Excess Inventory Hurts Customer Service!

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Many companies adopt a philosophy of “it’s better to have it and not need it, then to need it and not have it.” Planning initiatives such as implementing inventory optimization software in order to optimize reorder points, safety stocks, and order quantities are often seen as narrowly focused on reducing inventory and not pursued. Stock-out costs may very well be extremely high. However, resources are finite. The opportunity cost of keeping too much of one product means less space, cash, and resources for another product. Overstocking on one item reduces the ability to provide adequate levels of service on other items. Justifying overstocks by stating it is good for the customer is a poor excuse at best that hurts the customer and ignores what inventory optimization is really about – properly reallocating inventory investments.

Diminishing Returns and Inventory

Each additional unit of inventory that you carry buys proportionally less service. Inventory optimization software can help you understand the exact stock out risk given a certain level of stock. For example, say your stock-out risk with 20 units of inventory is 10%. If you add another 10 units and carry 30 units, the stock out risk might get cut in half to 5%. If you then add an additional 10 for a total of 40 units, the stock-out risk may only drop to 4%. At some point, the additional inventory just isn’t worth the extra service it buys. This is especially so if the cash used to buy that extra 10 units to get a small service level bump on one item could have been spent on another equally important item for a larger increase in service.

Carrying more than you need means you aren’t efficiently managing assets, which costs money, which means you can’t offer the best price to your customer, which hurts your ability to beat the competition. It also means there is less money for investment in other items. This results in the common adage “We have too much of the stuff we don’t need and not enough of the stuff we do.”

Inventory Optimization is about reallocation

The example presented in the blog’s main image highlights the benefits of reallocating inventory.  We used probability forecasting to estimate the service levels and inventory costs that would result from the current stocking policy. We then conducted a “what-if” scenario by modifying the policy. In the benchmark shown in the first column, the current stock levels were forecasted to yield a 84.78% service level and required $1.67 Million in inventory. Nearly 12% of the items numbers had reached their point of diminishing return and were forecasted to achieve a 100% service level. By imposing a maximum service level of 99% and a minimum service level of 80%, we reallocated inventory.  As a result, the inventory investment dropped to $1.5 Million and service level increased by 3%!

The exact point of diminishing returns will differ depending on the item, the customers involved, and the company making the stocking decision. It is important to understand the inherent levels of stock-out risk that result from current inventory policies and how changes to current policies will impact risk and costs. This enables the reshaping of inventory so that service can be maximized at the minimum possible cost.

Download Smart Inventory Optimization product sheet here: https://smartcorp.com/inventory-optimization/

Leave a Comment

Related Posts

Quantum Inventory Theory?

Quantum Inventory Theory?

Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management.

Stop Leaking Money with Manual Inventory Controls

Stop Leaking Money with Manual Inventory Controls

An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock.

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

Consider what is meant by “demand management”, “demand planning”, and “forecasting”. These terms imply certain standard functionality for collaboration, statistical analysis, and reporting to support a professional demand planning process.  However, in most ERP systems, “demand management” running MRP and reconciling demand and supply for the purpose of placing orders

Recent Posts

  • Quantum atom software illustrationQuantum Inventory Theory?
    Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management. […]
  • Stop Leaking Money with Manual Inventory Controls
    An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock. […]
Managing the Inventory of Promoted Items

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

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.

Reviewing terms, recall that “service level” is the probability of not stocking out while waiting for a replenishment order to arrive, while “fill rate” is the percentage of demand that is satisfied immediately from stock. In my previous post, “The Scourge of Skewness”, I pointed out that a certain type of demand distribution, having a “long right tail”, will lead to fill rates that can be much lower than service levels. I also pointed out that sometimes the only way to improve the fill rate is to increase the target service level to an unusually high level, which can be expensive.

In this post, I’ll look at solving the problem in one special case: skewness resulting from effective sales promotions mixed with “intermittent demand”. Intermittent demand has a large proportion of zero values, with nonzero values mixed in at random. Successful sales promotions, obviously positive, have a downside: they can confuse the “demand signal” with spikes in your demand history, and can undermine forecasts and bias safety stock calculations. When intermittent demand and effective sales promotions are the source of your data’s skewness, methods exist to work around the problem to achieve both higher fill rates and more accurate demand forecasts.

