The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
Physicists like my Smart Software co-founder, Dr. Nelson Hartunian, tell us civilians that everything is different when we drill down to the tiniest level of the world. Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.” Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management.
Consider quantum superposition. Briefly, superposition means any quantum entity can be in two states at once. Schrödinger’s cat is the most famous illustration of this idea. But how many of you readers are also in a state of superposition? Don’t you find yourself being a manager of a team yet a member of your supervisor’s team, a trouble-shooter yet also a forecasting expert or an inventory optimizer and…? And doesn’t all this make you sometimes feel, like that cat, that you are simultaneously both dead and alive? Modern software can ease some of this burden by automating the tasks of demand planning and inventory optimization. The rest is up to you.
A second quantum analog is entanglement. Briefly, entanglement is the linkage between two elements of a system. They can be light years apart, yet changing one part of an entangled system will instantaneously change the other part. This bugged Albert Einstein, who derided it as “spooky action as a distance.” In our regular world, demand planning and inventory optimization are entangled, since the process of inventory optimization sits on top of the process of demand forecasting. Modern software links the two in an efficient interface.
Finally, the quantum foam – one of my favorite ideas. As I understand it, quantum foam is a substitute for empty space: there is no empty space, rather a constant bubbling of “vacuum energy” accompanied by a flux of “virtual particles” being born out of nothing and then disappearing back into nothing. In the supply chain world, the analogs of virtual particles are customer orders. Often it seems that they pop up with no warning out of thin air, and sometimes they disappear by cancellation in an equally random and mysterious process. This kind of demand fluctuation is the basis for all the theory of inventory control. Modern software therefore begins with probability models of customer demand. Those models then have implications for such tangible quantities as safety stocks, reorder points, and order quantities.
Does it really help demand planners and inventory managers to think about these ideas from quantum physics? Well, it’s a bit of fun to see the analogies to our regular world of work. And they do remind us of more macroscopic matters: the basic concepts of the need to deal with more than one task simultaneously, the linkage between forecasting and inventory management, and randomness as the fundamental feature of the supply chain.
Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.
Let’s start by recognizing that increased revenue is a good thing for you, and that increasing the availability of the spare parts you provide is a good thing for your customers. But let’s also recognize that increasing item availability will not necessarily lead to increased revenue. If you plan incorrectly and end up carrying excess inventory, the net effect may be good for your customers but will definitely be bad for you. There must be some right way to make this a win-win, if only it can be recognized.
If you both make and sell things, you own two inventory problems. Companies that sell things must focus relentlessly on having enough product inventory to meet customer demand. Manufacturers and asset intensive industries such as power generation, public transportation, mining, and refining, have an additional inventory concern: having enough spare parts to keep their machines running.
This technical brief reviews the basics of two probabilistic models of machine breakdown. It also relates machine uptime to the adequacy of spare parts inventory.