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1
FYP 1 REPORT
TITLE :
CRIPS STOCK FORECAST SYSTEM USING LEAST SQUARE METHOD
(CASE STUDY: USAHA GIGIH ENTERPRISE COMPANY)
NAME : MUHAMMAD HAFIZZUDDIN BIN NASRUDDIN
MATRIC NO : BTAL 15039528
PROGRAMME : BACHELOR OF COMPUTER SCIENCE
( SOFTWARE DEVELOPMENT )
SUPERVISOR : EN. ABD. RASID BIN MAMAT
2
ABSTRACT
Usaha Gigih Enterprise is a small industrial company that produce many type of
chips. Technological advancement towards Industrial Revolution 4.0 provides an idea
to help this company grow its business using modern technology. Usaha Gigih
Enterprise manages its business manually like most other small businesses. Basically,
this company runs a crips business for supplying to buyers. The process involved in this
business is the sale of crips and record the inventory of crips.
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.
The company is difficult to estimate the amount in producing each product for each
month. Product revenue estimates are the main thing in the company to avoid excessive
production and ensure the supply is always sufficient in meeting customers' needs.
To overcome the problems faced, there are techniques that can be implemented
to assist the company's business operations. The methods and techniques that can be
used to solve the problem are by using time series modeling 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. It will predict the amount of production that must be produced
by the company with the appropriate quantity and be able to avoid excess or deficiency
in production.
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Table of Contents
ABSTRACT .............................................................................................................................. 2
CHAPTER 1 ............................................................................................................................. 5
1.1 Background............................................................................................................... 5
1.2 Problem Statement ................................................................................................... 7
1.3 Objective ................................................................................................................... 8
1.4 Scope .......................................................................................................................... 8
1.1 Admin .................................................................................................................... 8
1.2 Staff ....................................................................................................................... 9
1.3 Customer ............................................................................................................... 9
1.5 Implementing and Planning .................................................................................. 10
1.6 Limitation of Works ............................................................................................... 12
1.7 Expected Result ...................................................................................................... 12
CHAPTER 2 ........................................................................................................................... 13
2.1 Introduction .................................................................................................................. 13
2.2 Related Research Techniques and Tools .................................................................... 14
2.3 Least Square Method ................................................................................................... 17
2.4 Summary ....................................................................................................................... 19
CHAPTER 3 ........................................................................................................................... 20
3.1 Introduction .................................................................................................................. 20
3.2 Spiral Model ................................................................................................................. 21
3.3 Methodology Phase ...................................................................................................... 22
3.3.1 Initial Planning Phase ........................................................................................... 22
3.3.2 Planning Phase ...................................................................................................... 22
3.3.3 Analysis and Design Phase ................................................................................... 23
3.3.4 Implementation Phase .......................................................................................... 24
3.3.5 Testing Phase ......................................................................................................... 24
3.3.6 Deployment and Evaluation Phase ...................................................................... 24
3.4 Hardware and Software Requirement ....................................................................... 25
3.4.1 Hardware Requirement ........................................................................................ 25
3.4.2 Software Requirement .......................................................................................... 26
3.5 Trend Analysis.............................................................................................................. 27
3.5.1 Linear Trend ......................................................................................................... 27
3.5.2 Estimation of Trend Analysis by Least Square Method ............................. 28
3.5.3 Example data and calculation ....................................................................... 29
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3.6 Context Diagram .......................................................................................................... 32
3.7 Data Flow Diagram ...................................................................................................... 33
Data Flow Diagram Level 0 ........................................................................................... 33
Data Flow Diagram Level 1 ........................................................................................... 36
3.8 Entity Relationship Diagram ...................................................................................... 39
3.9 Data Dictionary ............................................................................................................ 40
1. Table user................................................................................................................ 41
2. Table staff ............................................................................................................... 41
3. Table customer ....................................................................................................... 42
4. Table address .......................................................................................................... 42
5. Table chips .............................................................................................................. 43
6. Table purchase ....................................................................................................... 43
7. Table order ............................................................................................................. 44
8. Table payment ........................................................................................................ 44
9. Table chipsmanagement ........................................................................................ 45
REFERENCES ....................................................................................................................... 46
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CHAPTER 1
INTRODUCTION
1.1 Background
Crips refer to a kind of snack that is cut thin and fried until crisp. Crips are made
from various materials and named according to the type of material used to produce the
crips. Most crips companies are located in the village area and business processes have
been conducted traditionally or manually. Usaha Gigih Enterprise is a small industrial
company that produces many types of crips. Technological advancement towards
Industrial Revolution 4.0 provides an idea to help this company grow its business using
modern technology.