How Promotions Increase Skewness

Successful promotions abruptly increase item demand. This creates anomalies, or “outliers”, which contribute to forming a skewed distribution. Knowing when promotions occurred in the past, we can adjust an item’s record of past demand. We produce an alternate demand history as if there had been no promotions, by replacing the outliers with values more representative of the “natural” level of demand. These adjustments reduce demand skewness. Reduced skewness can lead to significant reductions in both expected forecasts and safety stocks, which add together to form reorder points.

Successful promotions are likely to be repeated. When that happens, the promotion effects can be added in to demand forecasts to increase their accuracy. The effect of future promotions on inventory management will be to increase the risk of stockouts, so a sensible response is to work at the operational level to build up temporary supply, in a quantity keyed to the estimated impact of prior promotions on the effected items.

Using Event Modeling to Improve Demand Forecasting

It’s possible to model the impact of like events, and apply this to planned events in the future. Doing so can improve your forecast in two big ways: by projecting the demand jolt you expect from a planned event; and rationalizing the spikes in the past that were caused by events, making your baseline activity more visible and more accurately forecastable. We do a lot of this in SmartForecasts, so allow me to use our experience there to show you what I mean.

Event Modeling entails the following steps:
• Automatically estimating the impact of previous promotions (which is a useful result in itself).
• Adjusting historical demand to statistically remove the effect of promotions.
• Creating promotion-free forecasts.
• Revising the forecasts for any future time periods in which promotions are planned.

We call this this type of analysis “Promo forecasting”. We use the word “promotions” to describe what you do yourself to improve your results. We use “events” to describe what the world does to you, usually to your detriment; examples include strikes, power outages, warehouse fires and other unlucky happenings.

To understand how Event Modeling can help you cope with skewness when doing demand forecasting for high-volume items, consider Figures 1-3.

Figure 1 shows that this item’s demand pattern is clearly seasonal, and the forecast is both seasonal and “tight”, meaning that the forecast uncertainty interval (“margin of error”, shown in cyan lines) is very narrow.

Figure 2 shows an alternative history in which a promotion in June 2014 reversed the usual seasonal low associated with June sales. This demand pattern was forecasted using the Automatic forecasting tournament in SmartForecasts, as in Figure 1. This time, the promotion scrambled the seasonal pattern enough to create an inappropriate non-seasonal forecast, and one that has a much larger margin of error.

Finally, Figure 3 shows how Promo forecasting handles the same promoted scenario, retaining a seasonal forecast and building into the forecast an estimate of the effect of a planned repeat promotion in 2015.

The Case of Intermittent Demand

In Figure 1, the item was a high-volume finished good and the task was demand forecasting. Promo modeling is also useful when dealing with the task of setting safety stocks and reorder points for items with intermittent demand, whether the items are finished goods, components or spare parts. Intermittent demand very often has a skewed distribution that makes it difficult to achieve high item availability with a small investment in inventory.

Figure 4 illustrates the problem that a successful promotion can accidentally create for inventory management. If such a spike arises from the natural, un-promoted demand, then the only way to maintain high fill rates is to provide safety stocks large enough to cope with these random surges. In this case, the big spike in demand of 500 units in February 2013 was the result of a one-time promotion.

Taking Account of Promotions to Improve Inventory Management

Unwittingly treating the spike in the example above as part of the natural demand variability results in a poor fill rate. To achieve a target service level of, say, 95% with a lead time of one month would require a reorder point of 38 units, computed as the sum of an expected forecast over the one month replenishment lead time of 21 units supplemented by a safety stock of 17 units. This investment would result in a disappointing fill rate of only 36%.

However, recognizing that the spike is a one-time promotion and replacing the 500 units with 0 obviously would make a big difference. The reorder point would drop from 38 units to 31 (the sum of an expected demand of 7 units and a safety stock of 24 units) and the fill rate would increase to 94%.

Of course, it is not ok to just throw out inconvenient demand spikes whenever they make life uncomfortable; there has to be a valid “business story” behind the adjustment of historical demand. If the spike is the result of a data processing error, then by all means, fix it. If the spike coincides with a promotion, then replacing the spike with, say, the median demand (often zero, as in this example) will result in a much more sustainable inventory investment that still meets aggressive performance targets. Future promotions of the same type on the same item will require some extra effort to prepare for the temporary surge in demand, but the recommended reorder point will be correct in the long run.

