19463.5 5 CC

THSASSUMPTION UNIVERSITY r,

OPTIMIZATION OF PRODUCTION PLANNING AND SCHEDULING: A LINEAR PROGRAMMING APPROACH AT

UNILEVER THAI HOLDING LIMITED

By

THICHARAS CHANAKULSETHACHAI

A Final Report of the Six-Credit Course SCM 2202 Graduate Project

Submitted in Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCE IN SUPPLY CHAIN MANAGEMENT

Martin de Tours School of Management Assumption University

Bangkok, Thailand

October 2008

Project Title Optimization of Production Planning and Scheduling: A Linear Programming Approach

Name Ms. Thicharas Chanakulsethachai

Project Advisor Dr. Athisarn Wayuparb

Academic Year October 2008

ABAC School of Management, Assumption University has approved this final report of the six-credit course, SCM 2202 Graduate Project, submitted in partial fulfillment of the requirements for the degree of Master of Science in Supply Chain Management.

Approval Committee:

(7)41-YYVV8

(Dr. Athisarn Wayuparb) (Dr. Cslxayakrit Charoensiriwath)

Committee Advisor

(Dr.Ismail Ali Siad)

Chairman

October 2008

ii

Assumption University ABAC School of Management

Master of Science in Supply Chain Management

Form signed by Proofreader of the Thesis/Project

Asst. Prof. Brian Lawrence , have proofread this thesis/project entitled

"Optimization of Production Planning and Scheduling: A Linear Programming Approach at Unilever

Thai Holding Limited"

Ms. Thicharas Chanakulsethachai

and hereby certify that the verbiage, spelling and format is commensurate with the quality of internationally

acceptable writing standards for a master degree in supply chain management.

Signed

(Asst. Prof. Brian Lawrence)

Contact Number / Email address blawrence@au.edu

Date: I I

I 1,1 -1..5C\:› •?S

ABSTRACT

Today many companies try, as much as possible, to produce their products to

meet customer demand. Companies have inventory management and production

planning strategies to meet the customer service levels. These, sometimes, in each

stage in a supply chain face conflict strategies, such as that marketing and sales would

like to have more stock to protect against product shortage, but another department

would like to manage the inventory level as low as possible to meet the company

target. Trying to reduce the manufacturing production cost as much as possible, large

batch production is preferred so as to reduce the setup cost.

Supply Chain Management is the management of flows between and among all stages

in a supply chain in order to maximize the profitability or minimize the cost for the

whole supply chain. Common targets are to achieve the company goal and smooth the

operations throughout the stages in the supply chain, which means that at each stage

real information must be shared so as to try and fix problems and find the solutions

that will help the company to drive the business.

Many companies have implemented Enterprise Resource Planning (ERP) which

integrates application programs in finance, manufacturing, logistics, sales and

marketing, human resources, and the other functions in a firm. ERP enables people to

run their business with high levels of customer service and productivity, and

simultaneously lower costs and inventories. The company which is the subject of this

report implemented the ERP system called SAP/APO in two modules: Demand

Planning (DP) and Supply Network Planning (SNP). Production planning and

inventory management are contained in the SNP module. The expectations for

implementing the SAP/APO system are operation time reduction, data accuracy, and

cost reduction. After the company started SAP/APO, there were some problems in

terms of the result of Master Production Scheduling (MPS) from APO in SNP module

in running heuristic and optimization. The master production scheduling (MPS) result

might be not the optimal production planning system, as in the study of Kreipl et al

(2004). Thus, this paper studies another mathematical method, linear programming

(LP), to compute master production scheduling and cross check with the result from

SAP/APO, using the same environment constraints in SAP/APO master data setup.

iii

ACKNOWLEDGEMENTS

In the completion of this project, I would like to take this opportunity to give

particular recognition and thanks to all the following people.

First, I wish to express sincere gratitude to my project advisor Dr. Athisarn

Wayuparb, for continuous patient assistance, guidance, and constant encouragement

which has led me to the project's completion. He taught me to analyze research

problems and to develop approaches systematically. He showed me different ways to

approach research problems, and the need to be persistent to accomplish any goal.

Without his support and inspiration, my project might not be completed.

Next, I also would like to thank all the professional professors in the Master of

Science in Supply Chain Management program, from whom I gained knowledge

which helped me to apply the approaches applied in this paper.

Third, I would like to express appreciation to all my graduate project

committee members, Dr.Ismail Ali Siad and Dr. Chayakrit Charoensiriwath, who

gave insightful comments and guidance for the accomplishment of this project.

And lastly, let me say 'thank you" to the following people at Unilever Thai

Holding Ltd: Mr.Pichayane Tanprasert (Manufacturing Director- Food & Ice Cream),

Ms.Siriporn Meeboonkerd (Regional Customer Service Director), Ms.Suwanna

Mulakul (Regional Sourcing Unit Planning Manager-Food & Ice Cream) and all my

colleagues who enabled, organized and supported me with all the relevant information

collection until this project was completed.

iv

Tor ASSIT/WPTION' VISTIVERSITYTM

Table of Contents

Chapter Page

TITLE PAGE ii

ABSTRACT iii

ACKNOWLEDGMENTS iv

TABLE OF CONTENTS

LIST OF ABBREVIATIONS vii

LIST OF TABLES viii

LIST OF FIGURES

1. INTRODUCTION 1

1.1 Company background 1

1.1.1 Company's products 1

1.1.2 Company's supply chain model 2

1.2 Problem analysis 3

1.3 Problem statement 5

1.4 Objectives 8

1.5 Scope of the project 8

1.6 Deliverables 8

1.7 Outcome of project 9

2. Literature Review 10

2.1 Master Production Scheduling (MPS) 11

2.1.1 Definition of MPS 11

2.1.2 Function of the MPS 11

2.2 SAP/APO system 12

2.2.1 Definition of SAP/APO 12

2.2.2 APO-SNP Process 12

2.2.2.1 Supply network planning flow 12

2.2.2.2 Master data setup 13

Chapter Page

2.2.2.3 Transaction data and data input 14

2.2.2.4 Heuristic and Optimizer run 14

2.2.2.5 Data output 15

2.3 The analytic methodology approach for MPS 16

2.3.1 Comparison of analytic methodology model approaches 16

2.3.2 Analytic methodology: LP approach 20

3. METHODOLOGY 23

3.1 Data collection 23

3.2 Develop Linear programming function 26

3.3 Run MPS by LP solver 39

3.4 Comparing MPS between SAP/APO system and LP solver 44

4. RESULTS AND ANALYSIS

46

5. CONCLUSION AND RECOMMENDATION

55

5.1 Conclusions 55

5.2 Limitations 57

5.3 What do we learn from this project? 57

5.4 Recommendations 58

REFERENCES 59

vi

LIST OF ABBREVIATIONS

APO: Advanced Planning and Optimization

ATP: Available to Promise

BOM: Bill of Material

BPCS: Business Process Control System

CP: Constraints Programming

CPRF: Collaborative Planning, Forecasting & Replenishment

CRM: Customer Relationship Management

DP: Demand Plan

GA: Genetic Algorithm

IP: Integer Programming

IWC: Interest Working Capital

LDR: Linear Decision Rule

LP: Linear Programming

MP: Master Planning

MPS: Master Production Scheduling

PPM: Production Process Model

SAP: System Application and Product in Data processing

SNP: Supply Network Planning

LIST OF TABLES

Page

Table 1.1: Conflict performance indicators between category planning 5

and production department

Table 1.2: MPS from SAP/APO week no.1 -12 6

Table 1.3: MPS from actual production run week no.1-12 7

Table 2.1: Comparison for application, advantage and limitation of each 17

methodology model

Table 3.1: Production cost, Production standard cost and Inventory holding cost 24

Table 3.2: Historical demand plan volume for six products in weekly bucket 25

Table 3.3: Constraints planning and production constraints 26

Table 3.4: Stock on-hand level at the beginning of week no.1 31

Table 3.5: Stock on-hand level of weeks no.2, 3, 4 and 5 35

Table 3.6: Final solution of MPS for 6 products during 4 weeks period 43

Table 3.7: The production cost in THB for 6 products during 4 weeks period 43

Table 3.8: The inventory holding cost in THB for 6 products during 4 weeks 43

period

Table 3.9: The total cost in THB for 6 products during 4 weeks period 44

Table 3.10: Comparison MPS between SAP/APO and LP solver 44

Table 4.1: Comparison detail master production scheduling result between 47

SAP/APO and LP solver for product TH COA

Table 4.2: Comparison detail master production scheduling result between 47

SAP/APO and LP solver for product TH COB

Table 4.3: Comparison detail master production scheduling result between 47

SAP/APO and LP solver for product TH COC

Table 4.4: Comparison detail master production scheduling result between 48

SAP/APO and LP solver for product TH COD

Table 4.5: Comparison detail master production scheduling result between 48

SAP/APO and LP solver for product TH COE

Table 4.6: Comparison detail master production scheduling result between 48

SAP/APO and LP solver for product TH COF

viii

Page

Table 4.7: Total production cost and total inventory holding cost between 53

SAP/APO and LP solver in THB

Table 4.8: Total cost result between SAP/APO and LP solver in THB 54

ix

LIST OF FIGURES

Page

Figure 1.1: The portfolio of categories in year 2005 2

Figure 1.2: Powerful category positions and brands 2

Figure 1.3: Company's supply chain process model 3

Figure 1.4: Process flow of company's supply chain model 4

Figure 1.5: Total production run volume (TL) 2008 8

Figure 2.1: Supply network planning process flow 13

Figure 2.2: Master production scheduling report 15

Figure 2.3: Cost functions used in linear decision rules 18

Figure 3.1: Methodology proposed framework 23

Figure 3.2: Demand plan volume in weekly bucket of 6 products 25

Figure 3.3: Stock on-hand level at the beginning of week 1 30

Figure 3.4: Stock on-hand level remaining at the end of week 1 30

Figure 3.5: Production run at week 1 30

Figure 3.6: Stock on-hand level at the beginning of week 2 30

Figure 3.7: Demand plan volume at week 2 and week 3 33

Figure 3.8: Stock on-hand level at the beginning of week 2 33

Figure 3.9: Maximum production scheduling at PR GF machine 35

Figure 3.10: The integer function 37

Figure 3.11: Setting to defme target cell for calculation total cost 39

Figure 3.12: Setting changing cell, defme the integer function and created cells 39

formulation for computing MPS

Figure 3.13: setting to defme all constraints for this case study 40

Figure 3.14: LP solver started running by click "solve" button 40

Figure 3.15: LP solver is running 41

Figure 3.16: The LP solver found a solution and compute MPS at 41

cells formulation

Figure 3.17: The cell formulation for calculated total inventory holding cost, 42

production cost and total cost

Figure 4.1: Detail production scheduling before SAP/APO and LP solver 49

generated master production scheduling

x

ASSIVIPTiON UNIVERSITY UM Ai*

Page

Figure 4.2: Detail production scheduling after SAP/APO and LP solver 49

generated master production scheduling at week 1

Figure 4.3: Detail production scheduling after SAP/APO and LP solver 50

generated master production scheduling at week 2

Figure 4.4: Detail production scheduling after SAP/APO and LP solver 50

generated master production scheduling at week 3

Figure 4.5: Detail production scheduling after SAP/APO and LP solver 51

generated master production scheduling at week 4

Figure 5.1: Cost comparison between Sap/APO and LP solver 56

Figure 5.2: Master production scheduling result following with production 56

constraints and constraints planning between SAP/APO and LP solver

xi

Chapter 1

Introduction

1.1 Company Background

In the 1890s, William Hesketh Lever, founder of Lever Bros, wrote down his ideas

for Sunlight Soap — his revolutionary new product that helped popularizes cleanliness

and hygiene in Victorian England. It was 'to make cleanliness commonplace; to lessen

work for women; to foster health and contribute to personal attractiveness, that life

may be more enjoyable and rewarding for the people who use our products'.( Source

of company's data year 2008)

This was long before the phrase 'Corporate Mission' had been invented, but these

ideas have stayed at the heart of our business. Even if their language - and the notion

of only women doing housework — has become outdated. (Source of company's data

year 2008)

In a history that now crosses three centuries, Unilever's success has been influenced

by the major events of the day — economic boom, depression, world wars, changing

consumer lifestyles and advances in technology. And throughout the company created

products that help people get more out of life — cutting the time spent on household

chores, improving nutrition, enabling people to enjoy food and take care of their

homes, their clothes and themselves. (Source of company's data year 2008)

1.1.1 Company's Product

The major product group can be separated into three groups: Home care, Personal

care, Food and Ice Cream.

