The Smart Forecaster
Pursuing best practices in demand planning, forecasting and inventory optimization
The destructive impact of Hurricane Sandy has been both staggering and instructive. Our thoughts and best wishes for rapid recovery go out to all who have suffered personal or economic loss or damage. Now, in Sandy’s aftermath, we find ourselves thinking about accelerating recovery and planning for the next unforeseen event.
Our work with clients in the heavily hit mass transit sector presented a sobering view of damaged infrastructure, heavy equipment, and losses of essential inventory. Those most affected have seen a crush of work as inventory managers take stock of what they have, what they need and procure a mountain of replacement parts and products. This uniquely massive replenishment cycle presents all sorts of opportunities and considerations. For those who are still in this phase, and to help our collective preparation for the Next Big Event, here are a few thoughts:
Opportunity to immediately “right size” inventory
You may be in a position to receive a large, one-time infusion of funding for replacement inventory. It could be insurance money, federal relief or rainy day funds from your own treasury. Use the funding to establish the best possible inventory mix. Do not order to previously established Min/Max levels. Doing so may simply repeat excesses and shortfalls of the past.
A major event like Sandy presents a rare opportunity to transform your inventory. Start with an accurate demand forecast over the replenishment period, and generate safety stocks and reorder points that would address your critical needs. This can be accomplished in a matter of hours or days. Ordinarily, implementing optimal inventory levels may occur over several years, as excess inventory is gradually depleted. Now, however, you have a one-time opportunity to jump to the right answer. This shift can substantially reduce replenishment spending, freeing hundreds of thousands of dollars for other, more critical recovery uses.
Prioritize classes to be replenished
Be clear on what you need for crucial operations, and prioritize your replenishment. Which parts have long lead-times, and which are readily available? Obviously short lead-time items can be acquired in stages—getting just enough now, making funds available for the longer lead-time items.
Determine how much is “just enough”
This is where an accurate demand forecast, safety stocks and reorder point calculations come into play. Consider the service level you require—the likelihood that products will be on the shelves when you need them—which is really your tolerance for risk. Do this for each item, or class of items. This will tell you how much safety stock, in addition to your expected lead time forecast, you should have on hand. Iterating on service level-driven requirements will enable you to maximize the value of the replenishment budget at hand.
Statistical forecasting for intermittent demand vs. ‘rule of thumb’ methods
Now is the time to shift from ‘the way we’ve done it’ to the most accurate demand forecasting and inventory optimization process available to you. Greater forecast accuracy requires less safety stock—again, making inventory dollars available for other users. The greatest single category for improvement is intermittent demand. Most organizations do not apply solid statistical methods to this, instead resorting to the “heavy hammer rule”—have lots on hand because no one knows. Here is an area where SmartForecasts is especially adept, with a patented solution for forecasting intermittent demand. The resulting safety stock recommendations hit the service level goal nearly 100% of the time. Getting this right will save lots of spending now, and help minimize the potential for excess, obsolete inventory in the future.
Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.
The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three helping you to reduce inventory costs, improve on time delivery and service levels, while running a more efficient supply chain.
We just need to feed our demand histories into our new statistical methods, and we can start planning more effectively. Not quite: it’s about the technology and the process. You are investing in a new business process to develop forecasts for driving business strategy and inventory planning decisions.
No, not that kind of regime change: Nothing here about cruise missiles and stealth bombers. And no, we’re not talking about the other kind of regime change that hits closer to home: Shuffling the C-Suite at your company. In this blog, we discuss the relevance of regime change on time series data used for demand planning and forecasting.