DEMAND FORECASTING IN WALMART FOR

EFFICIENT INVENTORY MANAGEMENT

23PMC306

MINI PROJECT REPORT

Submitted by

SHAKRATISH S
Register No: 730923632042

in partial fulfillment of the requirement for

the award of the degree

of

MASTER OF COMPUTER APPLICATIONS

In

DEPARTMENT OF COMPUTER APPLICATIONS

EXCEL ENGINEERING COLLEGE

An Autonomous Institution, affiliated to Anna University Chennai Approved
by AICTE, New Delhi

KOMARAPALAYAM – 637303

NOV - DEC – 2024

 EXCEL ENGINEERING COLLEGE
KOMARAPALAYAM - 637303

BONAFIDE CERTIFICATE

Certified that this project report titled “DEMAND FORECASTING IN WALMART FOR
EFFICIENT INVENTORY MANAGEMENT ” is the bonafide work of SHAKRATISH S
(Reg No: 730923632042), who carried out the project under my supervision. Certified further,
that to the best of my knowledge the work reported here in does not form part of any other
project report or dissertation on the basis of which a degree or award was conferred on an
earlier occasion on this or any other candidate.
.

SIGNATURE SIGNATURE

Dr.P.S.VELUMANI,MCA.,Ph.D., Mrs. K.YASODHA ,MCA,.

HEAD OF THE DEPARTMENT GUIDE

Professor/ Head Assistant Professor
Department of MCA Department of MCA

Excel Engineering College Excel Engineering College

Komarapalayam – 637303 Komarapalayam - 637303

Submitted for the viva-voce examination held on

Internal Examiner External Examiner DECLARATION

 I jointly declare that the project report on “DEMAND FORECASTING IN
WALMART FOR EFFICIENT INVENTORY MANAGEMENT” is the result of original
work done by me and best of my knowledge, similar work has not been submitted to “ANNA
UNIVERSITY, CHENNAI” for the requirement of Degree of MASTER OF COMPUTER
APPLICATIONS. This project report is submitted on the partial fulfilment of the requirement
of the award of Degree of MASTER OF COMPUTER APPLICATIONS.

SIGNATURE

SHAKRATISH S
730923632042

Place: Komarapalayam

Date:
 ACKNOWLEDGEMENT

We wish to express our sin cere gratitude to our honorable chairman Dr.A.K. NATESAN,
M.Com.,MBA.,Ph.D.,FTA., for providing immense facilities at our institution.

We are very proudly rendering our thanks to our Principal Dr. K. BOMMANNA RAJA,
M.E.,Ph.D., for the facilities and the encouragement given by him to the progress and
completion of our project.

We proudly render our immense gratitude to the Head of the Department

Dr.P.S.VELUMANI,MCA.,Ph.D., for his effective leadership, encouragement and guidance
in the project.

We are highly indebted to provide our heart full thanks to our supervisor
Mrs.K.YASODHA,MCA., Assistant Professor for him valuable ideas, encouragement and
supportive guidance throughout the project.

We wish to extend our sincere thanks to all faculty members of ours Computer Applications
Department for their valuable suggestions, kind cooperation and constant encouragement for
successful completion of this project.

We wish to acknowledge the help received from various Departments and various
individuals during the preparation and editing stages of the manuscript.
 TABLE OF CONTENTS

S NO TITLE PAGE NO

ABSTRACT
1 01

INTRODUCTION
02
1.1 Background on Walmart’s Business Model
2
1.2 Importance of Demand Forecasting in Retail
1.3 Objectives of the Study
1.4 Challenges Faced in Retail Demand Forecasting
1.5 Purpose and Scope of the Study

LITERATURE REVIEW
3 07
2.1 Overview of Demand Forecasting in Retail
2.2 Key Demand Forecasting Models and Techniques
2.3 Challenges in Forecasting Demand for Retail Giants
2.4 Role of Technology in Demand Forecasting
2.5 Comparative Analysis of Forecasting Models in Retail

SYSTEM ANALYSIS
15
3.1 System Overview
3.2 Demand Forecasting Requirements
3.3 System Components
3.4 Process Workflow
4
3.5 System Integration
3.6 Performance Evaluation
3.7 User Interface
3.8 Security and Data Privacy
 SOFTWARE SPECIFICATIONS
20
4.1 Hardware Requirements
5
4.2 Software Requirements

MODULE DESCRIPTIONS 21

5.1 Data Collection Module

6 5.2 Forecasting Model Development Module

5.3 Demand Prediction Module

5.4 Inventory Optimization Module

APPENDICES
23
8.1 Source code
7 8.2 Screen Shots

32
CONCLUSION & FUTURE ENHANCEMENT
8
9.1 Conclusion

9.2 Future Enhancements

9 34
BIBILOGRAPHY
 ABSTRACT

The study incorporates factors such as historical sales data, promotional activities,
seasonal trends, and external variables like economic conditions and local events. Using time
series analysis and predictive modeling approaches like ARIMA, Random Forest, and Neural
Networks, the research aims to develop a robust forecasting model capable of handling
largescale data from multiple locations.

