PREDICTIVE DATA MODEL FOR
SUPPLY CHAIN DEMAND FORECASTING
DOMINO’S PIZZA SRI LANKA
FINAL YEAR PROJECT PROPOSAL - BSc (Hons) BUSINESS INFORMATION SYSTEM
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Title Page
Sponsoring Company:
Jubilant Foodworks Lanka (Pvt) Ltd
No.164, Galle Road, Dehiwala, Sri Lanka.
Contact Person:
Mr. Eranga Perera
Head of Information Technology
eranga_perera@jublfood.com
+94 773563588
Project Carried Out by:
M.G Ruchira Anuradha Senevirathne
Ruchira.senevirathne@ipotenza.com
+94 777192949
Date:
14th October, 2017
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Executive Summery
The intend of this project is to build a predictive data model for Jubilant Foodworks Lanka LTD
to forecast the demand of supply chain department.
The supply chain department of jubilant Foodworks Lanka ltd plays a critical role in the
organization by catering to the needs of raw materials for the production environment. Therefore, it
is a mandatory requirement to maintain a sufficient inventory throughout the year.
If the supply chain department overestimates the demand, it will end up with more inventory
than is necessary. This can incur a loss due to obsolescence of unsold inventory. In such a case,
organization might need to sell inventory or product at a discount price or in some cases wasting the
inventory without gaining a single cent out of it, which will reduce company's profit margins as well
as creating considerable losses. This will affect the organization other way around also, if the
department under estimate the demand, company’s entire production commissary and front-end
restaurant will end up with shortage of end products. This will eventually lead the company to lost
lose their customers and profits. A clear vision of the future demand will have a positive effect on
planning, performance and profit of the organization
The aim of this project is to build a predictive forecasting dashboard for Jubilant Foodworks Lanka to
predict the demand for supply chain department, by extracting historical data (sales, inventory
movements and purchasing) from the current supply chain department and restaurants operation
department and then analyzing the same with data model based on a Statistical model and a Neural
Network.
After completing this project, the organization will be able to use the predictive data model
and dashboard to forecast the demand of supply chain department more effectively. As a result of
this forecast of the supply chain demand, the organization will be able to save a considerable amount
of revenue by reducing wastage.
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Contents
Title Page..................................................................................................................................................
Executive Summery............................................................................................................................... iii
Contents ................................................................................................................................................ iv
1. Introduction to the Company ............................................................................................................ 1
2.Problem Statement ............................................................................................................................. 1
3.Background of the Study..................................................................................................................... 2
4.Aims & Objectives of the Study .......................................................................................................... 2
4.1 Academic Question ...................................................................................................................... 2
4.2Aims ............................................................................................................................................... 2
4.3Study Objectives ............................................................................................................................ 2
5.Scope ................................................................................................................................................... 3
5.1 Sample Data ................................................................................................................................. 3
5.2 Time Period for this project ......................................................................................................... 3
6. Limitations.......................................................................................................................................... 3
7. Technical Approach ............................................................................................................................ 3
7.1 Methodology Used ....................................................................................................................... 3
7.2 Performance evaluation ............................................................................................................... 4
8.Work Breakdown structure ................................................................................................................ 5
9.Gantt chart .......................................................................................................................................... 6
10.Deliverables ...................................................................................................................................... 6
10.1 Non-Technical Resources ........................................................................................................... 6
10.2 Technical Resources ................................................................................................................... 7
10.2.1 Hardware ............................................................................................................................. 7
10.2.2 Software .............................................................................................................................. 7
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1. Introduction to the Company
Jubilant Foodworks Lanka (Pvt) Ltd is a subsidiary of Jubilant FoodWorks India which holds master
franchise for Domino’s Pizza in India, Nepal, Sri Lanka and Bangladesh. Jubilant Foodworks is a part
of Jubilant Bhartia Group.
In Sri Lanka, Jubilant Foodworks Lanka (Pvt) Ltd has 25 restaurants island wide. Head office is
located in Dehiwala area. Their main line of business is selling pizza products and they offer a wide
variety of localized products in their menu. Front end restaurants act as quick serving restaurants
while delivering pizza to the customer’s door steps is a main target of them.
Central commissary serves all 25 restaurants by producing level 2 products such as dough balls,
processed vegetables, etc. Further, it distributes raw materials for restaurants playing the role of
main warehouse of Jubilant Food Works Lanka.
Dominos Sri Lanka has been serving great quality products and services to Sri Lankan customers
since 2011, becoming the main competitor for Pizza Hut, which has more than 20 years of experience
and 50+ restaurants.
