The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items.

In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock.

In the meantime, however, your company’s accumulated inventory control policies keep on doing what they do, even if they are leaking money. A good manager will make time to listen to the “background noise” even when he or she hears loud crashing in the warehouse.

Consider the current settings for your inventory control parameters (e.g., reorder points and order quantities). It’s easy to think of these as “fire and forget” decisions. But these settings usually accumulate over time and end up comprising a mish-mash of forgotten judgement calls that may be misaligned with your current operating environment. Many factors can drift away from their previous levels, such as supplier lead times, ordering costs, or average item demand. These changes can force invisible tradeoffs that are not to your best advantage.

It’s wise to revisit these control settings now and then to see if it’s possible to align your day-to-day operations with current realities. Of course, it would be infeasible for a busy manager to manually calculate the effects of changing the control settings on, say, 10,000 items. But that’s what modern inventory optimization and demand planning software is for: making large scale analytical tasks feasible. Such software will allow you to automatically process new information and compute adjustments at scale. The result will be easy wins – many of which would otherwise go unrealized.  And continuously saving a little here and there adds up to significant dollars when you are managing thousands of items.

Consider this example. Company A uses a periodic review inventory system. Every 30 days, they check on-hand inventory for all their items and decide how much replenishment stock to order. Each of their 10,000 items has a specified Order-Up-To Level that determines the size of their replenishment orders.

For instance, suppose Item 1234 has an Order-Up-To Level of 74, determined by factoring in the average item demand of 1.0 units per day, an average replenishment lead time of 8 days, and a target fill rate of 90% for this item. The choice of 74 as the Order-Up-To Level lets Company A meet its 90% fill rate target for Item 1234, but it also results in an average on hand inventory level of 40 units. At $1,500 per unit, this item alone represents $45,000 of inventory investment.

Now supposed that average item demand were to drift up from 1.0 to 1.2 units/day. Without anyone noticing, the fill rate for Item 1234 would drop to 82%!

Now suppose demand were to shift in the other direction and drift down to 0.8 units/day. As with the increase in average demand from 1.0 to 1.2 units/day, this kind of change is difficult to see when looking at a plot (see Figure 1) but can have a significant operational impact. In this case, the fill rate would zoom to a generous 96% but on hand inventory would also zoom: from 40 units to 46. Those six extra units would represent $9,000 in excess inventory.

Figure 1: Samples of daily demand with two different average values.  The difference in demand is unnoticeable to the naked eye but if not accounted for will have a large operational impact on inventory spend and service levels

Now imagine similar small shifts happening unnoticed across a full fleet of 10,000 inventory items. The total financial impact of all such shifts would be sufficient to get onto the radar of any CFO.  Trying to keep on top of this turbulence would be impossible if done manually but modern inventory optimization software could calculate the proper adjustments automatically as frequently as your company can handle, even daily helping you realize substantial improvements in service levels, inventory efficiency, while lowering stockout and holding costs!

 

Leave a Comment

Related Posts

Do your statistical forecasts suffer from the wiggle effect?

Do your statistical forecasts suffer from the wiggle effect?

What is the wiggle effect? It’s when your statistical forecast incorrectly predicts the ups and downs observed in your demand history when there really isn’t a pattern. It’s important to make sure your forecasts don’t wiggle unless there is a real pattern. Here is a transcript from a recent customer where this issue was discussed:

How to Handle Statistical Forecasts of Zero

How to Handle Statistical Forecasts of Zero

A statistical forecast of zero can cause lots of confusion for forecasters, especially when the historical demand is non-zero. Sure, it’s obvious that demand is trending downward, but should it trend to zero?

