What is to blame for having too much of the stuff you don’t need and not enough of the stuff you do need?  Demand and supply variability are often blamed.  These problems are significant and seems impossible to overcome leaving many organizations to simply accept misallocated stock as a cost of doing business.  However, the real problem it isn’t simply late supplier deliveries and unpredictable demand.  These are supply chain planning “facts of life” and it’s how your company addresses them that counts.  Watch Greg Hartunian’s vlog to hear his thoughts and what you can do about it.

Smart Inventory Planning and Optimization automatically calculates the stocking policy that yields the best return for your business considering holding costs, ordering costs, and stock outs.  To see it in action, register below to watch a 12 minute demonstration.

With the world once again in the grip of Jedi-mania, we can take a moment to think about what special powers are needed to turn an ordinary inventory professional into an Inventory Optimizer.

There’s a lot to know

Proficiency in the inventory arts requires mastery of a great deal of knowledge: Product knowledge, knowledge of suppliers and customers, teamwork skills, and a visceral understanding of the stochastic dynamics of inventory demand. One of the most fundamental types of knowledge is corporate self-knowledge, especially knowing where you want your organization to go.

How to know it

So much of that knowledge rests on an understanding of operational information: How to gather it, interpret it, and discern its implications. Most of that information is in the form of hard numbers, usually too many to absorb without computer help. Some of it comes from conversations with customers and suppliers, which let you know where they want their organizations to go and how you figure into their plans. Blending large quantities of numbers with knowledge of everybody’s goals and intentions provides situational awareness.

How to use the knowledge

Situational awareness must be translated into detailed operational decisions for every inventory item. For each item, you must decide on an inventory policy: As inventory decreases, at what point should we order more? How much more? How do we respond to stockouts?

In the good old days, these decisions were usually made based on gut instinct. You might say, or hope, that these decisions were guided by The Force. Unfortunately, what the good old days often bequeathed the present day was nothing more than a mish-mash of incoherent and dysfunctional policies. If there is “no try, only do or not do”, then the history of inventory management has seen a lot of not do.

Rather than hoping for mystical inspiration, the way forward is to systematically organize all that information into accurate, comprehensive probability models of inventory dynamics. Such models can relate all the key levers of performance to key performance indicators (KPI’s).

How software analytics can help

Software analytics can relate key drivers of performance to performance metrics. Key drivers include reorder points or min’s, order quantities or max’s, replenishment lead times, and the level and variability of demand. Also important are the costs of holding, ordering and running out of inventory. Using numerical values for these key inputs, inventory software can estimate the corresponding values of service level, fill rate, inventory operating costs and total inventory capital investment. In other words, the software can convert design decisions into consequences.

Now, some decisions might be a bit misguided, with consequences that are not appealing. Then the software becomes not just an analysis tool but a design tool. That is, it lets you play around a bit, exploring different decisions and hunting for system designs that yield better results.

This is where reliance on The Force reappears in practice, because you are left using the software to help you intuit your way to good system designs. We call this “hunt and peck optimization”. It amounts to a guessing game in which you try changing one or more of the drivers to see whether the KPI’s get better or worse.

The most advanced inventory software can take you to the next level. It is inventory optimization software. It eliminates the guesswork by automating the search through the very large “design space” to find desirable system designs for all your items.

For instance, you might ask the software to find that combination of reorder point and order quantity that minimizes the total cost of managing an item (i.e., the sum of holding, ordering and shortage costs) while insuring that the chance of a stockout is tolerably low. Even if your Jedi powers would eventually lead you to the same design, do you really want to whack your way through all 20,000 items you are managing? Let R2D2 figure it all out: That’s what droids are for.

Why we still need our light sabers

Despite all the productivity gains by inventory optimization software, you may still feel the need to take light saber in hand and finish off the design of inventory policy for selected items. You may want to do this for several reasons.

One is to see how sensitive the optimal design is to slight changes. For example, the most efficient designs might require more orders than your purchasing department can comfortably handle in one year. So you might want to see how much performance deteriorates if you make a practical concession and specify a larger order quantity.