2. Problem Statement
Supply chain department plays a vital role in the organization by maintaining the correct amount
of stocks and supplies for the production needs. With new strategic initiatives of the marketing
department, there is a need to maintain enough inventory to cater to the newly created demand and
avoid shortages of raw materials. As a result, the supply chain department tend to stock excess raw
materials to face such a situation.
Under certain circumstances, forecasted results cannot be achieved through marketing
campaigns. In such situations life time of food items and raw material expires before they are sold to
customers.
Therefore, Senior Management is very keen on finding a solution to reduce this wastage of food
products by forecasting the correct demand for production. Management is in the view that they can
forecast and minimize the over/under procurement situations by using a proper algorithm.
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3. Background of the Study
Even if there are number of software solutions available in the market to predict supply chain
demand, none of them are specially designed to cater to the food and beverage sector. Most of the
software solutions are designed to analyze previous purchasing history and inventory movements
but not focus on sales patterns.
Also these systems are very expensive and the organization is not ready to bear the cost at this
stage. But, the requirement for a reliable forecasting model or a system is a key element to achieve
less wastage and better demand handling in the organization.
As a IT professional, I have seen this requirement as an opportunity to develop a predictive data
model to support the decision making process of the supply chain department. Collecting historical
data from different departments will be a challenging task during the initial stage of the project.
By convincing the employees about the benefits and giving a clear picture of the deliverables of
the project, we will be able to get the necessary support and the information required to complete
this project successfully.
4. Aims & Objectives of the Study
4.1
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Academic Question
How artificial intelligence (Neural Networks) & Statistical analysis methods can
support the decision-making process in supply chain demand forecasting.
4.2
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Aims
Aim of this project is to build a data model that can predict the demand of supply
chain using advance algorithms & artificial intelligence like statistical data analysis
and Neural networks.
4.3
Study Objectives
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Gather historical data from relevant departments
Chose correct samples for data manipulation process
Create a data model using statistical analysis
Create a data model using artificial intelligence – Neural Networks
Develop a dashboard to identify the correct demand needs of the supply chain
Test and verifying the accuracy of the model created
Maintain the model to re-engineer after certain time period
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5. Scope
The scope of this project is to gather information, analyze information using an algorithm
purpose developed to predict supply chain demand using historical data and sales operation
data and present analysis output in an intelligible manner so that the management and
relevant business user can make effective business decisions on supply chain operations.
5.1
Sample Data
Two years of sample data will be collected from the departments for this analysis. The
sample time period will be.
2015 November to 2017 November
5.2
Time Period for this project
This project will be delivered to the client on 2018 May. Total day count of this project will
be 132 Days
6. Limitations
This project is only intending to develop a data model for supply chain department. All the predictions
will be based on the accuracy of the data collected from the departments and this project will not be
liable for the predictions if the data source is not reliable.
7. Technical Approach
7.1
Methodology Used
The software development life cycle models (SDLC) are the various processes or methodologies
that are being used for the development of the project depending on the project’s aims and goals.
There are many software development life cycle models (SDLC) that have been developed in order
to achieve different essential objectives. The life cycle models state the various stages of the process
and the order which they should carried out.
The selection of the correct SDLC model has very high impact on the project which is being carried
out. SDLC model will define the what, where and when the correct processes should be done.
There are various Software development models or methodologies available:
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Waterfall model
V model
Incremental model
RAD model
Agile model
Spiral model
Prototype model
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Waterfall Model is used for this project. The waterfall Model is a linear sequential design approach
for developing a IT information system projects. This has been used widely in the software
development industry to ensure the success of the projects. I have chosen this approach because of
its clear visibility of the phases like, planning, analysis, design, deployment and maintenance. An
inherent property of this model is that each step has a dependency on the previous step. This
property is in line with the activity flow of the proposed algorithm development project.
Based on waterfall method below steps are designed to archive the success of this project.
7.2 Performance evaluation
Performance and accuracy of the outcome will be measured at the testing stage with the help of
client and testing team. Accuracy of the data model predictions will be measured based on actual
data (purchasing).
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8. Work Breakdown structure
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9. Gantt chart
10.
Deliverables
Below items will be delivered at this project completion
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Dashboard system to access the predictive data
Report downloading facility
Actual vs predictive comparison portal
10. Required Resources
10.1 Non-Technical Resources
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Reliable Sample data from Supply chain department – purchasing and
inventory movement
Reliable sample data from operations department – sales data
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10.2 Technical Resources
10.2.1 Hardware
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Server PC - Dell R230 Power Edge
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Intel® Xeon® processor E3-1200
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Intel C236
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16GB DDR4 DIMM
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1 TB Hard Drive
10.2.2 Software
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JetBrains PyCharm Community Edition 2017.1.3
R version 3.4.2
Microsoft Power BI
Microsoft Excel and Word
Microsoft SQL Server 2012
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