I’ve stood in front of thousands of students. They’ve been more or less young, more or less technical, more or less experienced – and more or less interested.  I’ve done this as a university faculty member since 1972, first at Massachusetts Institute of Technology, then at Harvard University, finally in the School of Engineering at Rensselaer Polytechnic Institute. Between Harvard and RPI I dropped out of academia temporarily to co-found Smart Software with Charlie Smart and Nelson Hartunian. So since then, I’ve also been busy training business users to exploit the power of advanced analytics for forecasting and inventory optimization.

As I write this, I’ve just returned to my office at RPI after introducing first-year Industrial Engineering students to the basic concepts of inventory management. If they stick with the program, they will go on to take required courses in supply chain, system simulation, statistical analysis, and optimization. I told them stories about how useful they will be to their companies should they decide to make a career in the world of supply chain. If I’d had more time, I would have mentioned how capable they will be when they graduate relative to many of their corporate peers. These freshmen and ready and willing to stay the course, soaking up all the techniques and theories we can throw at them, and honing their practical skills in summer jobs or coop assignments.

What I didn’t tell them is that many of them will have to work to keep their intensity when they are on the job. It’s a sad truth that, for whatever reason, many inventory practitioners settle into a kind of stasis that impedes their companies’ ability to exploit the latest technologies, such as cloud-based advanced demand forecasting and inventory optimization. Gather enough of such people in one place and agility and improved efficiency go out the window.

I think one of the factors that dulls people is that the process of implementation frequently feels painfully incremental and prolonged. It often begins with a sobering inventory of relevant data, its correctness, and its currency. Then it moves to an often-awkward discovery that there really is no systematic process in place and the subsequent need to design a good one going forward. Next is the need to learn to use a new software suite. That step involves learning new vocabulary, some level of probabilistic thought, an ability to interpret new graphs and tables, not to mention a new software interface.  All this takes time and effort.

 

Forecast accuracy provides a statistically sound

 

We’ve found that a few things help new customers stay the course. One is having a champion among management, an executive sponsor, who can vouch for the commercial importance of a successful implementation while ensuring the users are supported with continuing education.  A second is identifying and training a super-user or two having unusual combinations of technical and communication skills.  A third is breaking the training into bite-sized chunks and testing for comprehension after each chunk and repeating this process until it is clear that the new concepts, vocabulary, and process are fully absorbed. But all those maneuvers will come to naught without management being all-in and ready to stay the course.  Inventory planning practices in place for many years are not going to be replaced entirely over a three-month implementation process.  You’ve got to want it to get it.

 

 

Leave a Comment
Related Posts
Call an Audible to Proactively Counter Supply Chain Noise

Call an Audible to Proactively Counter Supply Chain Noise

You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.

An Example of Simulation-Based Multiechelon Inventory Optimization

An Example of Simulation-Based Multiechelon Inventory Optimization

Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.

Fact and Fantasy in Multiechelon Inventory Optimization

Fact and Fantasy in Multiechelon Inventory Optimization

For most small-to-medium manufacturers and distributors, single-level or single-echelon inventory optimization is at the cutting edge of logistics practice. Multi-echelon inventory optimization (“MEIO”) involves playing the game at an even higher level and is therefore much less common.