Executing the Supply Chain

Executing the Supply Chain

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Executing the Supply Chain

Modeling Best-in-Class Processes and Performance Indicators

Alexandre Oliveira Anne Gimeno

Associate Publisher: Amy Neidlinger Executive Editor: Jeanne Glasser Levine Operations Specialist: Jodi Kemper Cover Designer: Chuti Prasertsith Managing Editor: Kristy Hart Project Editor: Andy Beaster Copy Editor: Keith Cline Proofreader: Sarah Kearns Indexer: Lisa Stumpf Compositor: Nonie Ratcliff Manufacturing Buyer: Dan Uhrig

© 2014 by Alexandre Oliveira and Anne Gimeno

Upper Saddle River, New Jersey 07458

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Company and product names mentioned herein are the trademarks or registered trademarks of their respective owners.

All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher.

Printed in the United States of America

First Printing July 2014

ISBN-10: 0-13-376438-9 ISBN-13: 978-0-13-376438-3

Pearson Education LTD. Pearson Education Australia PTY, Limited. Pearson Education Singapore, Pte. Ltd. Pearson Education Asia, Ltd. Pearson Education Canada, Ltd. Pearson Educación de Mexico, S.A. de C.V. Pearson Education—Japan Pearson Education Malaysia, Pte. Ltd.

Library of Congress Control Number: 2014937235

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

Chapter 1 Supply Network Governance Cycle . . . . . . . . . . . . . . . . . . 1

1.1 Scope Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Mapping the Processes . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Business Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Project Report: Sample . . . . . . . . . . . . . . . . . . . . . . . 29

1.3 Risk Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Chapter 2 Definition of Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.1 Specify Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402.2 Observe Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 412.3 Define Performance Pattern . . . . . . . . . . . . . . . . . . . . . 452.4 Assign Name for the Control. . . . . . . . . . . . . . . . . . . . . 462.5 Define Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472.6 Test Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492.7 Define Performance Goals . . . . . . . . . . . . . . . . . . . . . . 502.8 Understand the Learning Curve . . . . . . . . . . . . . . . . . . 522.9 Map Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.9.a Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532.10 Validate Control Dynamics . . . . . . . . . . . . . . . . . . . . . 552.11 Assign Control to People . . . . . . . . . . . . . . . . . . . . . . . 562.12 From Implementation to Fine-Tuning. . . . . . . . . . . . 572.13 Reviewing the Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . 582.14 Definition of Metrics in a Project . . . . . . . . . . . . . . . . 59

2.14.a Goals to Objectives . . . . . . . . . . . . . . . . . . . . . 592.14.b Objectives to Main Activities . . . . . . . . . . . . . 602.14.c Define the Controls . . . . . . . . . . . . . . . . . . . . 612.14.d Close the Loop . . . . . . . . . . . . . . . . . . . . . . . . 64Example: Customer Service . . . . . . . . . . . . . . . . . . . . 75Example: Quality Assurance. . . . . . . . . . . . . . . . . . . . 80Expert’s Opinion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

vi EXECUTING THE SUPPLY CHAIN

Chapter 3 Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

3.1 Efficiency, Efficacy, Adaptability . . . . . . . . . . . . . . . . . 883.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

3.2.a Planning Logistics . . . . . . . . . . . . . . . . . . . . . . . 983.2.b Synchronous Operations . . . . . . . . . . . . . . . . 1323.2.c Tactic Governance . . . . . . . . . . . . . . . . . . . . . 158

Chapter 4 Managing Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

4.1 Auditing Performance Indicators . . . . . . . . . . . . . . . . 1654.2 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1664.3 The Organizational Impact . . . . . . . . . . . . . . . . . . . . . 172

Business Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174The Cultural Web for the Toulouse Plant . . . . . . . . 175Part I: Second Assignment . . . . . . . . . . . . . . . . . . . . 184Part II: The Next 18 Months . . . . . . . . . . . . . . . . . . 192

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

Acknowledgments

As mentioned in the Authors’ Note, we are currently writing five books for Pearson, all of which benefit from contributions by a large number of individuals from both academic and business sectors.

Our special thanks to Professor Martin Christopher, Emeritus Professor of Marketing & Logistics at Cranfield School of Manage-ment, Cranfield University, United Kingdom, whose contributions are present in all five of our books. We also wish to express our kind-est appreciation to Mark Barratt, Associate Professor in Supply Chain Management at Marquette University, USA, and expert in collabora-tion and visibility across supply chains, for his contribution during the early stages of our academic background.

We would also like to thank Professor Milton Mori (State Uni-versity of Campinas, Unicamp, Brazil), Professor Mauro Sampaio (University Center, FEI, Brazil), Professor Richard Wilding OBE (Cranfield University, UK), Lecturer Carla Corte (State University of Campinas, Unicamp, Brazil), Professor Armando Dal Colleto (Busi-ness School São Paulo, BSP, Brazil), and Senior Lecturer Melvin Peters (Cranfield University, UK) for their contributions to our devel-opment over the last 20 years.

In our books, business cases/expert opinions were provided by the following:

Bernardo Faria: Operations Development at Cielo, Brazil; Cesar Righetti, Operations Director at Cielo, Brazil; Dr Alan Smart, Senior Lecturer at Cranfield School of Management, UK; Enrique Motilla, founding partner at Quad Tree S.A. de C.V., Mexico; Fikri Dweiri, Associate Professor in IndusOperation Engineering and Manage-ment Department and Vice Dean of College of Engineering at the University of Sharjah (UoS), UAE; Gianluca S. Cesare, Director at

viii EXECUTING THE SUPPLY CHAIN

Lombardia Informatica, Italy; Kimmochi Eguchi, Executive Direc-tor, International Marketing Institute, Japan; Lucas Costa: Opera-tions Planning at Cielo, Brazil; Luciano Bortoncello, Development Director at Transmiro, Brazil; Marcelo Pereira, Transport Manager at Transmiro, Brazil; Mauricio Ajzenberg, General Director at SPDL, Brazil; Robin Parsons, Supply Chain Manager, Maxinutrition (a GSK company), UK; Sharfuddin Ahmed Khan, lecturer in IndusOperation Engineering & Management Department at University of Sharjah (UoS), UAE; Shogo Kurokawa, President, Nippon Logistech Corpo-ration, Japan.

This project would have proven impossible without the inspira-tion from our colleagues active in several professional networks. The governance team of the Brazilian Institute of Supply Chain Profes-sionals ( www.ibpsc.net/IBS ), together with all associates, creates a unique technical environment that keeps us up-to-date on the best practices. Although we want to thank all IBS members, it is impos-sible to list anything more here than the advisory board:

Daniel Mello, Daines Toledo, Marcelo Alencar, Maricea dos Santos, Sérgio Romero, Daniel Okino, Ralph Martins, Cláudio de Sá, Alex Rocha, Luis Gonzaga, Marcelo Torres, William Marques, Antonio Berna, Carlos Pavanelli, Eduardo Junqueira, Antonio Souza, Luciano Bortoncello, Gelber Abe, Fabio Miranda, Luis Silva, Carlos Cirillo, Plínio Márcio, Luiz Ribeiro, Daniel Hermeto, Luiz Ferreira, Marco Palmeira.

We also want to thank our colleagues at CEBRALOG who have collaborated to develop and organize a significant part of the knowl-edge shared in our books: Andre Moraes, Eraldo Bertagnoli, Fernanda Silva, Adriana Freitas, Mauricio Cortes, Mirella Gomes, Francismar Lemos, Lucas Casagrande, and Fernando Fedato.

The Operations and Supply Chain Academic Group at Linked-In (with nearly 20,575 members) was another important source of inspiration.

http://www.ibpsc.net/IBS

ACKNOWLEDGMENTS ix

My kindest thanks to my editor, Jeanne Levine, and to Barry Render, who believed in and supported this book project from the early stages, and to Heather Simpkins, my proofreader, without whom this work would not be readable!

