Want to Optimize Inventory? Follow These 4 Steps

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Service Level Driven Planning (SLDP) is an approach to inventory planning. It prescribes optimal service level targets continually identifies and communicates trade-offs between service and cost that are at the root of all wise inventory decisions. When an organization understands this relationship, they can communicate where they are at risk, where they are not, and effectively wield their inventory assets.  SLDP helps expose inventory imbalances and enables informed decisions on how best to correct them.  To implement SLDP, you’ll need to look beyond traditional planning approaches such as arbitrary service level targeting (all of my A items should get 99% service level, B items 95%, C items 80%, etc.) and demand forecasting that unrealistically attempts to predict exactly what will happen and when. SLDP unfolds in 4 steps: Benchmark, Collaborate, Plan, and Track.

 

Step 1. Benchmark Performance

 

All participants in the inventory planning and investment process must hold a common understanding of how current policy is performing across an agreed upon set of inventory metrics. Metrics should include historically achieved service levels and fill rates, delivery time to customers, supplier lead time performance, inventory turns, and inventory investment. Once these metrics have been benchmarked and can be reported on daily, the organization will have the information it needs to begin prioritize planning efforts. For example, if inventory has increased but service levels have not, this would indicate that the inventory is not being properly allocated across SKUs.  Reports should be generated within mouse-clicks enabling planners to focus on analysis instead of time intensive report generation.   Past performance isn’t a guarantee of future performance since demand variability, costs, priorities, and lead times are always changing. So SLDP enables predictive benchmarking that estimates what performance is likely to be in the future. Inventory optimization software utilizing probability forecasting can be used to estimate a realistic range of potential demands and replenishment cycles stress testing your planning parameters helping uncover how often and which items to expect stockouts and excess.

 

Step 2. “What if” Planning & Collaboration

 

“What if” inventory modeling and collaboration is at the heart of SLDP. The historical and predictive benchmarks should first be shared with all relevant stakeholders including sales, finance, and operations. Efforts should be placed on answering the following questions:

– Are both the current performance and investment acceptable?
– If not, how should they be improved?
– Which SKUs are likely to be demanded next and in what quantities?
– Where are we willing to take more stock out risk?
– Where must stock-out risk be minimized?
– What are the specific stock out costs?
– What business rules and constraints must we adhere to (customer service level agreements, inventory thresholds, etc.)

Once the above questions are answered, new inventory planning policies can be developed.  Inventory Optimization software can reconcile all costs associated with managing inventory including stockout costs to generate the right set of planning parameters (min/max, safety stock, reorder points, etc.) and prescribed service levels.  The optimal policy can be compared to the current policy and modified based on constraints and business rules. For example, certain items might be targeted at a target service level in order to conform to a customer service level agreement.   Various “what if” inventory planning scenarios can be developed and shared with key stakeholders. For example, you might model how shorter lead times impacts inventory costs. Once consensus has been achieved and the risks and costs are clearly communicated,  the modified policies can be uploaded to the ERP system to drive inventory replenishment.

 

Step 3. Continually Plan and Manage by Exception

SLDP continually reforecasts optimized planning parameters based on changing demands, lead times, costs, and other factors. This means that service levels and inventory value have the potential to change.  For example, the prescribed service level target of 95% might increase to 99% the next planning period if the stock-out costs on that item increased suddenly. This is also true if opting to arbitrarily target a given service level or fix planning parameters to a specific unit quantity. For example, a target service level of 95% might require $1,000 in inventory today but $2,000 next month if lead times spiked.  Similarly, a reorder point of 10 units might get 95% service today and only 85% service next month in response to increased demand variability. Inventory Optimization software will identify which items are forecasted to have significant changes in service level and/or inventory value and which items aren’t being ordered according to the consensus plan. Exception lists are automatically produced making it easy for you to review these items and decide how to manage them moving forward. Prescriptive Analytics can help identify whether the root cause of the change is a demand anomaly, change in overall demand variability, change in lead time, or change in cost helping you fine tune the policy accordingly.

