My learning journey in Supply Chain Analytics

As someone who has always been interested in the world of business and logistics, I was drawn to the field of supply chain analytics early on in my academic career. Throughout my learning journey in this field, I have been exposed to a wide range of concepts, theories, and practices that have allowed me to develop a deep understanding of how businesses manage their supply chains in order to stay competitive and profitable.

My journey began with a foundational understanding of the supply chain, which involves the movement of goods and services from their source to the end consumer. I learned about the various components of the supply chain, including sourcing, procurement, manufacturing, transportation, and distribution, and how they work together to create an efficient and effective process.

From there, I began to explore the role of data and analytics in supply chain management. I learned about the importance of collecting and analyzing data from across the supply chain in order to identify inefficiencies, optimize processes, and make informed decisions. This involved learning about different data collection methods, such as RFID tags and sensors, as well as various tools and technologies used for data analysis, such as machine learning algorithms and predictive modeling.

As my knowledge and understanding of supply chain analytics grew, I began to explore more complex topics, such as supply chain network optimization and risk management. I learned about the various factors that can impact the efficiency and effectiveness of a supply chain, including demand variability, supplier disruptions, and transportation constraints. Through case studies and simulations, I developed the skills needed to identify potential risks and develop contingency plans to mitigate them.

Throughout my learning journey, I also had the opportunity to work with industry experts and practitioners through internships and research projects. This allowed me to gain real-world experience and apply my knowledge to real-world problems. I was able to see firsthand how businesses use supply chain analytics to improve their operations and achieve their goals.

Week 1:

- Introduction to supply chain analytics and its importance in supply chain management.
- Learned about the basics of data analysis and visualization.
- Discussed how data analytics can be used to improve supply chain performance.

I found some problems quite interesting:

Part 3: Special Applications

KNIT’ting Industries also has innovative applications for the industry. One of their applications include developing color baths for knitting wools. The color baths are mixed using two different ingredients – the color and an agent that makes the color more UV stable. Other ingredients go into the color baths that are not important to the analysis. The mix of the two ingredients depends on the product type (knitting wool variety).

Consider two products SKU M50 (Product A) and Z43 (Product B). You are asked to calculate the ‘amount of blue color bath’ for Product A and Product B. The following table indicates the amount of blue color (in liters) and UV agent (in liters) needed to prepare one litre of Product A and one litre of Product B.

Suppose you have to prepare the blue color baths for Product A and Product B at the same time. You have 308 liters of blue color and 586 liters of UV agent available. How many liters of blue color bath for Product A and Product B can you prepare using the available inventory of the color blue and the UV agent?

Week 2:

- Explored the use of statistical methods in supply chain analytics.
- Learned about different types of statistical methods, such as regression analysis and hypothesis testing.
- Discussed how statistical methods can be used to solve supply chain problems.

I found some problems quite interesting:

Brinell & Rockwell

Brinell & Rockwell (B&R) is a high-end manufacturer of cutting and drilling bits for lathe machines, drill presses, and other metal-working equipment. They pride themselves in producing only bits of the very best quality, guaranteed to last longer than the bits from any of their competitors. Brinell & Rockwell has a hotline where customers can call to report when a B&R bit fails before it is expected. At that time, Brinell & Rockwell will ship a replacement bit to the customer free of charge, and the customer will return the failed bit for examination. Back at the Quality Control Department of Brinell & Rockwell, a team of scientists and engineers – including several with PhDs in Metallurgy – will examine the failed bits under the microscope, X-Ray machines, and other diagnostics equipment to determine exactly what went wrong.

Brinell & Rockwell’s quality control team keeps the failed bits that they have collected from customers in large glass containers, using one glass container per bit model. They have gathered thousands of failed drilling and cutting bits of 73 different models, including 62 models of drilling bits. These 62 models of drilling bits can be grouped into four families:

In summary, they have collected failed bits of 73 different models, and they use one glass container for every bit model. Therefore, Brinell & Rockwell has in the lab a total of 73 different glass containers. Taking into account that drilling bits can be grouped into families, we know that 14 glass containers contain Diamond bits, 19 glass containers contain Tungsten bits, 20 glass containers contain Iridium bits, and 9 glass containers contain Adamantium bits.

Part 2

Gloria Perez is the second worker to arrive to the lab. Following Brinell & Rockwell policy, she will pick two bits from randomly and independently selected containers. She is equally likely to select any of the containers. Even the container selected to pick up the first bit has the same chance of being chosen as any other container at the time she picks the second bit (meaning that Gloria could grab the same model of bit twice).

What is the probability that Gloria will pick up at least one Iridium bit?

Week 3:

- Learned about forecasting techniques in supply chain analytics.
- Explored different types of forecasting methods, such as time series analysis and trend analysis.
- Analyzed how forecasting can be used to improve supply chain planning and reduce costs.

I found some problems quite interesting:

Shaving Cream

You are working with the marketing team for a FMCG firm that produces shaving cream. The team believes that sales of some of the products are closely related to sales of other products. They want you to explore this in a little more depth for two products, SKU 896 and SKU 401. Unfortunately, all of the base sales data for these products has been destroyed. All that you have is the weekly summary data:

Part 2

Now the marketing team wants to understand the potential weekly sales for these two products. Let the sales price for the two SKUs be 12.50, 7.75, respectively.

What is the expected weekly revenue?

Assume that marketing is correct and the correlation = 0.68.

What is the standard deviation of the weekly revenue?

Looking back on my learning journey in supply chain analytics, I am grateful for the breadth and depth of knowledge I have gained. From foundational concepts to advanced topics, I have developed a deep understanding of how businesses manage their supply chains and the role of analytics in this process. I am excited to continue my journey in this field and contribute to the ongoing evolution of supply chain management.

To view the full journey, please visit: