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FYP 1 REPORT

TITLE:

KEROPOK LEKOR SALES AND STOCK FORECAST SYSTEM

(CASE STUDY: D’NO CORNER KEROPOK LEKOR)

NAME : NUR ATHIRAH BINTI FAZILAH

MATRIC NO : BTAL 17046179

PROGRAMME : BACHELOR OF COMPUTER SCIENCE

( SOFTWARE DEVELOPMENT )

SUPERVISOR : EN. ABD. RASID BIN MAMAT

DECLARATION

This dissertation is submit as a partial fulfilment for the award of a Bachelor of

Computer Science (Software Development) with Honours at the University Sultan

Zainal Abidin (UniSZA), Terengganu, Malaysia. This work is a result of my

investigation. All section of the text and results, which have been obtaining from other

sources are fully referenced.

___________________________

Name : Nur Athirah Binti Fazilah

Date : …………………………...

CONFIRMATION

This is to confirm that this final year project entitled Keropok Lekor Sales and Stock

Forecast System using Weighted Moving Average Method has been prepared and

submitted by Nur Athirah Binti Fazilah , with matric number BTAL17046179 and has

found satisfactory in terms of scope, quality, and presentation as a part of the

requirement for the Bachelor of Computer Science in Software Development in

University Of Sultan Zainal Abidin (UniSZA). The research conducted and the writing

of this report was under my supervision.

____________________________

Name : Mr. Abd Rasid Bin Mamat

Date : …………………………….

DEDICATION

I would like to express my deepest appreciation to all who provided me the possibility

to completing my final year project Keropok Lekor Sales and Stock Forecast System

using Weighted Moving Average Method successfully. A special gratitude goes to my

supervisor, Mr. Abd Rasid Bin Mamat for guiding me in the development of this project.

My warmest gratitude for my Final Year Project panels for aiding my system and giving

the feedback and valuable guidance.

I take this opportunity to thank my parents and my family for giving moral support and

encouragement. Special thanks to all lectures under Faculty of Informatics and

Computing for their attentions, guidance and advice in developing this project. I, sincere

thanks to my fellow friends for their help in finishing this project.

ABSTRACT

D’no Corner Keropok Lekor is a small industrial company produce, sells and supplies

keropok lekor that located in Kuala Terengganu. D’no Corner Keropok Lekor manages

its business manually like most other small businesses. In order to help this company

expands their business, this system is created to meet the demands of today’s market as

technology advances towards Industrial Revolution 4.0.

Due to the fact that this business process is being run manually, it is difficult for the

company to run the data recording process. Businesses need to record every stock

inventory that has been produced and also record the sales and orders from customers.

It is difficult for the company to estimate the quantity of each product produced each

month. The estimation of product is very crucial to avoid excessive production and

ensure the supply is always sufficient in meeting customers' needs.

To overcome the problems faced, methods and techniques that can be used to solve the

problem are by using Weighted Moving Average Method. By using this technique,

sequence of data points measured and analysed in intervals of weekly, monthly,

quarterly or yearly. This analysis of time series in past can be used to make decisions

and predict future trends. It will predict the amount of production that must be produced

by the company with the appropriate amount and be able to avoid excess or deficiency

in production.

ABSTRAK

D'no Corner Keropok Lekor adalah syarikat perindustrian kecil yang menghasilkan,

menjual dan membekalkan keropok lekor yang terletak di Kuala Terengganu. D'no

Corner Keropok Lekor mengurus perniagaannya secara manual seperti kebanyakan

perniagaan kecil lain. Untuk membantu syarikat ini mengembangkan perniagaan

mereka, sistem ini diwujudkan untuk memenuhi permintaan pasaran hari ini sebagai

kemajuan teknologi ke arah Revolusi Industri 4.0.