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

Leave a Comment

Related Posts

Stop Leaking Money with Manual Inventory Controls

Stop Leaking Money with Manual Inventory Controls

An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock.

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

Consider what is meant by “demand management”, “demand planning”, and “forecasting”. These terms imply certain standard functionality for collaboration, statistical analysis, and reporting to support a professional demand planning process.  However, in most ERP systems, “demand management” running MRP and reconciling demand and supply for the purpose of placing orders

The 3 Types of Supply Chain Analytics

The 3 Types of Supply Chain Analytics

The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three helping you to reduce inventory costs, improve on time delivery and service levels, while running a more efficient supply chain.

Recent Posts

  • Quantum atom software illustrationQuantum Inventory Theory?
    Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management. […]
  • Stop Leaking Money with Manual Inventory Controls
    An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock. […]
The Scourge of Skewness

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Demand planners have to cope with multiple problems to get their job done. One is the Irritation of Intermittency. The “now you see it, now you don’t” character of intermittent demand, with its heavy mix of zero values, forces the use of advanced statistical methods, such as Smart Software’s patented Markov Bootstrap algorithm. But even within the dark realm of intermittent demand, there are degrees of difficulty: planners must further cope with the potentially costly Scourge of Skewness.

Skewness is a statistical term describing the degree to which a demand distribution is not symmetrical. The classic (and largely mythic) “bell-shaped” curve is symmetric, with equal chances of demand in any time period falling below or above the average. In contrast, a skewed distribution is lopsided, with most values falling either above or below the average. In most cases, demand data are positively skewed, with a long tail of values extending toward the higher end of the demand scale.

Bar graphs of two time series
Figure 1: Two intermittent demand series with different levels of skewness
Figure 1 shows two time series of 60 months of intermittent demand. Both are positively skewed, but the data in the bottom panel are more skewed. Both series have nearly the same average demand, but the one on top is a mix of 0’s, 1’s and 2’s, while the one on the bottom is a mix of 0’s, 1’s and 4’s.

What makes positive skewness a problem is that it reduces an item’s fill rate. Fill rate is an important inventory management performance metric. It measures the percentage of demand that is satisfied immediately from on-hand inventory. Any backorders or lost sales reduce the fill rate (besides squandering customer good will).

Fill rate is a companion to the other key performance metric: Service level. Service level measures the chance that an item will stock out during the replenishment lead time. Lead time is measured from the moment when inventory drops to or below an item’s reorder point, triggering a replenishment order, until the arrival of the replacement inventory.

Inventory management software, such as Smart Software’s SmartForecasts, can analyze demand patterns to calculate the reorder point required to achieve a specified service level target. To hit a 95% service level for the item in the top panel of Figure 1, assuming a lead time of 1 month, the required reorder point is 3; for the bottom item, the reorder point is 1. (The first reorder point is 3 to allow for the distinct possibility that future demand values will exceed the largest values, 2, observed so far. In fact, values as large as 8 are possible.) See Figure 2.

Histograms of two time series
Figure 2: Distributions of total demand during a replenishment lead time of 1 month
(Figure 2 plots the predicted distribution of demand over the lead time. The green bars represent the probability that any particular level of demand will materialize.)

Using the required reorder point of 3 units, the fill rate for the less skewed item is a healthy 93%. However, the fill rate for the more skewed item is a troubling 44%, even though this item too achieves a service level of 95%. This is the scourge of skewness.

The explanation for the difference in fill rates is the degree of skewness. The reorder point for the more skewed item is 1 unit. Having 1 unit on hand at the start of the lead time will be sufficient to handle 95% of the demands arriving during a 1 month lead time. However, the monthly demand could reach above 15 units, so when the more skewed unit stocks out, it will “stock out big time”, losing a much larger number of units.

Most demand planners would be proud to achieve a 95% service level and a 93% fill rate. Most would be troubled, and puzzled, by achieving the 95% service level but only a 44% fill rate. This partial failure would not be their fault: it can be traced directly to the nasty skewness in the distribution of monthly demand values.

There is no painless fix to this problem. The only way to boost the fill rate in this situation is to raise the service level target, which will in turn boost the reorder point, which finally will reduce both the frequency of stockouts and their size whenever they occur. In this example, raising the reorder point from 1 unit to 3 units will achieve a 99% service level and boost fill rate to a respectable, but not outstanding, 84%. This improvement would come at the cost of essentially tripling the dollars tied up in managing this more skewed item.