Finally, we wish to thank our relatives who have been a constant source of wisdom: our grandparents Pedro, Antônia, Ouriques, Clau-dina, David, Conchita, Leopoldo, and Clara; our parents Gerusa, Jorge, Lluis, and Maria Helena; our brothers Alan, Amanda, Andre, and Arnau; our sons Gabriel and Pedro; our nieces Mariana and Maria Paula; and our dearest Ana Claudia, Cristina, Maria Eduarda, Maria Etienne, and Rayder.

About the Authors

Alexandre Oliveira is a founding-partner at CEBRALOG (www.cebralog.com), a supply chain consultancy and training company headquartered in Brazil since 2001. Alexandre has also been Presi-dent of the Brazilian Institute of Supply Chain Professionals (I.B.S.) since 2007. He offers courses for MBA programs at Business School São Paulo and is a regular contributor to seminars, conferences, and congresses.

Alexandre began his career at Procter and Gamble, where he gained executive experience in manufacturing, quality assurance, and logistics in assignment in Brazil and in Europe, but for the past 15 years, he has developed his career as consultant. Alexandre has worked as a senior consultant, trainer, and expert advisor for compa-nies such as GE, 3M, Sony, Bayer, Pepsico, Pernod Ricard, Unilever, Avon, Adidas, John Deere, Eaton, Walmart, Motorola, and several others.

Alexandre holds a Bachelor of Arts degree in Chemical Engi-neering and a Master in Finance degree from the State University of Campinas (Unicamp, Brazil) and a Master of Science (Honors) degree in Logistics and Supply Chain Management from Cranfield’s University School of Management, United Kingdom.

Since 2001, Alexandre has actively influenced logistics thinking in several supply chain organizations. He chaired the Logistics Commit-tee at the American Chamber of Commerce (Amcham, Brazil, 2001–2004), chaired the Supply Chain Committee at the British Chamber of Commerce (Britcham, Brazil, 2006–2007), and he was Regional Vice-President of former Brazilian Logistics Association (2002–2003). He has lectured in MBA courses since 2004 in top regional universi-ties such as State University of Campinas (Unicamp) and State Uni-versity of São Paulo (USP).

http://www.cebralog.com

http://www.cebralog.com

ABOUT THE AUTHORS xi

Alexandre has published in the International Journal of Physical Distribution and Logistics Management and coordinates the Opera-tions and Supply Chain Academic group at LinkedIn, which currently has nearly 20,000 members (March 2014).

Anne Gimeno is a founding-partner at CEBRALOG, a supply chain consultancy and training company headquartered in Brazil since 2001. Anne has also been the Managing-Director of the Brazil-ian Institute of Supply Chain Professionals (I.B.S.) since 2007.

Anne began her career at Procter and Gamble, where she gained executive experience in materials planning, sales, and customer ser-vice. For the past 15 years, she has worked as a senior consultant for companies such as GE, 3M, Bayer, Pepsico, Pernod Ricard, and several others.

Anne holds a Bachelor of Arts degree in Chemical Engineering and a Master of Science (Honors) degree in Logistics and Supply Chain Management from Cranfield University’s School of Manage-ment, United Kingdom. She served as the director of the former Bra-zilian Logistics Association (2002–2003).

About I.B.S.

The Brazilian Institute of Supply Chain Management Profession-als, a leading regional professional association since 2007, benefits its members via technical events, courses, and an annual congress in São Paulo, Brazil.

I.B.S. promotes knowledge transfer with other knowledge cen-ters around the world. The Committee for International Cooperation (CCI) is the structure that builds partnerships for technical coopera-tion and knowledge exchange with foreign institutions. Ideal partners are national or regional professional associations or universities.

You can find more information about I.B.S. at www.ibpsc.net/IBS .

http://www.ibpsc.net/

Preface

“Vision without action is a daydream. Action without a vision is a nightmare.”

—Unknown

All companies have to deliver results to customers, to sharehold-ers, and to stakeholders. Each one within the organization must be aligned to contribute to this endless effort.

But as supply chains increase in complexity, it has become less clear what results each knowledge area within the organization is expected to deliver. Even worse, it has become unclear whether all members of the organization are really capable of contributing to ful-fill either customer or shareholder expectations.

The ability to understand the dynamic nature of modern supply networks and to assign practical actions to selected teams within the business has been lost over the years.

This book presents simple, though efficient, tools and methodolo-gies to guide supply chain professionals in their continuous challenge of delivering better results each day. Enriched with many examples, this work is a reference to map processes, define controls, manage operations, and promote change.

Authors’ Note

Over the years, the common understanding of world-class operations has evolved from the simplistic, focused management of functional silos to a comprehensive approach of supply network man-agement as the driver to deliver ultimate shareholder value. Although

PREFACE xiii

many commentators have tried to describe this evolution, most have failed to properly address the supply chain’s fundamental building block: knowledge management. Therefore, their analyses also over-looked the only element that delivers long-term sustainable share-holder value: people.

We are writing five books for Pearson that cover the most important features of this evolutionary journey. These books will provide detailed roadmaps and models to diagnose, implement, and sustain world-class supply chain network management in organizations of all types:

• A Guide to Supply Chain Management: The Evolution of SCM Models, Strategies, and Practices (an e-book) introduces the core concept of knowledge management as the only strategy capable of steering supply chains networks management to suc-cessfully compete in highly competitive markets. This introduc-tory work reviews supply chain practice from its earliest stages and presents reference models that support our view of this discipline as a business driver to deliver shareholder value.

This book introduces the Supply Network Alignment Reference Model (SNAR Model), which organizes the supply chain net-works into knowledge areas that enable accurate decision mak-ing from the strategic level to daily management decisions.

This book also introduces the Supply Network Knowledge Man-agement Maturity Roadmap (SKMap). Before the develop-ment of a supply network reference model, it was necessary to understand the intermediate evolutionary stages of knowledge management within the supply chain. The SKMap organizes and correlates several strategies and practices according to a unique structure that allows you to understand how to face the future challenges of managing supply chain networks in fluid and complex environments.

xiv EXECUTING THE SUPPLY CHAIN

• Supply Chain Management Strategy: Using SCM to Create Greater Corporate Efficiency and Profits explores how sup-ply chain management delivers shareholder value. The intro-duction covers topics such as the supply chain master plan, cash-management cycle, purchase-to-pay cycle, and manufac-turing-to-revenue cycle. This book introduces the Supply Net-work Business Value Model (SNValue Model) and discusses the supply chain mechanisms that generate value for the busi-ness. It addresses the following topics: enabling sales volume growth, enabling market-share growth, reducing revenue cycle, reducing lost sales, supporting marketing and sales initiatives, enabling customer experience by improving customer percep-tion, managing the cost to serve, offering differentiated service packages, enabling margin growth, reducing cost of sales, bal-ancing asset management, and balancing service level and cost structure.

This book also presents the Business Value Impact Chart (BV Chart) and the Balanced Control Panel (BC Panel). The third part of the book covers how each of the SNAR Model knowl-edge areas can contribute to each of the factors that enable shareholder value. The tool used to establish these relation-ships is the BV Chart.

• Executing the Supply Chain: Modeling Best-in-Class Processes and Performance Indicators covers the supply network governance cycle and explains the mechanisms needed to understand the business though process mapping, risk analysis, and the definition and use of performance indicators for all areas directly or indirectly related to supply chain management. The second part of the book presents how each of the SNAR Model knowledge areas can be monitored and controlled by performance indicators. Other chapters present real world

PREFACE xv

metrics from companies of different sizes, sectors and countries, and discuss benchmarking techniques.

• Customer Service Supply Chain Management: Models for Achieving Customer Satisfaction, Supply Chain Performance, and Shareholder Value focuses on the role of customer service as a strategic integrator for differentiated supply chain management. This book presents the Customer Service Management Model (CSM Model), a dynamic mechanism developed to evaluate the interactions present in the customer service environment. The model presents four pillars and provides a quantitative approach to understand the connection between them:

1. Customer Service Level Expectation

2. Supplier Service Level: Hired Performance

3. Customer Service Level Perception

4. Supplier Service Level: Delivered Performance

Although the book discusses some traditional customer service elements such as pre-transactional, transactional, and post-transactional service, the most important topics are customer service strategies, managing service levels, and customer ser-vice organization, respectively.