1

Savoury & Dressings 21%

1.- Ice Cream and Frozen Food 16%

Source of company's data year 2008

Beverages 8%

Weight Management

World Number I

World Number 2

j Local strength

Deodorants:" Laundry - #1 In D&E

Daily Hair Care - #1 In D&E

Household Care

I 1

fleccy Our 12 €1 bn brands

4.( %DM 0 1111. Lux_ 44110 '1>ote

Surf VIREO CUIZZI SUNSILK

rtIftettstiviTnownirvERsrryt,

Home Care 18% Spreads 11%

Figure 1.1 Portfolio of categories of each group in year 2005

Foods

Savoury & Dressings

Home & Personal Care

Skin

Ice Cream

Oral Care

Source of company's data year 2008

Figure 1.2 Powerful Category Positions and Brands

1.1.2 The Company's Supply Chain Model

In the company's Supply Chain Model, there are two main activities. The first is

primary activity that is in direct contact with the product, and the second is secondary

activity which is the support functions such as human resource management, finance.

The primary activity is divided into four parts. The first is Plan consisting of two

departments: Demand plan and Category Planning. The second part is Source, which

has one department, Buying department. The third part is Make, which consists of two

departments: Production planning and RSU planning. The last part is Delivery,

represented by the Distribution department or called outbound logistics.

2

Suppliers

Brand Developmen

Plan

Customer evelopment

Consumers

Customers

Supply Chain Process Model Supply Chain Mission & Strategy

Information Management i Human Resource Management ,11 Quality & Business Excellence 3

Finance Management a Safety. Health & Environment i

Technology Management I

Source of company's data year 2008

Figure 1.3 Company's Supply Chain process model

1.2 Problem Analysis

Supply chain management focuses on positioning the organization in such a way that

all participants in the supply chain benefit. Thus, effective supply chain management

relies on high levels of trust, cooperation, collaboration and accurate communication.

Referring to the Company's Supply Chain model, it was separated into four parts.

Each part contains many departments. The Company tries to be an excellent supply

chain partner, which means that it manages stages in the supply chain by managing

the operation activities, monitors performance indicators and improves the operation

to achieve the company target to drive the business to be in the market leader position.

But at each stage of the supply chain there is conflict in performance indicators to

meet the company target that is the common goal of accountability of all departments.

Conflicting performances, create a conflict operation and complexity between

departments in the supply chain.

3

Generate Sales Forecast

Monitroing FG Stock and actual

sale

To plan production run

and call off material

Produce Finished Goods

Send Finished Goods to Customer

Dem and Planning

Production Departm ent

\._ I - - - - - - - - - - - - - - - - - - i

W are housing ( Cold Store)

1 1 1 I 1 I I 1

I Planning I Category

f........iiiimlill Il

Open purchase order

DC/ Retailer

Source of company's data year 2008

Figure 1.4 Process flow of Company's Supply Chain model

Referring to the company's supply chain model, it starts from the demand plan

department to generate the demand plan volume of each product, and to send the

information to category planning to manage the finished goods stock to meet the

demand plan volume. The next department is RSU planning, which is accountable for

production planning and material planning that coordinate with category planning to

agree the finished good stock. It has contact with the buying department in terms of

material planning by sending material requirements to the buying department to open

a purchase order to buy material from suppliers and make an agreement with the

production department to produce the finished product by following the master

production scheduling that is generated by the system. After finishing the production

run, the finished product will be kept in the warehouse until the customer takes the

order, and then the distribution department send the product to the customer within

the lead-time commitment.

4

This paper is concerned with the production planning process. Referring to the

company's supply chain model, there are three department concerned with the

production planning process, which consist of Category planning, RSU planning and

Production department. As mentioned, there are conflicting performance indicators

among departments in the supply chain. In term of production planning, there are

common goals between department for a customer service level. Under the common

goal, the conflicting performance indicators between department create the

complexity and conflict in operation between departments. Table 1.2 shows the

common goal and conflict objective from the different performance indicators that

have the same concept in term of low operation cost.

Table 1.1 Conflict performance indicators between Category planning and production

departments.

Common Goal Category Planning

% Customer Service

Production Department

Company Target Level

Department Target Low Inventory Holding Cost Low Production Cost

Conflict Objective to - Small Batch Size - Large Batch Size Minimize cost - Simple Production Scheduling - Complicate Production

Scheduling - Short Lead time to customer - Long lead time to

customer - Sufficient Storage Capacity - Excessive Storage

Capacity - Leaves less capital tied up in - Leaves too much capital stock tied up in stock - Low value of the holding cost - High Value of the holding

cost

1.3 Problem Statement

The company tries to reduce the conflicting objectives to minimize the total costs of

the operation between departments. The company implemented a system that helps

the operation in term of time reduction, reduced complexity, and minimized cost. We

started to implement only two modules, which are demand planning (DP) module and

supply network planning (SNP) module. Production planning is in the "SNP" module.

5

The SNP module helps the operation to minimize the total cost by balancing

inventory holding cost and production cost. The expectation from the "System

Application and Product in Data processing (SAP)/ Advanced Planning and

Optimization (APO)" system should suggest an optimal master production scheduling

to minimize the total cost that balance inventory holding cost and production cost. In

contrast, the results of master production planning from SAP/APO are not optimal,

and some periods suggested more production planning that affect the creation of high

inventory holding cost but shows low production cost. A suggestion is that less

production planning created complexity for the production manager to have more

material wastage and high setup cost but generated a low inventory holding cost.

The optimal master production scheduling result from SAP/APO was not matched by

the actual operation for the production run. This means we cannot use master

production scheduling results after loading the master production scheduling report

from SAP/APO system. Planners have to review and revise master production

scheduling quantity to match with actual production running before distributing

master production scheduling information to the production department to produce the

finished product. The example for inaccuracy master production scheduling result is

shown in Table 1.2, and the actual production run is shown in Table 1.3.

Table 1.2 Master production scheduling from SAP/APO, weeks no. 1 - 12

r%71

IC CO TO M CO UM Production Plan IC PR GP 18,412 35,250 11,181 • - 11,625

IC CO TH COB CO UCH HIT Production Plan IC PR GP - 25,109 • 11,861 11,625 . 25,301 . • . 15,988

IC CO TH COC COBROWNIE Production Plan IC PR G1 11,615 11,625 • 11,625 19,394 11,185 • 19,935 • 11,625 • •

IC CO THSOB CO CHOCOLATE Production Plan ICPRGf - . • - 11,625 . • 11925 • . • •

Total quantity should not be IC CO TH_COB CO_STRANIBERRY Rohde° Plan IC_PR GP 11,625 • • 11,866 ,131

IC CO 111 4 CO BLUEBERRY Production Plan IC PR G1 • . . 21,1 . a more than 79,313 cases that be maximum production run

SAP/APO suggested total =

Total Prildn Plan production scheduling 81,075 cases 83,662 11,981 IT, 81,015 01,015 11,501 5901 31,560 • 98,100 25,960 52,326

,313 19,313 79,313 19, 19,313 79,313 /9,313 19,313 1913 19,313 19,313 19,313 Reduction output avail lle MEd

Production Plan Oyer Capacity 4,350 1,163 1,160 19,368

Source of company's data year 2008

6

Table 1.3 Master production scheduling from actual production run,

weeks no. 1-12

0 CO ALMON Production Plan ICPAGF . _•.1:..=! .;

32,324 35,001 11,625 IC CO TH COA _ 11,625

IC CO III COB CO BLACK FAT Production Plan IC PR GE • 29,000 • 11,625 11,625 . 25,450 . 22,600 .

IC CO TH_COC CO BROWNIE Production Plan IC PR GE 33,120 30,135 28,751 11,750 • 11,625 • •

IC CO TH_CO0 CO CHOCOLATE Production Plan IC PII_GI • . - 11,150 • 17,625 • • •

IC CO THCOE CO STRAWBERRY Production Plan IC PR G1 11,150 . - 11,625 11,099 - 31,900 • 17,625 . • 35,000

IC CO TH_COF CO BLUEBERRY Production Plan IC PR GE 11,750 11,750 20,500 33,000 33,000

Total production scheduling per week not be more than 79,3 13

Total Producion Plan Thin

`11,194 64,00064,000

. ol 60,210 41,090 40,611 49,250 50,350 29,315

.1. 50,615 50,625 22,000 52,6251

Production output avail? 19,313 79,313 79,313 79,313 79,313 79,313 /9,313 79,311 79,313 79,313 79,311 19,313

Production Plan Over C pocky

Source of company's data year 2008

The root causes for inaccurate master production scheduling results are:

1. Master Data Setup: The company implemented SAP/APO system by using a

common region process in a single IT system, which means the configuration to

input into the system are the estimated data the exact data, such as expected set up

time, average processing time.

2. Constraints. Planning or production constraints that cannot input all constraints

into the system, such as storage capacity and labor constraints.

3. Limitation of SAP/APO. Stephan and Michael (2004, pp.77-92) studied the SAP

APO tool for planning and scheduling in supply chain operations. APO adopts LP

models for solving MP problems and uses the LP optimization engine developed

by CPLEX. However, because the scale of a complete LP model is too large, such

model must be split into several sub-models. The computer running time would be

between 10 - 20 hours if each LP sub-model had 100,000 to 500,000 variables

and 50,000 to 500,000 constraints. Moreover, the final solution might not be

optimal.

Therefore, this paper will study a mathematic model to compute the optimal master

production scheduling by using the same environment constraint as the master data set

up in SAP/APO, and cross check the final solution from proposed methodology and

SAP/APO. The methodology studied in this paper is linear programming (LP) to

minimize an objective function subject to constraints in non-negative variables.

7

PR_GF PR_RA PR_FIB PR_RC PR_RD PR_RE PR_TF Machine

100%

90%

— 80%

70%

60%

50% F' 40%

30%

20%

10%

FI R PR_MN PR_LL

nr , PR_VE

Production Run Volume 1111111 1,

a=r Production Volume Percentage

8.000

7.000

6.000

5.000

). .""' • 4 000

3.000

2.000

1.000

1.4 OBJECTIVES:

1. To understand the mathematic methodology of Linear Programming.

2. To identify and develop a function of total cost, define a set of decision

variables and constraints.

3. To find and compare the optimal production planning and total cost between

SAP/APO and a linear programming technique.

1.5 SCOPE OF THE PROJECT:

1. This project will compare the final result for optimal master production

scheduling between SAP/APO system and linear programming.

2. To study only one production line (machine line name: PR GF) that produced

the major products and highest production run volume. There are six finished

products that were produced at this machine line, and they have the same

production rate and product characteristics (pack size).

Source of company's data year 2008

Figure 1.5 Total production run volume (TL) 2008 for each machine line

1.6 DELIVERABLES (Expected Results):

1. To develop an LP solution for computing the master production scheduling.

2. To create a tool for planning decision making.

3. To understand causes of the results that were suggested are not optimal by

SAP/APO.

8

nizts'sviiOnow 'UNIVERSITY LTT3

1.7 OUTCOMES OF THE PROJECT:

1. Minimize total supply chain cost in term of holding cost and production setup

cost.

2. Balance performance indicator confliction among the supply chain stages.

3. Improve customer satisfaction in order to increase demand and market share.

4. Reduce complexity for production planning

9

2.1 Master Production Scheduling (MPS)

2.1.1 Definition of the master production scheduling

2.1.2 Function of the master production scheduling

Chapter 2

Literature Review

This structure of this chapter can be separated into three sections, starting with a

literature review of master production scheduling (MPS). The second is SAP/APO

system and finally, an analytic methodology approach for calculation of the master

production scheduling.

The approach is as follows:

Section 1

Section 2

Section 3

2.2 SAP/APO system

2.2.1 Definition of SAP/APO

2.2.2 APO-SNP (Supply Network Planning) process

2.2.2.1 Supply network planning flow

2.2.2.2 Master data setup

2.2.2.3 Transaction data and Data input

2.2.2.4 Heuristic and Optimizer run

2.2.2.5 Data output

2.3 Analytic method approach for master production scheduling

2.3.1 Comparison of analytic method approach

2.3.2 Analytic method: Linear programming (LP) approach

10

TONASSOMPTIONTNIVERSITYLTIM

3./

Section 1

To understand what is master production scheduling and function of master

production scheduling

2.1 Master Production Scheduling (MPS)

2.1.1 Definition of the master production scheduling

The master production scheduling is a disaggregation of the production plan, stating

in terms of the specific end items the company plans for which it will produce and in

what quantities by what time period. (Smith, 1989).

The MPS provides a plan for production orders for these end items that is the

principal input to the MRP system, serving as the basis for determining capacity

requirements through the rough-cut capacity planning module and providing

information used for setting promised delivery dates for customers.

2.1.2 Function of the master production scheduling

The master production scheduling has four important functions (Smith, 1989):

1. It schedules production and purchase orders for MPS items. The MPS

states the items to be ordered, the quantities to be ordered, and the due

dates.

2. It is a principal input to the MRP system. The MPS is exploited using the

the BOM to determine the need for lower-level assemblies, parts, material

to support the MPS, and MRP plans orders to meet these needs.

3. It is the basis for determining resource requirements, such as manpower,

machine hours, or energy through the rough-cut capacity requirements

planning module. The MPS may be run in simulation mode to try a

number of different schedules and determine the resources need for each.