6
Usaha Gigih Enterprise manages its business manually like most other small
businesses. Basically, this company runs a crips business for supplying to buyers. The
process involved in this business is the sale of crips and record the inventory of chips.
In this system, it is proposed to use the least square method forecasting technique to
predict the amount of production that needs to be produced based on current trends.
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1.2 Problem Statement
1.2.1 Management of crips order system
Customers need to book directly from the factory or by contacting the
company's operators via telephone. Booking by phone will cause the customer
to be confused with the booking. The potential loss of reservation information.
The manual method employed by Gigih Enterprise Company by recording the
booking information in the booklet will potentially lost information while
booking through the phone will cause the company's manager to be misled and
misinformed by the customer.
1.2.2 Difficulties in estimates production of crips
The increase in orders every month will affect the production of the
product. The company needs product information that is a customer's favorite.
At the same time, they need an effective method of identifying customers'
favorite products. Based on product sales data on a monthly basis, the company
also needs a method to get an estimate of the exact amount of product produced
in order to avoid excessive or lack of production in producing the product so
that it can always meet customer demand.
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1.3 Objective
1.3.1 To design a process flow, structure of user interface and database from the Crips
Stock Forecast System (CSFoS).
1.3.2 To develop a system that can manage the user order and forecast stock of crips
1.3.3 To test the capabilities of the Crips Stock Forecast System (CSFoS) and generate
the report to the user.
1.4 Scope
The scope is important to set a boundary on what the area will cover in the
system development. Thus CSFoS using Least Square Method is focused on online and
walk in purchase by customer to Usaha Gigih Enterprise premise. The forecast will be
produced using the sales data collected from online order dan walk in purchased.
Forecasting will generate the expected stock will be produced on the next month.
Scope of this system is Admin, Staff and Customer of Usaha Gigih Enterprise.
1.1 Admin
1.1.1 Login
1.1.2 Manage Profile
1.1.3 Manage Stock
1.1.4 Cash Purchase
1.1.5 Manage Stock Prediction
1.1.6 View Report
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1.2 Staff
1.2.1 Login
1.2.2 Manage Profile
1.2.3 Manage Stock
1.2.4 Cash Purchase
1.2.5 View Report
1.3 Customer
1.3.1 Login
1.3.2 Manage Profile
1.3.3 Order
1.3.4 Payment
1.3.5 View Report
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1.5 Implementing and Planning
Using a Gantt Chart that describes key of activities and timescales involves in
implementing this project as shown in Table 1.1
Table 1.1 : Gantt Chart
No Task Week
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 FYP Briefing
2 Project Title Proposal
and Registration
3 Proposal Writing
(Introduction)
4 Proposal Writing
(Literature Review)
5 Proposal Progress
Presentation and
Evaluation
6 Discussion and
Correction of the
Proposal
7 Proposed Solution –
Methodology
8 Proof of Concept
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9 Seminar Preparation –
Project Poster and
Slide
10 Seminar Registration
– Project Poster and
Slide
11 Seminar Presentation
and Evaluation
12 Finalizing Report of
the Proposal
13 Final Report
Submission and
Evaluation
12
1.6 Limitation of Works
This system only involves customer in order crips process and staff in
managing the order and stock. From this system, only admin is allowed to view the
stock prediction and make a forecasting in order to make a new stock production.
1.7 Expected Result
The expected results of the project are facilitating the each party-
CUSTOMER, ADMIN and STAFF in manage the crips order and view the prediction
of chips production. The system has been designed keeping in view the present and
future requirements in mind and made very flexible.