The model’s effectiveness is evaluated through metrics such as Mean Absolute Percentage
Error (MAPE) and Root Mean Squared Error (RMSE), which measure the accuracy of
predictions. Furthermore, the integration of external data sources, such as weather forecasts
and local holidays, is considered to enhance the model's predictive power. This paper
demonstrates the potential for machine learning and AI-driven solutions to improve Walmart's
sales forecasting, leading to better inventory control, cost efficiency, and improved customer
experience.

This approach minimizes stockouts and overstock situations, reduces waste, and
enhances customer satisfaction. This study explores Walmart’s demand forecasting
methodologies and the impact on its overall supply chain efficiency, highlighting the strategies,
tools, and innovations that drive its world-class inventory management.

1
 CHAPTER -1

INTRODUCTION

1.1 INTRODUCTION TO WALMART'S BUSINESS MODEL

Founded in 1962 by Sam Walton, Walmart has evolved into one of the largest retail
corporations in the world, operating a vast network of stores across numerous countries. Its
business model centers on offering a wide range of products at competitive prices, with the
core mission of "saving people money so they can live better." This guiding principle is
embedded in every facet of Walmart’s operations, from its sourcing and supply chain
management to customer service and technology integration.

Walmart's business model primarily follows a "low-cost leadership" strategy, which

drives its ability to provide everyday low prices. The company leverages economies of scale,
vast logistics infrastructure, and efficient supplier relationships to reduce costs and pass the
savings on to consumers. This model has been instrumental in Walmart’s success, positioning
it as a dominant force in the retail sector. The company operates through various segments,
including Walmart U.S., Walmart International, and Sam’s Club, catering to diverse markets
and customer needs. Additionally, Walmart has embraced digital transformation, expanding its
online presence and blending e-commerce with physical retail to meet evolving consumer
demands in an increasingly competitive landscape.

Walmart’s business model continues to adapt and evolve, with recent initiatives aimed at
sustainability, innovation in supply chain logistics, and enhancement of the customer shopping
experience. By understanding Walmart’s foundational business principles, one can gain insight
into how the retailer maintains its position as a global retail leader. The company leverages
economies of scale, vast logistics infrastructure, and efficient supplier relationships to reduce
costs and pass the savings on to consumers. This model has been instrumental in Walmart’s
success, positioning it as a dominant force in the retail sector

2
1.2 IMPORTANCE OF DEMAND FORECASTING IN RETAIL
Demand forecasting is a cornerstone of effective retail management, providing insights
that guide inventory decisions, supply chain efficiency, pricing strategies, and customer
satisfaction. By accurately predicting future customer demand, retailers can ensure they have
the right products in stock at the right time, minimizing costly issues such as stockouts and
excess inventory.

Optimized Inventory and Cost Efficiency: Demand forecasting helps retailers align
inventory levels with expected customer demand, ensuring that products are available without
overstocking. This balance reduces storage and holding costs, minimizes waste, and prevents
capital from being tied up in excess inventory. For retailers, this is crucial for managing costs
and maintaining profitability in a competitive environment.

1. Enhanced Customer Satisfaction and Sales: Accurate demand forecasting enables

retailers to meet customer expectations by ensuring the right products are available at
the right time. This reduces stockouts, improves order fulfillment rates, and supports a
positive shopping experience, fostering customer loyalty. It also allows retailers to
capitalize on sales opportunities during peak seasons or special promotions, driving
higher revenue.
2. Improved Supply Chain Efficiency: With better demand visibility, retailers can plan
their supply chain operations more effectively. Accurate forecasting allows for better
coordination with suppliers, optimized order quantities, and more efficient logistics.
This reduces lead times, improves delivery schedules, and minimizes the risk of
disruptions, creating a smoother and more resilient supply chain.
3. Strategic Decision-Making and Market Responsiveness: Forecasting empowers
retailers to make data-driven decisions about pricing, promotions, and product
offerings. By understanding demand trends and consumer preferences, retailers can
strategically plan marketing campaigns and adjust pricing to optimize sales.
Additionally, demand forecasting helps retailers stay agile, allowing them to respond
quickly to market shifts or unexpected demand changes, such as during economic
fluctuations or seasonal events.

Demand forecasting, therefore, plays a critical role in creating an efficient, customer-focused,

and adaptable retail operation, making it an essential practice for sustained success in the retail
industry.

3
1.3 OBJECTIVES OF STUDIES

To Analyze the Role of Demand Forecasting in Inventory Management: This objective

aims to understand how demand forecasting contributes to effective inventory management,
including reducing stockouts and overstock situations, thereby optimizing inventory costs.

To Evaluate the Impact of Forecasting Accuracy on Customer Satisfaction: This examines

the link between accurate demand forecasting and improved customer satisfaction by ensuring
product availability, especially during peak seasons or promotional periods.

To Identify Key Techniques and Tools Used in Demand Forecasting: This objective seeks
to identify and evaluate the demand forecasting methodologies, technologies, and tools (such
as AI and machine learning) commonly used by retailers to improve forecasting accuracy.

To Assess the Impact of Demand Forecasting on Overall Supply Chain Efficiency: This
objective focuses on understanding how demand forecasting aids in aligning supply chain
operations, reducing lead times, and improving the coordination with suppliers to enhance
operational efficiency.

To Explore the Strategic Benefits of Demand Forecasting in Retail Decision-Making: This

objective aims to investigate how demand forecasting informs strategic decisions, such as
pricing, promotional planning, and inventory procurement, ultimately contributing to better
business outcomes.