 

Step 4. Track Ongoing Performance

 

SLDP processes regularly measure historical and current operational performance.   Results must be monitored to ensure that service levels are improving and inventory levels are decreasing when compared to the historical benchmarks determined in Step 1.  Track metrics such as turns, aggregate and item specific service levels, fill rates, out-of-stocks, and supplier lead time performance.  Share results across the organization and identify root causes to operational inefficiencies.  SLDP processes makes performance tracking easy by providing tools that automatically generate the necessary reports rather than placing this burden on planners to manage in Excel. Doing so enables the organization to uncover operational issues impacting performance and provide feedback on what is working and what should be improved.

Conclusion

The SLDP framework is a way to rationalize the inventory planning process and generate a significant economic return. Its organizing principle is that customer service levels and inventory costs associated with the chosen policy should be understood, tracked, and continually refined. Utilizing inventory optimization software helps ensure that you are able to identify the least-cost service level.  This creates a coherent, company-wide effort that combines visibility into current operations with scientific assessments of future risks and conditions. It is realized by a combination of executive vision, staff subject matter expertise, and the power of modern inventory planning and optimization software.

See how Smart Inventory Optimization Supports Service Level Driven Planning and download the product sheet here: https://smartcorp.com/inventory-optimization/

Leave a Comment

Related Posts

Worst Practices in Forecasting

Worst Practices in Forecasting

Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?

The Trouble With Turns

The Trouble With Turns

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.

Managing the Inventory of Promoted Items

Managing the Inventory of Promoted Items

In a previous post, I discussed one of the thornier problems demand planners sometimes face: working with product demand data characterized by what statisticians call skewness—a situation that can necessitate costly inventory investments. This sort of problematic data is found in several different scenarios. In at least one, the combination of intermittent demand and very effective sales promotions, the problem lends itself to an effective solution.

Recent Posts

  • When implementing inventory optimization, don’t swing for the fences when a single will do!
    When the pressure is on to cut inventory and improve performance, you might want to move fast much like a hitter who wants to hit a home run. And in some cases, swinging for the fences might be the recommended approach. More often than not, a progressive approach to inventory optimization is more effective
  • Want to Optimize Inventory? Follow These 4 Steps
    Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.
Managing Demand Variability

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Anybody doing the job knows that managing inventory can be stressful. Common stressors include: Customers with “special” requests, IT departments with other priorities, balky ERP systems running on inaccurate data, raw material shortages, suppliers with long lead times in far-away countries where production often stops for various reasons and more. This note will address one particular and ever-present source of stress: demand variability.

Everybody Has a Forecasting Problem

 

Suppose you manage a large fleet of spare parts. These might be surgical equipment for your hospital, or repair parts for your power station. Your mission is to maximize up time. Your enemy is down time. But because breakdowns hit at random, you are constantly in reactive mode. You might hope for rescue from forecasting technologies. But forecasts are inevitably imperfect to some degree: the element of surprise is always present.  You might wait for Internet of Things (IOT) tech to be deployed on your equipment to monitor and detect impending failures, helping you schedule repairs well in advance. But you know you can’t meter up the thousands of small things that can fail and disable a big thing.

So, you decide to combine forecasting with inventory management and build buffers or safety stock to protect against surprise spikes in demand. Now you have to work out how much safety stock to maintain, knowing that too little means vulnerability and too much means bloat.

Suppose you handle finished goods inventories for a make-to-stock company. Your problem is essentially the same as in managing service parts: You have external customers and uncertain demand. But you may also have additional problems in terms of synchronizing multiple suppliers of components that you assemble into finished goods. The suppliers want you to tell them how much of their stuff to make so you can make your stuff, but you don’t know how much of your own stuff you’ll need to make.

Finally, suppose you handle finished goods in a build-to-order company. You might think that you no longer have a forecasting problem, since you don’t build until you are paid to build. But you do have a forecasting problem. Since your finished goods might be assembled from a mixture of components and sub-assemblies, you have to translate some forecast of finished goods demand to work out a forecast of those components. Otherwise, you will go to make your finished goods and discover that you don’t have a required component and have to wait until you can re-actively assemble everything you need. And your customers might not be willing to wait.

So, everybody has a forecasting problem.

What Makes Forecasting Difficult

 

Forecasting can be quick, easy and dead accurate – as long as the world is simple. If demand for your product is 10 units every week, month after month, you can make very accurate forecasts. But life is not quite like that. If you’re lucky and life is almost like that – maybe weekly demand is more like {10, 9, 10, 8, 12, 10, 10…} — you can still make very accurate forecast and just make minor adjustments around the edges. But if life is as it more often is – maybe weekly demand looks like {0, 0, 7, 0, 0, 0, 23, 0 …} – demand forecasting is difficult indeed. The key distinction is demand variability: it’s the zigging and zagging that creates the pain.