• Managing Supply Chain Networks: Building Competitive Advantage in Fluid and Complex Environments presents a solid roadmap for managing knowledge within organizations across all industries. You learn how to build, implement, and sustain long-term knowledge management as a consistent strategy to deliver business value through supply chain innovation leadership.

This book presents the Supply Network Governance Diamond Model (SNG Diamond) which is executed through…people!

xvi EXECUTING THE SUPPLY CHAIN

The SNG Diamond Model is a common governance structure focused on the long-term success of the entire supply network that connects knowledge management and risk management and reviews policies that promote the innovative environment required to face the challenges of managing fluid and complex supply networks

1

1 Supply Network Governance Cycle

Despite the need to coordinate efforts within various elements of the supply chain has recently increased, companies do not yet under-stand the process of building efficient performance indicators for their operations knowledge areas.

Companies pursue continuous improvement and monitor perfor-mance through a set of lengthy and diverse indicators that allegedly represent a process or a cluster of processes.

Performance indicators should be used as decision supporting tools. For that reason, some attributes are expected: 1

• They have to be quantitative. No performance output should be identified by words such as good, regular, bad, weak, par-tial, or equivalent. Metrics must be numbers—either absolute or relative (percentages).

• Performance indicators should promote the right behavior and appropriate organizational culture; therefore, it is desirable for a multifunctional approach to define them.

• Performance indicators must balance the effort to collect and analyze data and the benefits this indicator delivers to the business.

1 Source: Adapted from “Keeping Score: Measuring the Business Value of Logis-tics in the Supply Chain,” CSC, University of Tennessee.

2 EXECUTING THE SUPPLY CHAIN

• Performance indicators must be visible to people, accepted by the organization, and shared within various departments.

• Only what is really important should be measured. The chal-lenge is to define a short list of metrics that are relevant and capable of representing the health of the processes.

• Performance indicators are sensitive to process performance oscillation. A good performance indicator reacts according to minimal process performance deviation.

• The performance indicator must be easily understood by all stakeholders.

Ideally, any performance indicators have all attributes described here. This book introduces a methodology to support companies to define a precise and valuable set of metrics entitled the Supply Net-work Governance Cycle (SNG Cycle).

The SNG Cycle proposes a five-step roadmap that guides compa-nies from the early stages of acquiring fundamental business visibility through process mapping to advanced people management policies. These five steps are as follows:

1. Define the scope.

2. Map the process.

3. Analyze the risks.

4. Define the metrics.

5. Review the SNG Cycle.

CHAPTER 1 • SUPPLY NETWORK GOVERNANCE CYCLE 3

Supply Network Governance Cycle - SNG Cycle

DefineScope

ApplySNARModel

ProcessMapping

EnvironmentDefine Strategies,

Policies, and PracticesDefine Supply

Network Governance

Understand Supply NetworkEnhancement Needs

UnveilSupply

Network

CaptureBusiness

Needs

ProcessMap

AnalyzeRisks

DefineMetrics

ReviewCycle

Figure 1.1 Supply Network Governance Cycle

1.1 Scope Definition

The mechanism the Supply Network Governance Cycle uses to identify the exact process scope (set of activities) to be mapped is the Supply Network Alignment Reference Model (SNAR Model) and its coding system (Oliveira and Gimeno 2014_01).

The SNAR Model provides a structured approach to map the extended supply network from different perspectives. These perspec-tives may vary both in scope (number of functions) and depth (func-tional details), using as an example a customer service team that is to map and monitor the transportation processes and its impact on several key customers.

4 E

XECU

TING TH

E SU

PPLY CH

AIN

Supply Network Alignment Reference Model - SNAR Model

SELECTED SUPPLY CHAINMANAGEMENT

EXTENDED SUPPLY CHAINMANAGEMENT

FEW SELECTED SUPPLIERS

FEW SELECTED NONCOMPETITORS

FEW SELECTED COMPETITORS

SEVERAL KEY NONCOMPETITORS

ALL SIGNIFICANT SUPPLIERS

ALL SIGNIFICANT CUSTOMERS

ALL SIGNIFICANT LSPs

SEVERAL KEY SUPPLIERS

SEVERAL KEY CUSTOMERS

SEVERAL KEY LSPs

FEW SELECTED CUSTOMERS

FEW SELECTED LSPs

DEMANDPLANNING &

FORECASTING

PURCHASING &PROCUREMENT

PLANNING

STOCK &INVENTORYPLANNING

CUSTOMERSERVICES

SUPPLY CHAINPROJECTS

SUPPLY CHAINBUSINESS

INTELLIGENCE TACTICALIGNMENT

SALES

SUPPLY CHAIN INTEGRATED INTO THE BUSINESS

SUPPLY NETWORK MANAGEMENT

OTHER AREAS OF THE ORGANIZATION

CORPORATE GOVERNANCE

FINANCE

CONTROLLERSHIP

QUALITY ASSURANCE

ENGINEERING

R&D

HS&E

MARKETING

IT

HUMAN RESOURCES

REGULATORY

OTHERS

MANUFACTURINGWAREHOUSINGTRANSPORTATION

Synchronous Operations

TACTICINTEGRATION

SUPPLY CHAIN GOVERNANCE

DISTRIBUTION

INTERNATIONAL LOGISTICS

HUMAN RESOURCESBUSINESS PARTNER

PRODUCTIONPLANNING

SupplyChainFoundation

Planning Logistics

SUPPLY CHAIN RISK MANAGEMENT

INFORMATIONTECHNOLOGY

SUSTAINABILITY

KEY KNOWLEDGE AREAS

Figure 1.2 SNAR Model

C

HA

PTER 1 • SU

PPLY NETW

OR

K GO

VERN

AN

CE C

YCLE

5

Supply Network Alignment Reference Model - SNAR Model

SELECTED SUPPLY CHAINMANAGEMENT

EXTENDED SUPPLY CHAINMANAGEMENT

FEW SELECTED SUPPLIERS

FEW SELECTED NONCOMPETITORS

FEW SELECTED COMPETITORS

SEVERAL KEY NONCOMPETITORS

ALL SIGNIFICANT SUPPLIERS

ALL SIGNIFICANT CUSTOMERS

ALL SIGNIFICANT LSPs

SEVERAL KEY SUPPLIERS

SEVERAL KEY CUSTOMERS

SEVERAL KEY LSPs

FEW SELECTED CUSTOMERS

FEW SELECTED LSPs

DEMANDPLANNING &

FORECASTING

PURCHASING &PROCUREMENT

PLANNING

STOCK &INVENTORYPLANNING

CUSTOMERSERVICES

SUPPLY CHAINPROJECTS

SUPPLY CHAINBUSINESS

INTELLIGENCE TACTICALIGNMENT

SALES

SUPPLY CHAIN INTEGRATED INTO THE BUSINESS

SUPPLY NETWORK MANAGEMENT

OTHER AREAS OF THE ORGANIZATION

CORPORATE GOVERNANCE

FINANCE

CONTROLLERSHIP

QUALITY ASSURANCE

ENGINEERING

R&D

HS&E

MARKETING

IT

HUMAN RESOURCES

REGULATORY

OTHERS

MANUFACTURINGWAREHOUSINGTRANSPORTATION

Synchronous Operations

TACTICINTEGRATION

SUPPLY CHAIN GOVERNANCE

DISTRIBUTION

INTERNATIONAL LOGISTICS

HUMAN RESOURCESBUSINESS PARTNER

PRODUCTIONPLANNING

SupplyChainFoundation

Planning Logistics

SUPPLY CHAIN RISK MANAGEMENT

INFORMATIONTECHNOLOGY

SUSTAINABILITY

KEY KNOWLEDGE AREAS

Figure 1.3 SNAR Model Customer Service and Transportation


A more specific approach identifies this environment according to the SNAR Model codification system: 01.02.01 (Transportation), 01.03.01 (Customer Service), and 02.01.02 (Preferred Customers).

