Extend Microsoft 365 BC and NAV with Smart IP&O

Microsoft Dynamics 365 BC and NAV can manage replenishment by suggesting what to order and when via reorder point-based inventory policies. The problem is that the ERP system requires that the user manually specify these reorder points and/or forecasts. As a result, most organizations end up forecasting and generating inventory policies by hand in Excel spreadsheets or using other ad hoc approaches. Given poor inputs, automatic order suggestions will be inaccurate, and in turn the organization will end up with excess inventory, unnecessary shortages, and a general mistrust of their software systems.  In this article, we will review the inventory ordering functionality in BC & NAV, explain its limitations, and summarize how Smart Inventory Planning & Optimization can help reduce inventory, minimize stockouts and restore your organization’s trust in your ERP by providing the robust predictive functionality that is missing in Dynamics 365.

 

Microsoft Dynamics 365 BC and NAV Replenishment Policies

In the inventory management module of NAV and BC, users can manually enter planning parameters for every stock item. These parameters include reorder points, safety stock lead times, safety stock quantities, reorder cycles, and order modifiers such as supplier imposed minimum and maximum order quantities and order multiples.  Once entered, the ERP system will reconcile incoming supply, current on hand, outgoing demand, and the user defined forecasts and stocking policies to net out the supply plan or order schedule (i.e., what to order and when).

 

There are 4 replenishment policy choices in NAV & BC:  Fixed Reorder Quantity, Maximum Quantity, Lot-For-Lot and Order.

  • Fixed Reorder Quantity and Max are reorder point-based replenishment methods. Both suggest orders when on hand inventory hits the reorder point.  With fixed ROQ, the order size is specified and will not vary until changed.  With Max, order sizes will vary based on stock position at time of order with orders being placed up to the Max.
  • Lot-for Lot is a forecasted based replenishment method that pools total demand forecasted over a user defined time frame (the “lot accumulation period”) and generates an order suggestion totaling the forecasted quantity. So, if your total forecasted demand is 100 units per month and the lot accumulation period is 3 months, then your order suggestion would equal 300 units.
  • Order is a make to order based replenishment method. It doesn’t utilize reorder points or forecasts. Think of it as a “sell one, buy one” logic that only places orders after demand is entered.

 

Limitations

Every one of BC and NAVs replenishment settings must be entered manually or imported from external sources.  There simply isn’t any way for users to natively generate any inputs (especially not optimal ones). The lack of credible functionality for forecasting and inventory optimization within the ERP system is why so many NAV and BC users are forced to rely on spreadsheets.  Planners must manually set demand forecasts and reordering parameters.  They often rely on user defined rule of thumb methods or outdated and overly simplified statistical models.  Once calculated, they must input the information back into their system, often via cumbersome file imports or even manual entry.  Companies infrequently compute their policies because it is time consuming and error prone. We have even encountered situations where the reorder points haven’t been updated in years. Many organizations also tend to employ a reactive “set it and forget it” approach, where the only time a buyer/planner reviews inventory policy is at the time of order–after the order point is already breached.

 

If the order point is deemed too high, it requires manual interrogation to review history, calculate forecasts, assess buffer positions, and to recalibrate.  Most of the time, the sheer magnitude of orders means that buyers will just release it creating significant excess stock.  And if the reorder point is too low, well, it’s already too late. An expedite is required to avoid a stockout and if you can’t expedite, you’ll lose sales.

 

Get Smarter

Wouldn’t it be better to simply leverage a best of breed add-on for demand planning and inventory optimization that has an API based bidirectional integration? This way, you could automatically recalibrate policies every single planning cycle using field proven, cutting edge statistical models.  You would be able to calculate demand forecasts that account for seasonality, trend, and cyclical patterns.  Safety stocks would account for demand and supply variability, business conditions, and priorities.  You’d be able to target specific service levels so you have just enough stock.  You could even leverage optimization methods that prescribe the most profitable stocking policies and service levels that consider the real costs of carrying inventory. With a few mouse-clicks you could update NAV and BC’s replenishment policies on-demand. This means better order execution in NAV and BC, maximizing your existing investment in your ERP system.

 

Smart IP&O customers routinely helps customers realize 7 figure annual returns from reduced expedites, increased sales, and less excess stock, all the while gaining a competitive edge by differentiating themselves on improved customer service.