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

Leave a Comment

Related Posts

Stop Leaking Money with Manual Inventory Controls

Stop Leaking Money with Manual Inventory Controls

An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock.

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

Consider what is meant by “demand management”, “demand planning”, and “forecasting”. These terms imply certain standard functionality for collaboration, statistical analysis, and reporting to support a professional demand planning process.  However, in most ERP systems, “demand management” running MRP and reconciling demand and supply for the purpose of placing orders

The 3 Types of Supply Chain Analytics

The 3 Types of Supply Chain Analytics

The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three helping you to reduce inventory costs, improve on time delivery and service levels, while running a more efficient supply chain.

Recent Posts

  • Quantum atom software illustrationQuantum Inventory Theory?
    Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management. […]
  • Stop Leaking Money with Manual Inventory Controls
    An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock. […]
A CFO’s Perspective on Demand Planning – “More Strategic Than You Think”

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Bud Schultz, CPA, Vice President of Finance for NKK Switches, presented his company’s experience with demand planning during a recent webinar. The following is a brief summary of Bud’s key points; view the complete webinar by clicking here.

Q: Tell us about NKK’s business and demand planning challenges.

NKK Switches, based in Scottsdale, Arizona, is a leading manufacturer and supplier of electromechanical switches. The business involves many different switch types—toggles, push-button, rotary, even some programmable switch types. We are known for our high quality, and for our ability to meet an exceptionally broad range of customer requirements on a turnkey (custom configuration) basis. NKK Switches produces customized solutions from component parts sourced exclusively from manufacturing facilities in Japan and China.

There are literally millions of possible switch configurations, and we never know what configured solutions our customers will order. This makes our demand highly intermittent and exceptionally difficult to forecast. In fact, until fairly recently we considered our demand unforecastable. We operated on a build-to-order basis, which meant that customer orders could not be fulfilled until their component parts were produced and then fashioned into finished goods by NKK. This resulted in long lead-times, painful for our customers and a competitive challenge for our sales organization.

Q: What did you expect to get from improved product demand forecasting?

When we began to investigate the value of demand forecasting software (SmartForecasts from Smart Software), we tried to view the decision from a Return on Investment (ROI) point of view. We did some capital budgeting, making assumptions about potential reductions in inventory levels, reduced inventory carrying costs and other potential savings. Although the capital budgets returned positive returns on investment, we nevertheless were unable to move forward based on that information. We lacked confidence in our assumptions, and we were worried that we wouldn’t be able to justify the safety stock and inventory levels that the software would suggest.

What we didn’t expect was a challenge from our parent company. In light of the capabilities of a newly implemented ERP system, they would consider a new approach. If we could produce demonstrably reliable demand forecasts, they would consider procuring raw materials and producing switch components on a build-to-forecast rather than build-to-order basis. This opened the door to a much more profound impact. We tracked actuals against forecasts over a twelve-month period and found that our forecasts, particularly in aggregate, were exceptionally accurate: actual demand was within 3% of forecast. Once we were able to prove the validity of our forecasts, we were able to move forward with the parent company’s plan to manufacture product based on those forecasts.

Q: How did accurate forecasts of product lines with intermittent demand data transform NKK’s operations?

From the many different combinations we manufacture to order, individual switch parts can show very intermittent demand (long periods with zero orders and then seemingly random spikes), but we can identify more consistent patterns across switch series. All of the part numbers in a given series have common components and raw materials, such as plastic housing, brackets and other hardware, gold, silver and LEDs.

Providing our manufacturing facilities with reliable forecasts ended up allowing us to make dramatic changes. Our manufacturing plants could start procuring raw materials that in the aggregate would eventually be used in production of different part numbers within that series, even if the specific part numbers to be produced were unknown at the time the forecasts were made. And in many instances, despite the irregular demand history data, it was even possible for the suppliers to manufacture specific part numbers based on the forecast.

Once the program is fully implemented, we anticipate our leads times will be reduced to half the time or even less. Shorter lead times will result in lower reorder points, resulting in higher service levels while reducing our inventory requirements.

Bud Schultz leads all finance and accounting functions at NKK. His background as a Certified Public Accountant, attorney, engineer and pilot for the US Air Force provide unique perspective on finances for engineering and manufacturing operations.