If current capacities are out of line with the needs of the MPS, some

change in resources must be planned or else the MPS must be modified.

4. It provides the basis for making deliveries promised to customers. By

allocating units of product in the schedule to customer orders, it keeps

track of unit so far unallocated and therefore available to keep the promise.

11

Section 2

SAP/APO is a ERP system to help the company calculate the master production

scheduling. This section explains what SAP/APO system process is and how to

generate the master production scheduling.

2.2 SAP/APO system

2.2.1 Definition of SAP/APO

SAP stand for System Application and Product in Data Processing. SAP is the

world's largest enterprise software company and the world's 3rd largest independent

software supplier. SAP was founded in 1972. The headquarters was established in

Walldorf, Germany. SAP employs over 27,800 people in more than 50 countries. SAP

built their business on SAP R3 which is an ERP system (a grand version of BPCS),

and has since moved into e-business solutions, CRM, APO and many more

applications.

APO stands for "Advanced Planning and Optimization". APO consists of many

modules such as APO-CPRF Collaborative Planning, Forecasting & Replenishment,

APO- PPDS Production planning/ Detailed Scheduling, APO-ATP Global Available

To Promise. Unilever has implemented APO-DP (Demand Planning module) and

APO-SNP (Supply Network Planning module).

2.2.2 APO-SNP (Supply Network Planning) Process

Supply Network Planning is a planning approach to create Tactical Plans and

Sourcing Decisions that takes the complete supply network into consideration. SNP

system will generate a plan meet forecast and actual demand by optimal use of

Manufacturing, Distribution, and Transportation Resource that consider all constraints

in the supply chain. Supply network planning is a mid-long term horizon.

2.2.2.1 Supply Network Planning Flow

Supply Network planning consists of three main parts: master data, transaction data,

and Heuristic and Optimizer run. In addition, one important data input that has to be

12

Master Data

Calendar, Produit, Resource, PPM,

Transportation Lane

Demand

4 Supply Plannin' g

I

RS U Planning

TISASSUMPTIONTINIVERSiTYLIBRATC

sent to the APO-SNP module is demand plan volume It is generated from APO-

Demand plan module, as shown in Figure 2.1.

Source of company's data year 2008

Figure 2.1 Supply Network Planning Process Flow

2.2.2.2 Master Data Set up

Calendar, Location, Product, Product split,

Resource PPM, Transportation lane

Master data is the statistical data that is set in the SAP/APO system for production

planning, and consists of seven parts.

1. Calendar: Set up calendar production planning such as how many days to run

production in each week that has a production run from 7.00 am to 12.00 pm.

2. Locations: Set up where the product can be stored at this location, demands to

be covered from this location, production location and distribution location

3. Products: Set up all statistical data related to the product such as product

characteristics of finished product i.e. category, brand, market, size, work in

process and component.

4. Product Split: Defines the mapping of product code between manufacturing

and customer for releasing the demand plan.

13

5. Resource: defines capacity of equipment, machine, personnel, means of

transport and warehouse. Resource data are relevant for planning order dates,

taking working time and the capacity of resource into account.

6. Production process model (PPM): Consists of routing and Bill of materials.

PPMS define the cost of production, rate and discrete lot size constraints, and

is directly related to the resource capacity.

7. Transportation lanes: Represent a business relationship between locations and

can be created by dragging from the source location and dropping on the target

location in the Supply Chain Model.

2.2.2.3 Transaction data and data input

Opening Stocks Open Orders

In-transit Firm Plans

Demand t. <1.1 iti:YP M:

Transactions data will be loaded from another system called Business Planning and

Control System (BPCS) and transfer data to SAP/APO that consists of opening stock,

customer order, and firm production plan. One important data input is demand plan

that transfers the data from DP (Demand plan) module to SNP module with SAP/APO

system.

2.2.2.4 Heuristics and Optimizer run

The heuristics run object is to obtain a good, although not necessarily optimal,

solution with a reasonable amount of computation. In this step, the system generates

the net requirement planning, for which the formulation is as follow:

14

o '`; J:r.jactr f Wt.!, kiy

11114LitalbOLJIMILIIllet

To suggest master production scheduling

'THOONIIF 4 `TO GT nor la

1)06; •I4

Net requirement = Demand + Safety Stock — Starting projected available

balance

This step, the system dose not computes master production scheduling based on

constraint planning and production constraint.

After finishing the heuristics run, the system runs the second step called optimizer run

that generates the optimal master production scheduling by computer based on

constraint planning and production constraint; the object is to obtain minimized cost.

2.2.2.5 Data output

After finishing the optimizer run, the system generates the optimal master production

scheduling, computer based on all constraint, the object is to minimize the total cost.

Global MPS 'Report Weekly/Detall 1,11111res DORPOTSI API COO I Oil 00 /OM

Source of company's data year 2008

Figure 2.2 Master production scheduling (MPS) report.

15

Section 3

To study an analytic methodology that computes master production scheduling for

planning in manufacturing.

2.3 The analytic methodology approach for master production schedule (MPS)

The MPS is a breakdown of the production plan into specific end items, and it must

be consistent with the production plan. Preparing the MPS requires consideration of

detailed problems not dealt with directly at the production plan level. These

frequently include setup costs, safety stocks to avoid shortages of specific products,

capacity limitation of key work center, and trade-offs between products when output

is limited by resources. (Martinich, 1997).

2.3.1 Comparison of analytic methodology model approaches

There are many analytic methodology model approaches for computed the master

production scheduling. This paper studied analytic methodology model approaches as

follow;

1. Linear Programming (LP)

2. Linear Decision Rule (LDR)

3. Integer Programming (IP)

4. Family Setup

5. SAP/APO

This paper studied differences in application, advantages and limitations of each

analytic method approach, as shown in Table 2.1.

16

Table 2.1 Comparison of application, advantage and limitation of each methodology

model

Author Method Application Advantage Limitation Fred Hanssmann and Sidney W.Hess 1960

lb. Linear Programming (LP)

110- Linear Decision Rule (LDR)

0- Production Planning and Employment Scheduling in manufacturing

I. Production planning in a paint plant

A group at

1111,- The method is quite flexible in that various additional constraint

IP- The solution also provides additional valuable information, namely, the marginal costs associated with the constraint.

Ow The model can be extended to include multiple product, invnetory cost, a work force divide among a number of department and product labor requirement

0.- Software packages for LP are readily available for most computers from

PP. The relation assumed be linear

1lb- Difficult to determine the cost of shortage

Carnegie -mellon University in the late 1950s

110- The LDR model would normally be chosen over LP only if the cost functions can be approximated much more accurately by quadratic than by linear function

» The regular payroll cost could be approximated as a linear function of the size of the work force

» The hiring and layoff costs, overtime costs and inventory costs could be approximated by quadratic function_

110. There is no easy way to put capacity constraint on production, inventory, size of work force, or amount of over time

Lasdon, L. S. and R.

C. Terjung, 1971s

110- Integer

Programming CEP)

III. Production planning

in the tire industry.

11,- The method is suitable for

production planning with

capacity constraint and setup cost are significant

IP. There are not all the

possible schedules but

rather some that seem attractive.

Bedworth and Bailey, 1987

110- Family Setup IP' Production planning

for the product line

consists of a number of

families which are composed of item

11,- There is a significant setup

cost incurred in each period in

that a family is produced.

10. The setup cost

associated with

producing an individual

item of the family is

negligible and can be

ignored.

Stephan and Michael, 2004

III.- SAP/APO system III. Planning and scheduling in at beer brewer Carlsberg in Denmark

11.- APO adopts LP models for solving MP Problem

10- APO has various approaches, including Constraint Programming, Genetic Algorithms and Repair algorithms.

1' APO creates a Mixed Integer Program and tries to find a solution with minimum cost

II.- The computer running time would be between 10-20h if each LP sub model had 100,000 to 500,000 variables and 50,000 to 500,000 constraints

110- The final solution might not be optimal because the scale of a complete LP model is too large, model must be split into several sub-models

17

1. Linear programming (LP) was studied by Hanssmann and Hess (1960).

They studied two approaches: production scheduling and employment scheduling.

After receiving monthly demands for the product, followed by a factory plan for

production scheduling, what should be the monthly production rates and work force

levels in order to minimize the total cost of regular payroll and overtime, hiring and

layoffs, inventory and shortages incurred during a given planning interval of several

months.

The industries use liner programming to determine the best mix of products to

produce, taking into account constraints on capacity. Products have a different profit

per unit, and the problem is to determine the schedule that will produce the greatest

total profit without violating the capacity constraints.

2. Linear Decision Rules (LDR) were developed by a group at Carnegie-

Mellon University in the late 1950s (Holt et al.,1960). The study developed an

aggregate planning model for a paint plant, and publication of this model stimulated

research by many other analysts on various aspects of the problem.

The group analyzed the cost records of the company and found that the regular payroll

cost could be approximated as a linear function of the size of the work force, but that

hiring and layoff costs, overtime costs, and inventory costs could be better

approximated by quadratic function, as show in Figure 2.3. There are some real

disadvantages to LDR relative to LP. First, there is no easy way to put capacity

constraints on production, inventory, size of work force, and amount of over time.

Figure 2.3: Cost functions used in linear decision rules

18

3. Integer programming (IP) was studied by Lasdon and Terjung (1971). IP

is similar to linear programming with the exception that some or all of the variables

are restricted to take on integer values. An integer programming model has been used

in the tire industry to schedule products where there is a capacity constraint and set up

costs are significant. Some possible schedules for each product are not all the possible

schedules but rather some that seem attractive. In each schedule, each lot size is equal

to the demand in the current period or the demand in the current and following

periods, or the current and two following periods, and so forth. The cost of schedule

computing is based on setup cost and the cost of holding inventory at the end of

period. The aim is to select one schedule for each product such that total cost will be

minimized and capacity constraints will not be allowed.

4. Family set up method developed by Bedworth and Bailey (1987) that

applies in some companies where the production line consists of a number of families

composed of items. There is a significant set up cost incurred in each period in which

a family is produced, but the set up cost associated with producing an individual item

of the family is negligible and can be ignored. Creating the MPS involves deciding,

first, which families to run each period, and second, the order quantities for the items

in those families. The total production volume must be in agreement with the

production plan

5. The SAP/APO system for planning and scheduling in supply chain

operations was studied by Kreipl et al. (2004, pp.77-92). APO adopts LP models for

solving MP problems and uses the LP optimization engine developed by CPLEX.

Because of the scale of a complete LP model is too large, such a model must be split

into several sub-models. The computer running time would be between 10 — 20h if

each LP sub-model had 100,000 to 500,000 variables and 50,000 to 500,000

constrains. Moreover, the final solution might not be optimal because APO exploits a

solution engine, such as constraints programming (CP) and genetic algorithm (GA).

CP uses constraint propagation, tree search, forward tracking, backtracking, and

consistency techniques to reduce the search region. Thus, the quality of the feasible

solution using CP depend heavily on the initial solution point and feasible region

reduction techniques chosen.

19

In summary, after studying other analytical method that adopt and adapt from linear

programming methods, as mentioned, Kreipl et al (2004) studied SAP/APO system

that found the final solution not be optimal, the same as this case study. Therefore this

paper starts to study linear programming for calculating the optimal master production

scheduling, the object being minimized cost depending on the existing constraints that

are setup in the SAP/APO system.

2.3.2 Analytic methodology: Linear programming (LP) approach

Linear programming (LP) is a mathematical technique used to minimize or maximize

a linear objective function subject to linear constraints in non-negative variables. In

planning, it is used to decide at what levels certain activities are able to be engaged in

and how resources in short supply are to be allocated to those activities so that an

objective such as minimum cost or maximized profit will be achieved.

Linear programming common approach is as follow:

(Martinich, 1997)

1. Define a set of decision variables, such as production, inventory, and work force

levels, the number of employees to hire or lay off, and quantities to subcontract.

2. Develop an expression giving total cost as a function of these variables.

3. Define a set of constraints that will require that demands will be satisfied, safety

stocks will be maintained, and capacity will not be exceeded.

4. Use an iterative procedure construct linear programming equation to determine

values of decision variables that will minimize the total cost function while

satisfying the constraints

Standard Equation of Linear programming is defined as

Objective:

Minimize cost = E i=i

Subject to

,i= 1, 2... m

X . > 0 , j= 1, 2.. n -

20

Where

C. = coefficients at j

X . = variables or parameters j, j= 1, 2...n, include the surplus or slack, if

any

bi = constrains at i

= constant

Properties of Linear Programming

(Render et al., 2006)

1. One objective function: All problems seek to maximize or minimize some

quantity, usually profit or cost.

2. One or more constraints: what LP problems have in common is the presence of

restrictions, or constraints, that limit the degree to which we can pursue our

objective.

3. Alternative courses of action: There must be alternative courses of action to

choose from. For example, if a company produces three different products,

management may use LP to decide how to allocate among them its limited

production resources.