Oleh kerana proses perniagaan ini dijalankan secara manual, sukar bagi syarikat untuk

menjalankan proses merekod data. Perniagaan perlu merekodkan setiap inventori stok

yang telah dihasilkan dan juga merekodkan jualan dan pesanan dari pelanggan. Adalah

sukar untuk syarikat menganggarkan kuantiti setiap produk yang dihasilkan setiap bulan

untuk mengelakkan pengeluaran yang berlebihan dan memastikan bekalan sentiasa

mencukupi untuk memenuhi keperluan pelanggan.

Untuk mengatasi masalah yang dihadapi, kaedah dan teknik yang boleh digunakan ialah

“Weighted Moving Average Method”. Dengan menggunakan teknik ini, urutan titik

data diukur dan dianalisa dalam selang mingguan, bulanan, suku tahunan atau tahunan.

Analisis masa ini boleh digunakan untuk membuat keputusan dan meramalkan trend

masa depan. Ia akan meramalkan jumlah pengeluaran yang mesti dihasilkan oleh

syarikat dengan jumlah yang sesuai dan dapat menghindari kelebihan atau kekurangan

dalam pengeluaran.

CONTENTS

CHAPTER I .............................................................................................................. 1

INTRODUCTION .................................................................................................... 1

1.1 Introduction ....................................................................................................... 1

1.2 Problem Statement ............................................................................................ 3

1.3 Objective ........................................................................................................... 4

1.4 Scope ................................................................................................................ 4

1.4.1 Admin ......................................................................................................... 4

1.4.2 Staff ............................................................................................................ 5

1.4.3 Customer .................................................................................................... 5

1.5 Implementing and Planning ............................................................................... 6

1.6 Limitation of Works .......................................................................................... 8

1.7 Expected Result ................................................................................................. 8

CHAPTER II ............................................................................................................ 9

LITERATURE REVIEW ......................................................................................... 9

2.1 Introduction .................................................................................................... 9

2.2 Related Research Techniques and Tools ....................................................... 10

2.3 Weighted Moving Average Method .............................................................. 13

2.4 Summary ...................................................................................................... 14

CHAPTER III ......................................................................................................... 15

METHODOLOGY ................................................................................................. 15

3.1 Introduction .................................................................................................. 15

3.2 Iterative Model ............................................................................................. 15

3.3 Methodology Phase ...................................................................................... 16

3.3.1 Initial Planning Phase................................................................................ 16

3.3.2 Planning Phase .......................................................................................... 17

3.3.3 Analysis and Design Phase........................................................................ 17

3.3.4 Implementation Phase ............................................................................... 18

3.3.5 Testing Phase ............................................................................................ 18

3.3.6 Deployment and Evaluation Phase ............................................................ 19

3.4 Hardware and Software Requirement ........................................................... 19

3.4.1 Software Requirement............................................................................... 20

3.4.2 Hardware Requirement ............................................................................. 21

3.5 Example of Implementation on Data and Calculation for Forecasting by

Using Weighted Moving Average Method ............................................................ 22

3.6 Context Diagram .......................................................................................... 24

3.7 Data Flow Diagram ...................................................................................... 25

Data Flow Diagram Level 0 ................................................................................ 25

Data Flow Diagram Level 1 .................................................................................. 28

3.8 Entity Relationship Diagram ......................................................................... 31

3.9 Data Dictionary ............................................................................................... 32

Table staff ............................................................................................................. 33

Table customer ...................................................................................................... 33

Table product ........................................................................................................ 34

Table purchase ...................................................................................................... 34

Table order ............................................................................................................ 35

Table payment....................................................................................................... 35

Table product detail .............................................................................................. 36

Table position detail .............................................................................................. 36

REFERENCES ....................................................................................................... 37

1

CHAPTER I

INTRODUCTION

1.1 Introduction

Keropok lekor is one of the well-known traditional Malay snacks originated from

east coast Malaysia which is Terengganu. Keropok lekor are made of fish usually wolf

herring or mackerel with a combination of salt and sago flour. This mixture will be hand

rolled into shape of long tubes similar to sausage or cut into thin slices and immediately

cooked in deep fried or boiled.

