an ever increasing number of

people (SONAE, 2017c).

Sonae culture is marked by constant reinvention and adaptation to diverse conjunctures. The

company evolved through acquisitions, mergers and fusions, sometimes with businesses far away

from the company core, and that allowed the firm to grow in knowledge and wisdom, and to

penetrate in different and varied markets(SONAE, 2017b).

In figure 1.1 it is possible to analyse the vast business portfolio that Sonae possesses, as well

as, Sonae MC Brands. Sonae MC is 100% owned by Sonae and is leader in the Portuguese retail

market. It counts with 41 hypermarkets (Continente), 130 supermarkets (Continente Modelo), 66

proximity supermarkets (Continente Bom Dia), 240 convenience stores, working as franchising

stores (Meu Super), 186 health and wellness stores (Wells) and also adjacent businesses like Bagga

and Note!.

Sonae Sierra and Sonae Sports and Fashion (SR) are also owned 100% by Sonae, the first

one manages investment projects in real estate, mainly working in shopping centers. Sonae SR

operates in the specialized retail market with brands like worten and sport zone.

Figure 1.1: Sonae Group Constituition

Source: Sonae Web Page

The Sonae MC unit, where this project was developed, is a food retailer that sells directly to

the final customer through its physical stores and the on-line store. It is the national market leader

in this sector, having been responsible for deep changes in Portuguese consume patterns. The

project will be focused on the food distribution chain encompassing products like canned food,

fresh fruit and vegetables, pet supplies and hygiene products. The food retail includes everything

that can be found at a supermarket, and this diversity contributes for the chain complexity.

1.2 Project Description and Goals 3

1.2 Project Description and Goals

In order to properly manage the supply chain of any kind of business, it is necessary to be

aware of the service levels of different operations. At Sonae MC the chain has lots of players and

different processes that need to be analysed in detail in order to understand which are the critical

ones and how they can be improved.

The retailer is the final responsible in food chain before the product reaches store shelves and,

consequently, the client. He is responsible for connecting the supply chain with the final consumer

and creating value added products for consumption. The retailer is fundamental in the chain and

the success of the supply chain depends upon whether the retailer is capable of selling its product

to the consumer.

Food Supply Chain is extremely volatile. The quality of the food needs to be controlled and

will depend on weather conditions and agriculture production, being influenced by governmental

or non-governmental players and, above all, by the market and consumer tendencies that are con-

stantly changing. The supply chain management needs to take into consideration these external

factors. Nevertheless internal constrains regarding the products specifications and the operation

needs, also have tremendous influence in the chain performance (Dani, 2015).

Depending on the provided service, the customer will decide if he buys the products or not

and if he comes back to the store. This project intends to identify the moments where the provided

service is bellow target and the reasons that motivate this kind of behaviour.

With this project the intent is to map Sonae MC supply chain in order to analyse the service

level during all the phases, as well as, identify the stakeholders along the process and, conse-

quently, their impact on the company service level. The main goal is to understand which are the

main causes for not having the desired service level.

Depending on the product sales and consequently its value for the company, it can be more or

less important to have a high service level. After conducting an analysis to understand which are

the critical products, an individual study will be developed in order to identify the most relevant

causes for the under target values. Furthermore this study will result in a model that allows to

understand the similarities and differences among the products with low service level.

1.3 Methodology

In order to achieve the proposed goals it was necessary to structure the process and define

the milestones of the project. At Sonae MC there is a large volume of information and systems

available and it was critical to understand which was the relevant data for this study, so the project

was divided in four main phases.

Firstly there is an extended process of data collection and analysis. In order to be able to

identify the critical products it is necessary to collect data for all SKUs that were active at Sonae

MC for the period of analysis. The chosen period is from November, 2016 to January, 2017. For

each SKU it is necessary to analyse:

4 Introduction

• Environment - If the SKU was stored at room temperature or not;

• Flow - If there is stock kept in the distribution center;

• Value - sales, price and cost;

• Order Quantity - orders to the supplier and the warehouse;

After having all the information required, a classification process was in order, the data re-

trieved had to be divided according to flow, warehouse, commercial structure and business unit.

Inside each division, each SKU needs to be classified accordingly to its sales and warehouse ser-

vice level.

The third phase consisted on evaluating and testing the causes for low service level in the target

products. After identifying the critical products, a panel with possible causes was formulated and

each cause was tested in order to understand how it can be eliminated and how its effect can be

reduced. It was also tested the possible relation between causes and similar products, as well as,

the profiles of the critical products and suppliers.

The culmination of the project is a value tree that maps the supply chain processes and col-

laborators along with their impact in the chain service level. This tree allows to identify among

the ranked causes the ones that have more impact, having in mind the improvement of the chain

service.

1.4 Document Structure

The present document is divided in five chapters. After this first introductory chapter, the theo-

retical contextualization provides in Chapter 2 the frame of reference for the work to be developed.

In order to have an accurate project the works previously developed in these areas were studied

and are presented in this part of the document.

The third chapter describes in detail Sonae MC Supply Chain and its logistics processes. It is

important to provide an accurate portrait of the warehouse operations and the process behind the

reported service levels.

Chapter 4 presents the indicators that the company already uses and analyses in detail those

more relevant to the project. The second part of this chapter describes the methodology followed

in the realization of the study, from the data collection to the value tree construction. The fifth

Chapter is composed by the results drawn from the models and analysis conducted enhancing the

best way to attack this problem and improve the service level. Chapter 6 has the conclusions of

the project and presents some guidelines for future work that could be developed.

Chapter 2

Theoretical Framework

In the current chapter the state of the art in relevant topics for this project will be discussed.

Firstly an overview of logistics and supply chain is going to be presented in order to show its

versatility and magnitude. Posteriorly particularizing for the food retail market which is the focus

of this study. The theory regarding service level and inventory management will then be laid out,

setting a framework for this document.

2.1 Logistics and Supply Chain Management

Logistics is present in every day to day situation. Considering a consumer that is going to the

supermarket and buying bananas does not reflect the real complexity of the process that allowed

the fruit to be in this same supermarket in those conditions (hygiene, package and others), at that

cost, in order to satisfy the consumer need in that moment.

Analysing the process to have the product in the store it may be likely to think about the farmer,

eventually in agriculture techniques or even in transportation means. However, since the moment

when fruit is collected in any place around the globe, it comes long way until reaching the selling

point. Throughout this process, there are several stakeholders that are responsible for assuring

the smoothness of the process and the simplicity that the regular consumer experiences in stores.

Logistics encompasses this entire process (Moura, 2006).

When the logistics concept emerged, it was associated with military strategies and operations,

the transportation of soldiers, protections and ammunitions to the war front and the continuous

supply of essential goods. Latter it evolved to industry and private companies, spreading around

the world through producers that wanted more than just to deliver their products. Logistics can

be as embracing as one wants it to be, and it may be applied to one single operation or an entire

network (de Carvalho et al., 2010).

Nowadays, logistics is much more strategic and focused in the human capital than in material

resources, and it can be seen from a more functional or structural point of view. Logistics can

be regarded as a fountain of costs or a provenance of competitive advantage, not only facing the

remaining players in the market, but also in relation to the customer.

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6 Theoretical Framework

Logistics is defined by Rushton et al., 2011 as the management of all activities which facilitate

movement and the coordination of supply and demand in the creation of time and place utility.

Logistics could also be defined as the capability of having the right product, in the right quantity

and the right condition, at the right time and place, for the right customer, at the right price (Rutner

and Langley Jr., 2000). The Council of Supply Chain Management Professionals defined logistic

as "The part of the supply chain process that plans, implements, and controls the efficient and

effective flow and storage of goods, services, and related information from the point-of-origin to

the point-of-consumption in order to meet customers requirements." (Lambert et al., 1998)

Supply Chain is an extension of logistics. While one might say that logistics only includes the

material management and distribution, supply chain is much more complete. Besides the opera-

tions referred it comprehends the management of suppliers and customers (Rushton et al., 2011).

Supply chain management is the integration of key business processes from end user through orig-

inal suppliers that provide products, services, and information that add value for customers and

other stakeholders (Lambert et al., 1998).

Supply chain Management is the management of relationships in order to achieve a more

beneficial solution for all parts involved than the one that could be achieve by each part separately,

therefore it can be defined as the management of upstream and downstream relationships with

suppliers and customers in order to deliver superior customer value at less cost to the supply chain

as a whole (Christopher, 2011).

With economic globalization, increasing importation and exportation flows, internationaliza-

tion processes, market tendencies constantly changing and consequent growth of different prod-

ucts in the market, the pressure to improve service levels and the environmental constrains, the

momentousness of supply chain management is increasing as never before. As the market com-

plexity increases, an adequate management of the supply chain is compelling in order to achieve

the business strategy, to keep and capture clients and markets, to efficiently manage operations and

to increase companies profitability (de Carvalho et al., 2010).

The supply chain decisions of an organization affect its logistics costs, incurred in the move-

ment of goods, its inventory costs, induced by inventory storage in distribution centers, warehouses

and retail locations, and labour costs contracted in the handling of goods throughout the supply

chain. These costs add up to a substantial fraction of the total value of a product sold by a firm.

Besides, these decisions may also impact the revenue of the company if they influence the product

availability or the speed of introduction of new products. (Gaur, 2013).

2.2 Food Retail Supply Chain Management

Supply chain management is a reality in most businesses, the path to reach the customer has

several intervinients and processes that need to be coordinated. It is necessary no manage the

internal processes, the relationships with the partners, suppliers and customers and the network of

interconnected businesses that are involved in the chain (Harland, 1996).

2.2 Food Retail Supply Chain Management 7

Food supply chain responds to a basic necessity. This factor to the perishability of the products

results in one of the most strained chains on the market. These chains are a reality since the

beginning of mankind. However its complexity has increased, the main goal is still the same,

having food available at the right time, quality and quantity (Dani, 2015).

Figure 2.1 presents the several participants in the food supply chain. They contribute to trans-

form the raw material in the products that can be shopped in hypermarkets.

Figure 2.1: Food Supply Chain Actors

Source: (Dani, 2015), Figure 1.1

The chain begins with the food producers that have to deal with the consumers expectations,

the demand instability, the weather conditions and the legal restrictions that are getting more rigid.

Then, the second participant is the food processor which is responsible for the transformation

of raw materials into the products that customers want to buy. Distributors are the link between

operators. They are responsible for moving the goods from the producer, to the processor and

finally to the market.

Retailers are the final step in the chain, before the product reaching the consumer. The retailer

presents goods to the consumer in physical markets and is the way that the retailer presents each

product that will influence the customer to buy or not. Retailing industry is extremely competitive.

The retailer needs to differentiate himself from the others and present merchandise that meets the

customer expectations, besides he is also responsible for dealing with the food processors to define

the terms and conditions of the contract. With proliferation of promotions, coupons and discount

cards, this task got even harder. No only the retailers compete among them due to the product

offer, they also compete in terms of promotions and space differentiation (Dani, 2015).