Leave a Comment

Related Posts

MAX-MIN OR ROP – ROQ

MAX-MIN OR ROP – ROQ

This guest blog details the differences between Min-Max and Reorder Point- Order Quantity replenishment logic and why it is important. It is authored by Phillip Slater, Founder of SparePartsKNowHow.com the leading educational resource for spare parts management. Mr. Slater is a global leader and consultant on materials management and specifically, engineering spare parts inventory management and optimization.

Excess Inventory Hurts Customer Service!

Excess Inventory Hurts Customer Service!

Many companies adopt a “customer first, better to have the inventory and not need it” approach to inventory planning. While well intentioned, this approach often ignores the role that diminishing returns and opportunity costs play in inventory management impacting the organizations ability to quickly respond to demand.

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?

Recent Posts

  • Quantum atom software illustrationQuantum Inventory Theory?
    Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management. […]
  • Stop Leaking Money with Manual Inventory Controls
    An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock. […]
6 Essential Steps to Better Recovery Planning

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

As we approach the midpoint in 2013, there is still a lot of economic uncertainty complicating your supply chain planning processes. Some look at this shaky economy and postpone needed investments that can position their organizations for a strong future.

However, this is not the time to retreat from your supply chain improvement initiatives. Rather, it’s a time to double-down on your efforts to prepare for the inevitable business opportunities that lie ahead.

Economic recovery is a time of sales opportunities. You want to make sure that you’re prepared to take advantage of them. Good demand and inventory planning can help.With the right software and planning processes, you can achieve a sound statistical basis for decision-making going forward while making informed adjustments as circumstances dictate. You can improve your ability to read demand signals, spot trends, model future events, and bring your inventory into balance with demand.

Here are six areas of demand and inventory planning where changes you make now can lead to big payoffs when new opportunities arise:

1. Optimize your inventories

When the customer calls, you want to be able to ship. At the same time, you want to control your costs. The surest way to meet that goal is to find the inventory “sweet spot.” That’s where you have the minimum amount of inventory required to satisfy product demand over a specified lead time and at a desired service level.

The ability to accurately set safety stock and inventory levels can set you apart from the competition, and make a difference in your bottom line. However, getting to that point requires a shift in your planning focus from just forecasting future demand to optimizing stocking levels to fill future orders.

If you’d like to know more about achieving the “sweet spot,” you can find a good article published in APICS Magazine here.

2. Implement intermittent demand forecasting solutions

Companies in the service parts, auto aftermarket, and capital goods industries commonly experience intermittent, “slow moving” demand for a large percentage of their inventory items. Accurately forecasting demand and estimating safety stock levels for these types of items is probably the toughest challenge demand planners face. If you can accurately forecast your intermittently demanded parts and products, and have the correct amount of inventory and safety stock on the shelf, you’ve got most of the competition beat!

The reason for this is that items that have intermittent demand do not have normal demand patterns or distributions, making them difficult to forecast using traditional forecasting methods (see the diagram below).

Bar chart illustrating intermittent demand

So, if you have an accurate means of forecasting intermittent demand and estimating safety stock requirements, you’ll be ahead of your competitors that don’t.

If you’d like to know more about forecasting and planning items with intermittent demand, you can find an informative white paper here.

3. Improve lead times

The economic downturn has forced companies to rethink their sourcing strategies because of uncertain demand back home, long lead times to obtain their goods, rising labor costs abroad, and increasing transportation costs. Shortening replenishment lead times can reduce the time required to get the products you need and helps make your supply chain more efficient. It also makes it easier to react to changes in demand when recovery comes.

4. Prioritize service levels

Prioritizing service levels for your products can help insure that the items important to your sales are given the attention they need. For items that are highly demanded, consider setting service levels higher than for those with less demand. Also try doing a revenue-based ABC analysis of your company’s stock-keeping units (SKUs) and set service levels accordingly in your software planning solution.

For example, you might set the service levels for your “bread and butter” items at 95-99% or higher, while setting service levels much lower (at 70-80% or even less) for other items. In this way, you may find that you need much less stock for some of your SKUs and more stock for others to effectively achieve your overall service level goals.