 

To see a recording of the Dynamics Communities Webinar showcasing Smart IP&O, register here:

https://smartcorp.com/inventory-planning-with-microsoft-dynamics-nav/

 

 

 

Inventory Planning Becomes More Interesting

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

Taiichi Ohno of Toyota is credited with inventing Just-In-Time (JIT) manufacturing in the 1950s. JIT ensures that a manufacturer produces only what is needed, only when required, and only in the necessary amount. That innovation has since had major impacts, some good, some less so.

A recent New York Times article “How the World Ran out of Everything” describes some of the “less so” impacts.  For example, JIT has kept inventory costs very low improving return on assets.  This in turn is rewarded by Wall Street, so many companies have spent the last few decades reducing their inventories dramatically. Focused as they were on financials, many companies ignored the risks inherent in reducing inventories to the point that “lean” began to border on “emaciated.” Combined with increased globalization and new risks of supply interruption, stock-outs have abounded.

Some industries have gone too far, leaving them exposed to disruption. In a competition to get to the lowest cost, companies have inadvertently concentrated their risk, been interrupted by shortages of raw materials or components, and sometimes forced to halt assembly lines. Wall Street does not look kindly on production halts.

We all know that random events have added to the problem. First among them has been the Covid pandemic. As the pandemic has hindered factory operations and spread disarray in global shipping, many economies worldwide have been tormented by shortages of an immense range of goods — from computer chips to lumber to clothing.

The damage is compounded when more unexpected things go wrong. The Suez Canal Blockage is a prime example, obstructing the main trade route between Europe and Asia. Recently, cyberattacks have added another layer of disruption.

The reaction creates its own problems, just as the cyberattack on the Colonial Pipeline created gas shortages through panic buying. Suppliers start filling orders more slowly than usual. Manufacturers and distributors reverse course and increase inventories and diversify their suppliers to avoid future stockouts. Simply expanding warehouses may not deliver the solution, and the need to determine how much inventory to keep is more urgent every day.Manager In Warehouse With Inventory Management Software

So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.

On the supply side, exploit the data you have on recent supplier lead times, which reflect the current turbulence. Don’t use average values when you can use probability distributions that reflect the full range of contingencies. Consider this comparison. Supplier A is now reliably filling orders in exactly 10 days. Supplier B also averages 10 days but does with a 78%/22% mix of 7 and 21 days. Both A and B have an average replenishment delay of 10 days, but the operational results they provide will be very different. You can only recognize this if you use probability models of inventory performance.

On the demand side, similar considerations apply. First, recognize that there may have been a major shift in the character of item demand (statisticians call this a “regime change”), so purge from your analysis any data that represent the “good old days.” Then, again, stop thinking in terms of averages. While the average demand is important, it is not a sufficient descriptor of the problem you face. Equally important is the volatility of demand. Volatility is the reason you keep inventory in the first place. If demand were completely predictable, you would have neither stockouts nor excess inventory. Just as you need to estimate the full probability distribution of replenishment lead times, you need the full distribution of demand values.

Once you understand the range of variability in both supply and demand, probabilistic forecasting will allow you to account for disruptions and unusual events. Software will convert your data on demand and lead times into huge numbers of scenarios representing how your next planning period might play out. Given those scenarios, the software can determine how best to meet your goals for such metrics as inventory costs and stockout rates. Using solutions such as Smart Inventory Optimization , you will confidently plan based on your targeted stockout risk with minimal inventory carrying cost. You may also consider letting the solution prescribe optimal service level targets by assessing the costs of additional inventory vs. stockout cost.

In inventory planning, as in science, we cannot escape the reality of uncertainty and the impact of unusual events. We must plan accordingly: using inventory optimization software helps you identify the least-cost service level. This creates a coherent, company-wide effort that combines visibility into current operations with mathematically correct assessments of future risks and conditions.

Inventory planning has become more “interesting” and requires a greater degree of risk awareness and agility. The right software can help.

 

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        Redefine Exceptions and Fine Tune Planning to Address Uncertainty

        The Smart Forecaster

         Pursuing best practices in demand planning,

        forecasting and inventory optimization

        Inventory Planning from the Perspective of a Physicist

        In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. If you can exactly predict what you need and where you need it and your suppliers can get what you need without delay, then you do not need to maintain much inventory locally.  But as the saying goes from famous pugilist Mike Tyson, “everyone has a plan until they get punched in the mouth.” And the latest punch in the mouth for the global supply chain was last week’s Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute[1].  Disruptions from these and similar events should be modeled and accounted for in your planning.