A survey made to executives from retail supply chain management and from industries that

support the sector showed that their main goal is the achievement of an agile process as well as

competitive advantages. In order to achieve competitive advantage the retailers can adopt different

strategies, as exhibited in the right chart from Figure 2.3, being the most valued one, by almost

fifty percent of the enquiries, the capacity to respond to market changes (Randall et al., 2011).


Figure 2.3: Retailers Analysis: [1] Internal Issues; [2] Drivers of Competitive Advantage

Source: (Randall et al., 2011), Table V. and Table VIII.

With the growth of retail market and the shift of power from the producers to the retailers there

is an increasing focus in improving the relation between the players in the supply chain, achieving

reliable flows across the entire network. As can be seen in the left chart from Figure 2.3. One of

retailers main issue is to align this kind of objectives with the company global goals and strategy.

As the second most voted issue, product availability (Figure 2.3), which directly relates with the

supply chain service level.

In the future the biggest challenge for retailers will be being able to find their space in the

market together with on-line stores. As the time spent in hypermarket and supermarkets reduces

and consumers choose more and more convenience stores and on-line shopping, it is necessary

that retailers innovate their stores structure and activities in order to attract the customer. The

main barrier is seducing the consumer to go to the physical store. Most people have to juggle

between family and professional careers, and having in account the speed of nowadays life, it is

impracticable to plan a trip to the supermarket and the culture change from monthly to convenience

shopping affects the large commerce areas (Dani, 2015).

Managing retail supply chains is extremely complex and requires coordination between the

players along the chain. In order to achieve the desired competitive results, companies need to be

focused on the client and in achieving operational efficiencies (Hübner et al., 2013).

At this moment, retailers not only need to differentiate themselves from other players in the

market, they also need to be innovative and responsive to the market preferences in order to attract

the modern consumers to their shops.

2.3 Service Level

Nowadays, the key to business success can be in logistics and its potential to reduce costs,

response time to customer orders and improve the service to clients. In the current competitive

2.3 Service Level 9

environment, the one who reaches the market first, who is more agile to deliver information, who

serves and understands the consumer needs and expectations better, has more probability to win

their preference and, consequently, get more orders (Moura, 2006).

Service Level is directly related to customer service, and consequently, to customer satisfac-

tion. However, customer satisfaction is a broader concept than customer service, it is influenced by

more factors. Nevertheless it encompasses the service. The operations that take place throughout

the chain have a direct impact in the chain service levels (Petridis et al., 2016).

In supply chain the service level is the probability of not having a stock out during the follow-

ing replenishment cycle, and thus, it is also the probability of not losing sales. The service level

can also be defined as the probability of being able to service the customers demand. While a

100% service level might seem desirable, it is usually not a feasible option.

Customer service can be defined as a process which takes place between the buyer, seller and

third party. The process results in a certain value added to the product or service exchanged. This

value added in the exchange process might be short term as in a single transaction or longer term

as in a contractual relationship. The value added is shared between the involved parties. It is

expected that after the transaction the situation of the ones involved is better than before. Thus, in

a process view: customer service is a process for providing significant value-added benefits to the

supply chain in a cost effective way (Rutner and Langley Jr., 2000).

Presently competition in retail is fiercer than ever, so retailers need to search ways to improve

performance. According to Corsten and Gruen, 2003 retailers can increase their earnings up to

5% by reducing their out-of-stock issues. This study showed that on average the extent of OOS is

8,2%. This average was calculated having in account eight product categories - Feminine Hygiene,

Diapers, Toothpaste, Shampoo/ Haircare, Laundry, Toilet Issue, Salted Snacks and Paper Towels -

ranging from 3,9% to 11,1%.

The retailer wants to maintain as much customers as possible. Analysing the first chart of

figure 2.5, it shows that in 31% of occurrences, the customer when facing an OOS goes to a

different store to buy the product and, consequently, the retailer completely loses the sale. When

the retailer has an OOS the consumer needs to make a choice, and that will depend upon the global

satisfaction the retailer provides him.

The consequences of an OOS to the retailer may reflect on loosing customers, they go to

another store and stop coming to the original retailer, or losing the sale, client does not buy the

item at all or buys a cheaper one. This consequences also reflect on the manufacturer that supplies

the retailer. The impact is not restricted to the first player. Worldwide the percentage of lost sales

due to OOS is around 3,9% of retailer total sales. Nevertheless there are losses due to logistics and

information inefficiencies in the supply chain.

There are several identified causes for out of stocks in retail environments. Within the scope

of this project the most relevant ones relate with the distribution center and the retail HQ or man-

ufacturer. The causes related with service levels add up to 24% of the causes for out of stock.

Currently, service levels in store, above 98% are very hard to achieve. Usually the target for

service level is defined according to an ABC analysis. The products are classified into categories


Figure 2.5: Out Of Stocks: [1] Consumer reaction to OOS; [2] OOS main causes

Source: [(Corsten and Gruen, 2003)], Figure 2. and Figure 7.

according to its sales volume or the proffit they grant to the company. The top products, "A"

products, have a target between 96% and 98%. The medium class products have a target between

91 and 95 percent. And the low class products have a target between 85% and 90% (Schalt and

Vermorel, 2014). This kind of approach allows to keep the costs of out of stock and inventory

management under control because it limits the targets according to product importance.

Service Level Indicators

In order to analyse service level and achieve performance improvements it is important to

define what is being measured. Both in store and warehouse, the calculation of the service level

might have in its base different factors and in order to understand the reasons for low performance

it is necessary to analyse which factors are being considered.

Mainly service level can be calculated based on three factors:

• Percentage of Total Demand in Units - if there is a demand for ten units and there are only

nine available, the service level will be 90%;

• Percentage of Total Demand in Orders - There are nine units available, the first order is for

nine and is met, but the second order that is only for one unit is not met. Service level is

50%;

• Percentage of Time Spent without the Product - If a store is open during ten hours and the

out of stock occurs one hour after the store opens, the service level is 10%.

Depending on what is being measured the results will differ. In this project the indicator that will

be deeper analysed is related with the percentage of total demand in units.

2.4 Inventory Management 11

2.4 Inventory Management

In order to achieve a desired service level it is necessary control all the processes that occur

since the product is bought from the supplier until reaching the store. When working with SKUs

that hold inventory in the warehouse, it is necessary to study the inventory policies.

When an SKU is working with stock it is necessary to have proper inventory management

systems in order to avoid increasing costs and still having low service levels.

Despite the cost of maintaining inventory in the company warehouses and stores, firms keep a

supply inventory in order to (Jacobs and Chase, 2011):

• Maintain independence of operations - if there are several operations or lines in the produc-

tion, having inventory reduces the need of waiting for the previous operation;

• Meet the variation on product demand - usually is not possible to predict exactly what the

demand will be, so it is necessary to have stock to avoid stock-out and also to backup during

sales peaks;

• Take advantage of economic purchase order size - if a product is know to have demand in the

future it may be worth to buy in larger quantities to obtain discounts and stock the product;

• Provide a safeguard for variation in deliveries - if the products are not perishable and the

supplier does not have a steady service level the company might keep stock to prevent these

occurrences

Working towards increasing product availability, retailers tend to flood the stores and ware-

houses with stock. In retail markets where variations in demand can be very high, the peaks could

be very difficult to respond without stockpiled products, however it is necessary to maintain a

balanced relation between both, avoiding unnecessary costs.

Retailers and manufacturers try to satisfy as many customers as possible since it maximizes

their sales. However, at the same time, maintaining the corresponding inventory is both costly and

risky. Products are expensive to buy and produce, they need space to be housed, they expire and

get obsolete.

Firms must combine inventory and service level decisions focusing on the global organization

objective (Salam et al., 2016). If the company goal is to achieve high levels of service it needs to

properly optimize the inventory. However, to increase service level it is not always necessary to

increase the inventory. The result achieved with this strategy might not be the expected one.

Improve service level can not always be achieved by increasing stock quantity. The higher the

service level, higher needs to be the reinforcement of stock quantity to achieve improvements. In

figure 2.7 this relation is shown clearly. To improve service level from 95% to 97% it is necessary

to increase inventory a lot more than when we want to improve the same percentage points but

from 85% to 87%. As we increase inventory quantities we also increase inventory management

costs.


Figure 2.7: Service Level Relation with Stock Quantity

Source: (Schalt and Vermorel, 2014)

Hence, the inventory management strategy needs to take into account three main factors (Ghi-

ani et al., 2004):

• Customer service - the inventory strategy needs to be aligned with the service level targets;

• Inventory costs - Holding costs (storage facilities, handling, breakage, obsolescence, depre-

ciation, among others), ordering costs and shortage costs, the cost of not having the product

to meet demand;

• Operational performance - the operational activities can not be disrupted by inventory man-

agement strategies.

There are four fundamental concepts regarding the types of inventory that need to be kept in

mind (Ghiani et al., 2004):

• On-Hand inventory - stock at the warehouse or store;

• Committed inventory - this products can not be used for upcoming orders;

• In Transit inventory - already ordered quantity;

• Inventory position - the quantity of product that is available, it is equal to On-Hand, plus In

Transit, minus Committed inventory.

Carrying inventory adds costs to the supply chain so it is necessary to make a trade-off analysis

between this cost and out off stock costs. Out of stocks in food retail have high costs, not only

the retailer looses the sale because it is hard to postpone buying food, but he may also loose the

customer loyalty, so the service level target is high.

2.4 Inventory Management 13

In the case of perishable food it is necessary to take in account the acquisition and handling

costs, but also the cost of potential losses due to expired products that can not be sold or sales with

discounts when retailers are trying to sell-out more just before expiration.

Replenishment

Replenishing is one of the warehouse activities. It consists in making stock full again in order

to avoid stock-out. Replenishment is typically initiated by a backorder that is placed to a supplier

or to a manufacturer (Vermorel, 2012).

In order to increase productivity most inventory management systems implement replenish-

ment rules to automate part of the operation. Usually replenishment is triggered when the stock

level hits the reorder point that is defined by the company or the system.

The most commond practises for stock management are models with periodic or continuous

review. Continuous review means that the inventory status is monitored in a continuously and

an order is placed to a certain quantity, previously defined. On the other hand, periodic review

indicates that the inventory is monitored regularly, with periodic intervals known as R, and the

reorder is made during the review period to increase inventory to a defined value (Jacobs and

Chase, 2011).

The order quantity should be defined for each SKU depending on its ordering cost, the lead

time, the demand, the price, the handling cost and the ordering cost (Jacobs and Chase, 2011).

In the Min/Max inventory model the Min represents the reorder point, the moment when an

order should be placed. The Max represents the optimal quantity to have in stock. The ordered

quantity is then the difference between the minimum and the maximum.

In the warehouse the replenishment cycle is often longer than in stores and the traded quantities

are larger. Hence, there are more factors to analyse, the lead time might vary from one day to

months and the order quantities are also distinct depending on the product.

At the store level, the orders are managed in a daily basis and in much smaller quantities.

