It seems all of the inventory management and service management platforms these days are touting the myriad of forecasting models in their latest and greatest release, but are they worth it?
It’s a hard question to answer. Here are some of the main arguments from both sides of the fence. You be the judge.
Supporter: You should check out this great new software for inventory and service management (SISM) from Hyper Yard Product Engineering (HYPE). It supports time series, casual, and combined forecasting models that allows you to define forecast types, horizons, target replacement rates, smoothing constants, variance parameters, seasons and trends, target costs, target fill rates, thresholds, and a slew of other parameters. It will save your buyers a lot of time, experts and novices alike.
Detractor: I don’t know. Sounds like a bunch of hoopla to me. Not only do most “experts” believe that only their models are the right models, they are likely to be suspicious of the results unless they can tweak every parameter and analyze the assumptions and algorithm in detail. And even then, if the resulting curve doesn’t adhere to their intuition, they are likely to call it a pile of junk and override it with a manual forecast. As for novices, it’s not very useful either as they aren’t likely to have the competence necessary to understand the inputs or outputs or the reason why one method is preferable to another for a given commodity or category and will likely choose the wrong method and screw-up the parameters.
Supporter:Oh no, not hoopla at all. It’s extremely useful to both parties. It’s a great help to novices as it automatically selects the right model given the commodity or category, sets the parameters based on contract data and historical inventory patterns, and correlates to similar commodities in the system. It greatly increases the accuracy of their results. And since it provides dozens of algorithms, where each parameter and assumption can be overridden, experts will love it as it will contain their algorithm, allow them to tweak the parameters, and override results with a manually derived model where needed.
Detractor:So, the expert can override the system at any time and the novice doesn’t have to choose anything?
Supporter:Correct. An expert can even define a fully manual forecast if the situation is unusual (such as a one of a kind promotion or rapid uptick in the economy) and a novice doesn’t have to do anything.
Detractor:So, the novice gets a free ride until the black swan decides to attack and cause a major catastrophe, and the expert gets to bypass the system entirely. In the first case it’s misused, as no system can compensate for human intelligence and every forecast needs a sanity check, and in the second case it’s not used at all. Sounds like a waste of money to me.
Supporter:No, no. You miss the point. By automating the process, it gives the user time to focus on learning the category so that she can tweak the parameters when she notices an unusual uptick or downtick and so that she can focus on analyzing the forecast and performing the sanity check instead of wasting hours or days trying to manipulate formulas and software to produce the forecast. And since it has so much power, the expert won’t need to override with a manual forecast very often.
Detractor:In theory, yes, but in practice no. Human nature is what it is. Most novices are tactical procurement personnel who don’t understand the math behind forecasting and don’t want to. They’ll drive whatever forecast the system spits out until they drive off the cliff. And most experts are arrogant know-it-alls who don’t believe a dumb system can ever come close to their years of experience and massive ego. They’ll tweak or override every forecast even if there isn’t a compelling reason and even if the forecast is probably right (because only they can be right) and tweaking it makes it wrong. Both ways, the company loses. Not only does it lose the investment in the system, but it loses out when it gets stuck with excess inventory or fails to meet demand.
Supporter:Well, you have to use the system properly to take advantage of its power.
Detractor:Right. And neither party will. Furthermore, if forecasting really was a science and if these tools always worked as advertised, the world would be an easy place. But the reality is that most demand is impulse and spike driven and most forecasting is nothing more than dumb luck in the end. As a result, all those continuous and curve fitting algorithms turn out to be worthless as they can’t model spikes. The reality is that you can get as much accuracy from a kindergartener’s hand drawn curve, especially if it looks “close” to historical demand curves.
Supporter:That’s why these tools also have extensive comparative graphing capabilities that allow you to compare current forecasts against past behavior for similar horizons. You can see whether or not the forecast is in line with what normally happens and whether or not there are any spikes consistent with your expert’s “gut feeling”.
DetractorThat’s nothing more than warm fuzzies, and warm fuzzies mean nothing. It’s as useful as Wall Street’s “risk” algorithms. Heck of a lot of good they did us.
Supporter:That’s why the tools allow you to update the forecasts at any time to take into account the latest changes in demand.
DetractorSo what? Spikes are, by their very nature, unpredictable. All you can do is “smooth” the curve to the new data. An unexpected spike is an unexpected spike is an unexpected spike and you lose millions in sales because of it. Just ask Apple or Nintendo or Sony who seriously underestimated initial demand for a number of their recent products.
Supporter:Look, we both know that no tool will solve all your problems and that spikes only occur in a few categories. For example, for many consumer goods, and many consumables in particular, demand is relatively constant year after year. Some brands or models may sell a little more or less than predicted, but most categories of household goods rise steadily with the population. We’re not going to suddenly start washing our clothes twice as often, start drinking twice as much coffee a day, or buying twice as many CDs. You need to start somewhere, and a tool that’s likely to get most of it mostly right out of the box is a great time saver. Plus, the best way to detect a spike or a drop is to instantly be able to see whether or not demand is following the plan. That’s the true power of these tools.
DetractorMaybe, but if that’s the case, what do you need all the advanced modeling for? Wouldn’t a simple time series with the ability to fully define a forecast by hand be just as good?
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