4. Objective function and constraints are linear: The objective and constraints in LP

problems must be expressed in terms of linear equation or inequalities. Linear

mathematical relationships just mean that all terms used in the objective function

and constraints are of the first degree (i.e., not squared, or to the third or higher

power, or appearing more than once).

Assumptions of Linear Programming

(Render et al., 2006)

1. Certainty: Assume that conditions of certainty exist; that is, the number of the

objectives and constraints are known with certainty and do not change during the

period being studied.

21

2. Proportionality: Assume that proportionality exists in the objective and

constraints. This means that if production of 1 unit of a product uses 3 hours of a

particular scare resource, then making 10 units of that product uses 30 hours of

resource.

3. Additivity: Meaning that the total of all activities equals the sum of the individual

activities.

4. Divisibility: Assume that solutions need not be in whole numbers (integers).

Instead, they are divisible and may take any fractional value.

5. Non-negative variables: Assume that all answers or variables are non-negative.

There are various LP models for planning problems, differing because the problem

differs from company to company and also because of differences in the approach

taken by different analysts.

22

rf Data Collection 3.1 Data Collection

3.2 Develop LP function Develop Linear Programming Function

Compute MPS by LP Solver

Compare MPS result between LP and SAP/APO

3.3 Run MPS by LP solver

3.4 Compare MPS result

Chapter 4 Analyst the result

TIMIStigittiedtrovERsrrnmpootr!

Chapter 3

Methodology

Figure 3.1 Methodology: proposed framework

This chapter describes the method used to fulfill the purpose of the project. It started

with data collection consisting of inventory holding cost, production setup cost,

constraints planning, and production constraints, MPS result from SAP/APO and the

demand plan. Then, the linear programming function was developed and all variables

and constraints data were input into the LP solver; after that the LP solver was run and

generated a final solution. Lastly, the master production scheduling results were

compared between LP solver and SAP/APO system.

3.1 Data Collection

The primary data for this project were collected through the company's historical

data. The historical data of master production schedules come from SAP/APO

systems. Production information is production work standard and production cost (set

up cost), sales forecast of each product, product standard cost, constraints planning

and production constraints. The secondary data were collected to achieve a broad

23

knowledge base within the scope of the project. .In the search for articles, databases

like Emerald, Ebsco, Science Direct and Google Scholar have been used and also data

collected from Unilever's website .

This paper mentions the production line named PR_FG that produced the major

product and highest production run volume. There are six finished products that were

produced by this machine line and have the same product format and production

output rate. Production cost, product standard cost and inventory holding cost are

shown in Table 3.1. Inventory holding cost is 30% of product standard cost. (30%

depends on 3% IWC, 7% financing charge and 20%Storage Cost), and demand plan

volume of each product in the weekly bucket is shown in Table 3.2.

Table 3.1 Production Cost, Product Standard Cost and Inventory Holding Cost in

THB

Product

Code

Product Inventory

Product Description Standard Cost Holding Cost

(THB) (THB)

Production

Cost

(THB)

TH COA CO ALMOND 125.83 37.75 49.40

TH COB CO BLACK FRT 141.16 42.35 53.80

TH COC CO BROWNIE 127.27 38.18 50.63

TH COD CO CHOCOLATE 131.00 39.30 53.88

TH COE CO STRAWBERRY 139.70 41.91 53.42

TH COF CO BLUEBERRY 138.74 41.62 52.32

24

14,000

12,000 - 3 10,000 -

8,000

6,000

4,000 -

2,000 -

0

Demand Plan Volume by weekly

TH COA -A- THCOB --X- TH_COC

TH_COD THCOE

-1-- THCOF

1 2 3 4 5 6 7 8 9 10 11 12

Week No.

Table 3.2 Historical demand plan volume for six products in weekly bucket

Product

Week no. TH_COA

4,666

TH_COB

6,476

TH_COC TH_COD TH_COE

5,635

TH COF

3,406 1 7,439 3,110

2 4,600 5,937 7,543 3,255 5,654 3,500

3 6,379 7,496 8,979 3,255 7,358 4,000

4 6,690 7,762 9,386 3,255 7,699 5,364

5 7,684 8,611 10,689 3,255 8,789 5,621

6 8,274 8,068 11,486 2,970 10,665 6,052

7 7,175 7,119 10,039 2,970 9,462 5,211

8 7,524 7,421 10,499 2,970 9,845 5,479

9 7,524 7,421 10,499 3,195 9,845 5,479

10 7,713 9,737 10,935 3,085 10,375 5,824

Figure 3.2 Demand plan volume for six finished products in weekly bucket

Constraints Planning and Production Constraints

This paper studied all finished products that have the same product format and were

produced by the same machine line named PRGF.

25

Table 3.3 Constraints planning and Production Constraints (Unit of measure: case)

Product Product Description

Code

Production

Machine output per

shift

Minimum Minimum

production mixing

(Filling) batch size

batch size

TH COA CO ALMOND PR_GF 5,875 11,750 5,000

TH COB CO BLACK FRT PR_GF 5,875 11,750 5,000

TH COC CO BROWNIE PR_GF 5,875 11,750 5,000

TH COD CO CHOCOLATE PR GF 5,875 11,750 5,000

TH COE CO STRAWBERRY PR GF 5,875 11,750 5,000

TH COF CO BLUEBERRY PR_GF 5,875 11,750 5,000

All products have the same production output rate at 5,875 cases per shift and have

production constraints in terms of minimum production (filling) batch size at 11,750

cases, which means the production will start to produce the products. In the case of a

special run, production allows running production based on a minimum technical

batch size equal to 5,000 cases.

Production department can produce the finished product at a maximum production

run of 13.5 shifts per week in one machine. Therefore the maximum production

output at IC_PR GF machine in each week is equal to = 79,313 cases.

Company policy requires having opening stock on-hand at the beginning of each

week at a level more than or equal to two weeks demand plan. This means the stock

on-hand level at the beginning of the current week should have opening stock equal to

demand plan volume for the current week plus demand plan volume for the next

week.

3.2 Develop Linear Programming Function

Referring to the statement of problem, there are conflict performance indicator

between the production department and category planning. This project studied how

to balance performance indicators between two departments, which are total

26

production cost and total inventory holding cost. This paper studied how to minimize

the total cost between production cost and inventory holding cost. In terms of

production planning, consider master production scheduling to generate the

production plan in daily buckets for only a four weeks period (frozen period).

Therefore this project studied master production scheduling for six finished products

that were produced at machine line PR GF during a four weeks period. So the LP

objective function is

Minimize total cost = Total production cost + Total inventory holding cost

=

Step 1: Define a set of decision variables:

Let X. = the number of product i produced in week t 1

X At = the number of product A produced in week t

X B t = the number of product B produced in week t

Xc, t = the number of product C produced in week t

X Dt = the number of product D produced in week t

X Et = the number of product E produced in week t

X F t = the number of product F produced in week t

Note •

X = Master Production Scheduling

I = Level of on-hand inventory at the beginning of week

Let Ii t = level of on-hand inventory for product i at the beginning of week t

/A,t = level of on-hand inventory for product A at the beginning of week t

/B,t — level of on-hand inventory for product B at the beginning of week t

/C t = level of on-hand inventory for product C at the beginning of week t

D,t = level of on-hand inventory for product D at the beginning of week t

/E,t = level of on-hand inventory for product E at the beginning of week t

I F,t = level of on-hand inventory for product F at the beginning of week t

27

Step 2: Develop an expression giving total cost as a function of these variables

The Objective Function;

This paper is to develop a function based on the existing operation that generates the

detail production scheduling for four weeks. We can write the part of the objective

function that deals with production cost as

Production Cost = 49.40 X t + 53.80 X Bt + 50.63 X.+ 53.88 X D,t + 53.42 X E,t +

52.32 X Ft

Total Production Cost =

49.40 X A j + 53.80 X )3 50.63 1 + 53.88 X Di + 53.42 X" + 52.32 X F

49.40X A2+ 53.80 X132+ 50.63 X 2+ 53.88 X D2+ 53.42X E2+ 52.32 X F2+

49.40 XA,3 + 53.80 X 83+ 50.63 X 3 + 53.88 X D3 + 53.42 X" + 52.32 X F,

49.40 X A4+ 53.80 X B4+ 50.63 Xc4 + 53.88 X D,4+ 53.42 X E4 + 52.32 X F,4

Eq. (3.1)

To include the inventory holding costs in the model, we can introduce a second

variable as

Inventory Holding Cost = 3 7. 75 /At + 42.35 1B,t + 38.18 I c, t + 39.301 Dt + 41.91 1Et

41.62 I F,,t

Total Inventory Holding Cost =

37.751,4,1 + 42.35 1,81 + 38.1816 + 39.301D,1 + 41.911„ + 41.62/F +

37.75 1 A,2+ 42.351B,2 + 38.18/C,2 + 39.30/13,2 + 41.914,2 + 41.62 I F,2+

37.75 1 ,4,3+ 42.35.4 3 + 38.181C,3 + 39.30/D3 + 41.911E,3 +41.621F3+

37.75 1 A,4+ 42.35 /BA 38.18/C,4 + 39.30/D 4 + 41.911E4 + 41.62 1F4 ...................... Eq. (3.2)

28

The total objective function becomes

Total cost = 49.40 X + 53.80 X Bt + 50.63 X c1+ 53.88 X Dt + 53.42 X Et +

52.32 X Ft + 37.751 At + 42.351 B,t + 38. 181 c,t + 39.30ID, + 41.911 Et + 41.621 Ft

Minimize total cost =

49.40 XAi + 53.80 X Bi + 50.63 Xc1+ 53.88 X Dj + 53.42 X Ei + 52.32 X Ei +

49.40 X A2 + 53.80 X B2+ 50.63 X c2+ 53.88 X D2+ 53.42 X E2 + 52.32 X E2 +

49.40X A3 + 53.80 X B3 + 50.63 .gc3+ 53.88 X D3 + 53.42 X E3 + 52.32 X E 3 +

49.40X 4 + 53.80 XB4+ 50.63 Xcsi + 53.88 XD4+ 53.42 X Est + 52.32 XF,4

37.75IA + 42.35 IB,1 + 38.181C,1 + 39.30 I DJ + 41.911 E + 41.62 Fi +

37.751,4,2 + 42.354,2 + 38.181C,2 + 39.30 1D2 + 41.914,2 +41.621F2 +

37.75/A,3 + 42.35 1B + 38.18/C3 + 39.30/D3 + 41.914,3 + 41.62/F3 +

37.75/A4 + 42.354 4 + 38.18/C4 + 39.30/D4 + 41.911E4 +41.621E4 •••• ....... (3.3)

Step 3: Define a set of constraints that will require that demand will be satisfied,

safety stock will be maintained, and capacity constraints will not be exceeded.

In setting up the constraints, we must recognize the relation between the beginning

inventory current week, the current week's production, and the demand plan current

week. The beginning inventory next week is

r -- r The Beginning > _ inventory next week

-- The Demand Beginning + Current's plan inventory Week current's current

J production week

29

nitial S ock Wk.1

5,000

Week No. 1

Figure 3.3 Stock on-hand level at the beginning of week 1 = 5,000 cases and has

demand plan volume at week 1 = 2,000 cases

Week No. 1

Figure 3.4 Stock on-hand level remaining at the end of week 1 = 3,000 cases after

being consumed by demand plan volume at week 1 = 2,000 cases

Week No. 1

Figure 3.5 Production run at week 1 = 2,000 cases

nitial Stock Wk.2

Week No. 1 Week No.2

Figure 3.6 Stock on-hand level at the beginning of week 2 = 5,000 cases after plus

production run at week 1 = 2,000 cases with remaining stock at week 1 = 3,000 cases

30

The stock on-hand level at the beginning of current weeks that were used for this

research is assumed be week no.1, as shown in Table 3.4

Table 3.4 Stock on-hand level at the beginning of week no.1

Product

Code Product Description Machine

The beginning

inventory current's

week (week no.1)

TH COA CO ALMOND IC PR GF 17,000

TH COB CO BLACK FRT IC PR GF 7,000

TH COC CO BROWNIE IC PR GF 12,000

TH COD CO CHOCOLATE IC PR GF 18,000

TH COE CO STRAWBERRY IC PR GF 9,500

TH COF CO BLUEBERRY IC PR GF 11,000

Subject to;

/A,2 > 17,000 + XAi - 4,666

X Aj > 12,334

/B 2 > 7,000 + X131 - 6,476

/B,2 - XB 1 — > 524

IC 2 — 12,000+ Xc,,1 - 7,439 ,

4,561

ID,2 > 18,000+ XDi - 3,110

1D,2- Xf;,,i > 14,890

4,2> 9,500 +XE 5,635

4,2 - XE1?.