Usually in retail stores with centralized warehouses there are deliveries from the distribution center

to the store every day, so the management of the inventory is in a short term perspective. Besides,

stores often have space constraints which limits the stock they can carry.

Cycle Stock is the inventory that is estimated to be needed since an order is placed until it is

received in the warehouse. The safety stock is the quantity that is kept in stock to prevent stock

out. If the demand is higher than what was predicted or the supplier does not delivered in the

estimated time the safety stock will cover the failures in most cases.

When using Min/Max replenishment methods, usually the minimum and maximum are calcu-

lated based on the estimated cycle and safety stock.

If the goal is to achieve a 95% service level (Figure 2.9), not all the stock outs will be avoided.

Despite having safety stock, in 5% of the cases there will be out off stock products. However, in

50% of the cases the safety stock will remain intact and in the remaining 45% of the cases, the

safety stock will be enough to cover the demand. If the desired service level is higher, the safety

stock also needs to be higher (King, 2011).


Figure 2.9: Safety Stock and Cycle Stock

Source: (King, 2011)

The safety stock calculation will depend on what is affecting service level the most, if it is

demand variability, lead time variability or both. In big retail companies the suppliers undergo a

thorough evaluation process to make sure they are able to meet the company requirements, so the

biggest concern is the demand variability.

The safety stock to prevent against demand variability can be obtained by multiplying the stan-

dard deviation of demand variability by the Z-score. The Z-score or standard score is a statistical

measure that is obtained through the normal distribution (King, 2011). In cases where the demand

periods do not equal the lead time, it is necessary to calculate the standard deviation of demand

in periods equal to the lead time. Hence, the safety sock is calculated as follows in equation 2.1

(King, 2011).

Sa f etyStock = Z ×√

T LTT1

×σD (2.1)

Where:

Z: Z-Score;

TLT: Total Lead Time;

T1: Time increment to calculate standard deviation of demand;

σD: Standard Deviation of Demand.

The total lead time includes the time needed to perform all functions until the order is received,

including deciding what to order, communicating orders to the supplier, manufacturing and pro-

cessing, delivery and storage, as well as the review period, the time since an order is received until

the stock being monitored again and re-start the cycle.

When studying SKUs that are managed with stock it is fundamental to understand how they

are stocked and the parameters defined for their replenishment. It will be possible to conclude that

the replenishment method has high impact in the supply chain performance being, consequently,

a relevant factor for this study.

2.5 Statistic Analysis 15

2.5 Statistic Analysis

Statistics is a branch of mathematics that deals with the collection, compilation, analysis and

interpretation of data. Statistics can be applied at several areas of knowledge.

Statistical methods can be divided in three main classes: predictive, descriptive and inference

methods. Predictive statistic analysis is based on historical data in order to predict and prevent

future occurrences.

Descriptive statistics intends to analyse information regarding past occurrences and represent

it in a more comprehensive way, usually by using graphics and tables. Statistical inference is more

ambitious than descriptive statistic, as it intends to classify a population by analysing a sample.

Statistic inference uses more complex methods and techniques (Guimarães and Cabral, 2011).

Regression is one of the most powerful and used statistical methods. This technique allows

to study which factors impact the dependent variable and what is its contribution. Regression is

a model that uses continuous independent variables in order to predict a continuous dependent

variable. Multiple regression models can also use categorical variables (Walker, 1989).

The linear regression modelsdescribe a relationship between a set of quantitative independent

variables, X j (j=1,2,..,J), and a quantitative dependent variable, Y (Equation 2.2).

Yn = α +β1 ∗ (X1n −µ1)+ ....+βJ ∗ (XJn −µJ)+En (2.2)

The hypotheses underlying the model are (Guimarães and Cabral, 2011):

1. The values XJn and µ j are constants and do not have associated errors;

2. The errors En are mutually independent, have a null expected value and constant variance,

this means that they are normally distributed.

In order to find the relevant factors - regressors - for the model the regressive method was

applied. Initially all the variables were included in the model, and, posteriorly, the regressors that

contribute least to explain a significant proportion of the total variance of the variable are removed

from the model one by one (Guimarães and Cabral, 2011).

Intending to evaluate the quality of the model the determination coefficient and he adjusted

determination coefficient are used. The second one penalizes the number of regressors, which

means that, if we are over fitting the function it will decrease. If the determination coefficient

increases, but the adjusted one decreases, no more regressors should be added to the regression.

The regression model not only indicates the significant relationships between dependent and inde-

pendent variables, but also indicates the strength of impact of the multiple independent variables

on a dependent variable.

This statistic analysis allows to validate the results that were obtained and to better understand

in quantifying terms which causes have more impact and how they are related with the problem

being studied.


Chapter 3

SONAE MC Supply Chain

Leader of national retail market, Sonae MC, has a supply chain that is influenced by four main

players: suppliers, warehouses, stores and customers. Currently there are four main distribution

centers located in Maia, Azambuja, Carregado and Madeira. Maia, Azambuja and Madeira work

with three different temperatures: room temperature, fresh and frozen. There are also the meat

and fish production centers that work separately. In these warehouses there is a total of 22.467

active items. Together, the distribution centers are responsible for supplying 477 stores spread

throughout the country (SONAE, 2017d).

The present project will be focused in Maia and Azambuja warehouses due to the fact that they

are the more relevant ones in terms of food distribution, besides supplying the stores in continental

Portugal, they also supply Madeira distribution center that further ahead supplies the stores in the

island.

Currently the company works with 4.300 suppliers that need to be aligned with the company

strategy and service goals. In order to improve performance and meet the imposed requirements,

the suppliers are analysed in detail prior to being chosen and the contracts contain strict penalties

in case of failure to deliver. The large number of stores, the extended range of products and the

amount of players are the biggest difficulties in supply chain management. They require proper in-

frastructures and extremely different operations that are not always easy to coordinate. Achieving

an efficient supply chain is one of Sonae MC main goals.

Sonae sales are distributed in five main commercial divisions - Food, Fresh, Food and Bakery,

Home and Bazaar - these are later divided in Business Units, Class, Sub Class, Base Unit and

finally SKUs (Figure 3.1). These divisions constitute Sonae MC marketing structure.

Figure 3.1: Sonae MC Marketing Structure

17

18 SONAE MC Supply Chain

In terms of sales, they are reasonably constant along the year, having their maximum during

holidays season and also a relatively high increment during the summer, this reality is common in

food sector. This pattern is typical in all the commercial directions, being more relevant in the food

and fresh ones. In appendix A it is possible to analyse this information in detail, having historical

data since December, 2014.

In the analysis, the main focus is in the grocery sector, so the commercial division bazaar was

excluded. It represents a completely different logistics system. This operation occurs in Carregado

warehouse and is processed separately. Also, this commercial division is not as relevant as the

others in terms of sales volume.

The food commercial division is responsible for more than 50% of Sonae MC sales, followed

by the Fresh unit, with around 30% of sales (Figure 3.2).

Figure 3.2: Sonae MC Sales distribution through Commercial Directions and Business Units

Sonae MC commercial divisions (CD) are divided in business units (BU), this is the character-

istic that will be used to classify items, giving it more detail than using only commercial divisions

and at the same time not being too specific. This analysis will include the main four commer-

cial divisions and twenty business units (Figure 3.3). Appendix B presents the detailed marketing

structure for Salt Grocery business unit, this BU was selected as an example of the structure since

it is one of the most relevant ones.

Figure 3.3: Sonae MC Commercial Divisions and Business Units

3.1 Supply Chain Mapping

Sonae supply chain is very similar to all retailers. The collaborators need to be coordinated in

order to assure clients satisfaction which is completely dependent on the performance of upstream

chain, so it is fundamental to study all the steps of the product life until it reaches the customer.

3.1 Supply Chain Mapping 19

The mission of Sonae MC Supply Chain Management team is to promote and lead innovative

processes that aim for transversal improvements in the supply chain. Striving to achieve logistics

continuous improvement, the General Direction of Supply Chain - DGCA - defines rules and pro-

motes the provision of technical support and advice to those involved in the supply chain (internal

and external) in addition to fostering a complete overview of the process.

This multidisciplinary team is responsible for identifying, implementing and following up the

standards, as well as motivating new initiatives towards innovative ways of approaching logistics.

This team is composed by three distinct areas: transportation of goods and its central storage, flows

of merchandise to the store and planning and control, the last being the one where this project was

developed (SONAE, 2015).

As the differences between Supply Chain Management and Logistics Management were ex-

plained above, Figure 3.4 intends to show that difference in the real process.

Figure 3.4: Sonae MC Supply Chain and Logistics Extension

The height of supply chain is much more embracing. In this project, the analysis will impact

the entire supply chain, however the main focus will be the supplier and the warehouse, the stores

will be seen as customers of the warehouse that need to be satisfied.

Warehouse Processes

The main activities in the warehouse are reception of products, inventory storage, picking and

expedition. (Bartholdi and Hackman, 2016) Receiving is the first activity that is carried out when

an order from a supplier arrives at the warehouse. It includes unloading the products from the

transporter, analyse if the received product matches the ordered one, if the quality standards are

fulfilled and finally, if the transaction is accepted, update the stock quantities. Inventory storage

consists in putting the received products in the designated positions, if the position assigned is not

the picking position, the product will have to be moved again in the future.

Picking is the most time consuming operation in the warehouse, the operator is responsible for

preparing the store order. He needs to go to the storage position, retrieve the product and update

the stock quantity. Finally, expedition, when the product is shipped from the warehouse to stores,

usually in pallets.


Figure 3.5 and Figure 3.6 show the two different flows that can be found at Sonae MC ware-

houses.

The Picking by Store (PBS) flow carries stock. When the warehouse stock reaches the mini-

mum, defined in the replenishment method, an order to the supplier is automatically placed. The

criteria to define the minimum stock includes the supplier service level. If the supplier is reliable,

the minimum can be lower.

Figure 3.5: Picking by Store Flow

When the warehouse receives an order from the stores, the workers will go through the ware-

house and pick all the products included in the store order. Each picker is allocated to a store order

and is responsible for completing it. The ground floor is the preparation zone, where the picking

is made, the stocking is done on the top floors of the racks.

At Sonae MC the most used replenishment method in the distribution center is Min/Max. In

this kind of approach, two parameters are defined for each SKU:

• Minimum stock value - each time the stock reaches this value an order to the supplier is

issue,

• Maximum value - value to which the orders are placed, it limits the order quantity.

These parameters are defined having into account the sales forecasting, the suppliers schedule

- lead time and days of delivery - and the forecast of supplier service level.

In the Picking by Line (PBL) flow, there is no stock in the warehouse. An order from the

store immediately triggers an order to the supplier and as soon as the product is received in the

warehouse it is picked and shipped. For products in PBL the picking is done by assigning the

picker to one specified pallet. He will be responsible for distributing all the products in the pallet

for the different stores. This kind of product flow is one type of cross docking.

