by Kumar Singh, Research Director, Automation & Analytics, SAPinsider
The Great divide !
If you are an active resident of analytics land, you know that Artificial Intelligence (AI) and Machine Learning (ML) tools are the new bosses in town. Every tool, technology and technology solution around you is trying to incorporate them in their solution in some form and every other guy you meet has “evolved” from being a data analytics or business analyst or system analyst into….yes, you got it right- a data scientist ! But all the hype ML is attracting is well deserved. If identified correctly and used strategically, these algorithms do have the potential to deliver transformational capabilities.
And all this limelight on AI and ML has pushed the classic analytics professionals like statisticians (who stood their ground and decided to stay with that title rather than getting “rebranded” as data scientists) and good old Operations Research (OR) professionals into a seperate category. Whether talking to executives, reading books or doing secondary research, a consistent theme that I see is that we have classified the community of advanced analytics professionals in the supply chain world into two primary categories : OR and ML professionals. But do they really need to always be in two distinct buckets in the world of supply chain ?
A Brief history of Operations Research (OR)
For starters, Operations Research (OR) is an applied mathematics discipline that leverages multiple algorithms and techniques like simulation, modeling, queuing, and other stochastic and probabilistic methods to optimize or improve a business process. The foundation for this discipline was laid during World War II and the key driver was to leverage applied math to find solutions to strategic operations and logistics problems. The discipline has evolved since then and OR techniques are used widely in non-military scenarios today. Operations Research has been employed in logistics Engineering for quite some time now, some of the classic applications being optimizing flows, identifying optimal locations, minimizing transportation costs, optimizing transportation assets, inventory optimization etc.
And then new analytical approaches started evolving
While Operations Research evolved over the years to find applications outside military and became widely used in Industries, other fields also evolved concurrently. An example can be Management Science, which was essentially leveraging applied math to solve business and economics problems. And then, with advancements in computing technology, data mining tools and database technologies, we saw emergence of new fields ML ,deep learning, NLP, reinforcement learning among many others. ML tools quickly grew in popularity as predictive analytics tools due to their wide gamut of applications made possible by today’s technology. And then, as they grew popular, we started thinking of them as tools that generally will work quite separately from OR tools and are useful in totally different context and scenarios.
What was the thinking behind drawing the line between OR and ML algorithms ?
In my opinion, our classic (and archaic) way of thinking has not only created the divide between OR and ML but has also thwarted opportunities to break what I call “analytics silos”. OR and ML tools are leveraged in silos but the key question that we need to ask is – do they really need to exist in silos ?
If we think about this from a supply chain perspective, a challenge that conventional OR approaches had was that they were essentially “one-off” modeling exercises in prescriptive analytics. The challenge or drawback of this approach is that today’s world of supply chain and manufacturing has become much more dynamic and complex. By the time you take recommendations from a prescriptive model like this one to the floor, many variables might have changed.
But one of the key bottlenecks in using these optimization algorithms “live” was that in the real world, where input variables, decision factors and constraints are insanely complex, the tool would not been able to spit out recommendation as fast as shop floor operations planning would like it to. Imagine a production schedule optimization model as an example, one which decides what set of parts should be processed together to minimize aspects like setup time, total production time etc. Running this model “live” to plan daily production runs may not be feasible since the run time (even with today’s computing power) will be too long to be useful as a live planning tool for complex SKU portfolios and complex manufacturing operations.
And this is what we generally cite as a drawback for OR. Machine Learning (ML) algorithms, when used in true sense (i.e used for scenarios where they should be used), can be “live” learning algorithms but they are more geared towards prediction scenarios. And hence, we see OR and ML as two distinct buckets.
But the real magic happens when you combine these two !
Did we think about it the wrong way ?
Let us re-visit our production planning example. I indicated that one of the challenges that these models run into is long run time, making them not suitable for live planning tools.
But here is another scenario. An optimization algorithm runs for days, and we build a massive data set of all input variables and output recommendations. We run these scenarios millions of times to essentially create a large data set. THEN…. we feed this data to a ML algorithm(can be a Deep Learning algorithm as well). So now, we have trained an algorithm that can predict, based on input variables, what the expected output will be. And then we can build a drag and drop interface around this ML algorithm that operators can use to select the optimal mix of parts that optimizes the production process aspects. And voila…we have created a solution combining optimization and ML alorithms.
This is just one example and there can be many opportunities. The key is to think beyond the silos of the “tools” and think in terms of the big picture solution. Note that some state of the art Enterprise AI tools have already started leveraging methodologies that combines these two powerful approaches to create solutions that align with today’s complex manufacturing and supply chain operations.
What does this mean for SAPinsiders ?
- Enterprise analytics is not just about running multiple analytical tools in silos. It is about finding opportunity to play around. Use your analytics canvas to draw beautiful solutions, by combining different analytical tools together to create robust solutions
- The suggested scenario is just one example but you can use the approach of combining these two approaches across end to end supply chain
- Create analytics teams that are not divided by expertise areas (like OR and ML). If they are, run workshops and sessions to help them think about opportunities as a high level. Push them to evolve as analytics artists vs modelers, programmers and data scientists. Designations do not do any magic, skills do
- SAP and SAP ecosystem partners provide a plethora of tools that you can leverage to create such innovative solutions. You can chose to build solutions from scratch using open source tools
- Third party Enterprise AI tools are also available in the market that provide these “combined” capabilities in off-the-shelf products
Kumar Singh, Research Director, Automation and Analytics, SAPinsider, can be reached at firstname.lastname@example.org