Numerous of keropok lekor stalls can be easily found along the road in

Terengganu and most of the business have been run traditionally or manually. D’no

Corner Keropok Lekor is a small industrial company produce, sells and supplies

keropok lekor that located in Kuala Terengganu. In order to help this company expands

their business, this system is created to meet the demands of today’s market as

technology advances towards Industrial Revolution 4.0.

D’no Corner Keropok Lekor runs it business manually like most other small

businesses. The process involved in this business is the sales of keropok lekor and

record the inventory of keropok lekor. In this system, it proposed to use weighted

2

moving average method technique to predict the amount of sales and production that

needs to be produced based on current trends.

3

1.2 Problem Statement

From investigation, there are a few problems that encountered by D’no Corner

Keropok Lekor: -

1.2.1 Difficulties for the customer to place an order

D’no Corner Keropok Lekor only runs direct selling and it make it harder for

customer if they want to place an order or do reservation. Customer need to directly

contact the owner by telephone that will cause miscommunication or loss information

with the staff during the process. This company manually record the booking

information in their booklet. This manual way can cause company’s manager to be

misled and misinformed by the customer.

1.2.2 Difficulties in estimating sales and production of keropok lekor.

The increase in orders monthly will affect keropok lekor productions. The higher

the orders, the higher sales they will earn. So, this company need a method to estimate

how much keropok lekor they need to produce every month to make sure that they don’t

produce excessive or lack of production based on product sales data of previous months.

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1.3 Objective

The objectives of this projects are as following: -

1.2.3 To design a process flow, structure of user interface and database for Keropok

Lekor Sales and Stock Forecast System.

1.2.4 To develop a system that can manage users order and a system that can estimates

sales and stock of keropok lekor.

1.2.5 To test the capabilities of the Keropok Lekor Sales and Stock Forecast System

and generate the report to the user.

1.4 Scope

Scope can be describe as the functionality of a system that relates to activities

between system user and system, which is the information that flows between system

and actors outside of the system. The scope of Keropok Lekor Sales and Stock Forecast

System is Admin, Staff and Customer of D’no Corner Keropok Lekor.

1.4.1 Admin

i. Login

ii. Manage profile

iii. Manage stock

iv. Manage stock prediction

v. Manage sales prediction

5

vi. Manage cash purchase

vii. View report

viii. Logout

1.4.2 Staff

i. Login

ii. Manage profile

iii. Manage stock

iv. Manage order

v. Verify transaction

vi. Manage cash purchase

vii. View report

viii. Logout

1.4.3 Customer

i. Login

ii. Manage profile

iii. View keropok lekor detail

iv. Make order

v. Make payment

vi. Logout

6

1.5 Implementing and Planning

Gantt chart are used to describe activities and timescale that involve in

implementing this project as shown in Table 1.1.

No Task Week

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 FYP Briefing and

discuss project title

2 Project Title

Proposal and

Registration

3 Proposal Writing –

Introduction

(Chapter 1)

4 Proposal Writing -

Literature Review (Chapter 2)

5 Proposal Writing

Progress

6 Proposal Progress

Presentation and Panel’s Evaluation

7 Methodology

Workshop

8 Proposal Writing –

Methodology

Proof Of Concept

(Chapter 3)

9 Final Year Project

Format Writing

Workshop

10 Drafting Report of

Proposal

11 Submit draft

proposal report to

supervisor

12 Preparation for

Final Presentation

7

13 Final Presentation

and Panel’s Evaluation

14 Final Report

Submission and

Supervisor’s Evaluation

Table 1.1: Gantt Chart

8

1.6 Limitation of Works

This system only allow admin to view and manage the stock and sales prediction.

Place an order on system only can be done by user and only staff can manage order and

verify transaction.

1.7 Expected Result

The expected results of this project are to facilitate three parties which is user,

admin and staff in manage the keropok lekor order and view the keropok lekor sales,

forecast stock and stock production. This project has been designed to keep in view the

present and future necessities in mind to make it adaptable.