Cross docking flows imply that there is no storing of the products, the products are received,

picked and shipped (Van Belle et al., 2012). This kind of flow is adequate when the product

demand is stable and constant and the cost of out of stock is low.

At Sonae in the PBL there is very little preparation of the products and they are expedited

in the same day in which they are received. At the end of the day the warehouse is empty and

prepared to start receiving the products for the following day.


Figure 3.6: Picking by Line Flow

In PBL the stores hold changeless positions in the warehouse and the pallet received from the

suppliers is the one that moves around, whereas in PBS the pallets with the products have steady

positions and the stores are the ones moving. Also, in PBS the pallets are mono product.

There is also the possibility of direct delivery from the supplier to the store, the supplier is

responsible for preparing the product and delivering it directly to the stores, however it represents

a very small percentage of the total number of transactions, around 2% of the total products the

store receives.

Besides the division in product flows, there is also another very important classification in

Sonae MC warehouses, the temperature based one. Due to their perishability, some products have

to be conserved in specific temperatures.

At room temperature can be found products that are not perishable and have long shelf life, like

canned food, beverages and hygiene products. In fresh temperature the warehouse is maintained at

5 degrees Celsius and keeps fruits, vegetables, yoghurt, delicatessen and cheeses. This temperature

works mostly in a PBL flow, this kind of products are extremely perishable and sensitive so they

need to be handled extremely carefully and can not stay in stock for long periods of time, the

quality standards are extremely high in this process. Only some specific cases of imported products

are kept in stock for more than one day.

There is also the frozen temperature warehouse. This operation works with minus 19 degrees

Celsius and is very demanding for the workers. The labour policies are much more strict than in

other operations. This operation works in PBS with mobile racks in order to make the most of the

space. The halls are opened according to the position of the SKU that the operator is picking.

Both in Maia and Azambuja, there are the three temperatures. In Azambuja, the frozen ware-

house is managed by an external operator, Frissul.

During 2016, 200 million boxes were prepared in Sonae MC warehouses and shipped to stores.

In Figure 3.7 it is possible to observe the growth that was felt in Sonae MC from 2015 to 2016,

there was an increase of 45 millions of expedited boxes. Until the end of May 2017 the number

of boxes prepared corresponds to 45% of last years total. Being that the sales peaks have not

passed yet, it is predictable that the growth will continue. The room temperature operations are,


on average, responsible for more than 65% of the total preparations, they are also the ones with

the highest productivity levels.

Figure 3.7: Boxes Dispatched from Warehouses yearly

Supplier Analysis

The supplier is the first player in supply chain. The supplier may be the manufacturer or just

the distributor, although, he is always the dealer. The stock management teams will deal with the

supplier and negotiate prices, quantities and delivery conditions. Normally the supplier negotiation

is closed with a contract for a defined period of time that can be extended. This contract can also

be terminated prior to the deadline in case of non-compliance. If the supplier fails, he will be

penalized and the contract should stipulate that.

Currently Sonae MC works with 2.530 suppliers in the commercial divisions that are being

analysed. There are main and secondary suppliers these ones are only used when the primary one

is not able to meet the orders or there is an unexpected increase in the order quantity that forces

the stock manager to go to this second supplier.

In order to be a Sonae MC supplier, it is necessary to meet the quality criteria defined and

have capacity to deliver in the quantities and times that are needed. The retail supply chain is very

strained, and the supplier need to comply with the expected performance.

In PBL the requirements made to the supplier are higher. It is an operation with no stock at

the warehouse, which means that if the supplier fails, the downstream chain will also fail. Despite

the possibility of having breakages, the warehouse service level will depend almost 10% of the

supplier one, so clearly the level of demandingness needs to be higher. In figure 3.8 the relation

between the supplier and the warehouse service level, according to flow, is clearly evidenced.

In PBS there are other factors impacting the service level and there is safety stock, so if the

product is not perishable and the supplier service level is low, probably it will be placed in the PBS


flow. Also, when the supplier has high lead times, especially foreigner suppliers, the product will

be processed in PBS.

Figure 3.8: Relation between Warehouse Service Level and Supplier Service Level

As can be seen in figure 3.8 when analysing SKUs processed in PBL the relation between

the supplier and the warehouse service level is linear, as the supplier service level increases, the

warehouse one also increases. There are few outliers that can be explained due to losses is the

warehouse.

In PBS this relation is not linear and the correlation is lower. In PBL the correlation coeffi-

cient is 0.8, while in PBS it is 0.6. Analysing the data regarding the PBS SKUs there is clearly

a concentration of the supplier service level in three moments, around 30%, 65% and 100%. In

these moments is clear that even with a low supplier service level is possible to have great ware-

house performance. The relation between the two indicators can be approximated by a polynomial

function with four degrees of freedom.

Store and Consumer Management

At Sonae MC stores the operation works from 6a.m. until after the store closing. The hy-

permarket stores open at 8a.m. and close at midnight which means that the operation works over

eighteen hours a day.

The store receives products from the distribution centers and suppliers that make direct deliv-

eries during the night, but it is past six a.m. that this flow gets more intense. The workers need

to make sure that what was received during the night matches the ordered products and need to

prepare the store for the opening.


With the growth of promotional activities this preparation is much more intense. It is necessary

to change the labels for all promotional items and change the disposition of the products in the

store to match the centralized decisions.

Usually there is the weekly promotional brochure that begins every Tuesday and embraces all

kinds of products. On Tuesdays each store changes over 3.000 price tags. Besides, there are also

the special campaigns like, summer promotions, back to school season and campaigns alluding to

the festivities.

Besides dealing with the promotions, the replenishment of the products on store shelves is

fundamental. If a customer goes to a retailer at eight a.m. he expects to find the most fresh

products and a full store, usually these are very loyal customers so it is of utmost importance that

the store is full and organized at the opening.

Usually, stores have little warehousing space so in many cases the stock they have is the one

on the shelves. If the store puts up an order for the next day and the distribution center fails to

deliver, most likely the store will have out of stock products which will directly impact customer

satisfaction.

With the current shopping tendencies the large commercial areas also need to adapt and Sonae

MC is not exception. There are more service offers in the stores, there is a constant preoccupation

in adapting the stores and having products that match customers needs and expectations.

Nowadays, the disposition of the products is more focused on the convenience than in per-

suading the customer to buy more products than they are initially willing to. Customers have less

and less time to spend in stores, so it is necessary that the companies adapt to this reality in order

to keep the customers loyalty.

In order to achieve customer satisfaction, it is necessary no proper manage all the supply chain

phases. Planning and executing these operations has costs, ans as the supply chain moves forward

higher are the costs that are added to the products.

3.2 Supply Chain Costs

Analysing the supply chain since the product is produced, until being sold in supermarkets

there are several costs that need to be considered. The total cost of ownership is an estimated

cost of an item that includes all the costs related to the procurement and the use of the item,

including the costs of disposing of the item when it is no longer useful. The stores are the chain

selling points and their supply involves a set of procedures that carry costs to the company. The

reception, the transportation of the goods and the placement of products up to sale in the store

shelves are examples of that.

The heterogeneity of the products and their packages adds complexity to the process and have

lots of influence in the works productivity, which increases the handling costs. Along the chain

the costs add up to the product value (Figure 3.9).

The costs can be classified in three main categories: acquisition cost, ownership costs and

post-ownership costs. The acquisition costs are associated with the first phase of product life,

3.2 Supply Chain Costs 25

Figure 3.9: Supply Chain Costs

production and buying costs. These are not long term costs. The ownership costs are the second

phase costs, the maintenance and repair costs and the supply chain costs, including inventory costs,

logistics costs, transportation costs and space management costs. Finally, the post-ownership costs

correspond to the salvage value and disposal costs.

During the several steps of the chain there is inventory carried, and this inventory adds to huge

part of supply chain costs. The efficiency of the supply chain can be measured accordingly to the

inventory that is carried throughout the chain. The inventory investment is measured relative to

the total cost of the goods that are provided through the supply chain.

Common measures to evaluate supply chain efficiency are inventory turnover and weeks of

supply, being that one is the inverse of the other (Equation 3.1 and 3.2).

InventoryTurnover =Costo f GoodsSold

AverageAggregateInventoryValue(3.1)

Weekso f Supply =AverageAggregateInventoryValue

Costo f GoodsSold×52Weeks (3.2)

Companies that face diverse sourcing, production and distribution decisions need to weigh the

costs associated with materials transportation, production, warehousing and distribution to de-

velop a comprehensive network designed to minimize costs and preserve environments (Jacobs

and Chase, 2011).

One example of effective cost reduction in the supply chain is Costco, the third biggest grocery

retailer in America. It manage a big cost reduction, and consequently profit increase with the

elimination of product handling stages between supply and sales. Nowadays the chain purchases

most of its merchandise direct from manufacturers (Bureau, 2017).

The more steps the chain has, more costs will be accumulated and this kind of strategies that

have their base in cooperation between retailers and suppliers contribute to improve the overall

chain performance.


Value Chain

Value Chain is a concept developed by the Harvard Busines School teacher, Michael Porter,

that analyses the interrelated operating activities businesses perform during the process of convert-

ing raw materials into finished products.

The main goal in analysing the supply chain as a value chain is to find the best way to add

value to the product along the process at the minimum cost possible. By looking at the chain as

a whole it is easier to find ways to improve the processes efficiency, the overall goal is to deliver

the maximum value to the customer at the lowest possible cost in order to create competitive

advantage (Cato, 2011).

The model developed by Michael Porter is presented in figure 3.10. It divides the production

activities in five main categories. Inbound Logistics that includes inbound shipping, warehousing

and inventory management. Outbound Logistics also warehousing, in this case of finished goods,

maintaining inventory, fulfilling orders and shipping the products.

Figure 3.10: Porter Value Chain

All the activities that add direct value to the product are included in the operations division.

Production, packaging and assembling are some of them. Marketing and Sales include all the

activities which the goal is to convince the customer to buy the product. The Service activities are

the final ones in the chain, they are directly related with the service provided to the customer, it

includes pre-sale counselling services as well as after-sales services.

By splitting all the activities into this categories it makes it easier to find improvement oppor-

tunities and consequently creating value at each one of the five product areas, so the value exceeds

the cost associated with the product. All the five branches of activities are essential to create sus-

tainable competitive advantage. Companies can increase their competitiveness by having highly

efficient outbound logistics processes, for example.

3.2 Supply Chain Costs 27

Achieving highly efficient chains allows the companies to collect higher profits, to invest in

new and innovative products and services or to reduce their selling price, captivating more con-

sumers.

The support activities are often seen as a fountain of costs, they are usually classified as over-

head costs. However, increasing the effectiveness of these activities will increase the efficiency of

at least one of the main activities, which will reflect on the overall company performance.

This analysis allows to understand the impact that all the activities have in the supply chain

costs. At Sonae, the biggest contributor to the supply chain costs are the logistics activities, this

includes the cost of receiving the goods, stocking them, picking them, expediting them and finally

delivering them to stores.