        The assumption that you can exactly predict the future was apparent in Isaac Newton’s laws. Since the 1920’s with the introduction of quantum physics, uncertainty became fundamental to our understanding of nature. Uncertainty is built into fundamental reality.  So too should it be built into Supply and Demand Planning processes.  Yet too often, black swan events such as the Suez Canal blockage are often thought of as anomalies and as a result, discounted when planning. It is not enough to look back in hindsight and proclaim that it should have been expected. Something needs to be done about addressing the occurrence of other such events in the future and planning stocking levels accordingly.

        We must move beyond the “thin tailed distribution” thinking where extreme outcomes are discounted and plan for “fat tails.”  So how do we execute a real-world JIT plan when it comes to planning inventory? To do this, the first step is to estimate the realistic lead time to obtain an item. However, estimation is difficult due to lead time uncertainty.  Using actual supplier lead times in your company database and external data, you can develop a distribution of possible future lead times and demands within those lead times. Probabilistic forecasting will allow you to account for disruptions and unusual events by not limiting your estimates to what has been observed solely on your own short-term demand and lead time data.  You’ll be able to generate possible outcomes with associated probabilities for each occurrence.

        Once you have an estimate of the lead time and demand distribution, you can then specify the service level you need to have for that part. Using solutions such as Smart Inventory Optimization (SIO), you will be able confidently stock based on the targeted stock-out risk with minimal inventory carrying cost. You may also consider letting the solution prescribe optimal service level targets by assessing the costs of additional inventory vs. cost of stockout.

        Finally, as I have already noted, we need to accept that we can never eliminate all uncertainty. As a physicist, I have always been intrigued by the fact that, even at the most basic levels of reality as we understand it today, there is still uncertainty. Albert Einstein believed in certainty (determinism) in physical law.  If he were an inventory manager, he might have argued for JIT because he believed physical laws should allow perfect predictability. He famously said, “God does not play with dice.”  Or could it be possible that the universe we exist in was a “black swan” event in a prior “multi-verse” that produced a particular kind of universe that allowed us to exist.

        In inventory planning, as in science, we cannot escape the reality of uncertainty and the impact of unusual events.  We must plan accordingly.

         

        [1] https://www.bbc.com/news/business-56559073#:~:text=Looking%20at%20the%20bigger%20picture,0.2%20to%200.4%20percentage%20points.

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              Engineering to Order at Kratos Space – Making Parts Availability a Strategic Advantage

              Introduction

              The Kratos Space group within National Security technology innovator Kratos Defense & Security Solutions, Inc., produces COTS software and component products for space communications, tailored products for individual customers, as well as complete satellite and terrestrial ground segment solutions.  Theirs is a highly demanding market often requiring engineered-to-order systems with exceptional performance and rapid delivery cycles.  Kirk Smith, Vice President of Business Systems Innovation, sat down with us to explain how parts management and planning has become central to their operational excellence, supporting numerous custom projects per year.

              The Challenge:  

              Engineering-to-order in Kratos’ world means that traditional finished goods forecasting won’t help you plan for the future.  In the tailored marketplace, the past does not provide a usable forecast for the future, even within the Space group’s focused technology areas. You just don’t know ahead of time everything your next tailored system customer is going to request.  This is problematic for the company’s contract manufacturers (CMs) that produce key lower level assemblies – they can’t know what to expect, and without some advice will have no ability to pre-order and stock requisite component parts.  Short forecast horizons and long component lead times makes competitive bidding for new projects difficult, where time to delivery is crucial.

               

              Leveraging a competitive advantage

              “With tailored and custom solutions, the Number 1 reason we win is that we solve very challenging problems for our customers,” says Smith.  But a close second is a strategic advantage – the ability to deliver those tailored systems quickly.  Kratos has an array of previously designed and engineered building blocks (chassis and board level assemblies) that can be applied to newly designed solutions.  This speeds design, but because these building blocks are tailored for each customer, stocking them for future sales is problematic – there are many variants.  If Kratos could find a way to effectively forecast their board and component level requirements, they would be able to reduce end-to-end production time, minimize part shortages that delay delivery, and prevent excesses that create obsolete inventory.

