IMPACT OF FORECAST MODELING ON COST

PERFORMANCE

TAN KAH EE

B050710002

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

2011

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

IMPACT OF FORECAST MODELING ON COST

PERFORMANCE

This report submitted in accordance with requirement of the Universiti Teknikal

Malaysia Melaka (UTeM) for the Bachelor Degree of Manufacturing Engineering

(Manufacturing Management)

by

TAN KAH EE

B050710002

FACULTY OF MANUFACTURING ENGINEERING

2011

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

BORANG PENGESAHAN STATUS LAPORAN PROJEK SARJANA MUDA

TAJUK: Impact of Forecast Modeling on Cost Performance

SESI PENGAJIAN: 20010/11 Semester 2 Saya TAN KAH EE mengaku membenarkan Laporan PSM ini disimpan di Perpustakaan Universiti Teknikal Malaysia Melaka (UTeM) dengan syarat-syarat kegunaan seperti berikut:

1. Laporan PSM adalah hak milik Universiti Teknikal Malaysia Melaka dan penulis.

2. Perpustakaan Universiti Teknikal Malaysia Melaka dibenarkan membuat salinan untuk tujuan pengajian sahaja dengan izin penulis.

3. Perpustakaan dibenarkan membuat salinan laporan PSM ini sebagai bahan pertukaran antara institusi pengajian tinggi.

4. **Sila tandakan (√)

SULIT

TERHAD

TIDAK TERHAD

(Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia yang termaktub di dalam AKTA RAHSIA RASMI 1972)

(Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)

Alamat Tetap: No. 31, Lorong Murni 12, Taman Desa Murni, 13800 Sungai Dua Butterworth, Pulau Pinang. Tarikh: 16th May, 2011

Disahkan oleh:

PENYELIA PSM

Tarikh: ___________________

** Jika Laporan PSM ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh laporan PSM ini perlu dikelaskan sebagai SULIT atau TERHAD.

DECLARATION

I hereby, declared this report entitled “Impact of forecast modeling on cost performance.”

is the results of my own research except as cited in references.

Signature : ……………………………………

Author’s Name : Tan Kah Ee

Date : 16th, May 2011

APPROVAL

This report is submitted to Faculty of Manufacturing Engineering of UTeM as a partial

fulfillment of the requirements for the degree of Bachelor of Manufacturing Engineering

(Manufacturing Management). The member of the supervisory committee is as follow:

……………………………………………

Supervisor

(En. Nor Akramin b. Mohamad)

i

ABSTRAK

Secara umumya, kebanyakan pentadbiran dan pengurusan industri cenderung untuk

mencari dan melaksanakan pendekatan sederhana untuk masalah-masalah operasi yang

dihadapi dalam aktiviti harian. Dalam projek ini, penulis merujuk kepada pendekatan

sederhana yang difahami oleh pengurus-pengurus. Para pengurus tidak akan merujuk

kepada pendekatan yang mereka kurang fahami bagi mengelakkan risiko yang akan

ditanggung akibat membuat keputusan yang salah. “Exponential Smoothing” dan

“Moving Average” merupakan kaedah ramalan yang paling umum bagi meramal

permintaan pelangan seperti mana dilapor dalam “Literature Review”. Selain itu, penulis

menggunakan inventori model di mana pengurus tidak jelas dengan corak permintaan

data-data yang dimiliki. Secara maklumnya, projek ini adalah lanjutan daripada

inventori model Lorenzo dan Stefano (2009) tentang sebuah pusat depot di mana data-

data sebelum digunakan dan kesan daripada kaedah ramalan dalam kos bagi para

pengurus dikenalpasti. Project ini dijalankan melalui contoh kes di mana pengurus

mengaplikasikan kaedah ramalan yang popular. Di samping itu, impak daripada kaedah

ramalan yang diaplikasikan dikaji melalui senario-senario khas yang disediakan. Selain

itu, hasil-hasil simulasi akan diambil sebagai bahan analisis bagi project ini. Pada akhir

projek, hasil analysis menunjukkan penentangan yang jelas daripada tanggapan asal.

