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SCDigest Expert Insight: Supply Chain by Design
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About the Author |
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Dr. Michael Watson, one of the industry’s foremost experts on supply chain network design and advanced analytics, is a columnist and subject matter expert (SME) for Supply Chain Digest.
Dr. Watson, of Northwestern University, was the lead author of the just released book Supply Chain Network Design, co-authored with Sara Lewis, Peter Cacioppi, and Jay Jayaraman, all of IBM. (See Supply Chain Network Design – the Book.)
Prior to his current role at Northwestern, Watson was a key manager in IBM's network optimization group. In addition to his roles at IBM and now at Northwestern, Watson is director of The Optimization and Analytics Group.
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By Dr. Michael Watson
November 13, 2012
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Advanced Analytics in Supply Chain - What is it, and is it Better than Non-Advanced Analytics?
Better Defining the Field of Analytics by Breaking it Down into Three Categories
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We've had readers ask what is "advanced analytics?" If you logically answer this question, you also have to define "non-advanced analytics."
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Dr. Watson Says: |
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| When you are evaluating analytics solutions, you should understand whether the solution is descriptive, predictive, or prescriptive. |
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What Do You Say?
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To no one's surprise, you don't see many vendors talking about their great "non-advanced" analytics solution or managers proposing a "non-advanced analytics" project to the CEO.
This discussion highlights that although the term analytics is widely used, it is very poorly defined. And, a poorly defined word with such good connotations is in danger of becoming a buzzword - vendors call everything they do "analytics" and managers put the word "analytics" in all their projects.
So, before we get into "advanced" analytics, we should define analytics. If we go back to the Davenport’s "Competing on Analytics" Harvard Business Review article that kicked off the analytics movement, he defines analytics as "the ability to collect, analyze, and act on data."
In other words, at a high level, analytics is the ability to use data to make better decisions.
Unfortunately, this does not help us much. Haven’t companies always tried to use data to make decisions? - Yes, they have. Aren't there thousands of ways to analyze data? Yes, there are.
No wonder people are confused.
Fortunately, academic and professional organizations have realized that the field of analytics should be broken down into three categories:
1. Descriptive analytics-- using historical data to describe the business. This is usually associated with Business Intelligence (BI) or visibility systems. In supply chain, you use descriptive analytics to better understand your historical demand patterns, to understand how product flows through your supply chain, and to understand when a shipment might be late.
2. Predictive analytics-- using data to predict trends and patterns. This is commonly associated with statistics. In the supply chain, you use predictive analytics to forecast future demand or to forecast the price of fuel.
3. Prescriptive analytics-- using data to suggest the optimal solution. This is commonly associated with optimization. In the supply chain, you use prescriptive analytics to set your inventory levels, schedule your plants, or route your trucks.
Having this definition gives you a better framework for evaluating analytics projects and understanding how they may help your supply chain. Note that this does not suggest that one type of analytics is better than another - different problems require different solutions.
Once we have this definition, we don’t need the generic term "Advanced Analytics." For various reasons, BI systems and some statistical solutions have become synonymous with the term analytics. So, to differentiate themselves, vendors offering optimization solutions, complex new statistical methods, or something that they thought was a breakthrough tried to label their solution as an "Advanced Analytics." Of course, once some vendors start using the term, others will follow.
Final Thoughts
When you are evaluating analytics solutions, you should understand whether the solution is descriptive, predictive, or prescriptive. Then, within each of these categories you can determine if the solution is rather basic or advanced and what will meet your needs.
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Recent Feedback |
Concise and on point without hyperbole! Thanks, Dr. Watson!
John Hill
Director
St. Onge Company
Nov, 16 2012
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Nice final thoughts and I agree that analytics "is the ability to use data to make better decisions."
Another interesting aspect that we can draw from this (apart from what the data is telling us) is what the data is not telling us.
In fact, it is the "not-telling" that contributes to a greater degree of complexity.
Koh Niak Wu, Ph.D.
Global Supply Chain and Logistics
Dell Singapore
Nov, 27 2012
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Great article Mike. It may be too late to worry about "Analytics" becoming a buzzword. Just like "analysis", "analyst" or "optimize" it's become part of the general vocabulary and has lost much of it's clarity. I think I moved to calling what I do "Advanced Analytics" as a way of highlighting that analytics can't all be done in Excel.
For the people buying such software and services this is a crucial thing to understand - just because it says analytics on the box does not mean you will get anything more than simple reporting when you use it. In fact, if my experience is at all representative, if its says "analytics" there is an excellent chance you will find nothing beyond reporting and perhaps visualization/alerting tools. If what you need is a predictive model, you had better understand what constitutes "predictive analytics".
Personally I tend to use the "What happened?", "What if?" and What's Best" categories to explain different sorts of analytics but perhaps it's time for me to make a change.
Andrew Gibson
Partner
Crabtree Analytics
Nov, 27 2012
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This is really a good definition. The categories are very helpfull to support the managers in their Business Analytics projects. A clear definition about what optimizations they would like to describe, predict and suggest will demand a deep understanding about the capabilities they have in the transactional process to generate the required level of data and information that they will need to create an efficient and value added business analytics process. So maybe they will realize that they need to start with some process improvements before starting a more sofisticated business analytics process.
Valério Machado da Silva
Supply Chain Executive
Seeking for a new position.
Mar, 18 2013
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