The goals that are achieved by the system are:-
1. Instant access
2. Improved productivity
3. Efficient management of records
4. Simplification of the operations
5. Less processing time and getting required information
6. User friendly and flexible for further enhancement
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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
A literature review is a body of text that aims to review the critical point of
current knowledge and particular topic. In my research its related to the method on
forecasting. It is an evaluative report of studies found in the literature that related to my
selected area. In this chapter, the idea of previous research is compared to make clear
description of the least square method as an added value in this system. There are so
many methods that had been used in order to make an accurate forecasting but each
method are different approach for different business.
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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 least square method 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 performance
evaluation of an organization at certain intervals helps to keep pace with the market.
For developing models to achieve better policy and planning results, forecast of sales
volume is a must. The objective of this study is to apply forecasting techniques to a
beverage production company and notice whether the forecast errors are irrationally
large and require an improvement in the statistical models and process of producing
these forecasts. Statistical time series modeling techniques like – Moving Average,
Simple Exponential Smoothing and Least Square methods are used for the study which
is compared with the value of actual sales volume and a large gap is found between
forecasted value and actual one. There are three forecasting models, namely, Winter’s,
decomposition, and Auto-Regressive Integrated Moving Average (ARIMA), were
applied to forecast the product demands and it is found that the decomposition and
ARIMA models provide lower forecast errors in all product groups which minimizes
the total overtime and inventory holding costs based on a fixed workforce level and an
available overtime.
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The author [2] applied various statistical time series models to observe forecast
errors in the demand of juice production are within the expected limit and to select a
forecasting technique which has a less relative error. The author [2] showed that Least
Square Method is more accurate than the others. To forecast milk production in India
using statistical time series modeling- Double Exponential Smoothing and Auto-
Regressive Integrated Moving Average and concluded that ARIMA performed better
than the other one [3]. Next, the author [4] applied methods to forecast the demand for
products of a food industry, which directs its sales to the foodservice market, in order
to base the short to medium term production planning. The forecasts were evaluated
using the error measure MAPE and compared to the demand considered by the
company. Authors concluded that the HoltWinters method, which was applied in the
time series analyzed in their work, showed its effectiveness for forecasting demand of
products that present trend and seasonality patterns in sales history.
A hybrid forecasting model for nonlinear time series by combining ARIMA with
genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting
models is proposed by [5], meanwhile [6] proposed a framework which serves as a
guide for practitioners when initiating and conducting long-term collaborative
forecasting partnerships. After reviewing the literature it has found that many works
have done on forecasting but sales forecasting of beverage product is in a very few
number.
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The study conducted by [7], most manufacturing organizations are in a
continuous effort for increasing their profits and reducing their costs. Accurate sales
forecasting is certainly an inexpensive way to meet the aforementioned goals, since this
leads to improved customer service, reduced lost sales and product returns and more
efficient production planning. Especially for the food industry, successful sales
forecasting systems can be very beneficial, due to the short shelf-life of many food
products. Production needs a long-term forecast for planning the development of the
plant and equipment and a more detailed short-term forecast for arranging the
production plan. Marketing needs a view of the future market in order to plan its actions
and assess the impact of changes in the marketing strategy on sales volumes. Food
companies are more concerned with sales forecasting due to their special characteristics,
such as the short shelf-life of their products, the need to maintain high product quality
and the uncertainty and fluctuations in consumer demands.
The third article conducted by [4], supply chain activities planning and control
depend on accurate estimates of the volumes of products and services to be processed
and the estimates come as forecasts.
Time series analysis is very important in a wide range of applications, especially
when it comes to forecasting, and it encloses many different forecasting models.
However, it is necessary to determine which model best suits each [8]. Besides choosing
the best technique, the forecasting to be generated by the model chosen should be as
close to real as possible [9]. In other words, the errors of forecasting should be
minimized, so the production managers plan the production inattention to the market
and minimizing the costs.
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2.3 Least Square Method
A time series is a sequence of data points, measured typically at successive times
spaced at uniform time intervals usually weekly, monthly, quarterly or yearly. An
analysis of past history can be used by management to make decisions, long term
forecasting and even planning. Time series forecasting employs various models to
predict future events based on past events.
The estimation of trend analysis can be use least square method as one of the
time series models. Least square method is a method of constructing a straight line
equation through data points to obtain the best fitting line.
This is the mathematical method of obtaining the line of best fit between the
dependent variable and an independent variable. In this, the sum of the square of the
deviations of the various points from the line of best fit is minimum or least. For straight
line,
𝑌′ = 𝑎 + 𝑏𝑡
where 𝑌′ is equal to forecast for period 𝑡 and 𝑡 is the number of time periods from 𝑡 =
0, 𝑎 is the value of y at 𝑡=0 (y – intercept ) and b is slope of the line.
(1)
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The above process can be summarized as below. However, the coefficient
computation between parameters a and b can be calculated using the least-squares
method, which minimizes the sum of squared errors. Steps to forecast using linear line
are as shown below :
Step 1 : Compute parameter 𝑏
𝒃 =∑ 𝒕𝒀 − (∑ 𝒀) (𝜮𝒕) 𝒏⁄
𝜮𝒕𝟐 − (∑ 𝒕)𝟐 𝒏⁄
Step 2 : Compute parameter 𝑎
𝒂 =∑ 𝒀
𝒏− 𝒃 (∑
𝒕
𝒏)
Step 3 : Generate the linear trend line
𝒀′ = 𝒂 + 𝒃𝒕
Step 4 : To make a forecast for dependent variable 𝑌′, substitute the appropriate
value for the independent variable 𝑡 .
(2)
(3)
(1)
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2.4 Summary
In conclusion, the selection of accurate technique is very important to make sure
that the system successfully implemented and achieved the objective. The selected
technique is least square method that can be able to predict the chips forecast correctly.
Based on the research study, it can be conclude that the least square method is suitable
for Chips Stock Forecast System Using Least Square Method.
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CHAPTER 3
METHODOLOGY
3.1 Introduction
The methodology is the set of the complete guideline that includes the models
of tools to carry out activities in the Software Development Life Cycle (SDLC). Which
splitting the work into the phases of activity for better planning and management of the
system development.
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3.2 Spiral Model
The methodology that will be used in Crips Stock Forecast System (CSFoS)
Using Least Square Method is Spiral Model. Spiral model is a combination of sequential
and prototype model. There are specific activities that are done in one iteration which
is spiral where the output is the small prototype of the large software. Thus, the same
activities are repeated for all the spirals until the whole software is built.
There are six phases involved in the spiral model which is initial planning phase,
planning phase, analysis and design phase, implementation phase, testing phase,
deployment and evaluation phase.
Figure 3.1 : Spiral Model
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3.3 Methodology Phase
The explanation of each phase involves for developing Crips Stock Forecast
System (CSFoS) Using Least Square Method as below :-
3.3.1 Initial Planning Phase
At this phase, the process occurred is brainstorming the project idea and
proposed the title of the project. Then, Chips Stock Forecast System Using Least
Square Method Time Series Analysis and Forecasting was decided.
3.3.2 Planning Phase
Planning phase is the most important phase as a guideline to develop the system.
During this phase, objectives of the system are identified and all the requirements
are gathered in order to develop the system. Research for the system are being
allocated and designing a schedule to ensure that the system follow the timeline
made. Research for the system is made by reading articles and journals related to
the system and the method used. System scheduling is created using a gantt chart to
ensure that the system will developed systematically and to make sure the project
can be done on time. In planning phase also getting the business and user
requirement by interview and collecting business document from Usaha Gigih
Enterprise to meet the functionality requirement of the system that will develop.
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3.3.3 Analysis and Design Phase
During analysis phase, some research has been done through articles, journals
in order to choose the best approach and added value in Crips Stock Forecast System
(CSFoS) Using Least Square Method Time Series Analysis and Forecasting. This
leads to selecting Least Square Method Time Series Analysis and Forecasting and
hence doing more research to understand the concept on it and how to applied in the
system. All of the disadvantages of the system are listed and come out with the
solution in developing this system. Methodology, techniques, hardware and
software requirements are also analyzed in this phase. This is to ensure that every
requirement and any related things need to be done are suitable with the system.
