The Trouble With Turns

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

In our travels around the industrial scene, we notice that many companies pay more attention to inventory Turns than they should. We would like to deflect some of this attention to more consequential performance metrics.

Recall the definition: Turns = Annual dollar cost of goods sold / Average dollar value of inventory. If you sell $1 million of stuff in a year and have an average of $100,000 of stuff on the shelf each day, you are running at an impressive 10 Turns (Walmart runs at around 8). Supposedly, having high Turns signals efficient management, and keeping your Turns higher than competitors’ signals competitive advantage.

But as happens with most performance metrics, there is more to the story. Turns may be very salient to the CFO, but they can be a straightjacket to the COO. This is because Turns are not directly related to customer service; in fact, high Turns can be synonymous with low service levels and fill rates. S&OP consultant Darrin Oliver calls Turns his “pet peeve metric” because “the customer doesn’t care about Turns.”

Suppose you are unhappy with your current Turns value. What can you do to boost the number? Since Turns is a ratio, you can increase it by either increasing the numerator (goods sold) or decreasing the denominator (inventory). Increasing sales is more difficult because it requires the cooperation of the customer. Decreasing inventory is easier because it’s completely under your control: just make smaller replenishment orders, which also saves money in the short run. Indeed, you can get very enthusiastic and cut inventory to the bone. You end up with a better looking number for Turns—and a serious problem with stockouts, backorders, lost sales, lost customer good will and lost market share. Who’s sorry now?

Here’s a lightly edited version of a story on this topic told by a very wise practitioner. “Back in my other life they were all about improving Turns. Why, I have no idea. So I pointed out the risks that you run. And they really weren’t interested. So we took our global inventories down to [a lower level], and then were breaking on stock left and right on a daily basis. Our turns were great, but we weren’t making any money, because we couldn’t get anything out the door, because we didn’t own it. The higher your turns, the lower your inventory’s going to have to be, or you’re just going to have really good flow. And in our industry that’s a very, very difficult thing to achieve. So if we can have reasonable Turns but still be in stock, I think that’s what we want to do. It can be very frustrating in an operations world to try to explain what we do every day and what the risks to the business are when the financial people are just looking at one or two metrics. They’re basically trying to plan the business in a vacuum, and it’s very difficult and very risky to do that.”

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselear Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

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

  • Fifteen questions that reveal how forecasts are computed in your companyFifteen questions that reveal how forecasts are computed in your company
    In a recent LinkedIn post, I detailed four questions that, when answered, will reveal how forecasts are being used in your business. In this article, we’ve listed questions you can ask that will reveal how forecasts are created. […]
  • 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. […]

    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. […]