fyp 1 report...abstract d’no corner keropok lekor is a small industrial company produce, sells and...
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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.
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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
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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
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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
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13 Final Presentation
and Panel’s Evaluation
14 Final Report
Submission and
Supervisor’s Evaluation
Table 1.1: Gantt Chart
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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.
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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].
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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.
15
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.
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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.
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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
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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
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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
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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
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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.
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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.
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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.
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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.
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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.
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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
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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).