To Measure the Financial Implications of Effective Demand Forecasting: This explores

how demand forecasting impacts a retailer’s financial performance by reducing carrying costs,
optimizing sales opportunities, and minimizing waste through efficient inventory control.

4
1.4 CHALLENGES FACED IN RETAIL DEMAND FORECASTING

To Analyze the Role of Demand Forecasting in Inventory Management: This objective

aims to understand how demand forecasting contributes to effective inventory management,
including reducing stockouts and overstock situations, thereby optimizing inventory costs.

To Identify Key Techniques and Tools Used in Demand Forecasting: This objective seeks
to identify and evaluate the demand forecasting methodologies, technologies, and tools (such
as AI and machine learning) commonly used by retailers to improve forecasting accuracy.

To Assess the Impact of Demand Forecasting on Overall Supply Chain Efficiency: This objective
focuses on understanding how demand forecasting ai.

1.5 PURPOSE OF STUDY

The purpose of a study is the guiding intention or primary goal that drives the research.
This might include identifying gaps in existing knowledge, testing a hypothesis, exploring
relationships between variables, or providing insights for practical application. A clearly
defined purpose directs the study’s design and methodology, shaping the research questions
and objectives.

For instance, if a study aims to explore the impact of online learning on high school students’
performance, the purpose could be described as:

"To examine how online learning affects academic outcomes among high school students,
thereby contributing to the understanding of its advantages and limitations in the education
sector."

Scope of the Study

The scope outlines the study’s boundaries, detailing what will and won’t be covered. This may
include:

Subject/Population: Who or what is being studied (e.g., specific demographic, region, or age
group).

Context/Setting: Where the study will take place or the particular field of study.

Time Frame: The period during which the study is conducted or the timeline it examines.

5
Limitations: Any factors that limit the study’s generalizability or depth (e.g., a focus on only
certain variables, reliance on particular data sources, etc.).

Using the same example, the scope could be framed as:

"This study focuses on high school students in public schools within an urban setting, analyzing
data collected over the 2023 academic year. It will specifically examine test scores and
participation rates to assess the influence of online learning."

In short, the purpose states why the study is conducted, and the scope defines what the study
covers. Together, they guide the research approach and help readers understand the study's
intentions and limitations.

6
 CHAPTER-2
LITERATURE REVIEW
2.1 Overview of Demand Forecasting in Retail
Demand forecasting is a critical function in retail, enabling businesses to anticipate future
customer demand and make informed decisions on inventory management, supply chain
operations, and overall business strategy. Accurate forecasting ensures that retailers can meet
customer needs without overstocking or running into stockouts, thus optimizing inventory costs
and enhancing customer satisfaction.

2.2 Importance of Demand Forecasting in Retail

In retail, demand forecasting is essential for maintaining a balance between supply and demand.
Proper demand forecasting helps retailers:
Optimize Inventory Management: Forecasts help in determining the ideal inventory levels,
reducing storage costs, and minimizing waste, especially in categories with perishable goods.
Enhance Customer Satisfaction: By accurately predicting demand, retailers can prevent
stockouts and ensure that products are readily available when customers want them. Improve
Supply Chain Efficiency: Forecasting supports better coordination with suppliers, allowing
for more efficient logistics, order quantities, and production planning.
Financial Planning: Accurate demand forecasting contributes to improved budgeting and
financial planning, leading to better cash flow and reduced risk from over- or under-stocking.
Support Promotional and Pricing Strategies: Retailers can anticipate demand for specific
items during promotional periods, seasonal events, or holidays, enabling them to tailor pricing
and promotions accordingly.

Methods of Demand Forecasting in Retail

Several methods and models are commonly used for demand forecasting in retail, including:
Qualitative Methods:
1. Expert Opinion: Leveraging insights from industry experts or internal
stakeholders.
2. Market Research: Gathering customer feedback and conducting
surveys to predict future demand trends.
3. Delphi Method: A structured, iterative approach where experts provide
insights and refine them based on feedback.

7
Quantitative Methods:
o Time Series Analysis: Utilizing historical sales data to predict future demand.
Techniques such as moving averages, exponential smoothing, and ARIMA
models are popular in time series forecasting.
o Causal Models: These models consider variables that directly affect demand,
such as economic indicators, promotions, or price changes.

2.3 CHALLENGES IN DEMAND FORECASTING FOR RETAIL :

Despite its benefits, demand forecasting in retail faces several challenges:
Seasonality and Trends: Demand often fluctuates with seasons, trends, and holidays, making
it challenging to predict consistently.
Data Accuracy and Availability: Forecasting requires high-quality data, which can be difficult
to obtain or inconsistent across sources.
External Factors: Economic shifts, supply chain disruptions, and unexpected events (e.g.,
natural disasters, pandemics) can significantly impact demand.
AI and Machine Learning: Leveraging AI for demand forecasting enables more sophisticated
models that can analyze patterns in vast datasets, improving accuracy.
Real-Time Data Integration: Integrating real-time data from sources like social media,
weather, and customer interactions allows for more responsive and adaptive forecasting.
Collaborative Forecasting: Sharing data across the supply chain, including with suppliers and
distributors, enhances forecasting accuracy and enables better coordination.
Conclusion
Demand forecasting in retail plays a pivotal role in optimizing inventory, enhancing customer
satisfaction, and reducing operational costs. With advancements in data analytics, AI, and
collaborative approaches, retailers are now better equipped to meet the challenges of demand
forecasting and stay competitive in a dynamic market environment.