Safety Stock Takes Over Where Forecasting Leaves Off

 

Statistical forecasting methods are an important part of the solution. They let you squeeze as much advantage as possible from the historical patterns of demand your company has recorded for each item. The job of forecasts is to describe what is typical, which provides the base on which to cope with randomness in demand. Statistical forecasting techniques work by finding “big picture” features in demand records, such as trend and seasonality, then projecting those into the future. They all implicitly assume that whatever patterns exist now will persist, so 5% growth will continue, and July demand will always be 20% higher than February demand. To get to that point, statistical forecasting methods use some form of averaging to smother the “noise” in the demand history.

But then the rest of the job falls on inventory management, because the atypical, random component of future demand will still be a hassle in the future. This inevitable level of uncertainty has to be handled by the “shock-absorber” called safety stock.

The same methods that produce forecasts of trend and/or seasonality can be used to estimate the amount of forecast error. This has to be done carefully using a method called “holdout analysis”.  It works like this. Suppose you have 365 observations of daily demand for Item X, which has a replenishment lead time of 10 days. You want to know how many units will be demanded over some future 10-day period. You might input the first 305 days of demand history into the forecasting technique and get forecasts for the next 10 days, days 306-315.

The answer gives you one estimate of the 10-day total demand. Importantly, it also gives you one estimate of the variability around that forecast, i.e., the forecast error, the difference between what actually happened in days 306-315 and what was forecasted. Now you can repeat the process, this time using the first 306 days to forecast the next 10, the first 307 days to forecast the next 10, etc. You end up with 52 honest estimates of the variability of total demand over a 10-day lead time. Suppose 95% of those estimates are less than 28 units. Then 28 units would be a pretty safe safety stock to add to the forecast, since you will run into shortages only 5% of the time.

Modern statistical software does these calculations automatically. It can ease at least one of the chronic headaches of inventory management by helping you cope with demand variability.

Leave a Comment

Related Posts

Worst Practices in Forecasting

Worst Practices in Forecasting

Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?

The Trouble With Turns

The Trouble With Turns

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.

Managing the Inventory of Promoted Items

Managing the Inventory of Promoted Items

In a previous post, I discussed one of the thornier problems demand planners sometimes face: working with product demand data characterized by what statisticians call skewness—a situation that can necessitate costly inventory investments. This sort of problematic data is found in several different scenarios. In at least one, the combination of intermittent demand and very effective sales promotions, the problem lends itself to an effective solution.

Recent Posts

  • When implementing inventory optimization, don’t swing for the fences when a single will do!
    When the pressure is on to cut inventory and improve performance, you might want to move fast much like a hitter who wants to hit a home run. And in some cases, swinging for the fences might be the recommended approach. More often than not, a progressive approach to inventory optimization is more effective
  • Want to Optimize Inventory? Follow These 4 Steps
    Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.
Assessing How Suppliers Influence Your Inventory Costs

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Software for inventory optimization is most often used to crank out the analytical results you need to run your day-to-day business, such as Reorder Points (also known as Mins) and Order Quantities. This specialized software helps you find the sweet spot that balances inventory costs against item availability during routine operations.

Inventory optimization software can also be used to perform “what-if” analyses on scenarios that describe changes from your current operating environment. What-if analysis (also called “sensitivity analysis”) lets you elevate your thinking from the tactical to the strategic. It helps you imagine how you should change your operations to adapt to potential changes in your operating environment. These changes might be negative pressures imposed on you from the outside, or they might result from your own positive actions. In this blog, we provide an example of how to conduct “what-analysis” on lead times and order quantities.  Outputs from the analysis can be used by the business to assess the impact of these changes on inventory costs and service level performance.

How Suppliers Limit Your Freedom of Maneuver

 

Discussing with our customers the data inputs required by inventory optimization software, we noted that suppliers are a prominent influence on their operations. We leave aside for now such important topics as sharing demand forecasts with suppliers and working out responses to supply chain disruptions, such as Hurricane Matthew last year in the southeastern US. Instead, we focus on two more common ways that suppliers influence producers’ inventory costs: replenishment lead times and restrictions on order quantities.

