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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      Backing into Safety Stock is the Safe Play

      The Smart Forecaster

       Pursuing best practices in demand planning,

      forecasting and inventory optimization

      We frequently encounter confusion about the process of setting safety stock levels. This blog hopes to clarify the issue.

      Safety stock is a critical component in any system of inventory management. Indeed, some inventory software treats safety stock as the key decision variable in the quest to balance inventory cost against item availability. Unfortunately, that approach is not the best way to strike the balance.

      First, realize that safety stock is part of a general equation:

      Inventory Target = Average Lead Time Demand + Safety Stock.

      Average Lead Time Demand is defined as the average units demanded multiplied by the average replenishment lead time. Example: If daily demand averages 2 units and the average lead time is 7 days, then the average lead time demand is 2 x 7= 14 units. Keeping 14 units on hand suffices to handle typical demand.

      But we all know that demand is random, so keeping enough stock on hand to cover the average lead time demand invites stockouts. As we like to say, “The average is not the answer.” The smart answer is to add in some safety stock to accommodate any random spikes in demand. But how much?

      There’s the problem. If you try to guesstimate a number for the safety stock, you are on thin ice. How do you know what the “right” number is?  You may think that you don’t have to worry about that because you have a good-enough answer now, but that answer has a sell-by date. Lead times change. So do demand patterns. So do company priorities. That means today’s good answer may become tomorrow’s blunder.

      Some companies try to wing it using a crude rule of thumb approach. For instance, they may say something like “Set safety stock at an additional two weeks of average demand.” This approach is seductive: It only needs simple math, and it is clear.  But for the reasons listed in the previous paragraph, it’s foolish. Better to get a good answer than a convenient answer.

      You need a principled, objective way to answer the question that takes account of the mathematics of randomness.  More than that, you need an answer that is linked to the key performance indicators (KPI’s) of the system: inventory cost and item availability.

      Simple logic gives you some sense of the answer, but it doesn’t provide the number you need. You know that more safety stock increases both cost and availability, while less safety stock decreases both. But without knowing how much those metrics will change if you change the safety stock, you have no way to align the safety stock decision with management’s intent for striking the balance between cost and availability.

      Rather than flying blind, you can back into the choice of safety stock by first finding the right choice for inventory target. Once you’ve done that, the safety stock pops out by a simple subtraction:

       Safety Stock = Inventory Target – Average Lead Time Demand.

      Manager In Warehouse With ClipboardOften times, companies will state that they don’t carry safety stock because the safety stock field in their ERP system is blank. Nearly always, safety stock is built into the targeted inventory level they have established.  So, using the above formula to “back out” how much safety stock you are building into the plan is quite helpful.  The key is not just to know how much safety stock you are carrying but the link between your inventory target, safety stocks, and its corresponding KPI’s.

      For instance, suppose you can tolerate only a 5% chance of stocking out while waiting for replenishment (inventory texts call this interval the “period of risk.”). Software can examine the demand history of each item and work out the odds of stockout based on the thousands of different demand scenarios that can occur during the lead time. Then the right answer for the inventory target is the choice that leads to no more than a 5% stockout risk. Given that target and knowing the average lead time demand, the appropriate safety stock value falls right out by subtraction. You also get to know the average holding, ordering and shortage costs.

      That’s what we mean by “backing into the safety stock.” Start with company objectives, determine the appropriate inventory target, then derive the safety stock as the last step. Don’t start with a guess about safety stock and hope for the best.

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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.”

         

         

         

        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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        An Example of Simulation-Based Multiechelon Inventory Optimization

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          Handling Extreme Supply Chain Variability at Rev-A-Shelf

          The Smart Forecaster

          Pursuing best practices in demand planning,

          forecasting and inventory optimization

          Does your extended supply chain suffer from extreme seasonal variability? Does this situation challenge your ability to meet service level commitments to your customers? I have grappled with this at Rev-A-Shelf, addressing unusual conditions created by Chinese New Year and other global events, and would like to share the experience and a few things I learned along the way.

          First, let me explain our situation. We import 60% of the parts we use to build our kitchen and bath accessories from China and Europe. Most of the year we were able to plan our inventory needs using a spreadsheet-based min/max approach. But not during Chinese New Year, which drives the planet’s greatest annual population migration. Chinese New Year shuts down production for up to two months, creating significant supply risk as we strive to meet our three day order fulfillment commitment.

          We solved our problem, introducing statistical demand forecasting with the flexibility to extend lead times when necessary, the ability to reliably establish safety stocks that achieve our required service levels and a continuous reporting system that lets everyone know exactly where we stand. However, success required much more than a new piece of software. We needed to change the way we view future demand, supply risk and safety stock. Here are a few key things we did that made all the difference.

          Stakeholder education and buy-in

          Regardless of the project, it’s always best to enlist the buy-in of all stakeholders. We knew we had to do something to solve our problem, but there was bound to be resistance. Senior managers, for example, had developed a healthy distrust of software and wondered whether demand forecasting software could help. Our buyers had developed their own perspectives and procurement methods, and felt personally at risk as we considered new approaches.

          People came around as they developed a common understanding of the problem and how we would address it. Education was a big part of the solution. We explained how forecasting works and key factors we should all understand: how to analyze trends, how to use “what if” scenarios, impact of shifting lead times, how to relate service levels to supply risk and safety stock and key performance indicators like inventory turns. Going through this process together, we all became stakeholders in the solution.

          Use the Right software

          When you have lots of part numbers and any sort of supply or demand variability, you just cannot forecast effectively with a spreadsheet. With our min/max forecasting system, we were planning to an average, and it wasn’t working. Average usage has inherent flaws for planning purposes—it’s always looking backward!

          You need software that looks ahead, recognizes seasonal patterns and enables you to determine how much stock you’ll need to meet required service levels over varying lead times.

          Fine-tune processes

          When the old ways don’t work, you need to be open to adjusting your assumptions. Think less about where you’ve been, and more about where you want to be. Take a look at your lead times and plan to your desired service level. Last year’s history may not be the best predictor of this year’s demand. The same forecast horizon may not be appropriate for all products or certain time of the year.

          Make the Forecast Actionable

          It’s not enough to produce an accurate forecast and estimated inventory stocking levels. You’ve got to develop a way to make the information actionable for those tasked with using it. We developed a set of reports that enabled buyers to leverage better forecast and safety stock information. Now, at the end of every month, we produce a forecast report that provides a clear picture of current inventory, safety stock, past usage, forecasted usage, incoming deliveries (PO’s) and recommended order quantities.

          Validate Results

          You can, and we did, test our new methods against our own demand history. Still, an authoritative outsider can make acceptance easier. We commissioned a study by a professor at Louisville University’s College of Business who set one of her graduate students to the task. Through them we were able to reinforce what we saw happening from our results, and feel comfortable that we were on a good path.

          All of these factors helped Rev-A-Shelf transform its demand planning process, to great effect. Today we are exceeding our service level targets, and our fill rate, based on a three day ship cycle, is showing steady improvement, and trending up. Overall, units-in-stock have stayed flat while supporting a 13% increase in sales.

          John Engelhardt is currently Director of Purchasing and Asian Operations for Rev-a-Shelf, LLC in Louisville, KY. He has held a variety of management positions both in private business and public organizations. At Rev-A-Shelf he held the position of International Sales Manager and Director of Sales Support before assuming his current position. He can be reached at johne at rev-a-shelf dot com.

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