8
2.4 ROLE OF TECHNOLOGY IN DEMAND FORECASTING
Technology plays a transformative role in demand forecasting, enabling retailers to handle
vast amounts of data, improve forecast accuracy, and respond more swiftly to market changes.
As retail becomes more complex and consumer demands fluctuate, advanced technologies in
data analytics, machine learning, and artificial intelligence (AI) are revolutionizing how
retailers predict future demand.

1. Data Collection and Integration

Modern demand forecasting relies on extensive data from various sources, including
historical sales data, online customer behavior, market trends, weather patterns, and social
media sentiment. Technology enables the seamless integration of these data sources into
forecasting models, allowing for a more holistic view of demand drivers.
Point-of-Sale (POS) Systems: Collect real-time sales data, which is crucial for identifying
trends and tracking demand at the store level.
Customer Relationship Management (CRM) Systems: Provide insights into customer
preferences and purchasing behavior, enhancing forecast accuracy.

2. Advanced Analytics and Big Data

With advancements in big data technologies, retailers can analyze vast datasets to identify
trends, patterns, and anomalies that influence demand. Big data analytics tools can process
structured and unstructured data from multiple channels, providing a comprehensive view of
demand factors.
Predictive Analytics: Uses historical data to predict future demand trends, making it easier to
anticipate fluctuations and optimize inventory.
Real-Time Analytics: Allows retailers to track demand changes as they happen, enabling
quick adjustments in response to events such as promotions or sudden shifts in consumer
preferences.

3. Machine Learning and Artificial Intelligence (AI)

AI and machine learning algorithms are key drivers of modern demand forecasting,
enabling models to automatically improve over time as they process new data. These
technologies can identify complex, non-linear relationships between variables, resulting in
more accurate and nuanced forecasts.

9
Machine Learning Models: Techniques such as regression analysis, decision trees, and
clustering algorithms help forecast demand by identifying patterns within large datasets. Deep
Learning Algorithms: Models like recurrent neural networks (RNNs) and long shortterm
memory (LSTM) networks are particularly useful for analyzing sequential data and time series,
making them ideal for forecasting demand patterns over time.
4. Cloud Computing and Data Storage
Cloud technology has transformed demand forecasting by providing scalable storage and
processing capabilities, allowing retailers to handle large datasets cost-effectively. Cloud
platforms enable seamless data sharing and collaboration across departments, improving
forecasting consistency and accuracy.
Scalability: Cloud computing can accommodate increased data loads, which is essential for
large-scale retail forecasting.
Data Security and Accessibility: Retailers can securely store vast amounts of data and access
it from anywhere, ensuring that forecasting teams have up-to-date information.

5. Internet of Things (IoT) and Real-Time Data Feeds

IoT devices such as sensors and smart shelves in stores provide real-time data on stock
levels, customer footfall, and shopping patterns. This data enhances demand forecasting by
providing immediate insights into product popularity and helping retailers make on-the-fly
inventory adjustments.
Smart Shelves: Monitor product stock levels in real time, reducing the risk of stockouts and
overstocks.
Connected Devices: IoT-enabled devices throughout the supply chain provide data on
transportation, warehousing, and distribution, which can inform more responsive demand
forecasting models.

6. Automation and Demand Forecasting Software

Demand forecasting software solutions automate much of the forecasting process,
reducing manual data entry and minimizing human error. Automated systems allow for quicker
adjustments and support frequent re-forecasting, keeping demand predictions relevant in
fastchanging retail environments.
Automated Forecasting: Automatically processes new data to update forecasts, which is
particularly useful for short-term demand planning.

10
Scenario Analysis: Advanced forecasting software can run multiple demand scenarios based
on different inputs, helping retailers prepare for various market conditions.

7. Blockchain and Enhanced Data Transparency:

Blockchain technology can improve transparency in demand forecasting by securely
recording transactions and sharing data across supply chain partners. This can lead to improved
data accuracy and consistency, supporting more reliable demand forecasts.
Traceability and Accuracy: Blockchain can track products across the supply chain, providing
precise information on supply and demand patterns.
Collaboration with Suppliers: Retailers can share demand forecasts securely with suppliers,
enhancing coordination and reducing supply chain inefficiencies.

2.5 COMPARATIVE ANALYSIS OF FORECASTING MODELS IN

RETAIL:

In retail, selecting the right forecasting model can significantly impact the accuracy and
efficiency of demand planning. Each forecasting model has strengths and limitations, and the
choice of model depends on factors such as the data available, forecasting horizon, complexity
of demand patterns, and the need for responsiveness. Here’s a comparative analysis of key
forecasting models in retail:

1. Time Series Models

Time series models rely on historical sales data to predict future demand, assuming that
past patterns, such as trends or seasonality, will likely continue.
Strengths: o Effective for products with stable and predictable
demand.
o Simple to implement and interpret, with minimal data requirements.

Limitations: o Less accurate for products with irregular or sporadic

demand.
o Requires extensive data preprocessing to handle outliers or data gaps.