IF,2>11,000 + XF 1 3,406

/ - X 7, 594 F,2 F,1—

3,865

.......................... Eq. (3.4)

.......................... Eq. (3.5)

......................... Eq. (3.6)

.......................... Eq. (3.7)

.......................... Eq. (3.8)

.......................... Eq. (3.9)

31

The constraints on demand in week no. 2, 3 and 4 is as follows:

Week no.2:

X A,2+ A,2- 1A,3

5_ 4,600

XB2+ 4,2 - 4,3 5,937

XC2 + 1C2 - 1C,3 7,543

XD2+1D,2 - 1D,3 3,255

XE2+ 42 4,3 <5,654

XF2+ 1

F2 - IF,3 < 3,500

Week no.3: X A,3+ 1A,3-

I A,4 X 6,379

X + I - < 7,496 B3 B,3 B,4 -

XC3+ 1C,3 - 1C,4 8,979

XD,3

+ ID,3

- /D,4 5_ 3,255

XE3 /E,3 - IE,4 5_ 7,358

X + 1 - 1 < 4,000 F,3 F,3 F,4 -

Week no.4: X A,4+ A,4- 1A,5 < 6,690

X.84+ IB,4 - IB5 5_ 7,762

XcA 1c,4-

IC,5 5_ 9,386

/ XD,4 D,4 - /D,5 < 3,255

XE,4+ 1E,4 - 1E,5 < 7,699 -

XF,4+ IF,4

- IF,5

< 5,364

.......................... Eq. (3.10)

.......................... Eq. (3.11)

.......................... Eq. (3.12)

.......................... Eq. (3.13)

.......................... Eq. (3.14)

.......................... Eq. (3.15)

.......................... Eq. (3.16)

.......................... Eq. (3.17)

.......................... Eq. (3.18)

.......................... Eq. (3.19)

.......................... Eq. (3.20)

.......................... Eq. (3.21)

.......................... Eq. (3.22)

.......................... Eq. (3.23)

.......................... Eq. (3.24)

.......................... Eq. (3.25)

.......................... Eq. (3.26)

.......................... Eq. (3.27)

The company wants to have a stock on-hand level at the beginning of each week to

cover demand plan volume for current week plus demand plan volume for next week.

/".

The Beginning inventory

current's week

Demand plan Current's week

Demand plan next week

32

Week No.2

Figure 3.7 Demand plan volume at week 2 = 2,000 cases and week 3 = 2,500 cases

Week No.2 Week No.2

Figure 3.8 Stock on-hand level at the beginning of week 2 = 4,500 cases based on

demand plan volume for week 2= 2,000 cases plus demand plan volume for week 3 =

2,500 cases

Demand plan volumes of each product in each week, is described in Table 3.2

Week no.2

I A,2 Demand week.2 of Product A+ Demand week.3 of Product A

I A,2 .., 4,600 + 6,379 .......................... Eq. (3.28)

IB2

Demand week.2 of Product B+ Demand week.3 of Product B

IB2

5,937 + 7,496 .......................... Eq. (3.29)

/C 2 ?. Demand week.2 of Product C+ Demand week.3 of Product C

Ic2 -?. 7,543 + 8,979 .......................... Eq. (3.30)

I D,2 Demand week.2 of Product D+ Demand week.3' of Product D

I D,2 3,255 + 3,255 .......................... Eq. (3.31)

1-E2 _>_ Demand week.2 of Product E+ Demand week.3 of Product E

I E,2 5,644 + 7,358

.......................... Eq. (3.32)

IF 2 Demand week.2 of Product F+ Demand week.3 of Product F

-1-F,2 3,500 + 4,000 .......................... Eq. (3.33)

33

Week no.3

Week no.4

I A,3 > Demand week.3 of Product A+ Demand week.4 of Product A

4,3 Demand week.3 of Product B+ Demand week.4 of Product B

Ic,3 ?. Demand week.3 of Product C+ Demand week.4 of Product C

Demand week.3 of Product D+ Demand week.4 of Product D

/E,3 Demand week.3 of Product E+ Demand week.4 of Product E

4,3 Demand week.3 of Product F+ Demand week.4 of Product F

(See stock on-hand level cover demand two weeks at table 3.5)

./A,4 ?. Demand week.4 of Product A+ Demand week.5 of Product A

IB,4 Demand week.4 of Product B+ Demand week.5 of Product B

/c,4 Demand week.4 of Product C+ Demand week.5 of Product C

/D,4 Demand week.4 of Product D+ Demand week.5 of Product D

IE,4 Demand week.4 of Product E+ Demand week.5 of Product E

IF,4 ?. Demand week.4 of Product F+ Demand week.5 of Product F

(See stock on-hand level cover demand two weeks at table 3.5)

Week no.5 IA,5 Demand week.5 of Product A+ Demand week.6 of Product A

4,5 Demand week.5 of Product B+ Demand week.6 of Product B

/c,5 > Demand week.5 of Product C+ Demand week.6 of Product C

I D,5 ?. Demand week.5 of Product D+ Demand week.6 of Product D

4,5 Demand week.5 of Product E+ Demand week.6 of Product E

Demand week.5 of Product F+ Demand week.6 of Product F

(See stock on-hand level cover demand two weeks at table 3.5)

34

79,313 cases

Machine Line

Table 3.5 Stock on-hand level of weeks no. 2, 3, 4 and 5 cover the demand plan

volume for two weeks cover.

Product

Code

Week. 2

/2

Week. 3

/3

Week. 4

14

Week. 5

15

TH COA 10,979 13,069 14,374 15,958

TH COB 13,433 15,258 16,373 16,679

TH COC 16,522 18,365 20,075 22,175

TH COD 6,510 6,510 6,510 6,225

TH COE 13,012 15,056 16,488 19,454

TH COF 7,500 9,364 10,985 11,673

Referring to production constraints, production department can produce the finished

product at maximum production output per week = 79,313 cases. Therefore

production department can produce six finished product at production line named

PR GF less than or equal to 79,313 cases during four weeks period.

Let XAi + X131 + Xci + XD1 4- XE 79,313 .......................... Eq. (3.34)

X A2+ XB2+

XC2 + XD2+XE2 + XF2 79,313 Eq. (3.35)

3 + X + Xc3 -F XD3 -E XE3+ XF3 79,313 Eq. (3.36)

X A4+ XB4+

XC4 + XD +XE4 + XF4 <79,313 Eq. (3.37)

Figure 3.9 Maximum production scheduling in each week at PR GF machine less

than or equal to 79,313 cases.

35

In addition to production constraints, the Production department was studied in terms

of what is the level of minimum production batch size which should generate an

optimal production setup cost when producing one finished product per time. They

suggested producing the product at a minimum production (filling) batch size of

11,750.

At a minimum production batch size = 11,750 cases, this created a problem for a slow

moving product for which there is low demand plan volume. The stock on-hand level

for slow moving products is very high, more than the demand plan volume of two

weeks cover of the company requirement, and creates a high inventory holding cost.

Then the production department studied another level of production batch size to

produce slow moving finished products. They allow producing finished products at a

mixing tank batch size of 5,000 cases per finished product per time.

Let KX = X +ky 1t it i,t .......................... Eq. (3.38)

X. t = Total master production scheduling for product i at week no. t

= integer value for produced product i at week no. t at minimum production

(filling) batch size

= integer value for produced product i in week no. t at mixing tank batch size

When

K = minimum production (filling) batch size = 11,750 cases, and

k = mixing tank batch size = 5,000 cases

The purpose of Eq. 3.38 is the total master production scheduling for producing one

product each week that was generated from LP solver running with integer function.

The first time LP solver generated the first integer value to meet with a minimum

production batch size = Xi t at the first stage, and then LP solver generated the second

integer value matching with a mixing tank batch size = yl

36

LEz.-ALJ SOF, SE$143 SEVICI - 1.9474W.]

Sft Target (& 11$1 1 174D-.

I Ck4a

cpc,), j

I n I

auc ty,

$.:$2.1717,SCS2G.12-11.51

rAever YA:31, t rt.e9er

Therefore the total master production scheduling is equal to summary total production

scheduling at minimum production batch size = K X, t and total production scheduling

at mixing tank batch size = k yo,

4

Filling (Machine) Line

x,,,

Minimum Production (filling) Batch Size ' Mixing Tank Batch Size = 11,750 cases = 5,000 cases

Figure 3.10 The integer function depends on minimum production (filling) batch size

and mixing tank batch size at LP solver.

The functions become:

— 11,750 XAt + 5,000 yA,t X A, t

X B t = 11,750XBt + 5,000 y B,t

Xc,t 11,750 Xct + 5,000 yct

X D,, 11,750 XDt + 5,000 y D,t

X E t = 11,750 XEt + 5,000 yz,t

X F. 1 = 11,750 X F + 5,000 y F

Let

Product A X A1 11,750Xim + 5,000y A,1

X A,2 11,750 X A2 + 5,000y A,2

X A3 = 11,750X A3 + 5,000 yA,3

X A,4 11,750 X „ + 5,000 y"

Eq. (3.39)

Eq. (3.40)

Eq. (3.41)

Eq. (3.42)

37

X B1 11,750 XB 1 + 5,000 yB,1

X B2 11,750 X B2 + 5,000 Y B,2

Product B Eq. (3.43)

Eq. (3.44)

X 133 = 11,750 X B3 ± 5,000 Y B,3

X B4 11,75OXB,4 + 5,000 YHA

Eq. (3.45)

Eq. (3.46)

C1 11,750.Xc 1 + 5,000 Yc

X C2 11,750.X.c2 + 5,000 Yc

Xc 3 = 11,750 Xc,3 ± 5,000 Yc,3

XC 4 = 11,75O X + 5,000 y C,4

X D1 11,750 X D ± 5,000 yD,1

X D2 11,750 X D2 + 5,000 Y D,2

X D3 = 11,750 X D3 + 5,000 yD,3

X D4 11,750 XDA 4- 5,000 Y D,4

X E1 11,750 XE 1 + 5,000 y

X E,2 = 11,750 XE2 ± 5,000 Y E72

X E3 = 11,750 XE3 + 5,000 Y E,3

X E4 11,750 X EA. ± 5,000 yEA.

Product C

Product D

Product E

.Eq. (3.47)

Eq. (3.48)

Eq. (3.49)

Eq. (3.50)

Eq. (3.51)

Eq. (3.52)

Eq. (3.53)

Eq. (3.54)

Eq. (3.55)

Eq. (3.56)

Eq. (3.57)

Eq. (3.58)

Product F X F1 11,750 X 1 + 5,000 yp, i

X F 2 11,750XF 2 5,000 yF,2

XF3 = 11,750 XF3 + 5,000 yF,3

X E4 11,750 X F 4 + 5,000 Y F,4

Eq. (3.59)

Eq. (3.60)

Eq. (3.61)

Eq. (3.62)

All variables > 0

38

3.3 Run Master Production Scheduling (MPS) by LP Solver

We started to create a Table for inputting all variables and constraints in Excel work

sheet. (Render et al., 2006)

1. Set the target cell with the objective function to minimize total cost Snaglt F )11 .6 .4E11.7.8113

)2; BE 1 135 : Invenitny Holding Cost (THB)F j Beaded 1 Week I 87 ' 37.75 : TH COA 0 80 42.35 _ TH COB 1 0

90 : 39.30 4 B3; 38.18 TH COT 1

TH COD 91 ' 41.91 r TH COE 92 . : Era

41.62 TH COF -......: 94 : Production Cost (THB) Plod.) k Week 1 9E 49.40 % : 53.80 921 50.63 981 53.88 99 1 53.42 1e01 5232 101

Week 2 Week 3 Week I Week 5 Solver Parameters

a

0

Week Week 3 Week 1

11

02 Total Cost r

T

P roduct -r Week 1 Week 2 Week 3 Week 4 Week 5 1 103: H COA , P.. 104', TH COB Cl2:: 05j TH COC I 0

TH_CITU 0 TH COF TH COF 0

This cell contain the formula for total cost

EX 107: loqj

11c( 111 Total Cost 1121Production Cost 113 Ttivelitimy Holding Cost 1141

1f11111. 0

me -.41Aod 0

Week 5

$C$20$1 r.10 5(035 .8[441 '3= SC$6T:8CT72 g 01T $581 T,•err $0055 1Clig 55161,4ETP.: $0955-15045 $E$E.T. TETT: 4FM1FT47 4= $0907 $F1,2

i_ 0 0 I 0 0

i 0 T LI

a 19) - -

: 0 0 0 C

TH COA TH COB TH COC TH COD TH COE TH COT

Solver Pdram

Se red gal Cal 451111 Et04 To, 144.e.0 C., Yekle et;

4,:$2:41,4310 20015

5cez:Vt7 = wevve )4044:4 )4s E,to,

Define the integer function and changing cell

T c 'T

Cells formulation for computed master production scheduling

ve. I Prorloo4 n000 ca

r L r

c

oeoe

toa r..0a00o (F111441,34444 e. 04.15 2 V..k 3 V*. 4 V.k

Wet Product

CCU

:N 043G

t

IwyO Valqi• • *Ia. 1.4144.5 Site

1 „e I

/1.750 11,7.

1.750 1 750

V.