Recent Posts

  • Businessman and businesswoman reading and analysing spreadsheetThe top 3 reasons why your spreadsheet won’t work for optimizing reorder points on spare parts
    We often encounter Excel-based reorder point planning methods. In this post, we’ve detailed an approach that a customer used prior to proceeding with Smart. We describe how their spreadsheet worked, the statistical approaches it relied on, the steps planners went through each planning cycle, and their stated motivations for using (and really liking) this internally developed spreadsheet. […]
  • Style business group in classic business suits with binoculars and telescopes reproduce different forecasting methodsHow to interpret and manipulate forecast results with different forecast methods
    This blog explains how each forecasting model works using time plots of historical and forecast data. It outlines how to go about choosing which model to use. The examples below show the same history, in red, forecasted with each method, in dark green, compared to the Smart-chosen winning method, in light green. […]
  • Factory worker engineer working in factory using tablet computer to check maintenance boiler water pipe in factory.Why Spare Parts Tradeoff Curves are Mission-Critical for Parts Planning
    When managing service parts, you don’t know what will break and when because part failures are random and sudden. As a result, demand patterns are most often extremely intermittent and lack significant trend or seasonal structure. The number of part-by-location combinations is often in the hundreds of thousands, so it’s not feasible to manually review demand for individual parts. Nevertheless, it is much more straightforward to implement a planning and forecasting system to support spare parts planning than you might think. […]
  • What to do when a statistical forecast doesn’t make senseWhat to do when a statistical forecast doesn’t make sense
    Sometimes a statistical forecast just doesn’t make sense. Every forecaster has been there. They may double-check that the data was input correctly or review the model settings but are still left scratching their head over why the forecast looks very unlike the demand history. When the occasional forecast doesn’t make sense, it can erode confidence in the entire statistical forecasting process. […]
  • Portrait of factory worker woman with blue hardhat holds tablet and stand in spare parts workplace area. Concept of confident of working with spare parts planning software.Spare Parts Planning Isn’t as Hard as You Think
    When managing service parts, you don’t know what will break and when because part failures are random and sudden. As a result, demand patterns are most often extremely intermittent and lack significant trend or seasonal structure. The number of part-by-location combinations is often in the hundreds of thousands, so it’s not feasible to manually review demand for individual parts. Nevertheless, it is much more straightforward to implement a planning and forecasting system to support spare parts planning than you might think. […]

    Inventory Optimization for Manufacturers, Distributors, and MRO

    • Businessman and businesswoman reading and analysing spreadsheetThe top 3 reasons why your spreadsheet won’t work for optimizing reorder points on spare parts
      We often encounter Excel-based reorder point planning methods. In this post, we’ve detailed an approach that a customer used prior to proceeding with Smart. We describe how their spreadsheet worked, the statistical approaches it relied on, the steps planners went through each planning cycle, and their stated motivations for using (and really liking) this internally developed spreadsheet. […]
    • Factory worker engineer working in factory using tablet computer to check maintenance boiler water pipe in factory.Why Spare Parts Tradeoff Curves are Mission-Critical for Parts Planning
      When managing service parts, you don’t know what will break and when because part failures are random and sudden. As a result, demand patterns are most often extremely intermittent and lack significant trend or seasonal structure. The number of part-by-location combinations is often in the hundreds of thousands, so it’s not feasible to manually review demand for individual parts. Nevertheless, it is much more straightforward to implement a planning and forecasting system to support spare parts planning than you might think. […]
    • Portrait of factory worker woman with blue hardhat holds tablet and stand in spare parts workplace area. Concept of confident of working with spare parts planning software.Spare Parts Planning Isn’t as Hard as You Think
      When managing service parts, you don’t know what will break and when because part failures are random and sudden. As a result, demand patterns are most often extremely intermittent and lack significant trend or seasonal structure. The number of part-by-location combinations is often in the hundreds of thousands, so it’s not feasible to manually review demand for individual parts. Nevertheless, it is much more straightforward to implement a planning and forecasting system to support spare parts planning than you might think. […]
    • Worker on a automotive spare parts warehouse using inventory planning softwareService-Level-Driven Planning for Service Parts Businesses
      Service-Level-Driven Service Parts Planning is a four-step process that extends beyond simplified forecasting and rule-of-thumb safety stocks. It provides service parts planners with data-driven, risk-adjusted decision support. […]