Design phase of the system is done based on the output produced during analysis
phase. First, all the required hardware and software requirements for the proposed
system are working properly. Design the Context Diagram (CD), Data Flow
Diagram (DFD), and Entity Relationship Diagram (ERD) to translate the process
flow of the Chips Stock Forecast System Using Least Square Method. Interface and
database designed based on the requirements stated during analysis phase. Then the
working prototype designed to get another further improvement to be added into the
proposed system.
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3.3.4 Implementation Phase
In this phase, all activities that have been planned during phase before are
executed. The system is developed using XAMPP, MySQL and Notepad++ .
Database and interface designed during design phase are started to be developed.
The process of writing the coding are being done and the progress of the system are
reported from time to time.
3.3.5 Testing Phase
When the system is fully developed, system are being tested. For this system,
the black box testing and white box testing is used to test the correctness of the
implementation coding and search for any errors and bug. If there are any errors, it
must be recheck and come out with the solution.
3.3.6 Deployment and Evaluation Phase
During this phase, the system is released to be used by the user. The users use
the system and give their feedback whether it needs to be improved or there is
anything that needs to be modify. Then the modifications are being made based on
the feedback from the user to make sure the system is completely fulfilling the
requirements.
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3.4 Hardware and Software Requirement
In developing a system, hardware and software play a great role as a standard
requirement which determines the accomplishment of the system. This standard
requirement relates to each other to build a successful system.
3.4.1 Hardware Requirement
Table 3.4.1 : Hardware Requirement
Hardware Description / Purpose
Microsoft Office Word 2010 Prepare documentation of the report
Draw.io An online software to create and design
Context Diagram and Data Flow
Diagram
PHPMyAdmin As a system database and generate the
Entity Relationship Diagram
Dropbox Storage and backup on all the document
Microsoft Powerpoint 2010 Prepare slide presentation
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3.4.2 Software Requirement
Table 3.4.2 : Software Requirement
Hardware Type
Asus Ultrabook Windows edition : Windows 8.1 Single
Language
Processor : Intel® Core ™ i5-3317U @
1.7- GHz
Installed memory (RAM) : 8.00GB
System type : 64-bit Operating System
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3.5 Trend Analysis
The concept of gathering information and spotting a pattern (or trend) in the
information is referred to as the trend analysis. Even though it is frequently applied to
foresee future events, it could also be applied to estimate uncertainties based on past
events.
3.5.1 Linear Trend
The long term trend of various business and economic time series such as sales
frequently approximates a straight line. The equation of straight line may be written as :
𝑌′ = 𝑎 + 𝑏𝑡
Where
• 𝑌′ represents the represent the estimated value of the variable 𝑌′ for a given
value of 𝑡
• 𝑎 is the intercept on Y-axis (estimated value of Y at 𝑡 = 0)
• 𝑏 is a slope of the line
• 𝑡 is any value of time that is selected
A straight line was drawn on a scatter diagram to approximate a regression line.
This method of assessment should only be used when a fast approach is needed.
(1)
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3.5.2 Estimation of Trend Analysis by Least Square Method
The common method of constructing straight line equation through data points
to obtain the best fitting line is called the least squares method. It uses calculus to
determine the minimum sum of squares of the vertical differences of each point from
the suggested straight line. To estimate two unknown parameters (a and b) that give the
least squares equation, two equations need to be solved simultaneously.
• 𝑏 =∑ 𝑡𝑌−(∑ 𝑌)(𝛴𝑡) 𝑛⁄
𝛴𝑡2−(∑ 𝑡)2 𝑛⁄
• 𝑎 =∑ 𝑌
𝑛− 𝑏 (∑
𝑡
𝑛)
(2)
(3)
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3.5.3 Example data and calculation
The sales of Sedap Foods, 2005 – 2009, are shown in Table 3.5.3
Table 3.5.3.1 : Annual sales of Sedap Foods
Year Sales ($ millions)
2005 7
2006 10
2007 9
2008 11
2009 13
Determine the least squares trend line equation.