Hasil analysis menunjukkan bahawa makin lemah interaksi antara ketepatan ramalan

dengan inventori system menghasilkan prestasi jumlah kos yang memuaskan.

ii

ABSTRACT

As it seems, in real-life industrial situations, management tends to find and implement

simple approaches for operational problems that they face in their day-to-day running of

business, the author in this regard refer to simple approaches to the approaches that are

well understood by managers. It is a known fact in management science that managers

will not use approaches that they cannot understand, obviously, due to the risk of

making the wrong decision when using an approach they truly not well understood.

Exponential Smoothing and Moving Average are two most common and the simplest

method to use for demand forecasting, as reported in the literatures. In this project, the

author considers an inventory model in which the manager does not know the exact

demand behavior. This project present an extension of the central depot inventory model,

reported by Lorenzo and Stefano (2009) that allows the author to quantify the historical

demand data and the impact of forecast modeling on the expected cost at the manager.

This project approach is demonstrated through examples in which the manager employs

the commonly used forecast technique and the impact is investigated through the

designed scenarios, in addition analyze in term of simulation output measures. At the

end of this project, in contrast to the expected finding, result analysis performed that, the

weaker the interaction among the forecast accuracy and inventory control system

provides the better solution to total cost performance.

iii

ACKNOWLEDGEMENT

First and foremost, the author would like to express the deepest gratitude to the Final

Year Project supervisor, En. Nor Akramin b. Mohamad for his supervision and

encouragement along the project duration. His advice and encouragement plays an

important role to the project completion.

The author would like to thanks also persons that provided support and assistance to the

project such as fellow course mates and lecturers. It was deeply appreciated regards to

their helpful gesture.

Lastly, special thanks also to the family members for their understanding, supports, and

encouragement. Their supports make the author grown stronger. Together with this, it is

offered with regards and blessings to all of those who provided the supports in any

aspect during the completion of the project.

iv

DEDICATION

This research is special dedicated to Professor Dr. Mohamed Khaled bin Omar regards

to his kindness and patient in helping the author for this project. Furthermore, it is also

dedicated to all those who are kindness in helping the author to the way of success in