11
 Best Use Cases: o Short-term demand forecasting for stable product
lines.
o Products with established seasonal patterns, such as holiday merchandise or seasonal apparel.

2. Causal Models
Causal models (also called econometric models) use statistical relationships between
demand and external factors, such as price, promotions, weather, and economic indicators, to
make forecasts.

Strengths:
o Ideal for understanding how specific factors impact demand, allowing for
strategic adjustments. o Provides a more holistic view of demand, as it considers
multiple influencing variables.
Limitations: o Complex to implement and interpret, particularly with multiple influencing
factors. o May struggle to handle non-linear relationships unless combined with
advanced techniques.
Best Use Cases: o Products with demand highly sensitive to external factors, like
weatherdependent items.
o Short- to medium-term forecasting for products with significant promotional or
seasonal variability.

3. Machine Learning and Artificial Intelligence (AI) Models

Machine learning models, including neural networks, decision trees, and clustering algorithms,
identify complex patterns in large datasets and can adapt over time with new data.
Strengths: o Highly adaptable, as they can handle non-linear relationships and large, complex
datasets.
o Capable of learning from new data, improving accuracy over time.
o Suitable for products with irregular or highly variable demand patterns.

Limitations: o Requires a significant amount of historical and real-time data, which may be
costly or challenging to obtain.

12
 o Requires high computational power and specialized knowledge to implement
and maintain. o Potential for overfitting, especially with limited or noisy data,
which may reduce generalization.
Best Use Cases: o Complex, dynamic retail environments with variable demand (e.g.,
ecommerce, fast fashion).
o Forecasting demand for products with highly unpredictable patterns, like tech
gadgets or seasonal trends.
o Long-term forecasting that benefits from model learning and adaptability over
time.

4. Simulation and Scenario-Based Models

Simulation models (e.g., Monte Carlo simulations) and scenario analysis use
hypothetical scenarios to evaluate potential demand outcomes under different conditions.
These models are particularly useful in uncertain or volatile markets.

Strengths:

o Useful for risk management and contingency planning, especially in uncertain

markets. o Allows businesses to evaluate multiple potential scenarios,
preparing for various market outcomes. o Enables retailers to test the impact
of hypothetical factors, such as a new competitor or a price increase.
Limitations: o Computationally intensive, requiring significant data and processing power.
o Not as precise for specific demand forecasts; rather, it provides a range of

possible outcomes.
o Can become complex and difficult to interpret, especially for non-technical
teams.

Best Use Cases:

o Planning for products in unpredictable markets or volatile demand
environments. o Evaluating the impact of business changes, such as new
product launches or entry into new markets.

13
5. Qualitative Forecasting Models

Qualitative forecasting techniques, such as expert opinion, the Delphi method, and market
surveys, rely on subjective inputs rather than historical data. These methods are valuable when
data is limited or when expertise is needed to interpret trends.

Strengths:

o Useful for new products, markets, or other cases with little to no historical data.
o Leverages human insight, providing context that data-only models might miss.

o Relatively low cost and simple to implement, requiring minimal data and

computational resources.

Limitations:

o Subjective and prone to bias, leading to variability in forecasts.

o Less precise than data-driven models, especially for quantitative forecasting

14
 CHAPTER - 3
SYSTEM ANALYSIS
SYSTEM OVERVIEW
In a retail context, a demand forecasting system integrates data collection, analysis, and
prediction capabilities to support accurate demand planning and inventory management. The
system typically combines a variety of technologies, data sources, and analytical models to
deliver timely and actionable forecasts, ensuring that retailers meet customer demand while
minimizing excess stock. Here’s a breakdown of the main components and flow within a
demand forecasting system.

1. DATA COLLECTION LAYER

The system’s data collection layer aggregates data from multiple sources to ensure a
comprehensive view of the factors influencing demand. This layer is critical for feeding
accurate, up-to-date information into forecasting models.

Sales Data: Collects historical sales data from Point-of-Sale (POS) systems, e-commerce
platforms, and customer relationship management (CRM) systems.

External Factors: Captures data on economic indicators, weather patterns, and social media
sentiment, which may impact demand.

Customer Data: Gathers insights from customer profiles, preferences, and transaction histories
to understand purchasing patterns and behavior.

2. DATA PROCESSING AND INTEGRATION LAYER

In this layer, the collected data is cleaned, normalized, and integrated to ensure
consistency and compatibility across the various data sources. This preparation step is crucial
for building accurate forecasting models.

Data Cleaning and Validation: Identifies and handles missing values, outliers, and
inconsistencies.

Data Transformation: Structures the data into a format suitable for analysis, converting
qualitative factors into quantifiable metrics if necessary.

15
3. ANALYTICAL ENGINE AND FORECASTING MODELS
The analytical engine is the core of the demand forecasting system, where data is analyzed,
and models are applied to generate predictions. A mix of statistical and machine learning
models may be.
DEMAND FORECASTING
Demand forecasting for Walmart store sales involves predicting future sales based on
historical data, trends, and other influencing factors. The goal is to anticipate demand
accurately, ensuring optimal inventory levels, reducing stockouts or overstocking, and
maximizing sales and profit. For Walmart or any large retailer, forecasting typically includes
several steps and considerations. Here’s an outline of what you’d need for a comprehensive
Walmart sales store forecasting system:
1. Data Collection
o Historical Sales Data: Gather historical daily or weekly sales data at the store,
category, and item levels.
o Promotion Data: Record any promotions, discounts, or campaigns that might
have impacted sales. o Weather Data: Include weather conditions if they’re
relevant to demand fluctuations.
o Local Events: Incorporate information on events that may affect foot traffic and
sales, like sporting events, concerts, or community activities.
o Store-Specific Factors: Account for factors unique to each store, such as
location, nearby competitors, and store size.

