Supply Chain Analytics | EveryEng

Skip to main content

Self-paced Beginner

Supply Chain Analytics

4(1581)

1 enrolled

785 views

FREE

1274 min

Anytime

English

785 views

Team EveryEngMechanical Engineering

Logistics & Shipment

Supply Chain Management

Lifetime access
Certificate of completion
Foundational Learning
Access to Study Materials

Why enroll

Participants join this course to understand how data analytics can improve supply chain performance and decision-making. It helps them learn practical techniques for analyzing supply chain data and identifying opportunities to reduce costs and increase efficiency. The course also provides valuable skills in data visualization and predictive analysis that are widely used in modern industries. By gaining these skills, participants can enhance their career opportunities in supply chain management and data-driven business operations.

Your instructor

Team EveryEng

Engineer

Mechanical Engineering

22+ yrs experience·India

Is this course for you?

You should take this if

You work in Logistics & Shipment
You're a Supply Chain Management / Data Science & Analysis professional
You prefer self-paced learning you can revisit

You should skip if

You need a different specialisation outside Supply Chain Management
You need live interaction with an instructor

Course details

This course focuses on how data analytics can be used to improve supply chain operations and support better business decisions. Participants will learn how to collect, analyze, and interpret supply chain data to identify patterns and trends. The course introduces important techniques such as data analysis, visualization, and predictive modeling. Learners will explore how analytics can help reduce costs, improve efficiency, and optimize inventory management. It also covers demand forecasting and performance monitoring using real data insights. Participants will gain practical knowledge on using data to solve supply chain challenges. The course explains how data-driven decisions can enhance logistics, procurement, and distribution processes. Simple analytical tools and techniques will be demonstrated for real-world applications. By the end of the course, learners will understand how to transform raw data into meaningful insights. This helps organizations make smarter decisions and achieve sustainable business growth.

Source: Supply Chain Analytics (YouTube Channel)
Dr. Rajat Agrawal, Dept. of Management Studies, IIT Roorkee

Course suitable for

Key topics covered

Analyze supply chain data for informed decisions
Apply data visualization and predictive analytics techniques
Optimize supply chain design and operations
Implement data-driven decision-making practices

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

40 lectures21 hr 14 min

Introduction to Supply Chain Management
30 min
Evolution of Supply Chain Management
31 min
Analytics in Supply Chain Management
34 min
Supply Chain Planning
29 min
Different views of Supply Chain
34 min
Supply Chain Strategy
29 min
Supply Chain Drivers
30 min
Developing Supply Chain Strategy
31 min
Strategic Fit in Supply Chain
33 min
Demand Forecasting in Supply Chain
32 min
Bullwhip Effect and Time Series Analytics
32 min
Exponential Smoothing Method of forecasting
30 min
Measures of Forecasting Errors
30 min
Tracking Signal and Seasonality Models
31 min
Forecasting using multiple characteristics in Demand Data and Inventory Management in Supply Chain
31 min
Inventory Management in Supply Chain
33 min
Multi echelon Inventory Management
34 min
Multi echelon Inventory Management (Continued)
30 min
Multi echelon Inventory Management for four stations
32 min
Multi echelon Inventory Management for four stations (Numerical Example)
31 min
Multi echelon Inventory Management for four stations (Numerical Example continued)
34 min
Network Design in Supply Chain
31 min
Network Design of Global Supply Chain
32 min
Alternative channels of Distribution
31 min
Location Decisions in Supply Chain
35 min
Network Optimization Models
30 min
Using Excel Solver for Network Optimization
36 min
Uncertainty in Network Design
32 min
Network Design in Uncertain Environment and Flexibility
31 min
Flexibility in Supply Chain
29 min
Optimal Level of Product Availability in Supply chain
34 min
Time Value of money in Supply Chain
29 min
Different types of Analytics in Supply Chain
33 min
Predictive Modelling in Forecasting in Supply Chain
35 min
Representation on Uncertainty in Supply Chain
31 min
Example of using Decision Tree incorporating Uncertainty in Single Factor
30 min
Using Decision Tree for handling Uncertainty
32 min
Example of using Decision Tree incorporating Uncertainty in two Key Factors
33 min
Modelling Flexibility in Supply Chain
36 min
Trends, Challenges and Future of Supply Chain
33 min

Opportunities that await you!

Career opportunities

Why people choose EveryEng

Industry-aligned courses, expert training, hands-on learning, recognized certifications, and job opportunities-all in a flexible and supportive environment.

FREE

Access anytime

Questions and Answers

Q: You're reviewing a KPI dashboard after a supplier OTIF collapse, searching for "why forecast accuracy drops but inventory rises supply chain analytics". Demand variance is stable, yet safety stock keeps inflating and service level still falls. What’s the most likely root cause?

A: Aligning forecast and inventory logic removes artificial stock inflation without fixing demand. B explains volatility but not stable demand, C raises stock but should protect service, and D inflates on-hand rather than degrading OTIF in parallel.

Q: A rail operator stores spare brake components in a coastal warehouse and later sees premature failures, prompting a search for "corrosion impact on spare parts inventory analytics". Which degradation mechanism dominates the observed increase in scrap rate?

A: Environmental exposure during storage drives slow, widespread material loss that analytics flags as rising scrap. B and D require cyclic or service stress, and C needs an assembled couple that isn’t present in storage.

Q: Your lead asks you to redesign a replenishment policy after seeing 15% higher actual cycles than assumed, googling "how to adjust safety stock with real cycle data". Which analytics-driven design choice best addresses the gap without overcapitalizing inventory?

A: Using real distributions targets protection where variability actually sits. B blindly scales stock, C can worsen service under variable demand, and D changes sourcing risk without fixing planning assumptions.

Q: An aerospace supplier data audit references "AS9100 requirements for supplier performance monitoring analytics". Why does the standard insist on trend analysis rather than single-point KPI checks?

A: Detecting drift prevents latent failures from compounding across the chain. B overstates statistics, C confuses certification with regulation, and D invents a domain separation the standard doesn’t make.