               

              The Solution: 

              Kratos pursued a hybrid planning approach, combining sales planning by its business development team with statistical forecasting from Smart Software.  Smith explained the process:

              Part 1 – Annual forecast at the CM built assembly level:

              • Use Smart to produce a rolling 12-month assembly level forecast for the CM.
              • Compare this with the Business Development Opportunity Forecast
              • Merge the insights from Smart with the Opportunity Forecast
              • Provide resulting adjusted assembly forecast to the CM for revenue and capacity planning.

              Part 2 – Provide component level forecasts to Contract Manufacturers:

              • Feed assembly level forecast into the ERP Bill of Material function, exploding component level demand for all parts.
              • Aggregating demand by part number, generate component level forecasts.
              • Provide forecasts to CM procurement to enable them to determine when to buy ahead or increase orders to capture volume price breaks. When they see an opportunity, they contact Kratos, get permission, and increase buys – with the effect of driving down material cost and lead times.
              • Also, providing annual forecasts reduces buy-back pressure from the CMs – Kratos is obligated to buy back unused components, but now the CMs can see opportunity at the component level and the value of retaining stocks.

               

              Results: 

              Over the past three years this approach has allowed Kratos to reduce material cost. Moreover, Kratos is able to work with its Contract Manufacturers to reduce stockout risk and achieve shorter delivery commitments.  While dealing with components with up to six month lead times, they are able to confidently propose and achieve customer delivery dates.

              Jon Good, General Manager at contract manufacturer NeoTech, shared their experience.  “We use the Smart forecast provided by Kratos’ Space group to assist in taking advantage of price breaks on material at higher quantities that wouldn’t otherwise be visible in our current business model.  This enables us to reduce material cost which translates into reduced pricing to Kratos in the long run.”

              Good added that another use is to predict probable material consumption over a longer period of time than would be visible only on open orders.  “This enables us to more realistically understand our inventory on hand position in terms of excess.  These two benefits allow NEOTech to make smarter decisions related to purchasing and inventory management while at the same time saving days and weeks in the front end of the process and delivering the end product to Kratos as rapidly as possible.”

              Looking forward, Smith sees even greater opportunity to team with Kratos Space CMs to streamline their supply chain and associated costs.  “The bottom line,” says Smith, “is that we are now able to more effectively communicate with our CM partners, despite the lack of forecastability in our business, and simultaneously reduce material cost and shorten lead times.”

               

               

               

              Ten Tips that Avoid Data Problems in Software Implementation

              The Smart Forecaster

               Pursuing best practices in demand planning,

              forecasting and inventory optimization

              We work with many customers in many industries to connect our advanced analytical, forecasting, and inventory planning software to their ERP systems. Despite the variety of situations we encounter, some data-related problems tend to crop up over and over. This blog lists ten tips that can help you avoid these common problems.

               

              Once a customer is ready to implement software for demand planning and/or inventory optimization, they need to connect the analytics software to their corporate data stream. In our case, we mainline transaction data directly into the analytical software. This provides information on item demand and supplier lead times, among other things. We extract the rest of the data from the ERP system itself, which provides metadata such as each item’s location, unit cost, and product group.

               

              These tips are important because it is not uncommon for implementation projects to start with great enthusiasm but then quickly bog down because of problems with the data that fuel for analytics. These delays can reduce team enthusiasm, embarrass project leaders, and delay (and thereby reduce) the ROI payoff that ultimately justified the implementation project in the first place.

              demand planning data stream.

              The importance of connecting the analytics software to the corporate data stream

              Here is the list of tips, grouped by the general themes of handling files safely, insuring data integrity, and dealing with exceptions.

               

              Handling Files Safely

               

              1. Have a test environment to use as a “sandbox.” Copy your current data to a test environment where you can safely experiment with the software without risking current operations. Besides helping users learn the ins-and-outs of the new software, having the latest data in the software allows end users to discover any problems with the data.

