Category: Module G

Posts related to Module G

OM in the News: Merck’s Move to Prevent Drug Shortages

Merck, the Germany-based pharmaceutical, needs to stockpile medications to make sure it has enough on hand because some expire before they can be used. Its supply-and-demand forecasts are about 85% accurate. To sharpen its predictions, Merck plans to use analytics and machine learning to predict and prevent drug shortages, a move that could also save it money. Its new platform, from TraceLink Inc., can analyze data in real time from organizations within Merck’s supply chain, including pharmacies, hospitals and wholesalers.

The U.S. had 600-1,200 drug shortages every year from 2014 and 2019, reports The Wall Street Journal (Oct. 15, 2019). Shortages can happen due to issues with manufacturing, supply-and-demand forecasts, and natural disasters. Drugs in short supply have included antibiotics, chemotherapy and cardiovascular treatments. More precise supply-and-demand forecasts mean pharmaceuticals could save hundreds of millions of dollars annually, a benefit of not having excess drugs on hand and avoiding expedited shipment costs.

On average, pharmaceutical companies carry 156 days of inventory. For retailers selling consumer products, it is 78 days. For IT equipment, it is 57 days. Pharmaceuticals traditionally have predicted demand for drugs based on historical data and input from sales teams. But as many as 10 entities handle a drug before it gets to a patient, including manufacturers, pharmacies and wholesale distributors. “It’s a highly complex supply chain,” said TraceLink’s CEO. The TraceLink network includes data from more than 275,000 organizations world-wide, including hospitals, retail pharmacies, wholesale distributors and drugmakers.

TraceLink’s algorithms give Merck signals about the days of inventory for a specific drug and how long it will take for a drug to get to a particular phase in the supply chain. A better supply-and-demand forecast also makes it easier for Merck to expand into locations without a reliable supply-chain infrastructure, such as parts of Africa and Southeast Asia.

Classroom discussion questions:

  1. Why do pharm firms carry such a large inventory?
  2.  How might data analytics improve forecasting at Merck?

OM in the News: Where OM Data Analytics Meets Chocolate

Todd Ferris uses advanced analytics to find solution to problems like ways to route peanuts.

Todd Ferris is a principal data scientist for Hershey Chocolates. He can track a cocoa bean from harvest to chocolate bar on a store shelf.  Here are some excerpts from  The Wall Street Journal (March 29, 2019)  interview that you might use in class when you cover our new chapter, Module G,  Applying Analytics to Big Data in Operations Management:

Our team goes after complex problems across the supply chain. Can we see our products from sourcing a cocoa bean in Western Africa all the way to manufacturing, shipping and getting it to the customers? It’s very difficult to keep track of all that data. 

We are always fighting this bullwhip effect, the phenomenon of small changes at one end of the supply chain creating huge issues once you get back to manufacturing. If customer demand varies by 100 chocolate bars at retail, by the time that information gets back to us at manufacturing that signal may be 1,000 bars. This creates a lot of inefficiencies.

We use a programming language called R, a counterpart to Python. You’ll do your modeling inside one of those programs; we’ll use SQL to access, manipulate and filter data before we bring it into analytical tools.

You can do procurement, like forecasting the health of crops or their availability. I’ve worked on manpower analysis—how we shape our manpower in our manufacturing plants in the best manner possible. We just worked on the best way to route peanuts from our suppliers to our plants. On one side we are forecasting crops and on the other we’re at the store level trying to determine if we have too much inventory or too little. We try to predict what’s going to happen and then make sense of how we need to respond.

Classroom discussion questions:

  1. Describe the “bullwhip effect” and its importance in OM.
  2. How would you describe “data analytics” to an executive and explain its role?

Our New Chapter, Applying Analytics to Big Data in Operations Management

The marriage of business analytics, big data, and operations/supply chain management is a revolutionary change in our field. We are the first text to include a chapter (Module G) on this subject, which includes sections on data management, data visualization, and predictive and prescriptive business analytics tools. The topics include heat maps, conditional formatting for cleaning data, and pivot tables. The module includes numerous exercises that will use students’ Excel skills and show them the power of Excel in Big Data. This is a topic instructors have asked for and students will really appreciate!

The new edition is now available, so contact your personal Pearson rep for your copy at:

http://www.pearsonhighered.com/educator/replocator/

Here is the first page of the new Module G.