01 INTERNAL NETWORK 02 EXTERNAL NETWORK

Preferred Supply ChainPreferred SuppliersPreferred CustomersPreferred Service ProvidersExtended Supply ChainSelected Suppliers

Selected Service ProvidersPreferred NoncompetitorsSupply Network ManagementAll Significant SuppliersAll Significant CustomersAll Significant Service ProvidersSelected NoncompetitorsPreferred Competitors

Selected Customers

02.0102.01.0102.01.0202.01.0302.0202.02.0101.02.0201.02.0301.02.0402.0302.03.0102.03.0202.03.0302.03.0402.03.05

Planning LogisticsDemand Planning and ForecastingProcurement & PurchaseStock and Inventory ControlProduction PlanningSynchronous OperationsTransportationWarehousingManufacturingDistributionInternational LogisticsTactic IntegrationCustomer ServicesSupply Chain ProjectsInformation TechnologyHuman ResourcesSustainabilityOther DepartmentsSales

FinanceControllershipQuality AssuranceEngineeringR&DHS&EMarketingIT

Human ResourcesRegulatorySupply Chain GovernanceKey Knowledge AreasSupply Chain Business IntelligenceSupply Chain Risk Management

01.0101.01.0101.01.0201.01.0301.01.04

01.02.0101.02.0201.02.0301.02.0401.02.05

01.03.0101.03.0201.03.0301.03.0401.03.05

01.04.01

01.04.0201.04.0301.04.0401.04.0501.04.0601.04.0701.04.0801.04.09

01.04.1001.04.1101.0501.05.0101.05.0201.05.03

01.04

01.03

01.02

Figure 1.4 SNAR Model coding system


The next example applies the methodology to the Demand Plan-ning and Forecasting Knowledge area (SNAR 01.01.01).

01. Internal Network

01.02. Synchronous Operations01.03. Tactic Integration01.04. Other Departments01.05. Supply Chain Governance

01.01. Planning Logistics

01.01.02. Procurement & Purchase01.01.03. Stock and Inventory Control01.01.04. Production Planning

02. External Network

-----

01.01.01. Demand Planning and Forecasting

01. Internal Network > 01.01. Planning Logistics > 01.01.01. Demand Planning and Forecasting

Figure 1.5 Selecting knowledge area

Once the knowledge area is selected, it is required that the pro-cess to be mapped has its scope detailed. The following example indi-cates two key subprocesses:

1. Finished goods demand forecasting [SNAR 01.01.01.a]

2. Sales & operations planning [SNAR 01.01.01.b]

The control chart is a tool that identifies the evolution in each step of the SNG Cycle. The rightmost column indicates the score each step can achieve; the maximum overall score is 16 points.


Code

Key Subprocesses (KSP):

Supply Network Governance Cycle

Process Owners:

Team Members:

Processes01.01.01

KSP a 01.01.01.a Finished goods demand forecasting

KSP b 01.01.01.b Sales & operations planning

KSP c

KSP d

KSP e

KSP a

KSP a

KSP b

KSP c

KSP d

KSP e

1.00 1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.00 - - - - 16.0

6%

100%

1. Define scope (apply SNAR model)

2. Process Map

3. Understand business needs

4. Define Supply network improvement needs

5. Elaborate strategies

6. Elaborate Policies

7. Define practices

8. Consolidate governance strategy

9. Implement cycle revision methodology

Scale

TOTAL SCORING

2.a Interviews

2.b Process observation

2.c Data analysis

2.d Organize data

2.e Elaborate flows

5.a Define metrics for strategies

6.a Define metrics for policies

7.a Define metrics for practices

Not started = 0.00 Started = 0.25 Completed = 1.00

KSP b KSP c KSP d KSP e Scale

Demand Planning and Forecasting

Dem.Planning Manager: T.Miros

S.J.Thomas, A.Xavier, T.Yian

02-feb

Figure 1.6 SNG Cycle, control chart


1.2 Mapping the Processes

The step of “mapping the process” is usually misunderstood to be the activity of drawing the process flows. Although the flows play an important role during any process mapping approach, alone they do not offer visibility to the supply network mechanisms.

Supply Network Governance Cycle - SNG Cycle – PROCESS MAP

DefineScope

ApplySNARModel

ProcessMapping

EnvironmentDefine Strategies,

Policies, and PracticesDefine Supply

Network Governance

Understand Supply NetworkEnhancement Needs

UnveilSupply

Network

CaptureBusiness

Needs

ProcessMap

AnalyzeRisks

DefineMetrics

ReviewCycle

Figure 1.7 SNG Cycle, process map

Managing the process mapping environment requires three core primary and coordinated activities: interviewing people, observing the process, and analyzing data. The next sections explain the ele-ments illustrated in Figure 1.8 .


These three core activities should follow some basic guidelines built after repeating experiences in different organizations. These guidelines are as follows:

• Interviewing principles: (1) One interviewee at a time; (2) open questions (avoid yes-no approach); (3) ongoing record and validation; (4) request complementary data; (5) draw prelimi-nary flow during interviewing process; and (6) double-check all information received.

ProcessesMapped

CommunicateMapped Processes

Flows Data

Process Mapping Environment

Interviews

DataAnalysis

Observation

Figure 1.8 SNG Cycle, process mapping environment


• Observation principles: (1) Information captured during ini-tial interviews should be double-checked before observation starts; (2) observe the process in different moments; (3) open questions to process owners and team; (4) request complemen-tary data; (5) use observations to revalidate preliminary flows drawn during interviews; and (6) double-check observation inputs whenever required.

• Data analysis principles: (1) Plan before requesting data; (2) understand the data received and evaluate whether it is exactly what had been required; (3) understand the context and check data adherence to the process; (4) analyze data; (5) double-check the analysis; and (6) revalidate flows previously drawn based on analysis outputs.

Following these three activities the organization needs to organize and communicate the acquired knowledge. A communication strategy is created and it is usually supported by two mechanisms: flowcharts and a supporting database (usually in the format of spreadsheets).

Once the process is mapped, the governance leaders need to capture real information on current business priorities ( Figure 1.7 , building block 5). Only by cross-checking the process maps and the business key demands is it possible to understand the supply net-work’s enhancement requirements.

The visibility of the gaps between the current process standards (“as is” scenario) and the expected performance (“to be” scenario) is the basis for enabling an appropriate risk analysis.

The evolution within different subprocesses can be simultane-ously registered in the control chart introduced in Section 1.1 . The following example explores this feature using the Warehousing knowl-edge area (SNAR 01.02.02).


01. Internal Network

01.02. Synchronous Operations01.03. Tactic Integration01.04. Other Departments01.05. Supply Chain Governance

01.01. Planning Logistics

01.02.01. Transportation

01.02.03. Manufacturing01.02.04. Distribution01.02.05. International Logistics

02. External Network

------

01.02.02. Warehousing

01. Internal Network > 01.02. Synchronous Operation > 01.02.02. Warehousing

Figure 1.9 Selecting SNAR 01.02.02

As explained, it is now necessary to detail which key subprocesses (KSPs) are to be observed during the SNG Cycle. The following sub-processes have been selected: (01) Receiving operations, (02) Picking operations, and (03) Shipping operations. Note that in Figure 1.10 that all three key subprocesses have an equal score (6.0).

In contrast to the previous example, the mapping process and the definition of controls may be at different stages for different subprocesses.

In this example, the organization focuses on the extended sup-ply network and the connections with all significant suppliers (SNAR 02.03.01), customers (SNAR 02.03.02), and service providers (SNAR 02.03.03). The management of the supply network also looks at selected noncompetitors (SNAR 02.03.04) and some preferred com-petitors (SNAR 02.03.05).


Code

Key Subprocesses (KSP):


Process Owners:

Team Members:

Processes01.02.02

KSP a 01.02.02.a Receiving operations

KSP b 01.02.02.b Picking operations

KSP c 01.02.02.c Shipping operations

KSP d

KSP e

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2. Process Map


4. Define Supply network improvement needs


6. Elaborate Policies

7. Define practices



Scale

TOTAL SCORING

2.a Interviews


2.c Data analysis

2.d Organize data

2.e Elaborate flows




Not started = 0,00 Started = 0,25 Completed = 1,00


Warehousing

Warehouse Supervisor: A.Adams

B.Smith, Q.Chang

02-feb

Figure 1.10 Control chart, subprocesses

Code

Key subprocesses (KSPs): Process owners:

Team members:

Processes02.03.

KSP a All significant suppliers

KSP b All significant customers

KSP c All significant service providers

KSP d Selected noncompetitors

KSP e Preferred competitors

Supply Network management

SC Director: A. Gimes

C. Glows, P. Zizau, N. Prix

02-feb

Figure 1.11 Selecting knowledge area, SNAR 02.03


The following figure shows the first key subprocess (KSP a: all significant suppliers) has been through all the SNG Cycle, whereas KSP e (preferred competitors) is in its early stages. This company is more comfortable in addressing its suppliers or its customers (KSP b), but the evaluation of significant service providers (KSP c) still requires the definition of metrics to monitor adherence to selected strategies.


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4. Define supply network improvement needs


6. Elaborate policies

7. Define practices



Scale

TOTAL SCORING

2.a Interviews


2.c Data analysis

2.d Organize data

2.e Elaborate flows




Not started = 0.00 Started = 0.25 Completed = 1.00


100%

100%

94%

45%

9%

Figure 1.12 Control chart, scoring

The elaboration of flows occurs as interviews, observation, and data analysis are conducted. This is a nonlinear process, and interac-tions of several types may be required.


This methodology also requires that diverse people are part of the process. It is important to take into account the hierarchical and func-tional barriers that create disconnected islands of knowledge.

From the beginning of this process until the validated flows are finished, the organization need to recurrently go through interview-ing, data analysis, or in loco observation.

It is possible to structure these dynamics according to six basic stages:

1. Start to map

2. First round flows

3. Preliminary flows

4. Refined flows

5. Validated flows

6. Organized data

VALIDATEDFLOWS

STARTTO MAP

Interviews

Interviews

Interviews

Observation

Observation

Observation

DataAnalysis

DataAnalysis

Figure 1.13 Process mapping spiral


The initial three stages are highly dependent on the interview-ing sessions, which should consider interviewees with complementary knowledge, whereas information cross-checking on specific processes or activities may be left for a future occasion.

Process Mapping Environment

Interviews

DataAnalysis Observation

STARTTO MAP

FIRST-ROUNDFLOWS

PRELIMINARYFLOWS

REFINEDFLOWS

VALIDATEDFLOWS

ORGANIZEDDATA

HANDWRITTEN

ELECTRONICVERSION

Build Process Flow

6

2

3

4

5

1

BuildProcess

Flow

Figure 1.14 Process mapping details

This methodology has been applied to several consulting projects, and the period from initial interviewing to validated flows when sup-ported by properly organized data varied from a few weeks to several months, depending on the scope range and depth. Figure 1.15 sum-marizes this feature based on some of the projects executed by the authors from 2001 to 2014.

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3M USA 3 2.02.52.02.01.01.51.01.02.02.52.04.02.53.03.53.01.02.01.51.72.0

8242121363947

12511312

AGCO USAAJR BRAZILBEIERSDORF GERMANYCAMOZZI ITALYDANISCO DENMARKDRAKA-COMTEQ HOLLANDEAGLEBURGMANN GERMANYFIRMENICH SWITZERLAND

FLEXTRONICS USAFMC GERMANYGE USAHUSSMAN USAHYPERCOM USAKATOEN NATIE BELGIUMMAGNA CLOSURES CANADAMARCOPOLO BRAZILPERNOD RICARD FRANCESONEPAR FRANCESONY JAPANZODIAC ARGENTINA

ClientCompany

Headquarters ProcessesMapped

MappingPeriod (months)

Figure 1.15 Process mapping period


Although it sounds curious, the best tip to give to someone who needs to draw a flow for any given process is this: Start it! In fact, the only resources you need are pencil, paper, and an interviewee.

Figure 1.16 is the result of a 1-hour conversation with a customer service manager in a beverage company. The session focused on the order management process and identified several key activities, some of which are listed after the image.

This initial session identified a total of 34 elements. As the process mapping occurred, complementary data was analyzed, and observa-tion happened; this initial picture evolved into a final validated ver-sion with 135 activities identified.

The methodology used to draw the flows may vary according to the final goals. Some of the best-known business process mapping meth-odologies 2 are, in alphabetic order, business model canvas, business process discovery, business process modeling, ethnography, IDEF, OBASHI, N2 Chart, organizational studies, process-centered design, SADT, systems engineering, value stream mapping, and workflow.

Once early, unconnected flows generated during first interviews are available, the mapping team can create more comprehensive dia-grams. Although some teams would prefer to go electronic, this phase is usually done with pencil and paper because there is still much to find out about the process and rework is likely to be necessary before any fine-tuning activities.

The first round of flows builds a scenario similar to a puzzle. Often, a wide panel (or wall) is used to offer a complete view of the processes being mapped. Recent and old versions are usually side by side to facilitate overall visibility.

2 http://en.wikipedia.org/wiki/Business_process_mapping , February 2014

http://en.wikipedia.org/wiki/Business_process_mapping


Some elements:

• 0 Client

• 1 Order request date

• 2 Order creation date

• 4 SKU analysis

Figure 1.16 Process mapping, start


• 5 Price analysis

• 6 Credit analysis

• 8 Define delivery date

• 12 Schedule delivery

• 15 Invoicing

• 16 Shipping

• 21 Delivery confirmation

Figure 1.17 First-round flows

Depending on the magnitude of the mapping efforts, a “war room” may be used to create a focused environment. More comprehensive projects involving larger teams and longer schedules will benefit from the structure of a war room. Figure 1.18 shows the war room used for a project at GE Transportation. This team worked simultaneously on 9 maps and created 49 flows connected to each other.

Some complementary interviews are expected during the phase of drawing the first round of flows. Additional sessions are usually required before the preliminary flows are done. The goal of prelimi-nary flows is to organize all information captured so far in a format detailed enough to go into a thorough presentation to those who were interviewed. The next figure shows a war room where processes are organized on different walls. These maps were to go through a two-day session with the interviewees.


DAY 1

DAY 2

Figure 1.19 Preliminary flows

Following the adjustments resulting from the presentation of the preliminary flows to the interviewees, it is now time to complete the process diagnosis with in loco observation and data analysis. Each project is unique. Therefore, the selection of what data must be ana-lyzed or what processes need observation do not follow a standard pattern. The process mapping team frequently plays detective in this phase.

Figure 1.18 War room


DataAnalysis Observation

REFINEDFLOWS

VALIDATEDFLOWS

ELECTRONICVERSION

04

05

BuildProcess

Flow

Figure 1.20 Refining flows, data analysis, and observation

Any process mapping must be executed with a clear purpose. The diagnosis will always be more efficient when a target has been defined in the early stages. Examples of these targets include the identifica-tion of :

• Low-productivity activities

• Activities with an interface with information systems

• Processes that generate inventory divergence

• Processes that cause customer dissatisfaction

• Any other approach that will improve organizational perfor-mance in any area of the business

There are infinite possible targets to a process mapping exercise.

Business Case

This process mapping was chosen to understand the root causes for recurrent inventory divergence in an external manufacturing facil-ity that serves a large multinational electronics industry headquar-tered in the United States. This case is inspired by an operation that includes three mobile assembly lines, one parts warehouse, and one finished goods distribution center.


Receiving Methodology: IDEF0

→activity-based mapping

→input, output, control, & mechanisms

→74 activities were mapped

Organizational commitment

→initial interview

→process in locus observation

→validation interview

Interview with 18 employees

Warehousing

Transfer

Production

Shipment

Figure 1.21 Case, scope

The mapping methodology used was IDEF0. Five processes were mapped: receiving, warehousing, transfer, production, and shipment. A total of 18 employees were interviewed and 74 activities identified. The goal of the mapping was to identify the cause of recurrent inven-tory divergence.

Each of the five processes was diagnosed after interviews, in loco observation, and data analysis. This diagram indicates 5 activities out of 12 are vulnerable and inventory divergence may occur. Note these items are parts for mobile devices. They are very small pieces; some of them can hardly be seen.

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GOODS TRANSFER (11 activities)VULNERABLE ACTIVITIES (8)POTENTIAL FAILURES (11)

Approverequisition inthe system

Figure 1.22 Case, map


CORRELATION INDEX

TOP 80 CORRELATIONDIVERGENCE VS INV. LEVEL

DIVERGENCE VS THROUGHPUT DIVERGENCE VS THROUGHPUT

AFTER TOP 80 CORRELATIONDIVERGENCE VS INV. LEVEL

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29% 90%

S no correlation no correlation SM MW W

Figure 1.23 Case, analysis

A detailed data analysis investigated the eventual correlation between inventory divergence and inventory holding or volume throughput. Throughput was shown to be correlated to the diver-gence of 80% of the items. These items represented only 45% of the divergence in value.

The challenge is then to identify the reason why 20% of the items generate 55% of the divergence.

Other analyses were done, such as inventory aging and divergence value by shift and by operator.

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REC EXABS EX

Figure 1.24 Case, analysis 2


UNIT COSTABOVE AVERAGE

492 ITEMS 90 ITEMS

UNIT COSTBELOW AVERAGE

DIVERGENCEBELOW EXPECTED

DIVERGENCEABOVE EXPECTED

33 1

458 89

Figure 1.25 Case, analysis 3

A possible cause was the unit value of these items. If these 89 items (20% of total) had a unit cost above the average unit cost, this could explain why their divergence summed such a high value. But all these items with high value divergence had a unit cost below the average unit cost.

It was during the observation in loco of the manufacturing opera-tion that one of the consultants noticed a junior operator was sweep-ing some items that were on the floor of the production area. When the consultant asked where these little items come from, the operator indicated three equipments (as circled in the map).

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Figure 1.26 Case, diagnosis


Conclusion

These three process equipments identified in the production flow faced some performance issues and several small parts that were processed through them were systematically being scrapped without being properly registered. The company would only acknowledge the missing components during inventory audit, but would not be able to track them back to the root cause. The combination of several factors affected 80% of the items that responded to 45% of inventory diver-gence. This required process improvements in all mapped areas. And a single factor related to the manufacturing operation caused 55% of divergence by not registering losses on 89 items, which represented 20% of total stock keeping units (SKUs). As a consequence of the process mapping, the following report was issued. Some information has been omitted due to confidentiality reasons.

Project Report: Sample

A) SUMMARY

• There is a weak correlation between the generated divergence and the product quantity in stock.

• A strong correlation exists between the generated divergence and the product quantity put into motion by the company.

• The items that generated more divergence (top 80) don’t fit into the patterns of correlation cited above, so they require bet-ter research.

• Some items have a higher % of losses than expected.

• Items with a % of losses higher than the expected can be origi-nated by production activities.

• There is strong evidence of failures in the records of material transferences for the production and from the process of fin-ished goods audit (FGA).


• There are generalized failures on all stages of material flow, with weak or nonexistent controls.

• There are three systems for the control/identification of divergences:

1. Cycle count, which is inefficient

2. FGA, which is a reactive system and therefore inefficient on the prevention of divergences

3. Physical inventory, an equally reactive system

• There are points of vulnerability in the production associated with not reporting all losses.

• There are failures in the internal communication of operational problems.

• There are conflicts of interest in the hierarchy of decision mak-ing, from the top down and the bottom up.

• The impact of failures in the material flows is reflected in the materials divergence on the work in progress (WIP), but is not generated totally in the production

B) Transformation Plan: Basic Steps

• Phase 1: Elaboration/review of approximately 40 procedures (receiving, warehousing, and transference to production). Implementation of actions plans.

• Phase 2: Parallel to phase 1. Elaboration and implementation of continuous training program.

• Phase 3: After phases 1 and 2. Complete review and implemen-tation of an independent cycle count program.

• Phase 4: Parallel to phases 1, 2, and 3. Elaboration or review of approximately 27 procedures (production & expedition). Implementation of action plans for those areas.


• Phase 5. After phases 3 and 4. Implementation of a process-based audit with total independence, on a daily basis, reporting to the site top management.

C) Potential Barriers to Implementation

• Reorganization of the internal communication system

• High turnover of employees

• Overloaded leading team (supervisors/managers)

• Leading team (supervisors/managers) with little experience on:

• Procedures elaboration techniques

• Internal audit techniques

• Cycle count program (design, start-up, rollout, ongoing)

• Many vulnerable activities that must be adjusted simultaneously

D) Loss-Elimination Scenarios

Loss-elimination scenarios (initial 12 months)

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Figure 1.27 Loss-elimination scenarios, 12 months


Loss-elimination scenarios (initial 24 months)

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Figure 1.28 Loss-elimination scenarios, 24 months

Validated Flows

At this time, nearly all the information has been captured in vari-ous interviews, innumerable data has been analyzed, and all signifi-cant processes have been observed in loco .

This session differs from previous ones because it is not an inter-view. Ideally, the interlocutor will only confirm that the presentation is accurate and represents the processes mapped with high fidelity. If relevant, new information comes up at this time; this will indicate that the previous stages have not been properly done.

The format this connected puzzle presents is varied; there is no golden rule. The more information the mapping team has captured, the more resources will be available to illustrate this final flow.

The following image is based on the process mapping of a ware-housing operation of a retailer. Note the arrows indicate correlations between different subprocesses. Some graphs (histograms) offer a time-based approach to the process map.

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Figure 1.29 Disclaimer: The processes identified in this map will vary based on the organization/industry goals, and so readability (or nonreadability, in this case) is not a factor for example purposes here.


Although the final version of the flow may offer a comprehensive perspective of the mapped processes, it is impossible to converge into it all the information captured during the interviews, data analysis, and in loco observations. The use of complementary organized data is therefore fundamental.

Given the popularity of spreadsheets, these are commonly the best way to organize all information captured during the mapping process. The format is highly customized according to the outputs of the project. Note that each line of the spreadsheet represents one specific activity mapped.

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Figure 1.30 Example of organized data


1.3 Risk Analysis

By having the processes mapped, a number of vulnerabilities are identified and properly registered, as discussed in the previous sec-tion. These vulnerabilities must be classified and prioritized. These dynamics may occur through three different mechanisms:

1. Reactive (focused) approach

2. Proactive (comprehensive) approach

3. Intermediate (business-driven) approach

The reactive approach to process vulnerability understanding (risk analysis) begins with a known issue. For example, a warehousing operation (SNAR 01.02.02) that registers a low picking productivity. This scenario calls for a focused process mapping to understand the activities, controls, and mechanisms mainly associated with picking operations.

Supply Network Governance Cycle - ANALYZE RISKS

REACTIVE (FOCUSED) APPROACH

DefineScope

KnownIssue

UnknownRoot Causes

KnownVulnerabilities

FocusedProcess

Map

ProcessUnderstood

ReviewedProcess

ProcessMap

AnalyzeRisks

DefineMetrics

ReviewCycle

Figure 1.31 Risk analysis, reactive approach

As the specific processes are mapped and the vulnerabilities are understood, process revisions may occur. It is important to remem-ber that process mapping necessarily includes the three pillars:


interviewing people, observing the process, and analyzing data. The difference between actual process and future processes defines the transformation effort.

The proactive approach to risk analysis occurs in two situations:

1. Several diffuse issues have disabled the reactive (focused) ap-proach.

2. It is a planning initiative, prior to the identification of any significant issues, and expects to give visibility to unknown inefficiencies.

