“Chaos, complexity, and uncertainty” often victimize an organization’s supply chain outstripping the ability of spreadsheet-based tools to respond intelligently. Sounds like 2020?  In fact, this comes from one of the original Jedi Knights INFORMS fellow Dr. Harlan Crowder in a 1997 paper titled “Helpful Hints for OR/MS Consultants”. In a time long ago in a galaxy far, far away, OR/MS pioneered analytics and built the original successful SCM applications. The purpose of this blog is to revisit this paper since the hints for success are as relevant now as they were then. The hints are: (1) clients know best, except when they are describing their own business; (2) is it scheduling or planning; (3) getting into an engagement is easy, getting out is the hard part – scope creep; (4) do not just storm the beaches, occupation is important; (5) engagements have less to do with building the model and more to do with data; (6) open the books.


Well before the current analytics and SCM technology landscape was occupied by data scientists, machine learning and artificial intelligence gurus, and SCM high priests; the analytics and SCM world was pioneered by a group of Jedi Knights referred to as OR/MS/IE/CS (usually shortened to OR/MS) professionals. OR- operations research; MS – management science; IE – industrial engineering; CS – computational scientists. It is these Jedi Knights that created SCM with a series of successes in demand management, central planning, and inventory management from 1987 to 1997. This group developed the core computational analytic tools as well as the critical “soft side”. The origin of this “band of brothers” is the analytics work to support the Allies in WWII which included breaking the German codes.

In preparing for an upcoming presentation from an “aging Jedi Knight”, I came across an article by one of the original Jedi Knights INFORMS fellow Dr. Harlan Crowder titled “Seven Helpful Hints for OR/MS Consultants” published in ORMS Today in February 1997 (Vol. 24, No. 1). These hints are as relevant today as they were in the 1990s.

Hint 1: Clients always know best – except when they are describing their own business problems. Listen carefully when your client describes their business problem, but make sure your OR/MS solution solves the right problem.

Harlan provides this example from his vast experience. “We performed an engagement for a manufacturer of agricultural chemical products. The initial problem they identified was too much inventory and they wanted an inventory policy model. After discussions with management and operational people, we found they indeed had too much inventory, but the root cause was not inventory policy but the lack of any idea about how to forecast annual demand for products. It turned out the two most important factors when demand occurred were customer history and the weather. We then build a better forecasting application that solved the inventory issue.”

Hint 2: Is it scheduling or planning?  Make sure you know; the client knows, and it is documented with questions one expects the model to answer.

Scheduling answers operational kinds of questions, planning gives answers to people who only talk about doing work. In fact, planning and scheduling are really two extremes of a spectrum of applications. In concert with the client, write a design document that has a section on the kinds of insights and answers that can be expected from the model and a list of the kinds of questions the model will not answer. In effect, this generates agreement on where the application is pegged on the planning scheduling spectrum.

Historically this spectrum (or decision tiers) was originally applied to planning and scheduling of production or actions, but it applies to estimating demand. This distinction is increasing in importance, just as the breadth and depth of analytics applied to demand estimation have increased substantially over the past 10 years.

Hint 3:  Getting into the engagement is easy, getting out can be the hard part

Here Harlan is referring to scope creep. He notes building and deploying models of real-world systems (the digital twin) usually generate lots of new questions and areas for investigation. There is a constant temptation to investigate these areas, but the diversion of effort to out of scope activities typically hurt the long-run success of the project. It is best to make these well documented for follow-on engagements.

Hint 4: Do not just storm the beaches and ignore the occupation’s forces

There are two phases to each project – generating the initial value and capturing interest. Integrating the models into the day to day business processes.

Hint 5: OR/MS consulting engagements have less to do with building the model and more to do with data: identification, extraction, cleansing, validation, and formatting.

Hint 6: Open the OR/MS Technology Book

Harlan found opening the methodology to people on how the technology works, in the long run, generate more valuable technology. More people are involved in thinking about the application area and what is a good solution. Share the fun!


At the start of Dr. Crowder’s article, he describes the current environment of the late 1990s as “Chaos, complexity, and uncertainty”. Public and private organizations are often the victims of these forces and increasingly models (analytics) are critical to helping navigate this environment. Recognizing that many of these sticky analytic questions can not be solved with just spreadsheets, firms to turn to OR/MS professionals, both their skills and toolkit, where “dirt-cheap” computers make it practical to bring to bear methods to provide insight into complex challenges not previously computationally feasible. I would content his six hints are as relevant today as in 1997.

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