11.750 II 750

0.750 11.75.1

34 10014.4.440 Pr 541•441•11,

110

21

.2•49 1,441.13.4414

11.750 N11': 11.760

11.7. 11.150 054/E II 754 II 750

7111: 11.7501

V 04.

400 .0

Figure 3.11 Setting defined target cell for calculating the minimized total cost

3. Defined the integer function and link cell formulation for computed master

production scheduling

eb, wt 14* 800 woe.: deb 4.1, A

e1,1 /env Oct • • -, •

Figure 3.12 Setting changing cells, define the integer function and crated cells

formulation for computing master production scheduling.

39

52f Target Cep: §111

Mn 0 14$, of: 0 ce•

0:4r$:5

712.725771,

2. F 7=0:5 L kid

4,53 1C

103.3 142. 3. $14.19117. $EF37:$E072 $F$E7:$F572

I g,0,9,

L Ode

l 5f I l arse ;

l

Equd To:

5413:7c

5--

152121111111111111111111111M

Define all constraints

T P

Solver Parameters

Bill • ;5, =B112,8113

5011313V Ced. gyp .111, Equal To: 3% me, :41 995 1

tak.

gr _'11132,7

$.42:$F$7.4.,S10.1 915

71. 71, rongrt,...-`,

$C$10:5F$I5 - $C$2:8F17 rteger 431135:1(541 .•• SC$67..1.3r2

24 o

add ...

t.E.43546542 Y 5/467 40412 $1335:$3312 t $E461,5E57:2. $7$15,$

3440 >= 113417:$,3172

Veet veet 2 Veet3 TVeele Vett 6 aeset rd WWI

T1,750 11 750 11,7511 11,760 71 760 Het° 11.750 0.751 11751 0,750 11.750 11 750 11,750 11,750

11760 tl 750 11,751 91,750

11.750 14750

11.750 11.750 tl 750 n 50 11 750 11:70 0 14,750 Click "Solve"

Vett 1 V0012 Vett 3 I Vr.43 Ve•t

button 5.000 5.000 5.000 5.000 5.900 6,0011 6,009: 5000 5.000 5.100 50011) 5 000 5.100 5,000 5000 ,000,

::0011.11.

5.000 5.000 5.001, 5.000 5.000

Po:40M Omit 1 Iket 2 Vett 3 Wok t Vett 5

THCON 0 0 I 0 0 11

0 d 0 d

111 COC 0 0 0 0 4 0 0 0 0 7 3.1 Cm19 1 rE 0 o 0 0 1

Di COF 0 0 0 0

A

Fit01lido0a Yakut dteptrulort P005100000 (F301093

0.50,1 V

2..

2

0000.4 $0040. VIM. bruit 0. tIsit,101141931,0% sire

'-- it Proton

- c -r--

Wet 1

o

Vett 2 Wyk 2

F

Vrttk 4

5

Vett 5

T71 COP Tt. coo TH COC TH Coo ,T .14

COC

,

rl 22 21

25

Main. T401. Baud. sire

27

35 35

12 xi

Total MILM1Pf04•Cd0lk SoMing

Po... Vatk 1 Vett 2 514•13 3 Vett 4 V** 0

TH COO TH COB TH COG TH COO III COE TH COE

Produettot tF401,9 Batt Size 11,00.21

TH COP TH

COC TH COD H COE

TH COF

Plorket

TH COO TO COB TH COC TH 4_3",933 TH COE TH COF

3. Defined all constraints

I H 1 I. M N 0 P 13

Product Wet 1 Vett 2 Ve03 0 Vett 4 Vett 5

TH CON 0 0 I I 0 TH COB 0 01 1 I I TH COG 1 Il I I 0 TH COO 0 I I 11 TH COE 0 0

1 I I

. 423 OH COP 17,000 74! 3243 .33,29 Tli COB 7,010' 5451 '1645 .3412 .50,476

45 TH COG 12.001 .2.342 13.531 .77833 5'.944 40 TH COO 18,1110'. RE* 5.53

17 TH COE £1.540! .1.71E• .14331 14354 .44.345 TH COF noel; 399 .12 CI .23.755

49 57

50 5644510a 2 Yeas

[T-9

59

it

' 67 18-

69

n

Total Hasty 133040ctia0 3a 0e40d*i.5 04

Product Yee* 1 ! Wet 2 wet 3 Peeks Vtet 5 TH CON 3146, X K2: ..3.563 14.374 15.1!-4 TO COB 5.412; 5.43°! 1525 .' 0.333 17.673 TH COC 1437t2: 15.7:2t 18,367. 22075 777175 111 COO 6. 51 653{ 6.513 6510 62,35 TH COE 5,29, 54112 '5051 11463 19.454 TH COE e.w! 7,5011 9.3,61 13965 13673

11.311 73663 44,641 TH COO 7.7141 3, TH COB 5.451 0.347: .14.101 514,. 37.154 TH CDC 2,522: H.5041 77.369 5330. 46,11 TH COEI .7,4.151 1381 7.055 1,110 TH COE 17E9' 1902 15933 44 345 65330 TH_COF .4,134'. .3.43e

12773 :3714 35.41

7.13 77 -7-1

79 60

9.2 43

TH_COA 11.000 12,434 TH COB TAO 524 .5.412 .12.403 TH COC 12.001 4.561 -2,982 4 TRLCOO 18.001 14.639 11635 6.300 TH COE 500 2,355 709 .9117 TH COE 130011 7,594 4314

= Demand plan two weeks -Initial stock on-hand level

Figure 3.13 Setting to define all constraints for this case study.

4. Running solver to find the solution results

1,.,11 96 [Mt I yisvd irvart DOB QV3 $10oamv tido

1I Snagit 37 ...Iwo

Figure 3.14 LP started running by clicking "solve' button.

40

it n3 .9) 7'1'. 1 II

Week 5 Week. 4 Weok 3

Week 5 Week 3 Week 4

Week 5 W••14 3 Wee; 4

11,750 15755 11.750

II ;L" 1 750 147.

11.750 11.750 11.750 II 750 11 750

1.750 11,750 11,75•

Week 4 Week 3

Week 1 Week 2

Week 1 Week 2

11.750 11150 II 750 II .50

11,750 "no 11.750 11 750 750

Week 1 Week 2

6 000 5 GOO 5,040

5.050 SOO 5,000

g. 5.000 5.000 5,010.

5 000 5.000 5,000

....-L141111111a. ],-ciame4Lolimutrawaitu5 ,̀Branch: 122 Trial Solution: I Set CoP: 9,772,626

Ej 4 S Arial

4 5

a

9 10 if

Second Yaeger Value depend on mating tank batch site

I Cancel Save Scenario...

El Microsoft Excel - Production schedulinpLP solver.xls

Ele Ecit mew rgert Format Doh Qata Window Eels,

rti 41 A -

1/ZI Smolt ! Window !!. 00

A I First Intern. Val. depend on

production IFillinpl baton size

1

Second Integer Value depend 9 oe mlalnO tank book el.

P:114

1 14

F

I " 1 2u

f: 1 25

28

I 34 36 g.

37 38 g.

40

Product

TH COA TH COO T11 COC TH COEI 11-1 COE TH COE

LP solver is running TN COO

5,050 5000 5,500 5.000 5.000 5.000 5.000)

5 000 5.000

TH COE

TH car

Product Veen I Week 2 Week 3 Week 4 Week 6

TH COO

o _9

0 0

0 p

0 o " 0

TH COC 0 0 0 0 0 TH COO TH COE

0 0

0 4 0

0 0 0

0

TH COF 0 0 0 0

Ptoduction Inning) Oaten 61.

dna Tank Baton sitre

Total Me.. PooduetIon Scheduling

:~J Era Edit taw Insert Format look Liata }Widow iaelp

! ..] wir' xi -1 _ji I 4 ZL IA 1 6 --4 Z - / I '1 - ' t. E - 11 II:, 0 Snocidt ,L11 1 Windt. - 11

.A34- • A Total Master Production Scheir ing A , ., r, - , -- H L i J ,,_ K

Product 'Week. 1 Week 2 Veet 3 Week 4 Week 5

Solver found a solution. N COGSkrants and optmaity

TH COO LO TH COB TH COC 1.0 1.0 TH COO TH COE to TH COF

ProdUct Week 1 lie.. 2 Week 3 Wee, 4 Week 5 conditions 50 catutbd. aports

answer TM COA 0.0 10 00 3 0 TH COB 1.0 3.0 3.0 3.13 cep Solver i4Atori;

tOnsdinty TH COC CO 1.0 1.0 4.0

Ct Restore Qtairnal Values knit1

TH COO 00 0.0 1.0 1.0 TH COE 1.0 2.0 3.0 1.0

22,

24 25

kitting Tank Batch rise

32

1Total Master Production 5ehed11111111

Product %testi Week 2 Week 3 We. 4 Week 5

TH COA 0 5.000 11.750 15.000 TH COO 5.000 15.000 15000 15.000 TH CDC 5,000 16.750 16.750 20.000 TH COO 0 0 5.000 5.000 TH COE 5,000 10,000 15,000 10,750 TH COF 0 5,000 10,000 10,000

----1'' Hest Interger Value depend on ?indention

1 (Fillinul batch rise 2 3

L _L o P E

Week 1 Product W•ek 2

Ptodoot

TH COA TH COO 1H L.01. TH COO

H COE

1 COE

Product

TH COA Tel COO TH COC

di 14 15 16

Production (Filing) Batch

i89_, Site

20 21

Product Week 1 Week 2 Week 3 Veek 4 Week 5

TH COO 11,750 11,750 11.750 11,750 11,750 TO COB 11.710 11.750 11.750 11.700 11.755 TH COC 11.750 11.750 11.750 11.750 11.755 TH COO 11.750 11.750 11350 1.750 11.7511 TH COE 11,750 11.750 11.750 11,750 11.750 TH COF 41,750 11,750 11.750 11,750 14750

Product Week 1 Week 2 Week 3 Week 4 Week 5

TH COA 5,000 5,000 5.000 5100 5.005 TH COB 5.900 5.000 5.500 5.000 5.000 TH COC 5.000 5.000 5.500 5.000 LOH TH COO 5100 5,000 5.500 5.000 5,000 TH COE 5.00 5,000 5100 5,510 5.000 TH COF 5.100 5,000 51001 5100 5_ 14

Computed master production scheduling at cells formulation

After LP solver found a solution, the cells formulation were computed in the master

production scheduling. The solution by LP solver suggested the first integer value and

then multiply it with minimum production (filling) batch size (11,750 cases), and plus

the second integer value multiply it with mixing tank batch size (5,000 cases)

respectively.

6. After LP solver found a solution, cell formulation computed total inventory

holding cost, total production cost and the minimized total cost

I~3 Microsoft Excel Production scheduling_LP solver.xls,

it) Elle kilt Slew Insert Fermat Tools Onto Window Help

;;;Lini A .43

iV 5na01t Window .

199 . 11

Arial . 10 - B z ar,g

96 99I 100 101 102 Total Cost 103 104 105 100 107 08

111.4

A Total Master Production

34 Scheduling 35

37 1-53

40 rgE" so Inventory Holding Cost (THB 87 37.75 DB 42.35 89 38.18 90 39.30 91 41,91 92 41.62 93 94- Production Cost (TOO) 95 49.40 96 53.80 97 50.63

53.88 53.42 52.32

B

Product

C

Week 1

D

Week 2

E

Week 3

F

Week 4

G

Week 5

H I

TH COA 0 5,000 11,750 15,000 0 TO COB 5,000 15 000 15,000 15,000 0 TH COC 5,000 16,750 16,750 20,000 0 TH COD 0 0 5,000 5,000 0 TH COE 5,000 10,000 15,000 16,750 0 TH COF 0 5,000 10,000 10,000 0

Product Week 1 Week 2 Week 3 1Neek 4 Week 5 TH COA 641,750 465,607 480,692 683,437 0 TH COB 296.450 233,958 617.782 935,571 0 TH COC 458,160 365,051 716,562 1,013,270 0 TH COD 707,400 585,177 457,256 525,834 0 TH COE 398,145 371,524 553,655 873,941 0 TH COO 457,820 316,066 378,496 628,216 1 0

Product Week 1 Week 2 Week 3 Week 4 Week 5 TH COA 0 247,000 580,450 741,000 0 TH COB 269,000 807,000 807,000 807,000 0 TH COC 253.150 848,053 848,053 1.012,600 0 TH COD 0 0 269,400 269,400 0 TH COE 267,100 534,200 001,300 894,785 0 TH COF 0 261.600 523,200 523,200 0

Product Week 1 1,061,142 1,424,782

2,025,870 585,177

665,245 TH COF

5,236

TH COD

TH COA TH COB TH COC

TH COE

3,748,975 457,820

711,310 707,400

641,750 565,450

Week 2

1,040,958 1,213,104

905,724 577

712,607 Week 3

7,033,844

1,564,

'354,955 901,696

,656

Week 4

8,908,253

1,768,726 1,151,116

1,424,437 1,742,571

795,234

Week 5

0 0 0

0 0 Compute the total cost after LP solver found a solution

111 Total Cost 112 Production Cost 113 Inventory Holding Cost

11,564,490 13,161,818

Figure 3.17 Cell formulation for calculated total inventory holding cost, total

production cost and the minimized total cost

After LP solver found a solution, the master production scheduling for six finished

products for the four weeks period were computed at cells formation and then total

production cost, total inventory holding cost and the minimized total cost were

calculated with cell formulation respectively. The results are shown in Tables 3.6, 3.7,

3.8 and 3.9.