Replenishment lead time is the number of days that elapse between inventory reaching or breaching a reorder point and the appearance of replenishment units in stock. Some portion of lead time is internal to the producer, perhaps due to slow reactions in a purchasing department. The rest of lead time is down to the supplier. In this discussion, we assume that suppliers’ contribution to lead times might be changed, for better or for worse. (But the same results could apply to changes in producers’ contributions to lead times.)

The restrictions on order quantities that we consider are order minima and order multiples. You might want to order 3 units of some item, but the supplier might impose a minimum order size of 6 units, so your 3 unit order would have to become a 6 unit order. Or you might want to order 21 units, handily exceeding the minimum order size of 6 units, but if the supplier also has an order multiple of 6, meaning every order must be a multiple of 6 units, then your 21 unit order would have to be increased to 24 units.

Scenario Analyses

 

To illustrate the use of inventory optimization software for what-if analysis, we examine two sets of scenarios. In the first set, lead times are varied from -20% to +20% of their values in a baseline scenario. In the second set, results are computed first with no supplier restrictions, then with order minima only, and finally with a combination of order minima and order multiples. We use Smart Inventory Optimization software for the calculations.

The baseline scenario uses real-world data on 2,852 spare parts managed by a progressive public transit agency. These parts have an extremely heterogeneous mix of attributes. Their per unit costs range from $1 to $23,105, and their lead times vary between 1 day and 300 days. Over 24 months, the mean demand ranged from less than 1 unit per month to 1,508 units per month, with coefficients of variation ranging from a manageable 10% to a scary 2,171%. Furthermore, the supplier picture is also very complex, involving 293 unique vendors, supplying an average of about 10 parts each. This heterogeneity implies that a real-world optimization would pick and choose among items and vendors. However, for simplicity of exposition and to develop basic insights, our what-if scenarios in this example treat every item and vendor equally. Similarly, we assumed in the baseline that holding costs equaled 20% of the dollar value of an item and that every replenishment order had a fixed cost of $40.

We conducted two what-if experiments. The first examined the effects of changing lead times. The second examined the effects of introducing restrictions on order quantities. In each experiment, we recorded the effects of the changes on two operational metrics: average number of units in stock and average number of orders per year. In turn, these influenced four financial metrics: average dollar value of inventory, average holding cost, average ordering cost, and the sum of the last two, which is total inventory operating cost.

In all scenarios, reorder points were calculated so as to achieve 95% probability of avoiding stockouts while waiting for replenishment. Order quantities, in the absence of supplier restrictions, were computed as what we call “feasible EOQ”. EOQ is the classic “economic order quantity” taught in Inventory 101; it is computed from average demand, holding cost and ordering cost. Feasible EOQ adds an additional consideration: inventory dynamics. If the reorder point is very low, it is possible for EOQ to be too small to sustain a stable, positive level of inventory. In these cases, feasible EOQ increases the order quantity above the EOQ to insure that average inventory does not go negative.

Effects of Changing Lead Times

 

Table 1 shows the results of changing the lead times. Working around the base case, we changed every item’s lead time by -20%, -10%, +10% and +20%.

It is no surprise that reducing lead times reduced the required level of inventory and increasing them did the opposite. Both the average number of units and the associated dollar value behaved as expected. What may be surprising is that the effects were somewhat muted, i.e., an X percent change in lead time produced a less-than-X percent response. For instance, a 20% reduction in lead time produced only a 7.9% reduction in on-hand inventory and only a 12.0% reduction in the dollar value of those units. Furthermore, the effects of reductions and increases are asymmetric: a 20% increase in lead time led to just a 7.3% increase in units (vs 7.9%) and only a 9.6% increase in inventory value (vs 12.0%).

Similar attenuated and asymmetric results held for operating costs. A 20% reduction in lead time decreased total operating costs by 7.0%, but a 20% increase in lead time caused only a 5.1% increase in operating costs.

Now consider the implications of these results for practice. In a competitive world, cost reductions on the order of 10% or even 5% are significant. This means that efforts to reduce lead times can have important payoffs. In turn, this means that efforts to streamline purchasing processes may be worth doing. Likewise, there is a case for engaging suppliers about reducing their part of lead time, possibly by sharing the savings to incentive them.