The goals that are achieved by the system are:

i. Be user friendly and flexible by allowing customers to view and place order

online.

ii. Improved productivity by allow staff to manage customers order, verify

transaction and view sales and stock forecast.

iii. Ease admin to manage stock and manage sales and stock forecast to avoid

excessive or lack of keropok lekor production.

9

CHAPTER II

LITERATURE REVIEW

2.1 Introduction

Forecasting can be viewed as a systematic approach used to analyse the trend of

historical sales data on future demand for projects as a framework for long-term

strategic preparation and as a consideration for several capacity-related decisions. In

this chapter, there are a few ideas of previous research that related to the method on

forecasting compared to make clear description of weighted average square method as

an added value in this system. There are so many techniques used to make precise

forecasts, but each method is different for different market strategies.

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2.2 Related Research Techniques and Tools

A review of the research paper has been conducted to study on how others

implemented the weighted moving average method and other similar forecasting

techniques into their system. As a result, a few research papers have been found.

The first article is conduct by author [1], the research stated that in retail stage of

food supply chain, food waste and stock-outs occur mainly due to inaccurate forecasting

of sales which leads to incorrect ordering of products. The time series sales in food retail

industry are characterized by high volatility and skewness, which vary by time. So, the

interval forecasts are required by the retail companies to set appropriate inventory

policy (reorder point or safety stock level). This paper attempts to develop a seasonal

autoregressive integrated moving average with external variables (SARIMAX) model

to forecast daily sales of a perishable food. The process of fitting a SARIMAX model

in this study involves: (i) the development of Seasonal Autoregressive Integrated

Moving Average (SARIMA) model and (ii) combining the SARIMA model and the

demand influencing factors using linear regression.

In study of retail unit in the city of João Pessoa by [2] state that it is known that

inventories are one of the key points in retail, in view of their management directly

impacts the level of service that the companies provide. While author [3] also

experiences same problems in predicting the number of products that must be bought to

suppliers every month to make sure that is no excessive or less in number of stock in

the Gajah Bintang Company. The importance of inventory management is to balance

the supply and demand of items, in order to reduce the capital retained in items that do

11

not present demand for their consumption. Both of this works was assisted by Weighted

Moving Average Method that is essential, given that past data show future sales

projections.

The next author [4] applied that there are methods and techniques that can be used

to solve the problem are by using time series modelling that is data with a pattern or

trend. There are two stages in time series modelling that is Univariate Forecasting for

one variable and Multivariate Forecasting for many variables. This project will use

Univariate Forecasting because it will forecast one variable from trend alone in

forecasting techniques. The study conducted by [5] implied that methodologies that

have been used in sales forecasting are typically time series algorithms that can be

classified as linear or nonlinear, depending on the nature of the model they are based

on. Linear models, like autoregressive moving average (ARMA) and autoregressive

integrated moving average are the most popular methodologies, but their forecasting

ability is limited by their assumption of a linear behaviour and thus, it is not always

satisfactory. The author [6] reveal that exponential smoothing method which results to

least forecast error is the best method to determine accurate demand forecasts using 12

monthly sales figures of a moderate busy pharmacy. Author concluded that the

exponential weighted moving average was preferred compared to the simple and linear

weighted moving average [7].

Even though the performance of the EWMA method significantly decreases when

applying to forecast data, it is most suitable for short-range forecasting. Exponentially

Weighted Moving Average (EWMA) method to forecast lime prices during January

2016 to September 2016 gives the smallest forecasting error measured by the Mean

12

Absolute Percentage Error (MAPE) [8]. Forecasting has always been an attractive

research area since it plays an important role in business planning. To achieve

competitive advantage in an environment subject to constant fluctuations, organizations

have to make correct and timely decisions based on accurate information-forecast [6].

13

2.3 Weighted Moving Average Method

A time series is a sequence of data points usually measured and analysed in intervals

of weekly, monthly, quarterly or yearly. This analysis of time series in past can be used to

make decisions and predict future trends.

One method that can be used to predict the time series is (WMA). Weighted Moving

Average Method gives you a weighted average of the last n prices by putting more weight

on recent data and less on past data.