this project and those who need this project

v

TABLE OF CONTENT

Abstrak i

Abstract ii

Acknowledgement iii

Dedication iv

Table of Content v

List of Tables x

List of Figures xii

List of Abbreviations xiii

1. INTRODUCTION 1

1.1 Overview 1

1.1.1 Forecasting 2

1.1.2 Inventory control system 3

1.2 Problem Statement 4

1.3 Research Objectives 6

1.4 Research Scope 6

1.5 Organization of Report 7

2. LITERATURE REVIEW 9

2.1 Forecasting 9

2.1.1 Steps in the forecasting 11

2.1.2 Advantages of forecasting 11

2.1.3 Forecast techniques 12

2.1.4 Forecast Error 13

2.2 Inventory Control System 15

3. METHODOLOGY 20

3.1 Research Methodology 20

vi

3.1.1 Define Problem, Objective, and Scope 20

3.1.2 Literature Review 21

3.1.3 Model Development 21

3.1.4 Model Testing 22

3.1.5 Result Analysis 22

3.1.6 Report Findings 23

3.2 Project Methodology 23

3.3 Solution Methodology 24

3.4 Gantt Chart 25

4. THE CONCEPT OF FORECASTING MODELS AND INVENTORY

MODELS 26

4.1 Forecasting models 26

4.1.1 The concept of Exponential Smoothing 26

4.1.1.1 Simple Exponential Smoothing 26

4.1.1.2 Double Exponential Smoothing 27

4.1.1.3 Smoothing Constant 31

4.1.2 The concept of Moving Average 31

4.1.2.1 Simple Moving Average 32

4.1.2.2 Weighted Moving Average 33

4.1.3 Forecasting Error 35

4.1.3.1 Mean Absolute Deviation (MAD) 35

4.1.3.2 Mean Squared Error (MSE) 37

4.1.3.3 Mean Absolute Percent Error (MAPE) 39

4.2 Inventory Modeling 40

4.2.1 Single-Period Model 40

4.2.2 Fixed-Period System 42

4.2.3 Basic Economic Order Quantity (EOQ) 44

5. PROBLEM DESCRIPTION 46

5.1 Case Study 46

vii

5.1.1 Inventory Control of Central Depot 48

5.2 Model Formulation 51

5.2.1 Description of the proposed model 51

5.2.2 Description of the Project Model 52

5.2.2.1 Demand Forecasting Model 52

5.2.2.2 Order Point 54

5.2.2.3 Order-up-to level 54

5.2.2.4 Order Quantity 54

5.2.2.5 Number of Pallet 55

5.2.2.6 Coordinated Replenishment Policy (CRP) 55

5.3 Model Contribution and Modification 55

5.3.1 Assumptions made 56

5.3.2 Simplifications 58

5.4 Design of Experiment 58

5.4.1 Input Validity 59

5.4.2 Forecasting Models Validity 61

5.4.3 Designed Scenario 61

5.4.3.1 Scenario A 61

5.4.3.2 Scenario B 61

5.4.3.3 Scenario C 62

5.4.3.4 Scenario D 62

6. RESULT AND DISCUSSION 63

6.1 Model Verification 63

6.1.1 Inventory Model 64

6.1.1.1 Phase 1: Input parameter column checking 64

6.1.1.2 Phase 2: Model components checking 65

6.1.1.3 Phase 3: Function testing 65

6.1.1.4 Phase 4: Model flow testing 67

6.1.1.5 Phase 5: Model verifying 67

6.1.2 Cost Model 68

viii

6.1.2.1 Phase 1: Input parameter column checking 68

6.1.2.2 Phase 2: Model components checking 68

6.1.2.3 Phase 3: Function testing 69

6.1.2.4 Phase 4: Model flow testing 69

6.1.2.5 Phase 5: Model verifying 69

6.2 Model Validation 70

6.2.1 Manual Calculation 72

6.2.1.1 Inventory control system 72

6.2.1.2 Cost model 75

6.2.2 Result from MS Excel Model 76

6.2.2.1 Inventory control model 76

6.2.2.2 Cost model 76

6.3 Results and findings 78

7. CONCLUSION 89

REFERENCES 91

APPENDICES

A Stationary demand

B Trend demand

C(I) Scenario A: Double Exponential Smoothing with trend demand (Forecast

result)

C(II) Scenario A: Double Exponential Smoothing with trend demand (Inventory

result)

C(III) Scenario A: Double Exponential Smoothing with trend demand (Total cost

result)

D(I) Scenario B: Moving Average with trend demand (Forecast result)

D(II) Scenario B: Moving Average with trend demand (Inventory result)

D(III) Scenario B: Moving Average with trend demand (Total cost result)

ix

D(IV) Scenario B: Simple Exponential Smoothing with trend demand (Forecast

result)

D(V) Scenario B: Simple Exponential Smoothing with trend demand (Inventory

result)

D(VI) Scenario B: Simple Exponential Smoothing with trend demand (Total cost

result)

E(I) Scenario C: Moving Average with stationary demand (Forecast result)

E(II) Scenario C: Moving Average with stationary demand (Inventory result)

E(III) Scenario C: Moving Average with stationary demand (Total cost result)

E(IV) Scenario C: Simple Exponential Smoothing with stationary demand (Forecast

result)

E(V) Scenario C: Simple Exponential Smoothing with stationary demand (Inventory

result)

E(VI) Scenario C: Simple Exponential Smoothing with stationary demand (Total

cost result)

F(I) Scenario D: Double Exponential Smoothing with stationary demand (Forecast

result)