There are key differences between this kind of “post-optimality” analysis and the old-fashioned hand-crafting of individual inventory policies. For one, the starting point is a very smart design, not a guess. For another, you can pick and choose the items that get your personal attention, assured that all the rest are well provided for.

We are often asked what the difference is between these two important performance metrics for inventory planning. While they are both important for measuring how successful a business is in meeting demand, their meaning is very different.  If not understood and incorporated into the strategic inventory planning process, inventory will be inefficiently allocated resulting in lower customer service and higher carrying costs.  We’ve illustrated the difference in this 4 minute recording using Microsoft Excel.

Smart Operational Analytics automatically calculates historical service levels & fill rates across any item.  To see how you calculate these and other operational metrics including inventory turns, supplier performance, and more register below to watch a five minute demonstration.  The demo will show how our cloud platform continuously calculates and reports these metrics across thousands of items helping you identify opportunities for service level improvement and inventory reduction.

Demand planning takes time and effort. It’s worth the effort to the extent that it actually helps you make what you need when you need it.

But the job can be done well or poorly. We see many manufacturers stopping at the first level when they could easily go to the second level. And with a little more effort, they could go all the way to the third level, utilizing probabilistic modeling to convert demand planning results into an inventory optimization process.

The First Level

The first level is making a demand forecast using statistical methods. Figure 1 shows a first level effort: an item’s demand history (red line) and its expected 12-month forecast (green line).

The forecast is bare bones. It only projects expected demand ignoring that demand is volatile and will inevitably create forecast error. (This is another example of an important maxim: “The Average is Not the Answer”). The forecast is as likely to be too high as it is to be too low, and there is no indication of forecast uncertainty accompanying the forecast. This means the planner has no estimate of the risk associated with committing to the forecast. Still, this forecast does provide a rational basis for production planning, personal scheduling, and raw materials purchase. So, it’s much better than guessing.

The Second Level

The second level takes explicit account of forecast uncertainty. Figure 2 shows a second level effort, known as a “percentile forecast”.

Now we see an explicit indication of forecast uncertainty. The cyan line above the green forecast line represents the projected 90th percentile of monthly demand. That is, the demand in each future month has a 90% chance of falling at or below the cyan line. Put another way, there is a 10% chance of demand exceeding the cyan line in each month.

This analysis is much more useful because it supports risk management. If it is important to assure sufficient supply of this item, then it makes sense to produce to the 90th percentile instead of to the expected forecast. After all, it’s a coin flip as to whether the expected forecast will result in enough production to meet monthly demand. This second level forecast is, in effect, a rough substitute for a careful inventory management process.

Figure 2. A percentile forecast, where the cyan line estimates the 90th percentiles of monthly demand.

Going All the Way to the Third Level

Best practice is the Third Level, which uses demand planning as a foundation for completing a second task: explicit inventory optimization. Figure 3 shows the fundamental plot for the efficient management of our finished good, assuming it has a 1 month production lead time.

Figure 3 shows the utilization of probabilistic forecasting and how much draw-down in finished good inventory might take place over a one month production lead time. The uncertainty in demand is apparent in the spread of the possible demand, from a low of 0 to a high of 35, with 15 units being the most likely value. The vertical red line at 22 indicates the “reorder point“ (or “min” or “trigger value”) corresponding to keeping the chance of stocking out while waiting for replenishment to a low 5%. When inventory drops to 22 or below, it is time to order more. The Third Level uses probabilistic demand forecasting with full exposure of forecast uncertainty to efficiently manage the stock of the finished product.

To Sum Up

Forecasting the most likely demand for an item is a useful first step. It gets you halfway to where you want to be. But it provides an incomplete guide to planning because it ignores demand volatility and the forecast uncertainty that it creates. Adding a cushion to the demand forecast gets you further along, because it lessen the risk that a jump in demand will leave you short of product. This cushion can be calculated by probabilistic forecasting approaches that forecasts a high percentile of the distribution of future demand. And if you want to take one step further, you can feed forecasts of the demand distribution over a lead time to calculate reorder points (mins) to ensure that you have an acceptably low level of stock-out risk.

Given what modern forecasting technology can do for you, why would you want to stop halfway to your goal?