The WMA is calculated by multiplying each bar’s price by its associated weighting

factors and totalling the values.

The formula for the WMA is as follows:

WMAs can have different weights assigned based on the number periods used in

the calculation. Because of its unique calculation, WMA will follow prices more closely

than a corresponding Simple Moving Average.

WMA = (Price * weighting factor) + (Price previous period * weighting factor-1) + … (1)

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2.4 Summary

In conclusion, it is very crucial in choosing the correct method to ensure that the

system has successfully implemented and accomplished the purpose. The technique

selected is the Weighted Moving Average Method that can correctly forecast the keropok

lekor sales and stock since it has more accurate measure of recent price action.

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CHAPTER III

METHODOLOGY

3.1 Introduction

Methodology is the set of the complete guideline that includes the Software

Development Life Cycle (SDLC) tool models for carrying out activities. That splits the

work into the phases of the operation for improved system development planning and

management.

3.2 Iterative Model

The methodology that will be used in developing Keropok Lekor Sales and

Forecasting System Using Weighted Moving Average Method is Iterative Model. This

model is driven by the risk analysis and evaluation requirements and keeps on iterating,

covering each phase of the model repeatedly,

until the final products produced satisfy all the requirements that were evolved before

and during the development process.

There are six phases involved in the iterative model which is initial planning

phase, planning phase, analysis and design phase, implementation phase, testing phase,

deployment and evaluation phase.

16

Figure 3.1: Iterative Model

3.3 Methodology Phase

The explanation of each phase involves for developing Keropok Lekor Sales and

Forecasting System Using Weighted Moving Average Method as below: -

3.3.1 Initial Planning Phase

This phase involves brainstorming the project idea and proposed the title of

project. Keropok Lekor Sales and Forecasting System Using Weighted Moving

Average Method has been decided as the project title.

17

3.3.2 Planning Phase

Planning phase is the most crucial phase as a guideline to develop the system. In

this phase, requirement is gathered through consistent interaction with D’no Corner

Keropok Lekor owner. All resources like product information, sales details and business

income are collected by interview the owner to make sure this system meet the system

requirement and functionality requirement. Clear project has been sort out during this

phase. To make sure this system follows the timeline, schedule has been made which is

Gantt chart to make sure that this project still on track and can be done on estimate time.

3.3.3 Analysis and Design Phase

In analysis phase, review on few research paper and journal has been conducted

to study on how others implemented the Weighted Moving Average Method and other

similar forecasting techniques into their system. This will give better understanding on

selected forecasting technique compare to technique that has been used by other

researchers. Weighted Moving Average Method was decided to be the approach in this

project. Methodology, technique, software and hardware requirement are also decided

during this phase to ensure that every requirement are compatible with the system.

Design phase of this system is done based on output of analysis phase. The

Context Diagram (CD), Data Flow Diagram (DFD), and Entity Relationship Diagram

(ERD) are designed at this phase to interpret the process flow of Keropok Lekor Sales

18

and Forecasting System. System interface and database are design based on

requirement.

3.3.4 Implementation Phase

This is a phase where activities that have been planned during previous phase are

executed. This system is developed by using XAMPP, MySQL and Notepad++. During

this phase, database and interface that has been designed are started to be developed.

The process of writing coding is started during this phase.

3.3.5 Testing Phase

After system has fully developed, testing is being done on the system. for this

system, gorilla testing has been used which this system is repeatedly tested to ensure

that it is working correctly and there is no bug in that module. In this phase, code and

programming are analyse to make sure it works according to customer requirements.

And while it's not possible to solve all the failures that might find during the testing

phase, it is possible to use the results from this phase to reduce the number of errors

within the software program.

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3.3.6 Deployment and Evaluation Phase

In this phase, the system is ready to be tested by user. The users will evaluate this

system and give their feedback based on their experienced. The evaluation will be use

to improve the system whether changes are needed to make sure that it fulfills all the

requirements.

3.4 Hardware and Software Requirement

Hardware and software play a major role in the development of a system as a

standard requirement that determines the system's achievement. To build a successful

system, this standard requirement relates to one another.

