This document discusses how analytics can help improve supply chain decision making. It describes how analytics is used to extract insights from data to improve efficiency, reduce costs, and enhance customer service. It also outlines some key challenges in supply chain analytics like data silos and lack of skills. Emerging technologies like IoT and AI are making it possible to better track inventory, predict demand, optimize routes and schedules, and implement predictive maintenance. Real-world examples from Walmart, Amazon, and Tesla are provided that illustrate how these companies use analytics across their entire supply chains.

1. INTRODUCTION
TO SUPPLY
CHAIN
ANALYTICS
By Ammar Jamshed (AJ)
How Analytics is part of
processes in supply chain
departments in companies and
how can it be introduced to
traditional processes

2. INTRODUCTION
TO SUPPLY CHAIN
ANALYTICS

3. INTRODUCTION
TO ANALYTICS
AND ITS ROLE IN
SUPPLY CHAIN
DECISION-
MAKING
Analytics is the process of collecting, analyzing, and
interpreting data to extract insights that can be used to
make better decisions for businesses and companies.
Supply chains are complex and generate a lot of data.
Analytics can help businesses and organizations to make
sense of this data and identify opportunities to:
Improve efficiency.
Reduce costs.
Improve customer service.

4. OVERVIEW
OF SUPPLY
CHAIN
MANAGEME
NT AND ITS
IMPORTANC
E
Management of all transaction-based
behaviors of goods, materials, finance and
data from start to end of the business
transport, warehousing and storage.
Strategy development for management of
goods within the ecosystem of delivery.
Information and data exchanges between
various connected stakeholders of the
inventory and its arrival at the business
sales point.

5. KEY
CHALLENG
ES IN
SUPPLY
CHAIN
ANALYTICS
Data silos: Many businesses have data scattered across
different systems, which makes it difficult to get a
complete view of the supply chain.
Lack of skills: There is a shortage of skilled data analysts
the workforce, which makes it difficult for businesses to
implement and use supply chain analytics effectively.
Complexity: Supply chains are complex and can be
difficult to model and analyze.
Cost: Supply chain analytics can be expensive to
implement and maintain.

6. KEY
OPPORTUNITI
ES IN SUPPLY
CHAIN
ANALYTICS
Improved decision-making: Analytics can help
to make better decisions about inventory, transportation,
pricing, and risk management.
Reduced costs: Analytics can help businesses to reduce
costs by identifying and eliminating waste and
inefficiencies in their supply chains.
Improved customer service: Analytics can help
to improve customer service by ensuring that products
are available when and where customers need them.
Increased visibility: Analytics can help businesses to
visibility into their supply chains, which can help them to
make better decisions and respond to problems more
quickly.

7. EMERGING
TECHNOLOGIES
IN SUPPLY CHAIN
ANALYTICS

8. INTERNET OF THINGS (IOT) AND
SENSOR DATA ANALYTICS USED IN
SUPPLY CHAIN
Tracking inventory levels and locations: IoT sensors can be used to track the movement of goods throughout the supply
chain, from raw materials to finished products.
Tracking
Monitoring product quality and safety: IoT sensors can be used to monitor the condition of products throughout the supply
chain.
Monitoring
Predicting demand and optimizing production schedules: IoT data from sensors can be used to predict customer demand
and optimize production schedules.
Predicting
Improving transportation and logistics efficiency: IoT sensors can be used to track the movement of vehicles and goods in
real time. .
Improving
Reducing downtime and maintenance costs: IoT sensors can be used to monitor the condition of equipment and predict
potential failures.
Reducing

9. ARTIFICIAL INTELLIGENCE (AI) AND
MACHINE LEARNING APPLICATIONS USED
IN SUPPLY CHAIN
Predictive maintenance: AI and ML can be used to predict when equipment is likely to
fail. This data can be used to implement predictive maintenance and reduce downtime
and maintenance costs.
Transportation optimization: AI and ML can be used to optimize transportation routes
and schedules. This can help organizations to reduce fuel costs and improve delivery
times.
Demand forecasting: AI and ML can be used to forecast demand for products and
services more accurately. This can help organizations to optimize inventory levels and
production schedules.

10. CASE STUDIES AND
REAL-WORLD
APPLICATIONS

11. Walmart uses analytics to optimize its inventory levels and ensure that
products are in the right place at the right time. Walmart collects data on sales,
customer demand, and inventory levels from its stores and warehouses.
Walmart uses analytics to predict when a product is likely to sell out. This
allows Walmart to order more of the product in advance and avoid stockouts.
As a result of using analytics, Walmart has been able to reduce costs and
improve customer satisfaction. Walmart's inventory turnover rate is one of the
highest in the retail industry.

12. Amazon uses analytics to optimize its entire supply chain, from product sourcing to
delivery. Amazon collects data on suppliers, products, customers, and orders.
Amazon uses analytics to predict which products are likely to be popular in the upcoming
holiday season. This allows Amazon to order more of those products in advance and
avoid stockouts.
Amazon also uses analytics to optimize its transportation network. Amazon's logistics
network is one of the most efficient in the world. Amazon is able to deliver products to
customers quickly and at a low cost.

13. Tesla uses analytics to analyze customer
data, sales data, and engineering data to
identify areas where its products can be
improved.
Tesla uses analytics to optimize its
manufacturing processes and reduce
costs. For example, Tesla uses analytics to
predict demand for different vehicles and
components, and to schedule production
accordingly.
Tesla collects data from its vehicles on a
variety of metrics, such as battery
usage, driving behavior, and environmental
conditions.

14. TOOLS USED FOR
BUILDING COURSE
 Microsoft Office.
 Chatgpt.
 Google Bard.
 Google Search engine.
 Google Scholar.