2. Data Preprocessing
o Data Cleaning: Handle missing values, remove outliers, and smooth noisy data.
o Feature Engineering: Create features like moving averages, lagged sales
data, and holiday flags. Consider creating interaction terms between variables,
like promotions during peak seasons.
o Data Normalization: Scale data to ensure consistent interpretation by
forecasting models.

16
3.Exploratory Data Analysis (EDA)
o Identify seasonal patterns, cyclical trends, and outliers.
o Analyze correlations among features, especially sales trends across stores and
time periods.

4. Model Selection and Development

o Time-Series Models: ARIMA, SARIMA, or Prophet can be used for individual
store-level forecasting.
o Machine Learning Models: Random Forest, XGBoost, or Gradient Boosting
methods work well for complex feature interactions.
o Deep Learning Models: LSTM (Long Short-Term Memory) or GRU (Gated
Recurrent Unit) models can capture temporal dependencies in the data.
o Hierarchical Forecasting: Given that Walmart operates at a multi-level
hierarchy (store, region, national), you may want to build a model that rolls up
forecasts from store to corporate levels.

5. Model Training and Validation

o Split the data into training and test sets, ensuring that the test set covers a recent

period to validate future forecasts. o Evaluate model performance using metrics like
Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and
Mean Absolute Error (MAE).

6. model deployment and monitoring

o Deploy the model in production for real-time or batch forecasting. o Regularly

monitor forecast accuracy and retrain the model periodically as new data becomes
available. o Implement alerts to detect when forecast performance significantly
deviates from expectations.

17
SYSTEM COMPONENTS
1. Data Ingestion and Processing: Collect historical sales, promotional data, weather, and
store-specific information, then clean and process it to create features like seasonality
indicators and moving averages.
2. Modeling and Forecasting: Use time-series models, machine learning, and deep learning
approaches to generate accurate forecasts, with model selection and tuning for optimal
performance.
3. Deployment and Real-Time Forecasting: Deploy models for both batch and real-time
forecasting, allowing updates as new data arrives and integrating the forecasts into
inventory management systems.
4. Monitoring and Feedback: Continuously monitor forecast accuracy and adjust models as
needed, incorporating feedback to refine predictions over time.

SYSTEM INTEGRATION
o ERP and Inventory Systems: Integrate forecasts with Walmart’s ERP and
inventory systems to automatically adjust stock levels based on predicted demand.
o Data Integration: Connect multiple data sources—like POS systems, promotional
databases, and external data feeds—for seamless, real-time data flow into the
forecasting system.
o API Access: Provide internal access to forecasts through APIs, allowing other
systems and teams to leverage forecasted data as needed.

18
 SYSTEM EVALUATION

o Accuracy Metrics: Evaluate model performance using metrics like Mean Absolute
Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error
(MAE) to measure forecast accuracy. o Cross-Validation: Use rolling-window or time-
series cross-validation to ensure that the model performs well across different time
periods and isn’t overfitted.
o Drift Detection: Monitor for data drift to detect changes in sales patterns, triggering
model retraining when performance declines.

USER INTERFACE
o Dashboard for Insights: Provide a user-friendly dashboard for store managers and
analysts to view forecasts, trends, and performance metrics visually.
o Customizable Reporting: Allow users to generate custom reports based on specific
stores, regions, or timeframes, enabling tailored insights for decision-making.
o Alert Notifications: Set up alerts for significant deviations from forecasts, helping
users proactively address potential stockouts or surpluses.

SECURITY AND PRIVACY DATA

o Access Control and Authentication: Implement strict user authentication and
rolebased access control to ensure that only authorized personnel can access sensitive
data and forecasting tools.
o Data Encryption: Use encryption for data at rest and in transit to protect customer and
business information from unauthorized access and breaches.
o Compliance with Regulations: Ensure adherence to data privacy laws (e.g., GDPR,
CCPA) by anonymizing personal data, tracking data usage, and maintaining transparent
data handling practices.

19
 CHAPTER – 4
SOFTWARE SPECIFICATIONS

HARDWARE REQURIEMENTS
Processor : Multi-core processor (Intel Xeon or AMD EPYC)

Memory : 4GB RAM or above

Cache Memory : 128 KB or above

Hard Disk : 512 GB

Pen Drive : 128 GB (BELOW)

SOFTWARE REQURIEMENTS
Operating System : Windows , LINUX

Font-End Tool : Python

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 CHAPTER – 5
MODULE DESCRIPTION
1. Data Collection Module
Objective: Gather relevant historical and real-time data for demand forecasting and
inventory management.
Key Components: o Data sources: sales history, seasonal trends, economic indicators,
customer demographics, and external data like weather.
o Data processing: clean, transform, and integrate data from various sources.
o Data storage: organize and store in a structured format (e.g., a data warehouse).
Outcome: A centralized and clean data repository for model development.