5. Use more recent demand history in creating your forecasts

Because the economy has been changing so fast, it may be time to shorten the demand history used in generating your forecasts so more emphasis is placed on recent trends and demand patterns—reflecting the “new normal”—rather than those contained in outdated history from 3 or 4 years ago. This, of course, should be done in consultation with your management team and preferably as part of an organized S&OP process that thoroughly evaluates both the risks and benefits of adopting this strategy.

6. Invest in technologies and resources that help you capitalize on opportunities

Investing in the right tools and processes increases your competitive advantage. If you aren’t doing so already, here are some valuable things to consider:

• Start an S&OP process, or fine tune your current process, to include key stakeholders in the supply chain and also ensure that demand forecasting and inventory planning provide key inputs in that planning process.

• If your forecasting software is not good at picking up trends, or cannot handle the portion of your inventory with intermittent demand, find software that’s up to the task.

• Find software that will take your forecast results and generate accurate inventory stocking levels to satisfy demand for your products, components or raw materials over specified lead times and at service levels you desire.

• Look for software solutions that are scalable, yet have a relatively low total cost of ownership, fast payback and high ROI.

• Finally, don’t scrimp on training; get all the training and consulting you need to get the “biggest bang” from your software investments.

Do you have anything to add? What are you doing to prepare for the economic recovery? Please leave a comment.

Charles Smart is the founding President of Smart Software. He currently serves as Vice Chairman, on Smart Software’s Board of Directors, as a company spokesman and in development of strategic business relationships. Prior to founding Smart Software, he was a management consultant at the Stanford Research Institute (SRI International) and Policy Analysis, Inc., and served as a Lieutenant in the U.S. Navy.

Leave a Comment

Related Posts

Stop Leaking Money with Manual Inventory Controls

Stop Leaking Money with Manual Inventory Controls

An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock.

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

5 Considerations When Evaluating your ERP system’s Forecasting Capabilities

Consider what is meant by “demand management”, “demand planning”, and “forecasting”. These terms imply certain standard functionality for collaboration, statistical analysis, and reporting to support a professional demand planning process.  However, in most ERP systems, “demand management” running MRP and reconciling demand and supply for the purpose of placing orders

The 3 Types of Supply Chain Analytics

The 3 Types of Supply Chain Analytics

The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three helping you to reduce inventory costs, improve on time delivery and service levels, while running a more efficient supply chain.

Recent Posts

  • Quantum atom software illustrationQuantum Inventory Theory?
    Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management. […]
  • Stop Leaking Money with Manual Inventory Controls
    An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock. […]
Discussing Intermittent Demand with Supply Chain Brain’s Bowman

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

The unique challenges of inventory planning for spare parts, large capital goods and other infrequently or irregularly moving items drives the importance of finding smarter methods to forecast this kind of intermittent demand. Robert Bowman, Editor of Supply Chain Brain Magazine, and I discussed this topic at the October APICS conference in Denver, and video of our conversation is available at Supply Chain Brain‘s website.

Why plan for intermittent demand? Well, why plan for any demand? If you can understand what the likely range of demand will be until you can get more, you will know how much stock to keep in reserve, so you have just enough. This is the heart of demand forecasting and inventory optimization. Intermittent demand is exceptionally difficult to forecast, but this same principle holds true.

Unlike other demand patterns, where historical data suggests regular trends, ebbs and flows, seasonality or other discernible patterns, intermittent demand appears to be random. There are many periods of zero demand interspersed with irregular, non-zero demand. This occurs frequently with service parts, where parts are replaced when they break, and you just don’t know when that will occur. Most service parts inventories (70% or more!) can experience intermittent demand. Demand for specialized or configured products is also likely to be intermittent.

Supply Chain Brain has made the more in-depth discussion of this topic Bowman and I shared available here. For new visitors to Supply Chain Brain, a quick account sign-up is required to access the video.

Jeff Scott serves as Vice President, Marketing & Alliances for Smart Software.

Leave a Comment

Related Posts

Excess Inventory Hurts Customer Service!

Excess Inventory Hurts Customer Service!

Many companies adopt a “customer first, better to have the inventory and not need it” approach to inventory planning. While well intentioned, this approach often ignores the role that diminishing returns and opportunity costs play in inventory management impacting the organizations ability to quickly respond to demand.

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.

Recent Posts

  • Quantum atom software illustrationQuantum Inventory Theory?
    Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management. […]
  • Stop Leaking Money with Manual Inventory Controls
    An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items. In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock. […]