 

Inventory Optimization - Effects of Changing Lead Times
Table 1: Effects of changing lead times

Effect of Order Quantity Restrictions

 

Table 2 shows the effect of imposing supplier restrictions on order quantities. In the base case, there are no restrictions, i.e., the order minimum is 0 and the order multiple is 1, implying that any order quantity is acceptable to suppliers. Working away from the base case, we first looked at imposing an order minimum of 5 units on all items, then adding an order multiple of 5 for all items.

Forcing orders to be larger than they otherwise would be had the expected impact on the average number of units on hand, increasing it by 0.9% with only an order minimum and by 3.4% with both a minimum and a multiple. The corresponding changes in the dollar value of the inventory were more dramatic: 22.4% and 23.3%. This difference in the size of the percentage response probably traces back to the large number of low-volume/high-cost replacement parts managed by the public transit agency.

Another surprise was the net reduction in operating costs when supplier restrictions were imposed. While holding costs went up by 22.4% and 23.3% in the two what-if scenarios, the larger order quantities allowed for fewer orders per year, resulting in offsetting reductions in ordering costs of, respectively, -24.4% and -32.7%. The net impacts on operating costs were then reductions of 3.7% and 7.9%.

In general, placing restrictions on producer actions would be expected to reduce performance. So the results in these scenarios were counter-intuitive. However, the real message here is that using EOQ, or even enhanced EOQ, to set an order quantity does not give optimum results. Paradoxically, the order quantity restrictions we investigated seem to have forced order quantities closer to optimal levels.

 

Inventory Optimization - Effect of Order Quantity Restrictions
Table 2: Effect of order quantity restrictions

Conclusions

 

The what-if analyses shown here do not lead to universal conclusions. For instance, changing the assumed cost per order from $40 to some smaller number could show that the supplier restrictions increased rather than decreased the producer’s inventory operating costs.

When doing what-if analysis in real-word situations, users would naturally craft scenarios at a lower level of detail. For instance, they might evaluate the effect of changes in supplier lead times on a supplier-by-supplier basis to find the ones that would have the highest potential payoffs. Or they might arrange for order minima, if they exist already for all items, to change by a specified percentage instead of a fixed amount, which might be somewhat more realistic.

The key takeaway is that inventory optimization software can be used in “what-if mode” to explore strategic issues, beyond its customary use to calculate reorder points, safety stocks, order quantities, and inventory transfers.

Leave a Comment

Related Posts

Worst Practices in Forecasting

Worst Practices in Forecasting

Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?

The Trouble With Turns

The Trouble With Turns

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.

Managing the Inventory of Promoted Items

Managing the Inventory of Promoted Items

In a previous post, I discussed one of the thornier problems demand planners sometimes face: working with product demand data characterized by what statisticians call skewness—a situation that can necessitate costly inventory investments. This sort of problematic data is found in several different scenarios. In at least one, the combination of intermittent demand and very effective sales promotions, the problem lends itself to an effective solution.

Recent Posts

  • When implementing inventory optimization, don’t swing for the fences when a single will do!
    When the pressure is on to cut inventory and improve performance, you might want to move fast much like a hitter who wants to hit a home run. And in some cases, swinging for the fences might be the recommended approach. More often than not, a progressive approach to inventory optimization is more effective
  • Want to Optimize Inventory? Follow These 4 Steps
    Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.
Getting “Halfway There” with Demand Planning
The Smart Forecaster
Pursuing best practices in demand planning, forecasting and inventory optimization

Demand planning takes time and effort. It’s worth the effort to the extent that it actually helps you make what you need when you need it.

But the job can be done well or poorly. We see many manufacturers stopping at the first level when they could easily go to the second level. And with a little more effort, they could go all the way to the third level, utilizing probabilistic modeling to convert demand planning results into an inventory optimization process.

The First Level

 

The first level is making a demand forecast using statistical methods. Figure 1 shows a first level effort: an item’s demand history (red line) and its expected 12-month forecast (green line).

 

 The first level: A forecast of expected demand over the next 12 months

 

The forecast is bare bones. It only projects expected demand ignoring that demand is volatile and will inevitably create forecast error. (This is another example of an important maxim: “The Average is Not the Answer”). The forecast is as likely to be too high as it is to be too low, and there is no indication of forecast uncertainty accompanying the forecast. This means the planner has no estimate of the risk associated with committing to the forecast. Still, this forecast does provide a rational basis for production planning, personal scheduling, and raw materials purchase. So, it’s much better than guessing.