F(II) Scenario D: Double Exponential Smoothing with stationary demand

(Inventory result)

F(III) Scenario D: Double Exponential Smoothing with stationary demand (Total

cost result)

G Nationwide Logistic Information

x

LIST OF TABLES

4.1 Table of Exponential Smoothing with Trend Adjustment 26

4.2 A review of past sales 27

4.3 Forecast with ∝= .2 and 훽 = .4 28

4.4 Smoothing Constant with weighted period 29

4.5 Demand for shopping carts for the last five periods 31

4.6 Demand data 32

4.7 Example 4a, MAD 34

4.8 Example 4b, MAD 34

4.9 Example 5, MSE 36

4.10 Example 6, MAPE 37

5.1 List of variable information based on products 57

5.2 Stationary demand before adjustment 59

5.3 Mean Value if trend demand 60

5.4 Stationary demand after adjustment 60

5.5 Trend demand 60

6.1 Check list for Phase 2 Verification 65

6.2a Verification for Phase 3 66

6.2b Verification for Phase 3 67

6.3 Check list for Phase 2 Verification 68

6.4 Verification for Phase 3 69

6.5 Input variables for model 71

6.6 Output performance measure of costs 78

6.7 Correlation coefficient between cost 78

6.8 Simulation output for number of truckload 79

6.9 Simulation output for unutilized capacity 80

xi

6.10 Correlation analysis of cost performance, truckload utilization, and number of

truckload 80

6.11 Simulation output of mean inventory control parameter for each product 81

6.12 Mean of mean analysis for inventory control parameter 81

6.13 Simulation outputs for forecast performance measure of Product A 84

6.14 Simulation outputs for forecast performance measure of Product B 85

6.15 Simulation outputs for forecast performance measure of Product C 85

6.16 Mean of forecast performance measures 85

6.17 Summary of simulation outputs 87

6.18 Correlation analysis on total cost and forecast error 87

xii

LIST OF FIGURES

1.1 The Problem layout 5

3.1 Project Methodology 23

3.2 Solution Methodology 24

4.1 Inventory Level in a Fixed Period (P) System 43

4.2 Inventory Usage over Time 45

5.1 The model of final year project 47

5.2 Inventory Level in a Fixed Period (P) System. 48

5.3 Inventory Model Simulation Flow Chart 50

6.1 Procedure flow for Model Verification 64

6.2 Process flow for Validation 71

6.3a Result of inventory control system (Part 1) 76

6.3b Result of inventory control system (Part 2) 76

6.4a Cost model (Part 1) 77

6.4b Cost model (Part 2) 77

6.4c Cost model (Part 3) 77

6.5a Total cost vs. order point 81

6.5b Total cost vs. inventory on-hand updated 82

6.5c Total cost vs. order up-to-level 82

6.5d Total cost vs. order quantity 83

6.6 Forecast error base analysis 86

6.7 Summary chart of simulation outputs 88

xiii

LIST OF ABBREVIATIONS

CRP - Coordinated Replenishment Policy

EOQ - Economic Order Quantity

MAD - Mean Absolute Deviation

MAPE - Mean Absolute Percent Error

MROs - Maintenance/ Repair/ Operating

MSE - Mean Square Error

ROP - Reorder Point

WIP - Work In Progress

SES - Simple Exponential Smoothing

DES - Double Exponential Smoothing

MA - Moving Average

1

CHAPTER 1 INTRODUCTION

This project investigate the impact of forecasting modeling on transportation and

inventory carrying cost of an environment that consists of inventory management of a

central depot. In this chapter, the following topics will be addressed: overview is first

introduced, followed by research problem, objectives, and scope of project.