Being the main contributors to the supply chain costs, these logistics activities need to be

managed accurately in order to avoid unnecessary costs. Having low service levels does represents

higher logistics costs to the company.


Chapter 4

Service Level Analysis

The current chapter is dedicated to Service Level analysis, which is the core of this project.

Firstly the indicators used at Sonae MC to measure service levels are going to be presented and

then the ones that are more relevant to the project are going to be analysed in detail.

In the second part of this chapter the methodology used is going to be explained, from the data

collection process to the mapping of the chain service level.

4.1 Service Level Indicators

At Sonae MC the service level is analysed in terms of different indicators, depending on the

operation that is being controlled. Service level can be studied in terms of five indicators, that

are presented in table 4.1. There is also the service level that is provided to the client, calculated

according to the product out of stock rate in store, however that is out of the project scope.

Service Level Indicator Calculation Metric

Warehouse Quantity Service Level ExpeditedQuantity(WH−>Store)OrderedQuantity(Store−>WH)

Warehouse Preparation Service Level QuantityPreparedWithoutDelay(WH)ExpeditedQuantity(WH−>Store)

Warehouse Expedition Service Level QuantityExpeditedWithoutDelay(WH)ExpeditedQuantity(WH−>Store)

Warehouse Global Service Level Quantity * Preparation * Expedition

Supplier Service Level ExpeditedQuantity(Supplier−>WH)OrderedQuantity(WH−>Supplier)

Table 4.1: Sonae MC Service Level Indicators

Regarding the warehouse we can identify four different indicators. Preparation service level

measures the quantity that was prepared on time divided by the amount that was sent to the store.

Expedition service level measures the quantity that was shipped to the store on time divided by the

total quantity shipped. Quantity service level analyses the amount of product that was expedited

to store divided by the one the store ordered. This is the indicator with the lowest value and it

29

30 Service Level Analysis

is fundamental to study its reasons. This low value indicates that the warehouse is not able to

respond to store demands accurately and one of this project goals is to understand its causes.

The last indicator regarding the warehouse can be named as global service level. It is composed

by the indicators previously explained. This combination gives a sense of the level of the global

operation. This value is currently around 80%, mostly due to the quantity indicator value.

In figure 4.1 it is possible to analyse the quantity service level of the warehouse and the supplier

service level. The target for this values was defined as 93,5%. The company target for the quantity

service level is 90%. However in this project the target defined is higher because it is perceived as

important achieving a global service level above 90%.

Currently the preparation and expedition service level target is 99% and this level is being

achieved. The quantity service level is, on average, 91% in PBL and 76% in PBS.

Figure 4.1: Warehouse Quantity and Supplier Service Level

The supplier service level is the first one to create impact on chain, especially when the product

flow does not carry stock. When the store makes an order to the warehouse, it will order from the

supplier, however in some cases the supplier is not able to fulfil this order and, consequently, the

warehouse will not be able to deliver to the stores.

Analysing the causes that lead to supplier failure is the first step to understand the entire cycle.

When the supplier is not able to meet the demand, it may be due to operational issues or lack

of planning. Sometimes the source of the problem is not on the supplier so it can not be held

responsible. Liability may be of Sonae MC.

As can be seen in Figure 4.1 the supplier service level is higher than the warehouse quantity

service level, which means that in some cases the supplier meets the warehouse orders but the

warehouse does not comply with the store demand and identifying the causes behind this event is

the second step of the process.

As was said previously, the supplier impact is higher if the flow does not carry stock, like the

PBL flow. In PBL the warehouse service level will be equal to supplier service level, except for

cases when there are losses in the warehouse, but this is an exception. Indeed, both these services

are very similar and are close to the target.

4.2 Data Analysis 31

Usually, if the supplier has a low service level, he will be placed in the PBS flow, thus, having

less impact in the operation. However, in this flow there is stock in the warehouse and the quantity

of stock kept is defined according to the supplier service level. Hence, theoretically the quantity

service level of the warehouse is being defined when these parameters are set.

After analysing the indicators that already exist at Sonae MC, it is necessary to conduct a

more detailed analysis of the data regarding the service level. The following section will detail the

process of data collection and analysis.

4.2 Data Analysis

In order to analyse the service level indicators it is necessary to collect and study the data

regarding all the parameters that can influence its value.

Data Collection

Firstly it is necessary to establish the time frame that is going to be analysed, the project begun

in February so the most recent data available was from January 2017. It was defined that the

analysis would comprise three months because one trimester is a significance sample of the year

and at the same time it provides a manageable volume of data.

Initially is was established that the analysis would include the last trimester of 2016, by choos-

ing this period, the Christmas season, which is the most critical time of the year, is included in the

analysis and that allows to understand if the suppliers and the warehouse are capable of respond-

ing well to this kind of pressure. However the calculation of the service levels only stabilized in

October, so it was decided to analyse November 2016, December 2016 and January 2017.

Due to the existence of different databases it was necessary to compile the data regarding the

warehouse service level. This was done using an Access file. First of all, it was necessary to

compile the marketing structure for the SKUs, the tables regarding the divisions were grouped in

one table with all the information.

Then the SKUs were classified by warehouse, with the respective codes. This classification led

to the duplication of some SKUs because they exist in Maia and Azambuja, however they may be

operated in different flows and with other suppliers which may result in contrastive performances.

After this analysis there were 48.789 different combinations (SKU - Flow - Warehouse) to analyse.

For each SKU it was necessary to compile the purchase orders that the stores had placed to the

warehouse during these three months. These orders are done in boxes, in order to have the same

base it was necessary to convert these boxes to units, using the pack size (see appendixC). After

having this information the quantity service level was calculated for each combination, as follows

in equation4.1.

Quantity Service Level =Units Shipped to Store

Units Ordered toWarehouse(4.1)


This information is obtained through EXE, which is Sonae MC Warehouse Management System

(WMS). This system does not keep records so daily backups are made, if there is any problem

with the backup, relevant information might get lost and can not be recovered.

For the supplier service level the process was similar, the puchase orders were retrieved from

RETEK that is Sonae MC Order Management System. From this source, it was also retrieved the

cost of each order, that was used to calculated the cost of each product unit.

The third tool used was Zoom, an analysis service with several information cubes. This was

used to study the product sales, in quantity, gross sales and net sales, and also the price of each

product having then its margin.

Posteriorly to these data being collect, it was organized in an Access file together with the

marketing structure and crossed, resulting in a table - Global Analysis - with the fields: SKU;

Description; Commercial Division; Business Unit; Distribution Center; Flow; Quantity Ordered

to the Warehouse; Quantity Shipped from the Warehouse; Warehouse Quantity Service Level;

Quantity Ordered from the Supplier; Quantity Delivered by the Supplier; Supplier Service Level;

Cost; Price; Quantity Sold; Gross Sales and Net Sales.

Data Classification

After having the data collected it was perceivable that the sales followed a Pareto rule (see

appendix D), 80% of the total sales, where achieve by 20% of the SKU. However, this was still

a large number to analyse, around 10.000 combinations. Further analysis showed that 20% of

the sales were due to 4% of the SKUs, so it was considered as a second Pareto rule because it

maintains the same ratio. It was decided to conduct this second analysis to limit the scope of the

project and consequently achieve a quantity of data that was easier to analyse.

These were the criteria used to classify the SKUs according to its rotation. The first class

- High - contains the SKUs responsible for 20% of sales (4% of the total), the second class -

Medium - contains the SKUs responsible for 60% of the sales (16% of the total). This two classes

correspond to 80% of the sales. The Low class contains the remaining SKUs that are responsible

for 20% of the sales. This method was done using gross sales.

The second part of the classification process was made according to the warehouse quantity

service level. Being the target defined in this project 93,5%, the class High included the SKUs

that were above this value. The medium class was defined between the target and 75%. Despite

75% not being an exceptional value it is close to the current values that this indicator has. So it is

important to separate these values from the lower ones that need to be completely eliminated.

Below 30% the service level was considered Null because the SKUs with this kind of values

also showed high performance variability, and consequently they are very unreliable. The Low

class was defined between 75% and 30%. In figure 4.2 is presented the twelve possible combina-

tions in terms of classification.

It was decided to analyse the more critical instances, the ones with high sales and low service

level. The SKUs classified as ’MH’, ’LH’ and ’LM’ were considered to have a good relation


Figure 4.2: SKU Classification

between service level and sales. These SKUs do not have considerable sales but they have good

levels of service so they were excluded from the analysis.

The ’MM’ and ’LL’ classes are borderline classes, if their service level gets worse or their

sales increase, they become critical. They are being kept out of this study but they should be the

first ones to be included in a second iteration of the process.

The class ’HH’ is a class that was considered worth to analyse. However the process would be

completely different. These are SKUs with great performance, and small changes could improve

their results even more or reduce the costs associated. They are not being considered in this

analysis, although it is recommended that they are studied in the future.

This analysis will create impact on the SKUs from the worst classes, being them: ’HM’,

’HL’, ’HN’, ’ML’, ’MN’ and ’LN’. These SKUs present relevant sales and poor service levels and

constitute the 419 critical SKUs that are going to be studied in detail.

After having identified the critical SKUs it is necessary to analyse them and start to map

possible causes for the low performance.

The critical SKUs have on average 63% of warehouse service level and 78% of supplier service

level. The following sections will be focused on several factors that can impact the warehouse

service level.

Replenishment Method

For each critical SKU in PBS it was analysed the replenishment method used, this analysis

showed that in 77% of the occurrences the method is Min/Max and in the remaining cases the

method is not defined in the system. Hence, it was assumed that the method used for all the SKUs

is Min/Max. The parameters of this method are defined as follows in table 4.2.

Supplier Service Level Minimum MaximumSSL >= 90% Min = Cycle Stock Max = MinSSL < 90% & SSL >= 50% Min = Cycle Stock Max = Min + 1

2 Safety StockSSL < 50% Min = Cycle Stock + 1

2 Safety Stock Max = Min + 12 Safety Stock

Table 4.2: Replenishment Method Parameters

This method may not be the most adequate way of defining stock levels for all SKUs. If the

goal is to improve service level there should be a higher level of safety. Considering that the stock


is reviewed periodically, in order to improve the service level it is considered that the time between

reviews should be included in the calculations of the cycle stock.

Currently the cycle stock is defined as [Lead Time * Average Daily Sales], however if it was

considered [(Lead Time + 12 Review Cycle Time) * Average Daily Sales] there would be less risk

of having out of stocks while waiting for delivery.

In particular the SKU - 4507690, which belongs to the House Cleaning business unit, is pro-

cessed in warehouse 104 and has one of the highest out of stock rate in the analysis. The out of

stock rate for this SKU is 30%, in comparison to 3,02% in the business unit average. In table 4.3

is presented the current replenishment parameters for this SKU and the ones that were estimated.