To simplify the calculations, as shown in Table 3.5.3, the years are replaced by coded
values. That is, we let 2005 be 1, 2006 be 2, and so forth. This reduces the size of the
values of ∑𝒕 , ∑𝒕𝟐 and ∑𝒕𝒀 , this is often referred to as the coded method.
30
Table 3.5.3.2 : Computations for determining the trend equation
Year Sales ($ millions) 𝒕 𝒕𝒀 𝒕𝟐
2005 7 1 7 1
2006 10 2 20 4
2007 9 3 27 9
2008 11 4 44 16
2009 13 5 65 25
∑ 50 15 163 55
𝑏 =∑ 𝑡𝑌−(∑ 𝑌)(𝛴𝑡) 𝑛⁄
𝛴𝑡2−(∑ 𝑡)2 𝑛⁄ =
163−50(15) 5⁄
55−(15)2 5⁄ = 1.30
𝑎 =∑ 𝑌
𝑛− 𝑏 (∑
𝑡
𝑛) =
50
5− 1.30 (
15
5) = 6.1
The trend equation is therefore : 𝑌′ = 6.1 + 1.30𝑡.
Sales are in millions of dollars. The origin, or year 0, is in the middle of 2004,
and t increases by one unit for each year.
The value 6.1 is the eastimated sales when t = 0. That is, the estimated sales
amount for 2004 (the zero year) is $6.1 million.
31
The least squares equation can be used to find the points on the straight line
going through the middle of the data. To get the coordinates of the points on the straight
line, insert the t valuesof 1 to 5 in the equation as shown in Table 3.5.3.3
Table 3.5.3.3 : Calculations for determining the points on the straight line
using coded method
Year Sales ($ millions) 𝒕 𝒀′
2005 7 1 6.1+1.30(1) = 7.4
2006 10 2 6.1+1.30(2) = 8.7
2007 9 3 6.1+1.30(3) = 10.0
2008 11 4 6.1+1.30(4) = 11.3
2009 13 5 6.1+1.30(5) = 12.6
The actual sales and the straight line trend are plotted in Figure 3.5.3
If the sales, production, or other data over a period of time trend to approximate
a straight line trend, the equation developed by the least squares method can be used to
estimate sales for some future period.
32
3.6 Context Diagram
Figure 3.2 shows Context Diagram
Figure 3.2 shows the context diagram for Chips Stock Forecast System Using
Least Square Method is shown above. There are three entities are involves in the
system, ADMIN, STAFF and CUSTOMER.
33
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 seven processes involve in admin module.
Admin can be login to the system as a first step to get into the system. After login,
process that involve admin is Manage Chips, Manage Staff, Cash Purchase, Forecast
Stock and generate a report from the system. At the end on the process, admin can be
logout from the system.
34
Figure 3.4 Data Flow Diagram Level 0 for Staff
Based on Figure 3.4 above, there are six processes involve in staff module. Staff
can be login to the system as a first step to get into the system. After login, process that
involve admin is Manage Chips, Manage Staff, Cash Purchase and generate a report
from the system. At the end on the process, staff can be logout from the system.
35
Figure 3.5 Data Flow Diagram Level 0 for Customer
Based on Figure 3.5 above, there are five processes involve in customer module.
Customer can be login to the system as a first step to get into the system. After login,
process that involve admin is Manage Order, Payment and generate a report from the
system. At the end on the process, customer can be logout from the system.
36
Data Flow Diagram Level 1
Figure 3.6 Data Flow Diagram Level 1 for Manage Chips
Based on Figure 3.6 above, there are four processes involve in Manage Chips.
Admin can register new chip, update chip’s details, remove chips. Admin and Staff
share the role to update or add the new quantity of chips.
37
Figure 3.7 Data Flow Diagram Level 1 for Manage Staff
Based on Figure 3.7 above, there are four processes involve in Manage Staff.
Admin can register new staff, update staff profile and remove chips.
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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 chip, update order and cancel order.
39
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.