The Second Level

 

The second level takes explicit account of forecast uncertainty. Figure 2 shows a second level effort, known as a “percentile forecast”.

Now we see an explicit indication of forecast uncertainty. The cyan line above the green forecast line represents the projected 90th percentile of monthly demand. That is, the demand in each future month has a 90% chance of falling at or below the cyan line. Put another way, there is a 10% chance of demand exceeding the cyan line in each month.

This analysis is much more useful because it supports risk management. If it is important to assure sufficient supply of this item, then it makes sense to produce to the 90th percentile instead of to the expected forecast. After all, it’s a coin flip as to whether the expected forecast will result in enough production to meet monthly demand. This second level forecast is, in effect, a rough substitute for a careful inventory management process.

 

A percentile forecast, where the cyan line estimates the 90th percentiles of monthly demand.

 

Figure 2. A percentile forecast, where the cyan line estimates the 90th percentiles of monthly demand.

Going All the Way to the Third Level

 

Best practice is the Third Level, which uses demand planning as a foundation for completing a second task: explicit inventory optimization. Figure 3 shows the fundamental plot for the efficient management of our finished good, assuming it has a 1 month production lead time.

 

Distribution of demand for finished good over its 1-month lead time

 

Figure 3 shows the utilization of probabilistic forecasting and how much draw-down in finished good inventory might take place over a one month production lead time. The uncertainty in demand is apparent in the spread of the possible demand, from a low of 0 to a high of 35, with 15 units being the most likely value. The vertical red line at 22 indicates the “reorder point“ (or “min” or “trigger value”) corresponding to keeping the chance of stocking out while waiting for replenishment to a low 5%. When inventory drops to 22 or below, it is time to order more. The Third Level uses probabilistic demand forecasting with full exposure of forecast uncertainty to efficiently manage the stock of the finished product.

To Sum Up

 

Forecasting the most likely demand for an item is a useful first step. It gets you halfway to where you want to be. But it provides an incomplete guide to planning because it ignores demand volatility and the forecast uncertainty that it creates. Adding a cushion to the demand forecast gets you further along, because it lessen the risk that a jump in demand will leave you short of product. This cushion can be calculated by probabilistic forecasting approaches that forecasts a high percentile of the distribution of future demand. And if you want to take one step further, you can feed forecasts of the demand distribution over a lead time to calculate reorder points (mins) to ensure that you have an acceptably low level of stock-out risk.

Given what modern forecasting technology can do for you, why would you want to stop halfway to your goal?

Leave a Comment
Related Posts
Worst Practices in Forecasting

Worst Practices in Forecasting

Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?

The Trouble With Turns

The Trouble With Turns

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.

Managing the Inventory of Promoted Items

Managing the Inventory of Promoted Items

In a previous post, I discussed one of the thornier problems demand planners sometimes face: working with product demand data characterized by what statisticians call skewness—a situation that can necessitate costly inventory investments. This sort of problematic data is found in several different scenarios. In at least one, the combination of intermittent demand and very effective sales promotions, the problem lends itself to an effective solution.

Recent Posts

  • When implementing inventory optimization, don’t swing for the fences when a single will do!
    When the pressure is on to cut inventory and improve performance, you might want to move fast much like a hitter who wants to hit a home run. And in some cases, swinging for the fences might be the recommended approach. More often than not, a progressive approach to inventory optimization is more effective
  • Want to Optimize Inventory? Follow These 4 Steps
    Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.
Beware of Simple Rules of Thumb for Managing Inventory

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Managing inventory requires executives to balance competing goals: high product availability versus low investment in inventory. Executives strike this balance by stating availability targets and budget constraints. Then supply chain professionals translate these “commander’s intentions” into detailed specifications about reorder points and order quantities.

A High-Stakes Race Between Supply and Demand

 

Let’s focus on reorder points (also known as mins). They work as follows. As on-hand inventory decreases in response to demand, it eventually drops down to or below a trigger value, the reorder point or min. At that point, it’s like a gun goes off to start a race between supply and demand. A replenishment order is sent to restock the item, but there is a replenishment lead time, so the restocking is not instantaneous. While your system waits for resupply, demand continues to whittle away at the stock on hand. It is bad news if demand wins the race, because then you won’t be in position to provide what somebody is demanding. Then they either get it from a competitor or get back-ordered and unhappy: either way, stocking out is a bad outcome for you and your customer.