Supplier Current Parameters Estimated ParametersLead Time = 1 Week Total Lead Time = Lead Time + 1

2 Review Time = 1,5 WeeksReview Time = 1 Week Z - Score = 1,54 (Service Lvel = 93,5%)Min = 3624 Min = Cycle Stock = 7883Max = 3624 Max = Cycle Stock + 1

2 Safety Stock = 9040Out Of Stock Rate = 30% Estimated Out of Stock Rate = 12%

Table 4.3: Estimation of Replenishment Parameters

Reception Problems

There is a tool at Sonae MC that keeps registry of all the receptions of products. For each

reception is recorded the supplier, the SKUs and quantity that was ordered and received, and

if there was any problem with the order. The most common problems are damaged good and

products that do not comply with the quality requirements.

Analysing this data, it was perceived that 18,5% of the critical SKUs present this kind of

occurrences that have impact in the service level. The quantities that are rejected are seen as if the

supplier had not delivered, impacting the supplier service level and consequently the warehouse

service level.

In 18% of the SKUs that present reception problems during the period studied, the quantity

rejected is superior to 5% of the quantity received. This analysis showed that reception problems

are a relevant cause for under target performance.

Out Of Stocks

For each critical SKU the out of stock rate was analysed in detail. For each business unit is

calculated the mean out of stock rate and the difference between the business unit rate and the

SKU rate. This analysis allowed to understand which were the SKUs with the worst performance.

Analysing figure 4.3 it is possible to understand that in fruit and vegetables business unit the

average out of stock rate is 3,2% while SKU four has a OOS rate of 18%. The business unit in

analysis has eleven critical SKUS, five of them with an higher OOS rate than the average of the


business unit. Not all the critical SKUs present high OOS rates, nevertheless they present critical

service levels. In this case, the study will be focused on the ones with higher OOS rate.

Figure 4.3: OOS Rate in Fruit & Vegetables Business Unit and OOS Rate in Critical SKUs

The SKU classified as four in figure 4.3, corresponds to strawberries. This is a seasonal fruit,

extremely perishable and sensitive which explains this value. The quality of the product depends

a lot on the weather conditions and the agriculture production, consequently the service level of

the supplier has a high standard deviation. Being a seasonal product, the ordered quantity and the

demand will be higher in summer. Hence, the supplier might not be able to respond.

Causes Classification

Having collected information regarding all these parameters an SQL query was constructed.

This query allowed to do the first classification process of the SKUs according to motive for low

performance. In this phase only three motives were considered: supplier, warehouse and other

causes.

This analysis is being done with PBS and PBL separately, not only due to the intrinsic differ-

ences between the flows but also because PBS carries stock and still presents lower performance.

In PBS flow during the three months analysed, in 40% of the orders the supplier has high-

est contribution to low performance. In 60% of the causes for low performance are related with

warehouse problems and other causes like management and planning failures, the inventory man-

agement policy and data problems.

Analysing the data for PBL flow, in 80% of the cases the problem is related to the supplier.

This result makes sense, like there is not stock in this flow, if the supplier fails, the warehouse

will also fail. In 20% of the cases the problem was related to other causes. Breakages due to

quality, errors in the operation and data errors are the main contributors to this result. Having this

information it was possible to map the service level.


4.3 Service Level Map

This analysis allowed to make a complete map of the supply chain processes and intervinients

which subsequently allowed to identify the factors that influence the service level throughout the

hole process.

The main goal of the supply chain is to deliver the products in the right place at the right time,

assuring customer satisfaction and, consequently achieving the service level targets.

There are three main indicators to analyse in Sonae MC supply chain and they are shown in

figure 4.4. As can be seen, they influence each other as the chain moves forward.

Figure 4.4: Service Level Impact throughout the Chain

The supplier service level will depend on the production. Whether the supplier is the man-

ufacturer or not, he is affected by the natural factors that might affect the food production. The

ordered quantity variation are also a very factor, the supplier scales its production according the

historical data, so if the warehouse decides to place an order that is a lot bigger than the usual, he

might not be able to respond on time.

There is also the case when the supplier simply is not able to meet the demand, all the factors

remain constant and he can not respond. In these cases, the supplier needs to be penalized. In cases

of extremely perishable products there is a more strict quality control, the suppliers are analysed

with even more detail and their failures are much more damaging for the company image.

In PBL as seen before, the warehouse quantity service level is extremely dependent on the

supplier service level. When analysing PBS there are other causes that were identified. The

replenishment method, the planning and long lead time of suppliers are the main ones.

Usually when a supplier has low performance, he is placed in the PBS flow because there

is stock to prevent the failures. However, if the stock quantity is not well dimensioned and the

planning is not done right, there will still be failures.

Some non perishable products are shipped from foreign countries by ship, which means they

will have long lead times. If this orders are not placed right and are not responded briefly, that will

cause low performance.

The customer service level is influenced by the previous two and the other indicators that were

not studied, like the preparation and expedition service level and also the store replenishment

capability. If the store receives the products correctly but is not able to place them on the shelves,

the service provided to the customers will still be low.

4.4 Challenges 37

4.4 Challenges

Throughout the development of this analysis the challenge that most stood out was the com-

prehensiveness of this topic and consequently the difficulties in limiting the analysis to the most

relevant causes and products.

Initially, due to the amount of information that was available it was necessary to understand

which information was more interesting for the analysis. For example, there was an excel infor-

mation cube that already had the supplier service level calculated. However the information that

was seen there had already been analysed so it hide some of the most critical orders, especially

during the holidays season.

It was chosen to analyse the raw data, just removing information that was clearly due to typing

or system errors. There was also the concern that some data might have been lost in the transfor-

mation, which justifies the choosing of the integral information used.

Also related to the suppliers, they are usually handled by the stock management team and

it was challenging to understand how they monitored the suppliers performance. Currently the

analysis that is made by them is only to see if the low performance must be charged to the supplier

or not.

Regarding the warehouse inventory management, the data available is still very basic and the

methods used are not quite well defined, so it was necessary to make some assumptions. The

approach to this topic was quite limited for the lack of reliable information available.

In order to validate the causes that this analysis allowed to identify, a regression model was

constructed. With this model it is possible to understand if the causes that were mapped are

responsible for the service level values.

Having as a concern the possibility of continuous monitoring the service levels and giving

the company a tool to analyse the impacts that future changes might have in the performance, a

dashboard was constructed. The following chapter is focused on analysing the results obtained

with the models and presenting this tool.


Chapter 5

Results and Discussion

This section will present the final results that were obtained with the analysis. To validate the

causes previously identified a regression model was constructed. This model allows to identify

the relation between the variables and consequently understand if the causes that were identified

are responsible for the service level values. Firstly this model will be presented and the results

described. Having validated the causes, the value tree is constructed classifying the causes for low

performance.

Having this value tree it was possible to identify some preventive and corrective actions in

order to improve service level. This actions are also presented in this chapter. To conclude the tool

that was constructed to monitor the performance regarding this topic is presented.

5.1 Regression Model

In order to study how the service level is affected by the various factors that were identified,

two regression models were constructed. One model for SKUs managed in PBS and other for

PBL.

The models were constructed using Excel and the quality of the regression model is measured

using the determination coefficient (R2) and adjusted R2. Due to the limitations of the software

used, only a sample of the global data was studied. The products were chosen randomly in order

to maintain the integrity of the test.

Model for PBL Flow

In order to analyse the quality of the model, several parameters need to be studied, for the

PBL case the results are presented in appendix E and the obtained charts in figure 5.1. There are

five relevant factors - the variance coeficient and the standard deviation of the order quantity, the

effective order quantity, the nationality of the supplier and the frequency of deliveries.

The residuals represent the difference between the predicted values with the achieved equation

and the real values. Analysing the residuals charts it is possible to observe that the error distribu-

tion is random as it should be. The values are concentrated around the zero value. They are not

39

40 Results and Discussion

systematically high or low, which shows that the model is correct on average for all fitted values.

The fit plots show the real values and the predicted ones. As previously seen the residual values

that correspond to the errors are low, so the fitting to the reality is satisfactory. Analysing the

normal probability plot it is clear that the residual values do not follow a normal distribution.

The value achieved for R2 is 76%, which means that 76% of the service level variation in PBL

is explained by these factors. In order to confirm the reliability of the results it is necessary to

analyse the Significance F obtained from the ANOVA test performed.

The ANOVA test analyses the variation of the data an works as a complement for the regression

model. In order to have a reliable model, the significance F should be lower than 0,05 (Guimarães

and Cabral, 2011). The value obtained was 2E−5, to a confidence level of 95%.

Also for the majority of the variables p-values are lower than 0,05. A low p-value shows that

the factor is likely to be a meaningful addiction to the model because changes in the predicted

value are related with changes in the dependent variable.

For each factor that is included in the model, there is a coefficient defined. These coefficients

give the equation for the variables.

Model for PBS Flow

In PBS there are more variables to take into consideration. The dependent variable is the

variation coefficient of the warehouse service level. The independent variables in study were

the ordered quantity to the warehouse, the supplier service level, the replenishment method, the

temperature of the products, the lead time of the product and the nationality of the supplier.

The value achieved for R2 is 93%. This model explains 93% of the service level variation and

it has eight relevant factors. The increase number of the variables did not spoil the overall set. The

adjusted determination coefficient is 89%, which means that the relation between the number of

factors and the fitness to the function is good.

In appendix E is presented the results for this model, and they show that the model is also

reliable. TO a level of confidence of 95% the significance F is 7E−8.

This model demonstrates that the most influential factor in the PBS flow service level is the

replenishment method, followed by the supplier service level that also has a high weight. In table

5.1 is a summary of the results for both models.

Variables PBL Model PBS ModelNumber of independent variables: 5 8

R2: 76% 93%Adjusted R2: 70% 90%

Significance F: 2E−5 7E−8

Table 5.1: Regression Models Results

5.1 Regression Model 41

Figure 5.1: Charts from PBL Regression Model


5.2 Main Causes Identified

After having the regression models correctly defined the previously identified motives for low

performance were ranked. This analysis was developed separately for PBS and PBL, and ulti-

mately resulted in the value trees that are presented in the end of this section.

The ranking of the causes allows the company to focuses on the most relevant problems. Using

the dashboard constructed allied with the ranking of causes it will be possible to implement and

monitor improvement actions.

Causes in PBL

After splitting the critical products according to flow, the analysis included 160 occurrences

for the PBL type of flow. In this flow the supplier performance is really meaningful, so the first

causes to be ranked were the ones related with the supplier.

In the studied period in 20% of the orders the problem is due to warehouse failures. In 4% of

the cases, the supplier had a 100% service level in all the deliveries throughout three months. The

remaining 16% correspond to orders where the supplier service level is stable between 96% and

99% and the warehouse service level is more than 10% bellow this value.

The causes identified for warehouse failure where: errors in the reception that account to 7,5%

of the occurrences, 3% of the orders were affected by warehouse operation errors and in 2% of

the cases there are errors while entering the data to the systems. The data errors might seem like

a low part of the overall causes, however, they can easily be avoided. Most of the cases are due

to incorrect parametrization or the articles, which can be avoided with the increasing use of EAN

128 labels.