40
3.9 Data Dictionary
A 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, such as data
ownership, data relationships to other objects, and other data. The data dictionary is a
crucial component of any relational database. Ironically, because of its importance, it is
invisible to most database users. 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 user
2. TABLE staff
3. TABLE customer
4. TABLE address
5. TABLE chips
6. TABLE purchase
7. TABLE order
8. TABLE payment
9. TABLE chipsmanagement
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1. Table user
Table 3.9.1 : Table user
No Column Type Length Null Key Description
1 username varchar 50 No PK
2 password varchar 10 No
3 rule varchar 10 No
2. Table staff
Table 3.9.2 : Table staff
No Column Type Length Null Key Description
1 staffID varchar 10 No PK
2 staffName varchar 50 No
3 staffIC varchar 12 No
4 phoneNo varchar 12 No
5 position varchar 10 No
6 username varchar 50 No
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3. Table customer
Table 3.9.3 : Table customer
No Column Type Length Null Key Description
1 customerID varchar 10 No PK
2 customerName varchar 50 No
3 customerIC varchar 12 No
4 phoneNo varchar 12 No
5 username varchar 50 No
4. Table address
Table 3.9.4 : Table address
No Column Type Length Null Key Description
1 username varchar 50 No PK
2 line1 varchar 50 No
3 line2 varchar 50 No
4 postcode varchar 6 No
5 city varchar 50 No
6 state varchar 50 No
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5. Table chips
Table 3.9.5 : Table chips
No Column Type Length Null Key Description
1 chipID varchar 10 No PK
2 chipName varchar 50 No
3 pricePerKG int 5 No
4 image blob No
6. Table purchase
Table 3.9.6 : Table purchase
No Column Type Length Null Key Description
1 staffID varchar 10 No PK
2 chipID varchar 10 No PK
3 datePurchase date No PK
4 quantity int 11 No
44
7. Table order
Table 3.9.7 : Table order
No Column Type Length Null Key Description
1 orderID varchar 10 No PK
2 customerID varchar 10 No
3 chipID varchar 10 No
4 dateOrder date 10 No
5 quantity int 11 No
8. Table payment
Table 3.9.8 : Table payment
No Column Type Length Null Key Description
1 orderID varchar 10 No PK
2 customerID varchar 10 No PK
3 datePaid date No PK
45
9. Table chipsmanagement
Table 3.9.9 : Table chipsmanagement
No Column Type Length Null Key Description
1 chipID varchar 10 No PK
2 staffID varchar 10 No PK
3 dateUpdate varchar 10 No PK
4 quantity varchar 5 No
46
REFERENCES
[1] Akhter, S., Rahman, M. A., Koushik, M. R. P., & Hossain, M. M. (2016).
Selection of a Forecasting Technique for Beverage Production: A Case
Study. World, 6(3), 148-159.
[2] Kumar, R and Mahto, D 2013, ‘A case study : Application of Proper Forecasting
Technique in Juice Production’, Global Journal of Researches in Engineering,
vol. 13, no. 4, pp. 1-6
[3] Pal, S, Ramasubramanian, V and Mehta, SC 2007, ‘Statistical Models for
Forecasting Milk Production in India’, Journal of Indian Society of Agricultural
Statistics, vol.61, no.2, pp. 80-83.
[4] Barbosa, N de P, Christo, E da S and Costa, KA 2015, ‘Demand Forecasting for
Production Planning in a Food Company’, ARPN Journal of Engineering and
Applied Sciences, vol. 10, no. 16, pp. 7137-7141.
[5] Lee, Y and Tong, L 2011, ‘Forecasting time series using a methodology based
on autoregressive integrated moving average and genetic programming’
Knowledge-Based Systems, vol. 24, pp. 66–72.
47
[6] Eksoz, C, Mansouri, SA and Bourlakis, M 2014, ‘Collaborative Forecasting in
the Food Supply Chain: A Conceptual Framework’, International Journal of
Production Economics, vol.158, pp.120–135.
[7] 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.
[8] De Oliveira Silva R., Da Silva Christo E. and Alonso Costa K. 2014. 'Analysis
of Residual Autocorrelation in Forecasting Energy Consumption through a Java
Program', Advanced Materials Research. Trans Tech Publ.
[9] Junior M. L. and Filho M. G. 2012. 'Production planning and control for
remanufacturing: literature review and analysis', Production Planning &
Control, Vol. 23, pp. 419-35.