The proactive approach begins with a comprehensive process mapping, covering several knowledge areas; it is suggested to follow the SNAR Model for defining the process mapping scope.


PROACTIVE (COMPREHENSIVE) APPROACH

DefineScope

ComprehensiveProcess Map

SNARMODEL


ClassifyVulnerabilities

Long-term action plan

BuildSNMP/SNCP

ProcessesUnderstood

AnalyzeRisks

RankedRisks

DefineStrategy

ProcessMap

AnalyzeRisks

DefineMetrics

ReviewCycle

Figure 1.32 Risk analysis proactive approach

After the processes are mapped, innumerable vulnerabilities are exposed that demand prioritization. As the risks associated with vulnerabilities are ranked, it is possible to define the implementa-tion strategy. Depending on the extent of the process mapping, it is


possible to build a Supply Network Master Plan (SNMP) as a refer-ence for the transformation process.


INTERMEDIATE (BUSINESS-DRIVEN) APPROACH

DefineScope

Experts’Panel

SNARMODEL


ClassifyVulnerabilities

Long-term action plan

BuildSNMP

ProcessesUnderstood

AnalyzeRisks

RankedRisks

DefineStrategy

SelectiveProcess

Map

ProcessMap

AnalyzeRisks

DefineMetrics

ReviewCycle

Figure 1.33 Risk analysis, intermediate approach

In addition, the implementation strategy may enable the elabora-tion of a Supply Network Continuity Plan (SNCP). The intermedi-ate risk analysis approach involves the contribution of experts in the operations to be mapped.

The experts panel selectively reduces the number of processes to be mapped, instead of a comprehensive process mapping a number of selected processes go through the risk analysis.

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Index

A achievement-oriented leadership, 181 activities, identifying objectives to

main activities, 60 - 61 adaptability, performance indicators,

93 - 97 additional costs as a percentage of

total freight cost, 133 adherence to vehicle type, 134 algorithms

defining, 47 - 48 efficacy performance indicators, 89 test algorithms, 49

applications, performance indicators, 97 - 98

planning logistics, 98 - 130 assigning

control to people, 56 - 57 names for controls, 46 - 47

attributes of performance indicators, 1 - 2

auditing performance indicators, 165 - 166

199

B benchmarking, 166 - 172

competitors, 172 efficiency, 171 performance indicators, 167 process-based, 169 traditional approaches, 167 - 168

business cases, 174 Change Management and

Leadership as Operations Management Tools, 174 - 175

FFC, 192 - 195 managing change, 184 - 191

map stakeholders, 52 - 53 cases, 53 - 55

monitoring the transport operation, 154 - 158

process mapping, 22 - 28

C case fill rate, 161 cases, monitoring the transport

operation, 154 - 158 . See also business cases

200 INDEX

change, managing. See managing change

Change Management and Leadership as Operations Management Tools, 174 - 175

charts, radar charts, 99 - 102 Christopher, Professor Martin, 82 - 85 communication strategies

process mapping, 11 project communication case, 65 - 75

Competencies and Skills for Tomorrow’s Supply Chain Managers, 82 - 85

competitors, benchmarking, 172 control, assigning to people, 56 - 57 control chart

scoring, 14 SNG (Supply Network Governance)

Cycle, 7 control dynamics, validating, 55 - 56 controls

assigning names for, 46 - 47 defining in metric projects, 61 - 64

cost avoidance, 116 cost per order, 148 Cultural Web Model, 175 - 184 customer order actual cycle time, 159 customer order promised cycle

time, 159 customers’ claims, 160

D data analysis principles, 11 days in hand (DIH), 121 days on the road per driver

(DOR), 157 deliveries per driver, 155 delivery in full, 133 delivery on time (DOT), 133 demand, 104 - 105 DIH (days in hand), 121

directive leadership, 180 distraction, 178 distribution service level (DSL),

153 - 154 dock-to-stock cycle time, 145 DOR (days on the road per

driver), 157 DOT (delivery on time), 133 DSL (distribution service level),

153 - 154

E efficacy, performance indicators,

89 - 93 efficiency

benchmarking, 171 performance indicators, 88

examples customer service example, 75 - 78 quality assurance examples, 80

expenses versus targets, 99 export freight cost, 152 export lead time, 153

F fast-moving consumer goods

(FMCG), 59 FCPSU (freight cost per shipped

unit), 150 - 151 FFC, 174 - 175

Cultural Web Model, 175 - 184 organizational bad governance, 177 organizational structure, 176 psychological moments, 183 - 184 ,

186 , 195 transition curves, 187 - 188

fine-tuning implementation, 57 - 58 flows, 18 - 20

preliminary flows, 21 process mapping, 14 - 15 validated flows, 32 - 35

INDEX 201

FMCG (fast-moving consumer goods), 59

forecasting, 108 forecasting warehousing occupation,

145 freight cost per shipped unit

(FCPSU), 150 - 151 freight invoice accuracy, 133

G-H GMROI (gross margin return on

inventory), 119 goals

metric projects, 59 - 60 performance goals, defining, 50

gross margins return on inventory (GMROI), 119

Grundy, Tony, 178 guidelines for process mapping, 10 - 11

I-J IBO (inventory level by origin), 124 ICC (inventory carrying cost), 119 identifying objectives to main

activities, 60 - 61 impact of performance indicators, 165 implementation, fine-tuning, 57 - 58 import freight cost, 153 inbound cost freight as a percentage

of purchase, 133 intermediate approach, risk analysis,

38 interviewing principles, 10 introduction phase, performance

patterns, 45 inventory, tactic governance, 158 inventory accuracy, 123

inventory by service level (INV.SL), 130

inventory carrying cost, 119 inventory carrying rate, 119 inventory divergence, 123 inventory excess, 126 - 127 inventory level by origin (IBO), 124 inventory record accuracy (IRA), 143 inventory replenishment cycle

time, 119 inventory turns, 119 inventory variation by item, 123 - 124 invoice matching order, 133 INV.SL (inventory by service

level), 130 IRA (inventory record accuracy), 143

K key subprocesses (KSPs), 12 - 31 KM/liter per driver, 156 knowledge areas, selecting, 7 KSPs (key subprocesses), 12 - 31

L leadership, 180 - 182

situational leadership, 188 - 189 leadership skills, 182 - 183 learning curves, 52 LFR (line fill rate), 93 , 160 load size, 135 loading time, 139 Locus of Control and Leadership

Model, 183 , 192 - 193 losses as a percentage of freight

cost, 133 lower left graph, 147

202 INDEX

M Managerial Grid Reference

Model, 179 managing change

auditing performance indicators, 165 - 166

benchmarking, 166 - 172 business cases, 174

Change Management and Leadership as Operations Management Tools, 174 - 175

FFC, 184 - 191 , 192 - 195 map stakeholders, 52 - 53

cases, 53 - 55 MAPE (mean average performance

error), 42 , 105 - 108 segmentation, 108

mapping the process. See process mapping

mean average performance error. See MAPE (mean average performance error)

mechanical failure index (MFI), 156 medium-term inventory variation

(MTIV), 121 metrics

algorithms defining, 47 - 48 test algorithms, 49

assigning, control to people, 56 - 57 assigning name for controls, 46 - 47 defined, 39 fine-tuning, implementation, 57 - 58 learning curves, 52 map stakeholders, 52 - 53

cases, 53 - 55 observing performance, 41 - 42 performance patterns, 45 projects, 59

closing loops, 64 customer service example,

75 - 78 defining controls, 61 - 64 expert opinions, 82 - 85

goals, 59 - 60 identifying objectives to main

activities, 60 - 61 project communication case,

65 - 75 quality assurance examples, 80

review cycle, 58 validating control dynamics, 55 - 56

metrics performance goals, defining, 50

MFI (mechanical failure index), 156 monitoring the transport operation,

cases, 154 - 158 MRO (maintenance, repair,

operations), 42 MTIV (medium-term inventory

variation), 121

O observation principles, 11 observing, performance, 41 - 42 obsolescence cost, 148 on-hold inventory, 123 on-time case count, 133 on-time delivery (OTD), 114 on-time in-full (OTIF), 89 - 93 on-time line count, 132 order size, 160 organizational bad governance,