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3.4.1 Software Requirement

Software Description / Purpose

Microsoft Office Word 2016

Use to prepare documentation of the report

Draw.io An online software use to draw Context

Diagram and Data Flow Diagram

PHPMyAdmin A database system to create Entity

Relationship Diagram

Google Drive Storage system use to backup all project

document

Microsoft Powerpoint 2016 Use to prepare slide presentation

Table 3.4.1: Software Requirement

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3.4.2 Hardware Requirement

Hardware Type

HP Laptop Windows edition: Windows 10 Home

Single Language

Processor: AMD A6-9220 RADEON

R4, 5 COMPUTE CORES 2C+3G

2.5GHz

Installed memory (RAM): 4.00GB

System type: 64-bit Operating System

Table 3.4.2: Hardware Requirement

22

3.5 Example of Implementation on Data and Calculation for

Forecasting by Using Weighted Moving Average Method

Table 3.5.1 shows the demand for defence machinery for a certain project for

month 1 to 5. The implementation of Weighted Moving Average Method on forecasting

are as following: -

Table 3.5.1: Demand for Defence Machinery

Determine the weighted moving average equation.

To simplify the calculations, as shown in Table 3.5.1, each year will have its own

weightage, which is more weight on recent data and less on past data. WMA are calculated

by multiplying each bar’s price by its associated weighting factors and totalling the values.

Implementation of the WMA method can be shown as below: -

Month Demand Weightage

1 120 5/15

2 110 4/15

3 90 3/15

4 115 2/15

5 125 1/15

23

From this example, demand of defence machinery for month 6 is 142.

WMA = (Price * weighting factor) + (Price previous period * weighting factor-1) + … (1)

WMA = (120× 515

) + (110×4

15) + (90×

3

15) + (115×

2

15) + (125×

1

15)

= 142

24

3.6 Context Diagram

Figure 3.2 shows context diagram for Keropok Lekor Sales and Stock Forecast

System using Weighted Moving Average Method. There are 3 entities involve in this

context diagram which is ADMIN, STAFF and CUSTOMER.

Staff

Figure 3.2 : Context Diagram

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3.7 Data Flow Diagram

Data Flow Diagram Level 0

Figure 3.3 : Data Flow Diagram Level 0 for Admin

Based on Figure 3.3 above, there are six processes involve in Admin module,

where Admin can log in as a first step to get into the system. After login, Admin can

Manage Profile, Manage Stock, Manage Stock Prediction, Manage Sales, Manage Cash

Purchase and Generate Report from the system. At the end of process, Admin can log

out from the system.

26

Figure 3.4 : Data Flow Diagram Level 0 For Staff

Based on Figure 3.4 above, there are six processes involve in Staff module, where

Staff can log in as a first step to get into the system. After login, Staff can Manage

Profile, Manage Stock, Manage Order, Verify Transaction, Manage Cash Purchase and

Generate Report from the system. At the end of process, Staff can log out from the

system.

27

Figure 3.5 : Data Flow Diagram Level 0 for Customer

Based on Figure 3.5 above, there are three processes involve in Customer module,

where Customer can log in as a first step to get into the system. After login, Customer

can Manage Profile, Make Order and Make Payment. At the end of process, Customer

can log out from the system.

28

Data Flow Diagram Level 1

Figure 3.6 : Data Flow Diagram Level 1 For Manage Product

Based on Figure 3.6 above, there are four processes involve in Manage

Product. Admin can register new product, update products details, remove

product. Admin and Staff share the role to update or add the new quantity of

product.

29

Figure 3.7 : Data Flow Diagram Level 1 For Manage Staff

Based on Figure 3.7 above, there are three processes involve in Manage Staff.

Admin can register new staff, update staff profile and remove staff.

30

Figure 3.8 : Data Flow Diagram Level 1 For Manage Order

Based on Figure 3.8 above, there are three processes involve in Manage Order.

Customer can make a new order, update order and cancel order.