               

              1. Protect your data extraction rules. If you aren’t utilizing a pre-built connector to your ERP system then you to need to ensure that you can create savable extract rules to move data from your ERP to a file.  Column orders, data types, date formats, etc. should not vary each time the same extract is re-executed.  Otherwise the project gets bogged down in manual errors or confusion in re-extracts after fixes to the data or when new data roll in. All data extraction rules should be saved and available to IT – we’ve encountered situations where files extracted were done so in ad hoc manner resulting in a slightly different formats with each new extract.  We’ve also seen customers work hard to develop a complex and accurate data extraction routine only to find all their work was lost when it was not properly archived.  Both situations led to confusion and project delays.

               

              1. Don’t use Excel native file formats for data transfers. If your planning solution doesn’t have a direct integration to your ERP system, then export ERP data to a flat file format, such as comma delimited (.csv) or tab delimited text files.  Don’t use MS Excel formats such as .xls or .xlsx as the export file type because Excel auto-reformats field values in unexpected ways. Many users assume they need to use .xlsx files if they want to manually review them, not realizing that .csv or .txt files can be opened just as easily and don’t carry the risk of auto-reformats.

               

              Insuring Data Integrity

              Data Problems and solutions in Software Implementation

              Data Problems and solutions in Software Implementation. Here is the list of tips, grouped by the general themes of handling files safely, insuring data integrity, and dealing with exceptions.

              1. Confirm the accuracy of your catalog data. Export your catalog data (i.e., list of products, list of customers, list of suppliers) and all their relevant attributes.  Check for wrong or suspicious values in the attributes (especially item lead times and costs).  Problematic values include blanks, zeros when you don’t expect zero as a data value, and text strings when you expect numeric values (or vice versa).  It can help to open each extract file in Excel and filter on each attribute field, looking at the unique values to see what jumps out as not like the others (e.g., “1”, “2”, “&&”, “3”…).

               

              1. Confirm the accuracy of your grouping data. Another useful activity that can be done while viewing the product catalog data in Excel is to check major grouping/filtering fields like product family, category or class to make sure no products are assigned to the wrong category, class, or family.  Likewise check any product status/product lifecycle fields, e.g., make sure that you have correctly identified all discontinued products.

               

              1. Check for spurious control characters within text fields. Check that there are no unusual characters extracted in your product descriptions, such as carriage returns or tabs within the description value itself.  If so, make sure you can extract that data using double quote enclosures around the description or else fix data entry errors in the ERP system directly.

               

              1. Verify that data have a standard layout. Check that your extracts of transactional data (e.g., customer orders, customer shipments, purchase orders, supplier receipts) contain no duplicate rows.  If they do, either identify what fields need to be added to make the rows distinct or, if they are truly duplicates, remove the extra copies in the ERP database.

               

              Dealing with Exceptions

               

              1. Detect and react to exceptions. Identify any attributes of transactional data that would mean they should not be used, such as cancelled orders.  Understand the process around mistakenly entered orders or cancelled orders to ensure against counting, or double counting, these types of transactions.  Watch for other data attributes that would imply that attribute should not be used, such as drop shipping to the customer directly from a supplier rather than shipping it from your own company. 

               

              1. Codify the handling of exceptional internal transfers. Define the idealized record of emergency internal stock transfers and then provide rules to edit any transactions done on an emergency basis that vary from the ideal pattern.  For example, if product P1 is supposed to be shipped out of location A, but there was an emergency shipment out of location B, the demand history for P1 at location A is hijacked and less than it should have been.  If possible, provide a rule on the preferred shipping location for each product so that the history can be corrected by the inventory optimization software for forecasting purposes.

               

              1. Devise a procedure to handle supersession. Supersessions arise, for instance, when adopting a new ERP which re-indexes the products, or an old product is replaced by an updated version, or an entirely new product obsoletes and old one. If product identifiers changed within the past few years for any reason, identify a mapping from the old product ID to the new.  These rules should be available to the demand planning and forecasting system and editable within the application.

               

              Failure to anticipate data problems is a major impediment to smooth implementation of new analytical software. No list can enumerate all the odd things that can go wrong in curating data, but this one highlights common problems and sensible responses.

               

              Note: For more on how data problems can stymie the application of advanced analytical  software, see Sean Snapp’s excellent blog on how this issue is obstructing the application of artificial intelligence and machine learning.  https://www.brightworkresearch.com/demandplanning/2019/05/how-many-ai-projects-will-fail-due-to-a-lack-of-data/

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                    MAX-MIN OR ROP – ROQ

                    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.

                     

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