The risk of stocking is controlled by your staff’s choice of reorder points. If they are set too high, stock-outs are rare but inventory is bloated. Set them too low and stock-outs abound. So how should reorder points be set?

Avoiding Foolish Follow-Through

 

Several factors govern stock-out risk. Each item in your inventory has its own demand history and lead time. Together with your chosen availability targets, these factors determine the best choice of reorder point. But the relationships are statistical and require good analysis to work out. Inventory Optimization Software can compute the proper reorder point for each of tens of thousands of items. But instead of relying on proper analysis, many companies fall back on simple rules of thumb or just “doing what we always do”.

In place of using the right math, companies often rely on rules of thumb that serve them poorly. Here are some examples in order of most common to least common.
 

1) Multiples of Average Demand

 

Setting reorder points at some (arbitrary) multiple of average demand starts to rely on actual facts. But it ignores the key demand attribute that drives stock-out risk: demand variability. Two items with the same average demand but very different levels of variability will require very different reorder points to insure the same low risk of stock-out. (See Figure 1)
 

2) Gut feel

 

Some companies have self-styled supply chain gurus. Even if they actually are Jedi masters, it’s impossible to keep up with tens of thousands of items whose reorder points should be reviewed frequently.  And if the logic that drives decision making is buried in a hard to use spreadsheet that only they know how to use, the company risks not being able to execute the inventory plan without that one individual –a risky proposition.
 

3) Average Demand + some multiple of Demand Variability

 

This approach is taught in many “Inventory 101” courses. But it implicitly assumes some facts about demand that are very often not true: that demand has a Normal (“bell-shaped”) distribution and that demand in one period does not relate to demand in the previous time period(s).  Assumptions of independence and reliance on normal distribution models just don’t cut it.
 

4) Nursery rhymes

 

Not at all the norm, hence being last on the list, but we heard of one company that used one simple rule for all items: “If it’s down to four, order more”. It’s crazy to believe that one rule applies to all items at all times. But at least it rhymes.

Your people can do better than to rely on any of these approaches. Do you know whether your company is using any one of them?

Getting It Right

 

The right way to set reorder points uses the tools of probability theory. The details depend on whether you are selling finished goods or spare parts. Spare parts are usually more difficult to manage because they have quirky demand patterns: high intermittency (lots of zero demands), high skewness (lots of small demands but with some whoppers too), and auto-correlation (“feast or famine” behavior). Modern Reorder Point Software takes these quirks into account to set reorder points that insure the desired level of item availability. Importantly, they also let your people see explicit trade-off curves, so they can strike the balance you want — at the item by location level – between stock-out risk and inventory investment.

Inventory is a major item on the balance sheet and needs high-level attention. At many manufacturers, service parts can represent up to half of revenue. Modern software lets the C-Suite move beyond, incomplete math and other inadequate approaches to managing inventory.

 

 

Figure 1:  Two equally important items with the same average demand get assigned the same stocking policy that determines the Min (reorder point) as 2 x average lead time demand.  Despite the “same” stocking policy service performance varies significantly with the stable Item A experiencing overstocks and the volatile Item B experiencing stock outs.

Leave a Comment

Related Posts

Want to Optimize Inventory? Follow These 4 Steps

Want to Optimize Inventory? Follow These 4 Steps

Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.

Managing Demand Variability

Managing Demand Variability

Anybody doing the job knows that managing inventory can be stressful. Common stressors include: Customers with “special” requests, IT departments with other priorities, balky ERP systems running on inaccurate data, raw material shortages, suppliers with long lead times in far-away countries where production often stops for various reasons and more. This note will address one particular and ever-present source of stress: demand variability.

Recent Posts

  • When implementing inventory optimization, don’t swing for the fences when a single will do!
    When the pressure is on to cut inventory and improve performance, you might want to move fast much like a hitter who wants to hit a home run. And in some cases, swinging for the fences might be the recommended approach. More often than not, a progressive approach to inventory optimization is more effective
  • Want to Optimize Inventory? Follow These 4 Steps
    Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.