31

3.8 Entity Relationship Diagram

Figure 3.9: Entity Relationship Diagram

An entity relationship diagram (ERD) illustrates an information system’s entities

and the relationship between those entities. ERD composed of three things such as

identifying and defining the entities, determine entities interaction and the cardinality

of the relationship. Figure 3.9 above shows the relationship between entities that exist

in this develop system database.

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3.9 Data Dictionary

Data dictionary is a file or a set of files that contains a database's metadata. The

data dictionary contains records about other objects in the database, other data, data

relationships to other objects, and such as data ownership. The data dictionary is

important component a relational database. For most relational database management

systems (RDBMS), the database management system software needs the data dictionary

to access the data within a database.

1. TABLE staff

2. TABLE customer

3. TABLE product

4. TABLE purchase

5. TABLE order

6. TABLE payment

7. TABLE product detail

8. TABLE position detail

33

Table staff

No Column Type Length Null Key

1 staffID varchar 10 No PK

2 staffName varchar 50 No

3 password varchar 50 No

4 position varchar 12 No

5 phoneNo varchar 12 No

Table customer

No Column Type Length Null Key

1 custID varchar 10 No PK

2 custName varchar 50 No

3 custAddress varchar 100 No

4 phoneNo varchar 12 No

5 password varchar 50 No

Table product

No Column Type Length Null Key

1 productID varchar 10 No PK

2 productname varchar 50 No

3 priceperKg int 5 No

4 image varchar 50 No

5 staffID varchar 10 No FK

Table purchase

No Column Type Length Null Key

1 purchaseID varchar 10 No PK

2 staffID varchar 10 No FK

3 paymentID varchar 10 No FK

4 datePurchase date 8 No

5 totalPurchase int 11 No

6 custID varchar 10 No FK

Table order

No Column Type Length Null Key

1 orderID varchar 10 No PK

2 custID varchar 10 No FK

3 productID varchar 10 No FK

4 dateorder date 10 No

5 quantity varchar 11 No

6 staffID varchar 10 No FK

Table payment

No Column Type Length Null Key

1 paymentID varchar 10 No PK

2 datepayment date 8 No

3 custID varchar 10 No FK

Table product detail

No Column Type Length Null Key

1 productname varchar 10 No FK

Table position detail

No Column Type Length Null Key

1 position varchar 10 No FK

REFERENCES

[1] Arunraj, N. S., & Ahrens, D. (2015). A hybrid seasonal autoregressive integrated

moving average and quantile regression for daily food sales forecasting. International

Journal of Production Economics, 170, 321-335.

[2] Mendonca, L. K. D. (2019). ANÁLISE DA FORMAÇÃO EXCESSIVA DE

ESTOQUE EM UMA UNIDADE VAREJISTA DA CIDADE DE JOÃO PESSOA.

[3] Yudianto, A. (2019). Sales Forecasting System with Weighted Moving Average On

Gajah Bintang Company (Doctoral dissertation, UNIKA SOEGIJAPRANATA

SEMARANG).

[4] Muhammad Hafizzuddin Bin Nasruddin (2018) , ‘Crisp Stock Forecast System

(CSFoS) Using Least Square Method’ . Universiti Sultan Zainal Abidin, Terengganu,

Malaysia.

[5] Doganis, P., Alexandridis, A., Patrinos, P., & Sarimveis, H. (2006). Time series

sales forecasting for short shelf-life food products based on artificial neural networks

and evolutionary computing. Journal of Food Engineering, 75(2), 196-204.

[6] Obamiro, J. K. (2019). Demand Forecasting and Measuring Forecast Accuracy in a

Pharmacy. Acta Universitatis Danubius. Œconomica, 15(3).

[7] Wagner, J. E., Rahn, J., & Cavo, M. (2019). A Pragmatic Method to Forecast

Stumpage Prices. Forest Science.

[8] Booranawong, T., & Booranawong, A. (2017). An exponentially weighted moving

average method with designed input data assignments for forecasting lime prices in

Thailand. Jurnal Teknologi, 79(6).