2.Forecasting Model Development Module

Objective: Develop and validate models for demand forecasting.

Key Components: o Model selection: choose methods such as time series, machine learning,
or hybrid models.
o Feature engineering: identify variables to enhance model accuracy.
o Model training and tuning: optimize parameters to improve predictions. o Model
validation and testing: evaluate model accuracy using metrics like MAPE, RMSE, etc.
Outcome: Accurate demand forecasting models ready for deployment.

3.Demand Prediction Module

Objective: Implement the forecasting model to predict future demand.

Key Components:
o Model deployment: apply trained models to predict demand at different levels (product,

region, or time). o Real-time and batch predictions: support both historical batch
predictions and real-time forecasting. o Monitoring: track and adjust the model as necessary
for accuracy.
Outcome: Reliable demand predictions for decision-making.

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4.Inventory Optimization Module
Objective: Use demand predictions to optimize inventory levels and minimize costs.

Key Components: o Safety stock calculation: account for demand variability

and lead time. o Reorder point calculation: determine when to place new
orders. o allocation: optimize across locations based on demand forecasts.
o Constraints and business rules: integrate practical considerations like budget and storage
limitations.
o Outcome: Optimized inventory levels, minimizing stockouts and excess inventory.

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 CHAPTER – 6
APPENDICES
Source code :

import plotly.express as px

import numpy as np import

pandas as pd import

matplotlib.pyplot as plt import

seaborn as sns

def split_date(df):

df['Date'] = pd.to_datetime(df['Date'])

df['Year'] = df.Date.dt.year df['Month']

= df.Date.dt.month df['Day'] =

df.Date.dt.day

df['WeekOfYear'] = (df.o Date.dt.isocalendar().week)*1.0

split_date(merged) split_date(testing_merged) missing_values =

merged.isna().sum() px.bar(missing_values, x=missing_values.index,

y=missing_values.values, title="Missing Values",

labels=dict(x="Variable", y="Missing Values")) typecounts =

merged.Type.value_counts().to_dict() df =

pd.DataFrame(list(typecounts.items()), columns=['Store_Type', 'Counts']) fig

= px.pie(df, values='Counts', names='Store_Type', title='Popularity of

Store Types',labels='Store_Type') fig.update_traces(textposition='inside',

textinfo='percent+label') fig.show() avgweeklysales =

merged.groupby('Type')['Weekly_Sales'].mean().to_dict() df =

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pd.DataFrame(list(avgweeklysales.items()), columns=['Store_Type',

'AvgSales'])

fig = px.bar(df, x="Store_Type",

y="AvgSales",

color_discrete_sequence=["Blue"])

fig.show()

avgweeklysales = merged.groupby('Type')['Weekly_Sales'].mean().to_dict() df =

pd.DataFrame(list(avgweeklysales.items()), columns=['Store_Type', 'AvgSales'])

fig = px.bar(df, x="Store_Type",

y="AvgSales", title="Avergae

Sales - Per Store",

color_discrete_sequence=["Blue"])

fig.show()

weekly_sales_2010 =
merged[merged.Year==2010].groupby('WeekOfYear')['Weekly_Sales'].mean()

weekly_sales_2011 =
merged[merged.Year==2011].groupby('WeekOfYear')['Weekly_Sales'].mean()
weekly_sales_2012

=merged[merged.Year==2012].groupby('WeekOfYear')['Weekly_Sales'].mean()

plt.figure(figsize=(22,8)) plt.plot(weekly_sales_2010.index,

weekly_sales_2010.values, 'red') plt.plot(weekly_sales_2011.index,

weekly_sales_2011.values, 'blue') plt.plot(weekly_sales_2012.index,

weekly_sales_2012.values, 'green')

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plt.xticks(np.arange(1, 53, step=1),

fontsize=16) plt.yticks( fontsize=16)

plt.xlabel('Week of Year', fontsize=18)

plt.ylabel('Sales', fontsize=18)

plt.title("Average Weekly Sales - Per Year", fontsize=24) plt.legend(['2010', '2011', '2012'],

fontsize=20); dept_sales =

merged.groupby('Dept')['Weekly_Sales'].mean().sort_values(ascending=False)

fig = px.bar(dept_sales,

x=dept_sales.index,

y=dept_sales.values, title="Average

Sales - Per Department",

labels={'x':'Dept', 'y':'Sales'},

color_discrete_sequence=["#DC143C"])

fig.update_xaxes(tick0=1, dtick=1)

fig.show()
plt.figure(figsize=(16,8))

sns.scatterplot(x=merged.WeekOfYear, y=merged.Weekly_Sales, hue=merged.Type, s=80);

plt.xticks( fontsize=16) plt.yticks(

fontsize=16) plt.xlabel('Week of Year',

fontsize=18) plt.ylabel('Sales',

fontsize=18); from plotly.subplots import

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make_subplots import

plotly.graph_objects as go import pandas

as pd from plotly.subplots import

make_subplots import

plotly.graph_objects as go

store_sales_2010 =
merged[merged.Year==2010].groupby('Store')['Weekly_Sales'].mean().to_dict() store2010_df
= pd.DataFrame(list(store_sales_2010.items()), columns=['Store',
'AvgSales2010'])

store_sales_2011 =
merged[merged.Year==2011].groupby('Store')['Weekly_Sales'].mean().to_dict() store2011_df
= pd.DataFrame(list(store_sales_2011.items()), columns=['Store',
'AvgSales2011'])

store_sales_2012 =
merged[merged.Year==2012].groupby('Store')['Weekly_Sales'].mean().to_dict() store2012_df
= pd.DataFrame(list(store_sales_2012.items()), columns=['Store',
'AvgSales2012'])

fig = make_subplots(rows=3, cols=1, subplot_titles=("Average Store Sales 2010", "Average