42

Table 3.6 The final solution of master production scheduling for six products during

the four weeks period (Unit: Case)

E Week. 1 Week.2 Week.3 Week.4

TH_COA 0 5,000 11,750 15,000

TH_COB 5,000 15,000 15,000 15,000

TH_COC 5,000 16,750 16,750 20,000

TH_COD 0 0 5,000 5,000

TH_COE 5,000 10,000 15,000 16,750

TH_COF 0 5,000 10,000 10,000

Table 3.7 Production cost for six products during the four weeks period in THB

Product

Code Week. 1 Week.2 Week.3 Week.4

TH_COA 0 247,000 580,450 741,000

TH_COB 269,000 807,000 807,000 807,000

TH_COC 253,150 848,053 848,053 1,012,600

TH_COD 0 0 269,400 269,400

TH_COE 267,100 534,200 801,300 894,785

TH_COF 0 261,600 523,200 523,200

Table 3.8 Inventory holding cost for six products during four weeks period in THB

Product Week. 1

Code Week.2 Week.3 Week.4

TH_COA 641,750 465,607 480,692 683,437

TH_COB 296,450 233,958 617,782 935,571

TH_COC 458,160 365,051 716,562 1,013,270

TH_COD 707,400 585,177 457,256 525,834

TH_COE 398,145 371,524 553,655 873,941

TH_COF 457,820 316,066 378,496 628,216

43

Table 3.9 Total cost for six products during the four weeks period in THB

Product

Code Week. 1 Week.2 Week.3 Week.4

TH COA 641,750 712,607 1,061,142 1,424,437

TH COB 565,450 1,040,958 1,424,782 1,742,571

TH COC 711,310 1,213,104 1,564,615 2,025,870

TH COD 707,400 585,177 726,656 795,234

TH COE 665,245 905,724 1,354,955 1,768,726

TH COF 457,820 577,666 901,696 1,151,416

3.4 Comparing master production scheduling result between SAP/APO system

and LP solver

Referring to the scope of the project, the master production scheduling was computed

by using a linear programming approach that used the Excel solver to find a solution.

Then comparing master production scheduling results was generated from SAP/APO

system and LP solver by weekly bucket for six products during the four weeks period

as shown in Table 3.10.

Table 3.10 Comparison of master production scheduling result between SAP/APO

system and LP solver for six products during the four weeks period

Product Code

TH_COA - - - - - - - - - - - - - - -

Master Production Scheduling

SAP/APO LP Solver

Week. 1

0 0

Week.2

18870 5,000

Week.3

0 11,750

Week.4

0 15,000

SAP/APO 0 22240 0 0 TH_COB

LP Solver 5,000 15,000 - 15,000 - 15 000 - , SAP/APO 18,736 0 19163 0

TH_COC LP Solver 5,000 16,750 16,750 20,000

SAP/APO 0 0 0 11750 TH_COD

LP Solver 0 0 5,000 5,000 SAP/APO 20,980 0 22071 0

TH_COE - - - - - - - - - - - - - - - LP Solver 5,000 10,000 15,000 16,750

SAP/APO 0 25000 0 0 TH_COF

LP Solver 0 5,000 10,000 10,000

44

The result of master production scheduling for six products during the four weeks

period as shown in Table 3.10, was that the LP solver and SAP/APO system

generated the master production scheduling with different figures and suggested

producing finished product at different timing periods.

Also, this project considered the same planning constrains and production constraints

that the data and configuration were setup at SAP/APO system, with exception for

allowance to produce finished product at mixing tank batch size that cannot be setup

at SAP/APO system because the system allows to key at one configuration for a

minimum production batch size of 11,750 cases.

45

Chapter 4

Result and Analysis

Table 3.10 showed the final result of master production scheduling using the LP

approach with LP solver, and compare it to the result from SAP/APO system. The

results showed different quantities and timing of master production scheduling, as

mentioned in terms of production constraints that setup an allowance to produce

finished product at mixing tank batch size = 5,000 cases at LP solver.

Therefore the master production scheduling result from LP solver generated

production of finished products almost every week for controlling the stock on-hand

level. At the beginning of weeks no. 2, 3 and 4 the level can cover demand plan

volume of two weeks (demand plan volume current week and demand plan volume

next week) that follows company policy. In addition, the master production

scheduling result followed production constraints requirement to produce the product

at the mixing tank batch size = 5,000 cases and minimum production (filling) batch

size = 11,750 cases.

In contrast, the master production scheduling result from SAP/APO generated

producing finished products at the high quantity and had the effect of have high stock

on-hand level at the beginning of weeks no. 2, 3 and 4. In this case the master

production scheduling result from SAP/APO generated a high inventory holding cost

but control in terms of production setup cost for producing product at the large

quantity in some weeks. This case, it is not suitable for balancing machine and labor

utilization. In real life, production department prefers to produce finished products

every week more than producing the product at high volume in some weeks. During

the four weeks period studied, the total master production scheduling at the PR GF

machine line was not greater than the production constraint at 79,313 cases per week,

and the master production scheduling quantity for each product was not lower than

production constraints of 11,750 cases.

The final result cannot be concluded by comparing only the master production

scheduling result to answer the overall picture for production planning. In addition,

46

ilnASSUMPTioNTINTVERSTITTanra or,

there are production constraints and constraints planning that must be considered for

production planning. Therefore the detailed production scheduling shown in Table

4.1, 4.2, 4.3, 4.4, 4.5 and 4.6 respectively should be studied for six products during

the four weeks period. .

Table 4.1 Comparison of detail master production scheduling result between

SAP/APO and LP solver for product TH COA

Product LP Solver Week 1 Week 2 Afeek 3 Week 4

TH COA. Iniatial Stock 17,000 12,334 12,734 18,104 Detnand11an 4,666 4,600 6,379 6,690

Production Schedulin 0 5 000 11 750 15 000 Product SAP/APO Week 1 Week 2 Week 3 Week 4

TH COA. Iniatial Stock 17,000 12,334 26,604 20,224 Demandaplan 4,666 4,600 6,379 6,690

Production Schedulina 0 18 870 0 0

Table 4.2 Comparison of detail master production scheduling result between

SAP/APO and LP solver for product TH_COB

Product LP Solver Week 1 Week 2 Week Week 4 TH COB Iniatial Stock 7,000 5,524 14,588 22,091

Demand_plan 6,476 5 937 7,496 7762 Production Schedulin 5 000 15 000 15 000 15 000

Product AP/APO Week 1 Week 2 Week 3 Week 4 TH COB Iniatial Stock 7,000 524 16,828 9,331

Demandplan 6 ,476 5,937 7,496 7,762 Production Schedulinl 0 22 240 0 0

Table 4.3 Comparison of detail master production scheduling result between

SAP/APO and LP solver for product TH_COC

Product LP Solver Week 1 Week 2 Week 3 Week 4 TH COC Iniatial Stock 12,000 9,561 18,768 26,539

Demandilan 7,439 7,543 8,979 9,386 Production Schedulin 5 000 16 750 16 750 20 000

Product AP/ APO Week 1 Week 2 Week 3 Week 4 TEl COC Iniatial Stock 12,000 23,297 15,754 25,938

Demand plan 7,439 7,543 8,979 9,386

Production Scheduling 18,736 0 19,163 0

47

Table 4.4 Comparison of detail master production scheduling result between

SAP/APO and LP solver for product TH COD

Product LP Solver Week 1 Week 2 Week 3 Week 4 TH COD Iniatial Stock 18,000 14,890 11,635 13,380

Demand plan 3,110 3,255 3265 3,265

Production Scheduling 0 0 5,000 5,000 Product SAP/APO Week 1 eek 2 Week 3 Week 4

TH COD Iniatial Stock 18,000 14,890 11,635 8,380 Demand plan 3,110 3,255 3,255 3,266

Production Scheduling 0 0 0 11,750

Table 4.5 Comparison of detail master production scheduling result between

SAP/APO and LP solver for product TH_COE

Product LP Solver Week 1 Week 2 Week 3 Week 4 TH COE Iniatial Stock 9,500 8,865 13,211 20,853

Demand plan 5,635 5,654 7,368 7,699

Production Scheduling 5,000 10,000 15,000 16,750 Product SAP/APO Week 1 Week 2 We k 3 Week 4 TH COE Iniatial Stock 9,500 24,845 19,191 33,904

Demand plan 5,635 5654 7,358 7,699

Production Scheduling 20,980 0 22,071 0

Table 4.6 Comparison of detail master production scheduling result between

SAP/APO and LP solver for product TH COF

Product LP Solver Week 1 Week 2 Week 3 Week 4 TH COP Iniatial Stock 11,000 7,594 9,094 15,094

Demand plan 3 ,406 3,500 4,000 5,364

Production Scheduling 0 5,000 10,000 10,000 Product /APO Week 1 Week 2 Week 3 Week 4

TH COF Iniatial Stock 11,000 7,594 16,094 12,094 Demand plan 3,406 3,500 4,000 5,364

Production Scheduling 0 12,000 0 0

The detailed production scheduling was generated from LP solver and SAP/APO

system for product TH COA, as shown in the bar chart at Figures 4.1, 4.2, 4.3, 4.4

and 4.5 for explanation of the master production scheduling result, constraint planning

and production constraints that must be considered in terms of production planning.

48

Detail Production Scheduling

Week. Week. 1 Week. 2 Week. 2 Week. 3 Week. 3 Week. 4 Week. 4 1 f SAP /LP f SAP /LP (SAP LP I SAP I LP

0 Production

0 Demand

0 Initial Stock

Figure 4.1 Detail production scheduling before SAP/APO system and LP solver

generated master production scheduling.

Referring to Figure 4.1, the initial stock decreased continuously in each week until

week no.4, and the initial stock did not cover demand plan volume for current week

demand plan which means the finished product stock was not enough to sell to

customers.

Quantity Detail Production Scheduling

30,000 El Production

28,0oo

El Demand

10,000

o

1f Week. Week.

SAP ILP 1 W

i ek. 2 Week. 2 Week. 3 Week. 3 Week. 4 Week. 4 • AP !LP ( SAP i LP / SAP /LP

17 Initial Stock

Figure 4.2 Detail production scheduling after SAP/APO system and LP solver

generated master production scheduling at week no.1

Referring to Figure 4.2, the stock on-hand level at the beginning of week no.1 has

finished product stock enough to sell the product following the demand plan volume

at week no.1 and week no.2, referring to company policy to have stock on-hand level

at the beginning of each week to cover demand plan volume of two weeks. Therefore

LP solver and SAP/APO system did not generate a master production scheduling to

produce product TH_COA at week no.1.

49

k. 2 Week. 2 W AP f LP

Week. Week. 1 W 1/ SAP / LP

k. 3 Week. 3 Week. 4 Week. 4 AP /LP I SAP /LP

Detail Production Scheduling Quantity 40,000

30,000

20,000

10,000

0

Production

0 Demand

❑ Initial Stock

Figure 4.3 Detail production scheduling after SAP/APO system and LP solver

generated master production scheduling at week no.2

Referring to Figure 4.3, the stock on-hand level at the beginning of week no.2 has

finished product stock enough to sell the product following with demand plan volume

at week no.2 and week no.3. But the remaining stock on-hand level at the end of week

no.2 which will be the stock on-hand level at the beginning of week no.3 did not

cover demand plan volume for week no.3 and week no.4.

Therefore LP solver generated master production scheduling to produce product

TH COA in week no.2 at 5,000 cases following the mixing tank batch size and

SAP/APO system at 18,870 cases following with producing finished product are not

lower than minimum production (filling) batch size at 11,750cases

Quantity 40,000

30,000

20,000

10,000

0

Detail Production Scheduling

❑ Production

❑ Demand

0 Initial Stock Week. Week. 1 Week. 2 Week. 2 Week. 3 Week. 3 Week 4 Week. 4 1/ SAP LP / SAP ILP I AP / LP ISAP /LP

Figure 4.4 Detail production scheduling after SAP/APO system and LP solver

generated master production scheduling at week no.3

50

Referring to Figure 4.4, the stock on-hand level at the beginning week no.3 from

SAP/APO system has high finished product stock that covers demand plan volume of

more than two weeks or covers demand plan volume around four weeks. Therefore

SAP/APO system did not generate master production scheduling to produce the

finished product at week no.3. In contrast, the stock on-hand level at the beginning of

week no.3 from LP solver has a finished product stock that covers demand plan

volume of two weeks following constraints planning based on company policy. But

the remaining stock on-hand level at the end of week no.3 that will be the stock on-

hand level at the beginning of week no.4 did not cover demand plan volume for week

no.4 and week no.5. Therefore LP solver generated master production scheduling to

produce product TH COA for week no.3 at 11,750 cases following the minimum

production (filling) batch size

Detail Production Scheduro

D Production

0 Demand

0 Initial Stock

Week. Week. 1 Week. 2 Week. 2 Week. 3 Week. 3 W ek. 4 Week. 4 1/ SAP / LP I SAP LP / SAP 1 LP /SAP / LP

Figure 4.5 Detail production scheduling after SAP/APO system and LP solver

generated master production scheduling at week no.4

Referring to Figure 4.5, the stock on-hand level at the beginning week no.4 from

SAP/APO system still has high fmished product stock that covers demand plan

volume of more than two weeks or covers demand plan volume around three weeks.