1.1 Overview

Forecasting is a method to predict the future condition by recognizing the existing

condition. It is common in the daily life, for instance, the daily weather forecast. In

industrial, generally, they used to forecast the demand for their production lines and

inventory level of replenishment. Since forecasting provides an estimated value which is

computed from the historical demands, the forecasted demand is closer to the actual

demands. In common, an appropriate demand forecasting method (DFM) generated a

small variation of forecasted demands from actual, while, a wrong DFM may lead to

either overestimate or underestimate of demands consequently. As mentioned by Ralph

Snyder (2001), an understanding of key features in demand data is important in the

process of simulation development for forecasting and inventory control. Mostly in

inventory management, managers encountered only in the accuracy of forecast, however,

this project highlighted the main concern of cost performance in term of annual carrying

cost and transportation cost. At the end of this project, the author will conclude the

finding whether is that a significant difference of total cost by applying difference

forecast modeling.

2

1.1.1 Forecasting

Forecasting is an art and science of predicting future events (Heizer and Render, 2010).

In an overview of forecasting, there is classified into two methods: quantitative

forecasting method and qualitative forecasting method. Qualitative forecast method is

based on educated opinion of appropriate person whereas quantitative forecast method is

more to time series analysis such as historical data analysis and component of time

series demand. Some examples of qualitative forecast method include Delphi Method,

market research, product life-cycle analogy, and expert judgment (“Forecast”, 1997).

For quantitative forecast method, which is the interested part in this project, there are

two parts of concerns. Part 1 is the time-series forecasting modeling which is forecast

the new demand based on historical data such as Moving Average, Exponential

Smoothing, ARIMA models, etc., while Part 2 is discussed in the components of time-

series demand such as trend, average, stationary demand, etc. As mentioned earlier,

forecasting is always not an actual value. Therefore, it is important to encounter for its

deviation from the actual, or called as error. The performance measures used to

determine the forecast accuracy are Mean Absolute Percent Error (MAPE), Mean

Absolute Deviation (MAD), and Mean Squared Error (MSE).

In this project, the author concerns in quantitative forecasting. The forecast models

applied are Simple Moving Average, Single Exponential Smoothing, and Double

Exponential Smoothing. The reason of author using selected forecast modeling is due to

its simplicity and popularity used in industrial. Literatures in Chapter 2 reviewed some

supportive statements according to author’s reason of choosing those forecast modeling.

Moreover, the concepts of these forecast modeling is explained in Chapter 4. These

forecasting models are then tested by using different component of time series as input

demand. These include stationary demand and trend demand. Forecasting accuracy will

be concerned as well in analyzing and explaining the resulted impact which will explain

in detail in Chapter 6 later.

3

1.1.2 Inventory Control System

Inventory control system (also called inventory management) is a policy, procedure, and

technique employed in maintaining the optimum number or amount of each inventory

item. It is applied in both service and manufacturing industry. The objectives of

inventory control are to prevent shortages, minimizing the production cost by reducing

the stock-holding cost, work-in-progress, and inventory cost. Moreover, the ultimate

goal of inventory control system is to achieve 100% demand fulfillment with as low as

possible the stock keeping. There are several types of inventory in industry which can be

classified into raw material inventory, work-in-progress (WIP) inventory, MROs

(maintenance/ repair/ operating), and finished-goods inventory. However, inventory

control is one of the most intriguing problems in industrial logistic.

In an inventory control, there are some general terms in inventory control to be

introduced. Reorder point, s(t), the inventory level (point) at which action is taken to

replenish the stocked item whereas order-up-to-level, S(t), is the optimum level of order

quantity. It is noted that the order-up-to-level is not necessitate for every inventory

policy, yet, depends on the chosen policy criteria. Next, replenishment lead time, L

represents the time between placing and receiving the order and replenishment period

(or also called review period), R is meant to the interval period which the order has to be

made. Most industry applied the inventory policy into their inventory control system

such as fixed period system and fixed quantity system. The concept applied for the

former is based on the review period while the later is based on the fixed order quantity.

Further description of inventory policy kindly refers to Chapter 4.