Source of Failed Vision, 177 organizational structure, FFC, 176 OTD (on-time delivery), 114 OTIF (on-time in-full), 89 - 93 outbound cost freight as percentage of

net sales, 151 outbound freight, 152

P participative leadership, 181 path-goal theory, 180 - 181 perfect order, 164 performance, observing, 41 - 42

INDEX 203

performance goals, defining, 50 performance indicators, 87

adaptability, 93 - 97 applications, 97 - 98

planning logistics, 98 - 130 attributes, 1 - 2 auditing, 165 - 166 benchmarking, 167 cases, monitoring the transport

operation, 154 - 158 efficacy, 89 - 93 impact of, 165 synchronous operations. See

synchronous operations tactic governance. See tactic

governance performance indicators efficiency, 88 performance patterns, 45 picking accuracy, 137 picking on time, 136 - 137 picking productivity, 137 planning logistics

cost avoidance, 116 demand, 104 - 105 DIH (days in hand), 121 forecasting, 108 GMROI (gross margin return on

inventory), 119 on-hold inventory, 123 IBO (inventory level by origin), 124 ICC (inventory carrying cost), 119 inventory accuracy, 123 inventory divergence, 123 inventory excess, 126 - 127 inventory replenishment cycle

time, 119 inventory turns, 119 inventory variation by item, 123 - 124 INV.SL (inventory by service

level), 130 MAPE (mean average performance

error), 105 - 108 MTIV (medium-term inventory

variation), 121

price variation, 111 - 112 price volatility, 104 - 105 purchase cycle, 116 purchase cycle time, 111 purchase reliability, 98 - 99 QPP (quality of purchased parts),

118 - 119 raw materials availability to

production, 110 sales forecast accuracy, 102 SDP (supplier’s delivery

performance), 114 stock aging, 129 stock-outs, 123 supplier’s delay, 112 supplier’s service level, 114 - 115 on-time delivery (OTD), 114 WCap (working capital), 126

plant managers, 178 POs (production orders), 132 post-introduction phase, performance

patterns, 45 preliminary flows, 21 price variation, 111 - 112 price volatility, 104 - 105 proactive approach, risk analysis, 37 process mapping, 9 - 10

business case, 22 - 28 communication strategies, 11 flows, 14 - 15 , 18 - 20

preliminary flows, 21 validated flows, 32 - 35

guidelines for, 10 - 11 project report sample, 29 - 31 starting, 18 - 19 subprocesses, 11 targets, 22 war rooms, 20 - 21

process mapping details, 16 process mapping period, 16 process mapping spiral, 15 process-based benchmarking, 169 production cycle time, 149 production orders (POs), 132

204 INDEX

production service, 132 production volume, 150 project communication case, 65 - 75 project report sample, process

mapping, 29 - 31 projects

customer service example, 75 - 78 metrics, 59

closing loops, 64 defining controls, 61 - 64 expert opinions, 82 - 85 goals, 59 - 60 identifying objectives to main

activities, 60 - 61 project communication

case, 65 - 75 quality assurance examples, 80

psychological moments, FFC, 183 - 184 , 186 , 195

purchase cycle, 116 purchase cycle time, 111 purchase reliability, 98 - 99

Q QPP (quality of purchased parts),

118 - 119 quality assurance examples, 80 quality of purchased parts (QPP),

118 - 119

R RAA (receiving schedule

adherence), 147 radar charts, 99 - 102 raw materials availability to

production, 110 reactive approach, risk analysis, 36 - 37 receiving schedule adherence

(RAA), 147 receiving seasonality, 145

receiving without divergence, 139 Region A, 158 returns, 148 returns by segments, 149 review cycle, 58 risk analysis, 36

intermediate approach, 38 proactive approach, 37 reactive approach, 36 - 37

S sales forecast accuracy, 102 scales, effect of, 42 scope

defined, 3 - 7 specifying, 40 - 41

scoring, control chart, 14 SDP (supplier’s delivery

performance), 114 segmentation, MAPE (mean average

performance error), 108 service level and inventory, 158 shipping errors, 144 shipping productivity, 140 shipping seasonality, 152 situational leadership, 181 , 188 - 189 SKU fill rate, 160 SKUs (stock keeping units), 29 SNAR (Supply Network Alignment

Reference) Model, map stakeholders, 52 - 53

SNCP (Supply Network Continuity Plan), 38

SNG (Supply Network Governance) Cycle, 2

control chart, 7 process mapping, 10

Source of Failed Vision, organizational bad governance, 177

specifying scope, 40 - 41 speed excess, 157

INDEX 205

speed excess per driver, 158 starting process mapping, 18 - 19 stock aging, 129 stock keeping units (SKUs), 29 stock-outs, 123 subprocesses, 11

KSPs (key subprocesses), 12 - 31 supplier’s delay, 112 supplier’s delivery performance, 114 supplier’s service level, 114 - 115 Supply Network Continuity Plan

(SNCP), 38 Supply Network Governance

Cycle. See SNG (Supply Network Governance) Cycle

supportive leadership, 180 synchronous operations

additional costs as a percentage of total freight cost, 133

adherence to vehicle type, 134 cost per order, 148 delivery in full, 133 dock-to-stock cycle time, 145 DOT (delivery on time), 133 DSL (distribution service level),

153 - 154 export freight cost, 152 export lead time, 153 FCPSU (freight cost per shipped

unit), 150 - 151 forecasting warehousing

occupation, 145 freight invoice accuracy, 133 import freight cost, 153 inbound cost freight as a percentage

of purchase, 133 invoice matching order, 133 IRA (inventory record

accuracy), 143 load size, 135 loading time, 139

losses as a percentage of freight cost, 133

lower left graph, 147 obsolescence cost, 148 outbound cost freight as percentage

of net sales, 151 outbound freight, 152 picking accuracy, 137 picking on time, 136 - 137 picking productivity, 137 POs (production orders), 132 production cycle time, 149 production service, 132 production volume, 150 RAA (receiving schedule

adherence), 147 receiving seasonality, 145 receiving without divergence, 139 returns, 148 returns by segments, 149 shipping errors, 144 shipping productivity, 140 shipping seasonality, 152 team productivity, 139 - 140 on-time case count, 133 on-time line count, 132 transit time, 133 transport cost, 135 transport tracking percentage, 134 TRT (total receiving time), 142 - 143 truck turnaround time, 134 TVST (total vehicle staying

time), 142 unplanned transport, 134 VCT (vehicle check-in time), 140 vehicle capacity utilization, 133 VUT (vehicle unloading time), 141 warehouse handling cost, 148 warehousing occupation, 145 warehousing productivity, 139

206 INDEX

T tactic governance

case fill rate, 161 customer order actual cycle

time, 159 customer order promised cycle

time, 159 customers’ claims, 160 DIH, OTIF, and inventory

value, 160 LFR (line fill rate), 160 order size, 160 perfect order, 164 Region A, 158 service level and inventory, 158 SKU fill rate, 160 value fill rate, 161

targets versus expenses, 99 process mapping, 22

team productivity, 139 - 140 test algorithms, 49 total receiving time (TRT), 142 - 143 total vehicle staying time (TVST), 142 transit time, 133 Transition Curve Model, 191 transition curves, FFC, 187 - 188 transport cost, 135 transport tracking percentage, 134 trips per driver, 155 TRT (total receiving time), 142 - 143 truck turnaround time, 134 TVST (total vehicle staying time), 142

U unplanned transport, 134

V validated flows, 32 - 35 validating control dynamics, 55 - 56 value fill rate, 161 VCT (vehicle check-in time), 140 vehicle capacity utilization, 133 VUT (vehicle unloading time), 141

W-X-Y-Z war rooms, process mapping, 20 - 21 warehouse handling cost, 148 warehousing occupation, 145 warehousing productivity, 139 WCap (working capital), 126

Documents

Executing the Supply Chain