Therefore SAP/APO system did not generate master production scheduling to produce

finished product at week no.4. In contrast, stock on-hand level at the beginning week

no.4 from LP solver have finished product stock that covers demand plan volume of

two weeks following constraints planning based on company policy. But the

remaining stock on-hand level at the end of week no.4 that will be the stock on-hand

level at the beginning of week no.5 did not cover demand plan volume week no.5 and

week no.6. Therefore LP solver generated master production scheduling to produce

51

product TH COA for week no.4 at 15,000 cases following the minimum production

(filling) batch size and mixing tank batch size.

As mentioned about production planning decisions, it cannot consider only one site

for master production scheduling but must consider the overall results that includes

constraints planning and production constraints that relate with real production

operation in manufacturing.

The overall result is of a comparison between SAP/APO system and LP solver. There

are different master production schedulings that affect different stock on-hand levels

at the beginning of each week for product TH _COA. The LP solver generated master

production scheduling following the constraint planning and production constraints,

which means the result from LP solver can apply in a real production operation.

In contrast, SAP/APO generated master production scheduling following the

production constraints but cannot following constraints planning to have a stock on-

hand level at the beginning of each week to cover demand plan for two weeks, which

means this result from SAP/APO created a high inventory holding cost and does not

balance machine and labor utilization. Therefore the master production scheduling

result from SAP/APO system cannot apply in real production operation in some

periods, that must be reviewed with a revised master production scheduling before

being distributed to all concerned using the data. This process will extend to more

than one day.

As mentioned about the current process, the computer running time takes 2 days or 48

hours for the heuristic and optimizer run at SNP module. In addition, some weeks

must be added to the additional process to review and revise master production

scheduling. Therefore the production planner needs time to make production

scheduling total 3 days, which is more than the original process at 2 days. For LP

solver, computer running time takes around 10 to 20 minutes for one hundred

variables and constraints. It takes a long time for system running. This point supports

Kreipt's study because that paper studied only one machine line that contained only 6

products and around 100 variables. Therefore an actual operation that has many

machine lines, and one machine consists of many products that have different

52

Cost (THB)

Total Cost

LP Solver

24,726,308

AP/APO

22,540,110

Production Cost

11,564,490 7,608,231

Inventory Holding Cost

13,161,818

14,931,879

production constraint and constraint planning, created many variables and constraints.

So the computer running time take a long time and may have the effect the final

solution is not optimal.

For total production cost, SAP/APO system generated a total cost equal to 7,608,231

THB, and LP solver equal to 11,564,490 THB. Thus, SAP/APO a generated total

production cost lower than LP solver because SAP/APO generated master production

scheduling at a high production size effect which has a low production setup cost.

In contrast, for the total inventory holding cost, LP solver generated total cost at

13,161,818 THB, and SAP/APO equal to 14,931,879 THB. Thus, LP solver generated

total inventory holding cost lower than LP solver because LP solver has production

constraints to allow produce finished product at mixing tank batch size. It effect, stock

on-hand level at the beginning of each week cannot be higher than company policy

and creates low inventory holding cost as shown in Table 4.7

Table 4.7 Total production cost and total inventory holding cost result between

SAP/APO and LP solver in THB for six products during the four weeks period

For total cost comparison between SAP/APO and LP solver, this consists of total

production cost and total inventory holding cost for six products during the four

weeks period. The result of the total cost is not much difference between total cost of

LP solver (equal to 24,726,308 THB) and total cost of SAP/APO (equal to 22,540,110

THB). For the total cost, SAP/APO system generated a cost lower than LP solver, as

shown in Table 4.8.

53

Table 4.8 Total cost result between SAP/APO and LP solver in THB

Product Code Code Total Cost Week. 1 Week.2 Week.3 j Week.4 Total

TH_C OA SAP/APO 641,750 1,397,785 1,004,284 763,467 3,807,286 LP Solver 641,750 712,607 1,061,142 1,424,437 3,839,936

TH_C on SAP/APO 296,450 1,218,720 712,646 395,185 2,623,001 LP Solver 565,450 1,040,958 1,424,782 1,742,571 4,773,761

TH_COC SAPAPO 1,406,764 889,492 1,571,710 990,323 4,858,289 LP Solver 711,310 1,213,104 1,564,615 2,025,870 5,514,898

TH_COD SAP/APO - - - - - - - - - 707,400 585,177 457,256 962,424 2,712,257 LP Solver 707,400 585,177 726,656 795,234 2,814,467

TH_COE S..kP/APO 1,518,897 1,041,246 1,983,310 1,420,908 5,964,360 LP Solver 665,245 905,724 1,354,955 1,768,726 4,694,650

TH COF _ SAP/APO 457,820 943,906 669,836 503,356 2,574,917 LP Solver 457,820 577,666 901,696 1,151,416 3,088,597

TOTAL COST SAP/APO 22,540,110 LP Solver 24,726,308

54

Chapter 5

Conclusion and Recommendation

5.1 Conclusions

The objective of this case study is to understand linear programming, identify and

develop a function for creating LP solver, find and compare the optimal master

productions scheduling and the total cost between SAP/APO system and LP solver.

Firstly, an understanding was developed of a methodology model, Linear

Programming, that is subject to linear constraints in non-negative variables, followed

by linear programming properties to find the optimal master production scheduling.

Secondly, LP solver was developed and run that found a solution and computed the

master production scheduling at cell formulation. The master production scheduling

results were computed dependent on production constraints and constraint planning

that must be considered. The total costs were compared and summarized between

total production cost and total inventory holding cost for six finished products during

a four weeks period.

• The total costs generated by LP solver were equal to 24,726,308 THB, and

SAP/APO system were equal to 22,540,110 THB, thus SAP/APO

generated a total cost lower than LP solver. The total cost was defined

from total production setup cost and total inventory holding cost.

• For total inventory holding cost for six products during a four weeks

period, the result showed that LP solver generated total inventory holding

cost at 13,161,818 THB, lower than SAP/APO system at 14,931,879 THB.

• For total production setup cost for six products during four weeks period,

the result showed SAP/APO system generated total production setup cost

at 7,608,231 THB, lower than LP solver at 11,564,490 THB.

55

Cost (THB)

25.000,000 -

M000,000

15.000.000

10.000.000

5,000.000

0 Production Cost Inventory Holding Total Cost

Cost tl SAP • LP Solver

511

Cost Comparison

Figure 5.1: Cost comparison between SAP/APO system and LP solver

In addition, there are constraints planning and production constraints that must

be considered after the system generated a master production scheduling result. The

master production scheduling generated by LP solver is better than SAP/APO system

because the overall results followed all constraints such as stock on-hand level at the

beginning of each week to cover demand plan volume around two weeks. The master

production scheduling suggested production of finished product based on minimum

production (filling) batch size and mixing tank batch size. In contrast, SAP/APO

system generated a master production scheduling result which did not follow all

constraints, and so the plan must be reviewed and a revised master production

scheduling done again after receiving the master production scheduling report from

SAP/APO system. This process when created will extend to more than one day.

Meet Requirement 11,111 Not Meet Requirement

Production Constraint Constraint Planning

Maximum Capacity Minimum Production Run Stock an-hand level

Figure 5.2: represents the master production scheduling result following the

production constraints and constraint planning between SAP/APO system and LP

solver.

56

Lastly, this case studied only one machine line, with six products during a four weeks

period. The line has the same product format and production output rate, production

constraints and constraints planning. The LP solver was developed to match with all

constraints and variables, and then the solver run was started. The computer running

takes around 10 -20 minutes before finding a solution based on around 100 variables

and constraints. In the real operation, Manufacturing has many machine lines for

producing finished product and has different production constrains and constraint

planning, so that the computer running takes a lot of time if there are a lot of variables

and constraints, and may mean that the final solution as not optimal, the same as the

master production scheduling result from SAP/APO system studied by Stephan and

Michael (2004, pp.77-92).

5.2 Limitations

This case study shows the master production scheduling results from LP solver based

on the existing constraint, using the following approach:

1. Study only one machine line which consists of 6 products that have simple

product characteristics.

2. All products have the same product format, production output rate and constraints

planning.

3. The demand rate and cost constraints are constant.

4. The linear programming approach must follow the properties below:

5. One objective function: The problem seeks to maximize or minimize an objective

6. One or more constraints: Constraints limit the degree to which the objective can

be attained.

7. Alternative courses of action: There must be an alternative available.

8. Objective function and constraints are linear: mathematical relationships are linear

5.3 What do we learn from this project?

1. To understand linear programming and develop LP solver with all constraints and

variables to match this case.

2. Excel solver flexible enough to develop a making decision tool that lowers cost

and is more powerful than using,only work sheet or spread sheet.

57

3. Decision making method should not decide from one site only, but should be an

overview, such as LP solver generated total cost more than SAP/APO but

SAP/APO generated the final result but is not be feasible solution.

4. SAP/APO is a big system that needs customization. It should not consider only

programming that comes from theory and the programmer.

5. The computer running time take a long time if there are a lot of variables and

constraints. Sometime the results are not feasible.

6. Product Characteristics, Master production scheduling was computed based on a

product characteristic that is driven by customer need or demand plan volume, and

then is pushed by constraint planning such as initial stock level, and finally has

production constraints such as production batch size.

5.4 Recommendations

1. Setting a low-cost production schedule over a period of weeks is a difficult and

important management problem in most plants. The company has to consider

many factors: labor capacity, inventory and storage costs, space limitation,

product demand, and labor relations. Because most companies produce more than

one product, the scheduling process is often quite complex.

2. Referring to the scope of this project, we consider some factors are inventory

holding cost, production cost, maximum capacity per week, minimum production

run and inventory level, that require covering a demand plan of two weeks, but did

not consider product shortage cost and machine and labor utilization.

3. Production characteristic. This project studied food products that have continuous

demand and stable demand. This paper studied production constraints such as

minimum production run, mixing tank batch size and inventory level, but did not

study how many minimum production run would match with demand plan volume

of each product and what stock on-hand level at the beginning of each week

would be suitable for each product.

4. In the actual operation, there are others constraint that can be added in the LP

solver, such as space limitation, labor requirement and adding more production

lines or product characteristics.

58

REFERENCES

Bedworth, David D., and James E. Bailey, Integrated production Control System,2nd

ed. New York: John Wiley, 1987.

C.-C.Chern*, J.-S.Hsieh (2007), Aheuristic algorithm for master planning that

satisfies multiple objectives, Computer & Operation research, Vol. 34 No. 1, pp.

3491- 3513

Hanssmann,F., and S. W. Hess (1960), A linear Programming Approach to

Production and Employment Scheduling, Management Technology, Vol. 1 No.1, pp.

46 — 52

Holt, Charles C., C. R. Modigliani, J.F. Muth, and H.A. Simon, Planning production

inventories nad Workforce, Englewood Cliffs, N.J.Prentice-Hall, 1960.

Joel D. Wisner, G. Keong Leong, Keah-Choon Tan (2004), Principles of supply chain

management: A balance approach, pp. 156 — 186, Thomson South Western

Joseph S. Martinich (1997), Production and operation management: An applied

modern approach, pp. 797 — 837

J. Jozefowska*, A. Zimniak (2008), International journal of production economics,

Vol 112, pp. 109 — 120

Kreipl S, Pinedu M (2004), Planning and Scheduling in supply chain: an overview of

issues in practice., Production and Operation Management, Vol.13 No.1, pp. 77 — 92

Parag C. Pendharkar (1997), A fuzzy linear programming model for production

planning in coalmines, Computer Operation research, Vol. 24 No. 12, pp. 1141 -

1149.

Render Barry, Ralph M. Stair,JR., Michale E. Hanna (2006), Quantitative analysis for

management (9. ed), Pearson Education International

Spencer B.Smith (1989), Computer based production and inventory control, pp. 159 —

223

Thomas E. Vollmann, William L. Berry, D.Clay Whybark, F. Rebert Jacob (2004),

Manufacturing Planning and Control for Supply Chain Management (5. ed), McGaw

Hill International

'if it ASO/VI:PITON trisavERsrrnammw

59