In some cases the quantity that is ordered to the supplier is smaller than the quantity the

stores have ordered to the warehouse, this accounts for 4,5% of the cases where service level is

unsatisfactory. This happens due to stock management teams decisions, however it negatively

impact the overall performance.

The causes related with planning impact the warehouse and supplier performance and they

account for 28% of the orders. Since this study embraces the holidays season, it is expected the

this causes are less relevant in the remaining of the year when the demand is more stable.

In 25% of the orders placed the quantity ordered was unusual, at least 30% more than the

average in the previous six months. This kind of orders result in supplier failure. In 2% of the

cases there are errors in the order placement and in 1% of the cases there are products that change

flow and before updating the system, this occurrence is seen as product breakdown.

In PBL as would be expected the service level of the supplier has most responsibilities in the

low performance, supplier performance is responsible for 50,5% of the orders with low service

level. In 3% of the cases the supplier never delivered in the period in analysis, being this a long

period it was decided to separate this cause because it is necessary to analyse directly with the

supplier what is the problem. It is a completely out of the ordinary situation.

5.2 Main Causes Identified 43

The biggest cause for low service level is the supplier simply not being able to deliver as

expected, the ordered quantity is normal and everything else is in order, however in 47,5% of the

orders the suppliers are not able to fulfil the orders.

There are also 4,5% of the orders where the cause is not possible to determine. In figure 5.2

are summarized the identified causes.

Figure 5.2: Ranked Causes for Low Service Levels in PBL

Causes in PBS

In the PBS flow 259 critical SKUs were analysed in order to identify the most relevant causes

for low performance. In PBS there are more factors to analyse and, consequently, more possible

causes.

This investigation showed that in that in the analysed periods the suppliers had a 100% service

level in 15% of the orders, this is a much higher value than the one found in PBL.

The causes that differ from the PBL flow are the ones related with the inventory management

process. In 32% of the cases, are these processes that result in poor performances, this type of

causes are affect the warehouse final performance.

The fact that there are SKUs without a warehouse replenishment method defined in the system

is the cause for 6% of the losses. It is not possible to know how the inventory is being managed,

so if the supplier is delivering as expected and the warehouse is not meeting the store orders the

problem is related with this topic.

In 12% of the cases the replenishment method is not the most adequate, changing the way the

parameters are defined would result in better performance.


The two remaining causes are related, when the lead time is long, namely in the case of inter-

national suppliers, it is necessary to have enough stock to fulfil demand in this period. In 9% of

the cases the safety stock defined is not enough to cover the lead time and the review period and

in the remaining 5% of the orders the lead time is not registered in the system so it is not possible

to determine if the method is working or not.

It was considered that the causes related with the inventory management affect the warehouse

performance, however if the replenishment method was correctly defined it could impact positively

the supplier service level because the supplier reliability rate would be correctly considered in the

calculation of the replenishment parameters.

In this flow, the supplier impact is much more reduced as well as the planning one. Having

warehouse inventory it is easier to deal with this kind of failures. To conclude, in figure is presented

a summary of the identified motives.

Figure 5.3: Ranked Causes for Low Service Levels in PBS

5.3 Value Tree

The construction of the value tree intends to facilitate the comprehension of which causes for

low performance are worth more attention. The causes have already been presented before so the

5.3 Value Tree 45

purpose of this section is to give to the company a clear view of where the main efforts should be

put.

These trees intend to show the kind of measures that should be applied in the future to improve

Sonae MC supply chain service levels.

PBL

The value assignment to the different causes (Figure 5.4) is not only based on the percentage

they represent in the overall performance, but also in the difficulty allied to its correction, the costs

associated and the impact it has in the global chain. The causes are ranked in a scale between 0

and 10 according to these factors.

Figure 5.4: Value Tree for PBL

Analysing the cause with punctuation 10, it only represents 3% of the orders that had low

performance. However, if the supplier never delivers, the store will have the product constantly

out of stock which reflects in the sales and, consequently, in the profits. Furthermore, having a

product constantly out of stock affects customer satisfaction and loyalty.

In order to correct this kind of problem, it is necessary to negotiate with the current supplier

or find a new supplier. It might be difficult to find a new supplier, however the costs that result for

the company due to out of stocks are higher.

The causes ranked as 9 are also related with the supplier, in PBL the supplier has higher

responsibility in low performance. The unusual ordered quantities are due to lack of warehouse

planning. If there is a concern with the service level, planning is of foremost importance.

All these causes should be address somewhere along this process if the goal is to achieve

optimal service levels, although these are the ones that need immediate action.


PBS

For PBS the process followed was the same and the value tree achieved is presented in figure

5.5.

Figure 5.5: Value Tree for PBS

For the PBS flow the main focus should be in the inventory management improvement oppor-

tunities. This causes are ranked between 10 and 8 according to the impact they have. Improving

the inventory management directly impacts the warehouse performance and it also has impact in

the supplier service level.

The improvement of the replenishment method calculations and the study of alternative meth-

ods as well as updating the systems in order to have all the information available are priority tasks.

The inventory handling costs are a large part of the supply chain costs, which adds up to the im-

portance of managing this part of the chain adequately. The inventory management is an internal

process that is defined by the company, so there will be less resistance to change than when dealing

with external players.

The supplier impact is still relevant in this flow. Usually the suppliers that work in PBS have

lower performance than the ones that are chosen to be in PBL, so it is important that there is a

current monitoring of the supplier performance in order to avoid constant failures.

Planning accordingly to the demand and stocking up before a predicted peak of sales are

also ways of improving the results. This kind of planning will relieve the supplier pressure and,

consequently, his performance will improve.

In order to analyse the current situation of the company and to monitor the improvements that

might be caused by these actions a Dashboard was constructed.

5.4 Supply Chain Service Level Dashboard 47

5.4 Supply Chain Service Level Dashboard

In order to allow an easy monitoring of the indicators analysed it was constructed a support

tool in Excel with VBA programming that gives a clean and easy view of the situation and allows

to analyse the studied indicators.

This kind of analysis is useful for other projects, which are being developed in parallel and

also aims the improvement of the chain performance monitoring. The creation of an actionable

tool intends that it can be used by different intervenients in the chain.

This tool was constructed having as base the period that was analysed, but it can easily be

adapted to include more recent data. If in the future, the improvement opportunities identified are

put into practise, by using this tool it would be possible to monitor the improvements in the overall

process.

The dashboard also contains information relative to store out of stocks and sales, promotional

and regulars, in order to allow the user to have a complete vision of the retail processes. This

information allows to monitor the impact that store promotions have in the supply chain service

levels.

The first page (Figure 5.6) alows to analyse a particular SKU performance. For a particular

SKU it is possible to analyse the supplier and the warehouse service level, the classification that

was attributed to the SKU, its sales and the variation of the indicators.

Figure 5.6: Dashboard, Page 1

In the second page (Figure 5.7), a more embracing analysis is made for the SKU or the Busi-

ness Unit. It is possible to see the last three months service level, as well as the regular sales,

promotional sales and the out of stocks that occurred. This division between promotional and reg-

ular sales permits understanding if the out of stocks are related with the promotions or if they are

recurring.

This dashboard is the culmination of the project, it gives an overview of the information anal-

ysed throughout the process and it serves as a support for future iterations of this process.


Figure 5.7: Dashboard, Page 2

Chapter 6

Conclusions and Future WorkPerspectives

The constant necessity to improve supply chain performance is aroused by the increasing

competitiveness felt in the markets, particularly in the food retail one. The customer expectations

are higher each day and the retailers strive to conquer their loyalty constantly adding services and

products to their portfolio.

However, if the chain that supports this kind of movements is not efficient, the retailer will face

extra costs and the service provided to the customer will not be the desired one. Improving the

service level throughout the chain is crucial to improve customer satisfaction and, consequently,

achieving competitive advantage.

This project arises from Sonae MC necessity to better understand the service level that is

provided throughout the chain. The food retail supply chain has lots of intervenients and combines

different types of processes. It is necessary to understand how ones influence the others in order

to identify the indicators that are more relevant for the chain performance.

The state of the art regarding food supply chain highlights the difficulties that are related with

handling perishable products, therefore making the food retail chain one of the most tense chains.

Concerning the service levels it is necessary to analyse the chain since the beginning, starting in

the producer and passing to the distributor, the retailer and finally to the customer because if the

failures start to accumulate and are not managed properly they will have high impacts in the end

of the chain.

In order to produce this document the analysis was structured in three main phases, data col-

lection, data classification and an iterative process for low performance causes identification. After

having collected the necessary data regarding all the active SKUs ate Sonae MC, it was necessary

to limit the scope of the process.

An analysis similar to the ABC/XYZ model was conducted in order to compare the SKU

service level with its sales and classifying it accordingly. After this analysis was finished there

419 critical SKUs identified, 160 from PBL flows and 259 from the PBS flows.

49

50 Conclusions and Future Work Perspectives

Corsten and Gruen, 2003 stated in his study that the retailer looses on average 3,9% of sales

due to out of stocks. This means that, if the product was always available the sales would be 3,9%

higher. Analysing the 419 SKUs classified as critical in the period studied, if their quantity service

level was 93,5%, which corresponds to the target defined, the gross sales would increase more

than two million Euro.

In order to identify the most relevant causes for low performance in terms of service level, it

was necessary to split the analysis according to product flow. This division led to the production

of quite different results, in PBL the supplier is clearly responsible for low performance. However,

in PBS causes related with inventory management have greater importance.

To improve the performance in PBL flow it is necessary to better manage the suppliers, en-

courage a closer relation might be a way of improving performance. In this flow the warehouse

service level is almost 100% dependent on the supplier service level, because there is no stock, so

it is of utmost importance to improve the suppliers management to improve performance.

Regarding PBS flows the dependence on the supplier is lower. However, having stock in-

creases the supply chain costs and if the inventory management is not accurate the performance

will still be low. In this case, it is effectively the inventory management that has highest negative

impact in the service levels. Being an internal process, which does not depend or directly affects

external partners, the improvement of this process should be a priority.

This analysis highlighted the main improvement opportunities that exist along the chain. It

is a project that is transversal to many areas and if the identified improvement opportunities are

taken advantage of, it can be a source of competitive advantage since it has a direct impact on the

quality on the service provided to the client.

In the future, it would be important to extend the period of analysis in order to validate the

results and also extend it to the remaining SKUs in order to map other causes and to improve the

study reliability.

It would also be interesting to analyse the SKUs that were classified as ’HH’. Despite being

a different analysis because the targets have already been achieve, it could show patterns between

this high performance products that could be tested in different SKUs.

With the new technologies that are available retailers have the opportunity to improve their

logistics decisions increasing their level of differentiation and the level of integration throughout

the chain (Donselaar et al., 2005). Having this possibility the retailer is able to improve the service

provided to the customers which nowadays is one of the major challenges in this industry.