Store Sales 2011", "Average Store Sales 2012"))

fig.add_trace(go.Bar(x=store2010_df.Store, y=store2010_df.AvgSales2010,),1, 1)

fig.add_trace(go.Bar(x=store2011_df.Store, y=store2011_df.AvgSales2011,),2, 1)

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fig.add_trace(go.Bar(x=store2012_df.Store, y=store2012_df.AvgSales2012,),3, 1)

fig.update_layout(coloraxis=dict(colorscale='Bluered_r'), showlegend=False, height=1500)

fig.update_xaxes(title_text="Store", row=1, col=1) fig.update_xaxes(title_text="Store",

row=2, col=1) fig.update_xaxes(title_text="Store", row=3, col=1)

fig.update_yaxes(title_text="AvgSales", row=1, col=1)

fig.update_yaxes(title_text="AvgSales", row=2, col=1)

fig.update_yaxes(title_text="AvgSales", row=3, col=1)

fig.update_xaxes(tick0=1, dtick=1)

fig.show() plotly.subplots import

make_subplots from

holiday_sales = merged.groupby('IsHoliday')['Weekly_Sales'].mean() holiday_counts

= merged.IsHoliday.value_counts()

fig = make_subplots(rows=1, cols=2, subplot_titles=("Holidays/Nonholidays Sales",

"Holidays/Nonholidays Counts"))

fig.add_trace(go.Bar(x=holiday_sales.index, y=holiday_sales.values,),1, 1)

fig.add_trace(go.Bar(x=holiday_counts.index, y=holiday_counts.values,),1, 2)

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fig.update_layout(coloraxis=dict(colorscale='Bluered_r'),

showlegend=False) fig.show() plt.figure(figsize=(16,8))

sns.scatterplot(x=merged.Size, y=merged.Weekly_Sales, hue=merged.Type, s=80);

plt.xticks( fontsize=16) plt.yticks(

fontsize=16) plt.xlabel('Size',

fontsize=18) plt.ylabel('Sales',

fontsize=18); storetype_values =

{'A':3, 'B':2, 'C':1}

merged['Type_Numeric'] = merged.Type.map(storetype_values)

testing_merged['Type_Numeric'] = testing_merged.Type.map(storetype_values)

import seaborn as sns import matplotlib.pyplot as plt

# Selecting only numeric columns for correlation numeric_data

= merged.select_dtypes(include=['number'])

plt.figure(figsize=(28,14)) plt.xticks(fontsize=20)

plt.yticks(fontsize=20)

sns.heatmap(numeric_data.corr(), cmap='Reds', annot=True, annot_kws={'size':12})

plt.title('Correlation Matrix', fontsize=30)

plt.show() input_cols =

merged.columns.to_list()

input_cols.remove('Weekly_Sales')

target_col = 'Weekly_Sales'

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X = merged[input_cols].copy() y

= merged[target_col].copy()

# Scale the values

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler().fit(merged[input_cols])

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 SCREENSHOTS
CORRELATION MATRIX :

AVERAGE MONTHLY SALES :

30
SALES :

SALES :

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 CHAPTER – 08
CONCLUSION & FUTURE ENHANCEMENTS
Conclusion
The Walmart Sales Store Forecasting project provides valuable insights into sales trends
and patterns, enabling better decision-making for inventory management, promotional
strategies, and resource allocation. By leveraging historical sales data, the forecasting model
predicts future sales with accuracy, helping Walmart optimize stock levels, reduce waste, and
enhance customer satisfaction.

This project demonstrates the importance of using data-driven methods, such as machine
learning algorithms, to analyze complex sales patterns influenced by factors like seasonality,
holidays, and market trends. The forecasting model serves as a powerful tool for improving
operational efficiency and maximizing profitability

Future Enhancements

1. Integration of advanced machine learning methods and models in sales

Implement advanced forecasting techniques like Long Short-Term Memory (LSTM)
networks, XGBoost, or Prophet to improve the accuracy of sales predictions by capturing
complex patterns and trends.

2. Real-Time Forecasting
Develop real-time forecasting capabilities by integrating live data streams from point-ofsale
systems. This will enable dynamic decision-making and rapid responses to changing market
conditions.

3. Incorporating External Data Sources

Enhance forecasting models by including external factors such as weather conditions,
economic indicators, and competitor pricing strategies to better understand demand
fluctuations.

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 BOOK REFERENCE
“Sales Prediction of Walmart Based on Regression Models"

Author: Jiayuan Zhang

Publisher: Atlantis Press (now part of Springer Nature)

Year:2023
This study focuses on using regression models to predict Walmart sales, emphasizing multiple
linear regression analysis.as

WEBSITE REFERENCE
1. www.google.com
2. www.w3school.com
3. www.kaggle.com

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