Analytics and Operations Research; a practitioner’s view

The topic whether the O.R. society should embrace (business) analytics is one that will probably go on for a while. It’s THE theme that keeps O.R societies occupied at this moment; all are busy with the question whether they should hook on to analytics since it could boost awareness and interest for O.R. Although the term “Business Analytics” is quite old, it dates back to Frederick Taylor’s time management practice in the late 19th century, it is presented as the latest trend in management and every CEO should start using it. Amazon lists over 1,500 books on the subject, nearly every one of these books has the theme “Start using Analytics in decision making, otherwise you will be doomed to the lower end of the performance ladder and go bust”. In promoting the use of analytics different terms are used which makes it hard to understand what people are really talking about. Terms like business intelligence, business analytics, descriptive analytics, predictive analytics, prescriptive analytics, and so on. From a practitioner’s point of view the discussion on the subject is rather academic, my clients don’t really care whether I use the term Analytics or O.R. in improving their operations or decision making. They just want me to help them solve their problem.

If have been working in O.R. consulting for over 20 years now and have learned something that Plato already knew over 2300 year ago, a good decision is based on knowledge not on numbers. It isn’t the analyses of data (=Analytics?) or building and solving a math models (=O.R.?) that leads to better decisions, it’s the knowledge gained in the process. It starts with understanding the problem and framing it right. This can best be achieved by gathering and analysing relevant data, measuring performance and identifying the applicable business rules. Analytics if you will. This analysis will increase the knowledge about the problem at hand and the environment in which it needs to be solved. Based on the data analysis and the identified business rules, directions for improvement (scenarios) can be identified. By analyzing the scenarios, the impact (consequences) of each of these can be identified, again increasing the knowledge about the problem, but also on how to solve it. The “do’s and dont’s” have come forward at this point. Next step is to use the knowledge about the challenge, the data and the business rules to build and use/solve a math model to find the best possible and achievable solution to the challenge (note: optimality in practice is something different compared to the textbook concept of optimality). With the knowledge gained during each of the above steps, implementing the solution is straight forward, apart from the “normal” potential change management issues. Result of it all is a solution to a practical challenge, and hopefully a satisfied customer.

My clients have never asked me what techniques I use to help solve the challenge they face and I also never tell them. In the past 20 years (See: Does O.R. Sell?) I have never come across a client that hired me because I could analyse data, build a forecast model, build/solve a linear programme or was able to build a simulation model. There is a simple reason for that, they don’t know the difference and they don't need to. Introducing yourself with that you are really good at building a math model, have been in Monte Carlo simulation or Markov chains for years, doesn’t help build your credibility. Talking about O.R. or Analytics doesn’t either. What counts is that you understand or show that you’re able to understand the business of your client, his organisation and the challenge he faces. So discussing whether Analytics and O.R. are the same, part of each other or complementary doesn’t really matter from my point of view. I’ll use the technique that is required to solve my clients challenge, no matter if it’s descriptive, predictive or prescriptive. Whoever thought of the term “Prescriptive Analytics” by the way? It makes O.R. to something that can only be applied when a specialist tells you how and when to use it. “Solve this LP model 3 times a day and your problem is solved?”

I once used just a blank sheet of paper to solve a business challenge, a real back of the napkin situation. One drawing was enough to identify and solve the business issue. The drawing was a simple graph. The shortest path in the graph was the solution to the client’s challenge. Calculations where not required, even my client could see the solution immediately. This shows that O.R. can be down to earth and within reach of everybody. That is also how we should go about in the Analytics vs Operations Research discussion, down to earth and for everybody including clients. I would suggest a small twist and add some special focus on the practical side of it all.

The Risk of being Just in Time

Most executives don’t realise that optimisation can make companies vulnerable to changes in their environment. They have been taught that efficiency and maximising shareholder value don’t tolerate redundancy. For that reason many manufacturers practice a Just in Time (JIT) stock keeping philosophy for raw materials. Since for most manufacturing operations over 70% of the cost is associated with purchasing of goods and services, keeping stock levels low seems a sensible thing to do. In JIT the focus is on controlling the stock levels of the sourced materials, since finished goods and internal sub-assemblies are within the control of the manufacturer. Unfortunately this is also where the highest risk in the supply chain resides; disruptions in the supply of raw material can cause missed customer shipments or worse shut your customer down.

Buying and shipping of supplies has changed in the past two decades. The locations, at which components are produced, are spread around the world, with technology and low costs as its driving forces. This causes supply chains to become longer and far more complex than in the past, making it hard to asses the risks. An example of such a risk is the bullwhip effect. When supply chains get longer, lead times increase, which in turn amplifies demand fluctuations leading to increased stock levels. Most manufacturing companies have deep knowledge of their primary suppliers; the risks however reside further down the supply chain, at the supplier’s supplier. An example from the electronics industry is the production of silicon wafers, the raw material used to make computer chips which in turn are used in smart phones or tablets. 60% of the world supply in silicon wafers comes from two Japanese companies one of which is the Shin-Etsu Chemical Company. Its main wafer plant in Shirakawa was damaged by the recent earthquake in Japan. Chip makers like Intel, Samsung and Toshiba depend on these wafers for their production of microchips. Typically, they keep stock levels for 4 to 6 weeks of production. So if their supply falls short, Apple and other smart phone/tablet producers might be impacted, just as a tablet war is about to start.

There are many more examples like the silicon wafer example, which can cause shareholder value to degrade when practicing a JIT philosophy. An empirical analysis of share price performance shows that firms facing a disruption in the supply chain experienced share price returns that were 30-40% lower than the industry and general market benchmarks. Showing that being prepared and therefore tolerating some redundancies is a better strategy that will at least preserve shareholder value. But which supplies of raw material to keep in stock and at what level? Not a question that can be solved easy.

First step in making that decision is to understand the dynamics of the supply chain. Since disruptions may have impact not only locally but globally a holistic and system wide approach is required to analyse the risks involved. A structured approach for describing and analysing supply chains therefore is needed to help unravel the supply chain complexity. From personal experience, I find that combining the SCOR method of the Supply Chain Council (SCC) with Operations Research is a good way to achieve that. SCOR is a framework and a methodology that allows companies to create high-level descriptions for supply chain systems. Combined with the power of the mathematical models and optimisation techniques from Operations Research you end up with an understandable and manageable models that can be used to create insights and put fact to beliefs. Second step is to identify the appropriate metrics to measure the supply chain reliability, responsiveness or agility, which allows you to measure supply chain risks and asses and mitigate the risks by evaluating different policies, like which stock levels to keep and where to keep them. In a third step, using techniques like Monte Carlo simulation, stock keeping policies can be evaluated. Last but not least, the model can be used to optimise (not minimise!) the stock levels, while minimising supply chain risks. As a result the best stock keeping locations and levels can be identified.

So, a stock keeping strategy should be a result from a thorough analysis of the full supply chain, focussing on the robustness of the complete chain, taking the risk of disruptions in supply into account. Following what’s common (like JIT) can have high impact on the performance of the supply chain, and destroy instead of create shareholder value. Adding OR to SCOR will enable you to asses risk in a standardized and fact based way, leading to informed choices on which risks should be mitigated and how to mitigate them. This allows you to make a fact based decision on which raw materials to keep on a Just-In-Time or Just-in-Case stock level.