The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

MAX-MIN OR ROP – ROQ

by Philip Slater

This guest blog is authored by Philip Slater, Founder of SparePartsKNowHow.com the leading educational resource for spare parts management. Mr. Slater is a global leader and consultant in materials management and specifically, engineering spare parts inventory management and optimization. In 2012 Philip was honored with a national Leadership in Logistics Education Award. To view the original blog post, click here.

There are essentially two ways that companies express their inventory control settings: either as MAX- MIN (sometimes MIN-MAX) or ROP-ROQ.

Some people will say that it doesn’t really matter which you use, just as long as you understand the definitions and the pros and cons. However, in my experience it does matter and this is one aspect of spare parts inventory management that you really do need to get right.

Let’s Start With the Definitions for MIN, MAX, ROP & ROQ

 

MIN = short for minimum

There is, confusingly, two schools of thought about what is meant by the MIN. Most typically this is the point at which the need to order more stock is triggered. Sometimes, however, the MIN is seen as the minimum quantity that can be safely held to cover expected needs. In this case the need to order more stock is set so that the reorder point is one less than the MIN value. That is. MIN -1.

The key to managing when using a MIN setting is to understand the configuration of the computer system you use, as different definitions will change the resulting holding level, the re-order point, and perhaps even the actual safety or buffer stock.

MAX = short for maximum

This value is most typically the targeted maximum holdings of the item. Usually, in a MAX- MIN system, where the MIN is the reorder point, the quantity reordered after reaching the MIN is the quantity required to get back to the MAX. For example, if the MAX- MIN is 5-2, when the quantity in the storeroom reaches 2, procurement would need to order 3 to get back to the MAX.

ROP = Reorder Point

As the name suggests, quite simply, this is the stock level at which the need to reorder is triggered. This is calculated by determining the safety stock level and the stock required to service needs during the reorder lead time.

ROQ = Reorder Quantity

Again, as the name suggest, this is the quantity to be reordered when the ROP is reached. This is not the EOQ but rather the quantity that both makes economic sense and is commercially available.

MAX-MIN OR ROP – ROQThe Differences are Meaningful and Important

It is essential that every inventory manager understands that the MAX- MIN and ROP-ROQ approaches are not simply interchangeable.

For example, in general terms:

MIN can be equated with the ROP, except if you have a system set up for reordering at a point of MIN-1. In that case, there is no equivalence.

For slow moving items the MAX can in some circumstances be equal to the ROP + ROQ. This is because for slow moving items it is possible that there will be no additional demand before the newly ordered item(s) arrive in stock.

However, with all other items the MAX is UNLIKELY to be equal to the ROP + ROQ as items may be issued between the time of reaching the MIN and the newly ordered items arriving. In fact, there is a logic that says that the MAX would never actually be achieved.

Do these differences matter? I think that they do.

For example, what if you change IT systems? If you move from one type of MAX-MIN system to another but they define the MIN differently then you cannot just migrate your data. This may not seem obvious if everyone is using the language of MAX-MIN but is classic trap where words are used in different ways.

Similarly, if you are benchmarking your holding levels with another company or site then you need to be aware of the different definitions and the outcomes that each approach would achieve. Otherwise you are comparing ‘apples with pears’.

Or what about what happens when a new team members arrives at your company and their previous company used the terms MAX-MIN but with different parameters or meaning to that your company uses. There will likely be an assumption that the terms are used in the same way and this could lead to stock shortages or overstocks, depending on the differences in the definitions.

To add further confusion, some software systems use the term ‘Safety Stock’ to represent the MIN holding level, despite this not being the universal definition of safety stock. This different nomenclature leads some people to assume that holding less than the so-called ‘safety stock’ according to your IT system is ‘unsafe’ or risky, when in fact it may not be at all. They may even be holding an excessive level of stock because they don’t properly apply the term ‘safety stock’. Calling it safety stock does not make it so.

Pros and Cons

MAX-MIN

Pros:

• Conceptually simple to understand.

Cons:

• Terms can be misleading in terms of safety stock and actual maximums.

• Terms are used in different ways and so caution required to ensure a common understanding.

• Values often set using ‘experience’ or intuition.

• Often leads to overstocking while reporting misleading overstock data

ROP-ROQ

Pros:

• Meaning of each term is clear and consistent.

• Values set using auditable logic.

• Safety stock values clearly established.

• Holdings more likely to reflect the actual needs and commercial constraints.

Cons:

• Requires more work to determine the appropriate values.

You Need to Get This Right

The differences between MAX-MIN and ROP-ROQ are not trivial and the terms certainly are not interchangeable. In my experience, the ROP-ROQ approach produces greater transparency and is easier to manage because there is no confusion about the meaning of the terms. This approach also produces a more appropriate and auditable level of inventory.

This suggests that if spare parts inventory management is important to you then you really do need to get this right.

 

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. […]