Briefly described for the project, the author will develop an inventory control template

in MS Excel to determine the order quantity per replenishment period. The inventory

control policy used is the fixed period inventory policy where order is made to a

maximum level (order-up-to-level) for every review period. Detail explanation for the

template will be reviewed in Chapter 5. It is known that the greater the safety stock, the

higher the service level, however not every items can be stored for long period such as

4

the items in this project, canned food. Therefore, in order to keep the inventory at the

minimum level, the author decided to keep the service level where two months

forecasted demand will be keep as the minimum quantity. This is not only to reduce

backorder, it also to standby for any emergency and uncertainty such as stock-out from

supplier. However, in the project, backorder is not allowed. Further explanation for the

last statement will be discussed in Chapter 5 under model formulation.

Another concern in this project is the total cost development. The cost model, reported

by Lorenzo and Stefano (2009) will be used as the model in this project in investigating

the impact of forecasting on cost performance. The proposed cost model is associated

with the inventory carrying cost and transportation cost. By implementing different

forecast approaches may bring to possible effects on inventory control policies.

Transportation may be affected as well due to the deviation of forecasted demand

resulted from different forecast approach. This final year project will study on the

possible impacts resulted from different forecast approach on the cost associated with.

1.2 Problem Statement

As it seems, in real-life industrial situations, management tends to find and implement

simple approaches for operational problems that they face in their day-to-day running of

business, the author in this regard refer to simple approaches to the approaches that are

well understood by managers. It is a known fact in management science that managers

will not use approaches that they cannot understand, obviously, due to the risk of

making the wrong decision when using an approach they truly not well understood.

In this project, a real case model, reported by Lorenzo and Stefano (2009), is studied to

quantify the cost associated with inventory carrying and transportation subject to future

demand which the historical data demand pattern is known.

5

The case involve a company that markets tinned food for hotels, restaurants and catering

segment which supply chain network consists of a central depot, three suppliers, and six

warehouses. The concern in this final year project is to determine the impact of

forecasting modeling on total cost performance, as a consequence of the type of data

used in inventory management, of the central depot where stocks received, distributed,

packaged, and sent to warehouses and the transportation cost for every replenishment

trucks from suppliers. The problem layout is illustrated as in Figure 1.1.

In a simple term, the problem address by this project is to investigate the impact of

forecasting modeling on the total cost associated with the inventory and transportation

situation depicted in Figure 1.

Figure 1.1: The Problem Layout

Supplier

Central Depot

Warehouse 1

Warehouse 6

Warehouse 5

Warehouse 4

Warehouse 3

Warehouse 2

6

1.3 Research Objectives

This project is dealing with an industrial situation as described in the problem statement

with the ultimate objective to investigate the impact of the forecasting modeling

selection transportation and inventory total cost, therefore the objectives of this project

are

1. To fully understand the concept of demand forecasting practiced in the industry.

2. To fully understand the inventory policy that commonly used in industry.

3. To develop a mathematical model that can represent the transportation and inventory

carrying cost of the case described in the problem statement using MS Excel.

4. To conduct comprehensive computational works to determine the impact of

forecasting modeling on the inventory carrying cost and transportation cost.

5. To report the findings.

1.4 Research Scope

The scope of this project is to develop a complex mathematical model that represents

carrying inventory costs and transportation costs using MS Excel for the case reported

by Lorenzo and Stefano (2009). Moreover, three forecasting modeling approaches will

be developed, namely Single Exponential Smoothing, Double Exponential Smoothing,

and Moving Average to conducted the investigation of the impact of forecasting

modeling on the total cost associated with the inventory carrying costs and

transportation costs for the case reported by Lorenzo and Stefano (2009). The

component of time series involved in this project is trend and stationary demand.

Furthermore, the scope of this project includes a solution methodology that has been

developed by the author to conduct the investigation suggested by this project which

detailed in this section under proposed solution methodology. It is not in the scope of

this project to investigate the impact of other forecasting modeling approaches and or