This kind of study allows to identify improvement opportunities in the company processes and

operating network. Being focused in continuous improvement and increasing the integration of

the supply chain it is fundamental to be aware of all the steps that are present in the supply chain

and this is transversal to all kinds of businesses.

References

Bartholdi, J. J. and Hackman, S. T. (2016), Warehouse & Distribution Science.URL: www.warehouse-science.com

Bureau, L. (2017), ‘A Grocery Chain Reduces Costs by Minimising Touch Points’.URL: www.logisticsbureau.com/reducing-supply-chain-costs

Butner, K. (2010), ‘The GMA 2010 Logistics Benchmark Report: Performance reaches all-timehigh during economic depression’.URL: http://www.gmaonline.org/downloads/research-and-reports/GMA_2010_Logistics_Benchmark_Report.pdf

Cato, S. (2011), ‘Pareto principles, positive responsiveness, and majority decisions’, Theory andDecision 71(4), 503–518.

Christopher, M. (2011), Logistics & Supply Chain Management, 4th editio edn, Prentice Hall -Financial Times.

Corsten, D. and Gruen, T. (2003), ‘Desperately seeking shelf availability: an examination of theextent, the causes, and the efforts to address retail out-of-stocks’, International Journal of Retail& Distribution Management 31(12), 605–617.URL: http://www.emeraldinsight.com/doi/10.1108/09590550310507731

Dani, S. (2015), Food Supply Chain Management and Logistics - From Farm to Fork, Kogan PageLimited, Philadelphia.

de Carvalho, J. C., Guedes, A. P., Arantes, A. J. M., Martins, A. L., Póvoa, A. P. B., Luís, C. A.,Dias, E. B., Dias, J. C. Q., de Menezes, J. C. R., Ferreira, L. M. D. F., Carvalho, M. d. S.,Oliveira, R. C., Azevedo, S. G. and Ramos, T. (2010), Logística e Gestão da Cadeia de Abastec-imento, 1a edição edn, Lisboa.

Donselaar, K. V., Woensel, T. V., Broekmeulen, R. and Fransoo, J. (2005), ‘Improvement Oppor-tunities in Retail Logistics’.

Gaur, V. (2013), ‘Supply Chain Management’, Operations Management - Havard Business Pub-lishing (September).

Ghiani, G., Laporte, G. and Roberto, M. (2004), Introduction to Logistics Systems Planning andControl, Wiley, England.

Guimarães, R. C. and Cabral, J. A. S. (2011), Estatística, 2nd editio edn.

Harland, C. M. (1996), ‘Supply Chain Management: Relationships, Chains and Networks’, BritishJournal of Management 7(March), 63–80.

51

52 REFERENCES

Hübner, A. H., Kuhn, H. and Sternbeck, M. G. (2013), ‘Demand and supply chain planningin grocery retail: an operations planning framework’, International Journal of Retail &Distribution Management 41(7), 512–530.URL: http://search.ebscohost.com/login.aspx?direct=true&db=eoah&AN=30584650&site=ehost-live%5Cnhttp://www.emeraldinsight.com/10.1108/IJRDM-05-2013-0104

Jacobs, F. R. and Chase, R. B. (2011), Operations and Supply Chain Management, 13th editi edn,McGraw-Hill.

King, P. L. (2011), ‘Crack the code’, APICS magazine (August), 33–36.URL: http://web.mit.edu/2.810/www/files/readings/King_SafetyStock.pdf

Lambert, D. M., Cooper, M. C. and Pagh, J. D. (1998), ‘Supply Chain Management: Implemen-tation Issues and Research Opportunities’, The International Journal of Logistics Management9(2), 1–20.

Moura, B. d. C. (2006), Logística: Conceitos e Tendências.

Petridis, K., Dey, P. K. and Emrouznejad, A. (2016), ‘A branch and efficiency algorithm for theoptimal design of supply chain networks’, Annals of Operations Research 253(1), 1–27.

Randall, W. S., Gibson, B. J., Clifford Defee, C. and Williams, B. D. (2011), ‘Retail supply chainmanagement: key priorities and practices’, The International Journal of Logistics Management22(3), 390–402.URL: http://www.emeraldinsight.com/doi/10.1108/09574091111181381

Rushton, A., Croucher, P. and Baker, P. (2011), ‘The Handbook of Logistics and DistributionManagement: Understanding the Supply Chain’, p. 665.

Rutner, S. M. and Langley Jr., C. J. (2000), ‘Logistics Value: Definition, Process and Measure-ment’, The International Journal of Logistics Management 11(2), 73–82.

Salam, A., Panahifar, F. and Byrne, P. J. (2016), ‘Retail supply chain service levels: the role ofinventory storage’, Journal of Enterprise Information Management 29(6), 887–902.

Schalt, S. and Vermorel, J. (2014), ‘Service Level Definition’.URL: https://www.lokad.com/service-level-definition

SONAE (2015), Retail Book of Innovation 2014, Innovation and Future Tech.

SONAE (2017a), ‘SONAE - Business Portfolio’.URL: https://www.sonae.pt/pt/sonae/o-grupo-e-os-negocios/

SONAE (2017b), ‘SONAE - Culture’.URL: https://www.sonae.pt/pt/sonae/cultura/

SONAE (2017c), ‘SONAE - History’.URL: https://www.sonae.pt/pt/sonae/historia/

SONAE (2017d), SONAE - Investor Presentation, Technical report, SONAE.URL: https://www.sonae.pt/fotos/apresentacoes_investidoresanceiro/investorpresentation_sonae_jan17_821960196588b79c77d499.pdf

Van Belle, J., Valckenaers, P. and Cattrysse, D. (2012), ‘Cross-docking: State of the art’, Omega40(6), 827–846.URL: http://dx.doi.org/10.1016/j.omega.2012.01.005

REFERENCES 53

Vermorel, J. (2012), ‘Replenishment Rules’.URL: https://www.lokad.com/stock-replenishment-definition

Walker, E. (1989), ‘Applied Regression Analysis and Other Multivariable Methods’, Technomet-rics 31(1), 117–118.URL: http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1989.10488486

54 REFERENCES

Appendix A

Year Sales

In figure A.1 it is possible to analyse the sales distribution throughout the years. The highestpeak occurs in the Holidays season, mainly affecting the food commercial division. There is alsoa substantial growth in the Summer season, this growth is especially felt in the fresh products,mainly affecting yoghurts and fruit.

Figure A.1: Distribution of Sales throughout the Months

55

56 Year Sales

Appendix B

Complete Marketing Structure

Analysing the case of the Commercial Division Food and the Business Unit Salt Grocery thereare the following Classes, Sub Classes and Base Units. The SKUs are distributed by the BaseUnits.

Table B.1: Salt Grocery Marketing Structure

57

58 Complete Marketing Structure

Appendix C

Pack Conversion

Analysing the case of sugar. The supplier delivers to the warehouse in half pallet which corre-sponds to one SKU, when the warehouse ships to stores there are two possibilities. Shipping thehalf pallet or the one kilogram package of sugar, which corresponds to a different SKU (FigureC.1).

Regardless of the unit shipped to store, the customer will always buy the kilogram package, sothe sales will be registered in this SKU not in the half pallet one.

Using the table RMS-MC-PACKITEM from RETEK the connection between the packs and theSKUs was established and the packs were converted to units, this allowed the comparison betweenthe units received at the warehouse and the units shipped to stores.

Figure C.1: Sugar Pack Conversion

In cases of packs with several products it was necessary to go through this procedure for eachSKU in order to properly calculate the units that were shipped and received.

There are also products received in boxes that are shipped in units as well as sold in units, inthis cases there is only one SKU. Despite only having one SKU if there are several suppliers thebox size might be different, so it was also made a conversion to units following the same principlesas in the first case.

59

60 Pack Conversion

Appendix D

Pareto Rule

The Pareto principle was named after economist the Vilfredo Pareto, that specifies an unequalrelationship between inputs and outputs.

The principle states that 20 per cent of the invested input is responsible for 80 per cent of theresults obtained (Figure D.1). That is, 80 per cent of consequences result from 20 per cent of thecauses. It can also be referred as the Pareto Rule or 80/20 rule (Cato, 2011).

The economist responsible for this principle defined it because he realized that the richness ofthe world was not evenly distributed and its distribution followed this rule. Eighty per cent of theworld wealth is controlled by twenty per cent of the population.

Figure D.1: Pareto Rule

The Pareto rule can be applied in a wide range of areas such as manufacturing, managementand human resources. It has been adopted by a variety of coaching and customer relationshipmanagement (CRM) software programs.

The Pareto principle can be applied to many businesses, especially those that are client-servicebased which is Sonae MC case. In the scope of the project it was necessary to study the distributionof the sales throughout the products and it was found that it followed the Pareto rule.

Later, in order to do the classification process it was identified a second Pareto rule. Thismeans that there were two divisions that followed the 80/20 principle (Figure D.2).

61

62 Pareto Rule

Figure D.2: Pareto Rule applied to Sonae MC SKUs

In Sonae MC, 16 per cent of the sales are caused by 4 per cent of the SKUs which correspondsto the 80/20 proportion, and 80 per cent of the sales are the result of 20 per cent of the SKU’s. Infigure D.2 this two points are marked.

The remaining 20 per cent of the sales are due to 80 per cent of the SKUs, this SKUs aremuch less relevant, despite being important that they have satisfying service levels, theis impact isminimum.

The Pareto rule can also be applied on a personal level and time management is the mostcommon use for it. Most people spend the majority of the time with irrelevant tasks so it berelevant to do this kind of analysis.

Appendix E

Regression Model

After several iterations the best results achieved for PBL regression model are presented bel-low. The results for the PBS Model are also presented bellow, as well as the graphics that showthe behaviour of the factors in this model.

The results obtained with the PBL model (Figure E.1) show, as expected, that the service levelis highly affected by the variations in the ordered quantity. In the future it could be interesting tostudy deeper the relations between the warehouse and the supplier performance. In this model itis assumed that the supplier service level is equal to the warehouse one. Ideally this should be truesince there is no stock, however in some cases it is not.

Figure E.1: Results of PBL Regression Model

In the results from the PBS model (Figure E.3), the impact is divided between the inventorymanagement and the supplier. The inventory management is the variable with highest importancein the model.

For PBS, as well as in the PBL model, the residuals do not follow a normal distribution (E.5),however they are distributed around zero which is desirable. This model counts with more vari-ables but the results are still satisfactory. Regarding the fit plots (Figure E.6), since the residualshave low significance, the fitting is good.

63

64 Regression Model

Figure E.2: Outputs from PBL Regression

Figure E.3: Results of PBS Regression Model

Regression Model 65

Figure E.4: Outputs from PBS Regression

66 Regression Model

Figure E.5: Residual Plots from PBS Regression Model

Regression Model 67

Figure E.6: Line Fit Plots from PBS Regression Model

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Service Level Analysis in Food Retail Supply Chain · no qual os produtos são rececionados, preparados e expedidos, por norma no mesmo dia. Trata-se de um ﬂuxo no qual a melhoria