There’s a stale old joke: “There are two types of people – those who believe there are two types of people, and those who don’t.” We can modify that joke: “There are two types of people – those who know there are three types of supply chain analytics, and those who haven’t yet read this blog.”

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.

Descriptive Analytics

Descriptive Analytics are the stuff of dashboards. They tell you “what’s happenin’ now.” Included in this category are such summary numbers as dollars currently invested in inventory, current customer service level and fill rate, and average supplier lead times. These statistics are useful for keeping track of your operations, especially when you track changes in them from month to month. You will rely on them every day. They require accurate corporate databases, processed statistically.

Predictive Analytics

Predictive Analytics most commonly manifest as forecasts of demand, often broken down by product and location and sometimes also by customer. These statistics provide early warning so you can gear up production, staffing and raw material procurement to satisfy demand. They also provide predictions of the effect of changes in operating policies, e.g., what happens if we increase our order quantity for Product X from 20 to 25 units? You might rely on Predictive Analytics periodically, perhaps weekly or monthly, when you look up from what’s happening now to see what will happen next. Predictive Analytics uses Descriptive Analytics as a foundation but adds more capability. Predictive Analytics for demand forecasting requires advanced statistical processing to detect and estimate such features of product demand as trend, seasonality and regime change.  Predictive Analytics for inventory management uses forecasts of demand as inputs into models of the operation of inventory policies, which in turn provide estimates of key performance metrics such as service levels, fill rates, and operating costs.

Prescriptive Analytics

Prescriptive Analytics are not about what is happening now, or what will happen next, but about what you should do next, i.e., they recommend decisions aimed at maximizing inventory system performance. You might rely on Prescriptive Analytics to best posture your entire inventory policy. Prescriptive Analytics uses Predictive Analytics as a foundation then adds optimization capability. For instance, Prescriptive Analytics software can automatically work out the best choices for future values of Min’s and Max’s for thousands of inventory items. Here, “best” might mean the values of Min and Max for each item that minimize operating cost (the sum of holding, ordering, and shortage costs) while maintaining a 90% floor on item fill rate.

Example

The figure below shows how supply chain analytics can help the inventory manager. The columns show three predicted Key Performance Indicators (KPI’s): service level, inventory investment, and operating costs (holding costs + ordering costs + shortage costs).

 Figure 1: The three types of analytics used to evaluate planning scenarios

The rows show four alternative inventory policies, expressed as scenarios. The “Live” scenario reports on the values of the KPI’s on July 1, 2018. The “99% All” scenario changes the current policy by raising the service level of all items to 99%. The “75 floor/99 ceiling” scenario raises service levels that are too low up to 75% and lowers very high (i.e., expensive) service levels down to 95%. The “Optimization” scenario prescribes item specific service levels that minimizes total operating costs.

The “Live 07-01-2018” scenario is an example of Descriptive Analytics. It shows the current baseline performance. The software then allows the user to try out changes in inventory policy by creating new “What If” scenarios that might then be converted to named scenarios for further consideration. The next two scenarios are examples of Predictive Analytics. They both assess the consequences of their recommended inventory control policies, i.e., recommended values of Min and Max for all items. The “Optimization” scenario is an example of Prescriptive Analytics because it recommends a best compromise policy.

Consider how the three alternative scenarios compare to the baseline “Live” scenario. The “99% All” scenario raises the item availability metrics, increasing service level from 88% to 99%. However, doing so increases the total inventory investment from $3 million to about $4 million. In contrast, the “75 floor/99 ceiling” scenario increases both service level and reduces the cash tied up in inventory by about $300,000. Finally, the “Optimization” scenario achieves an 80% service level, a reduction from the current 88%, but it cuts more than $2 million from the inventory value and reduces operating costs by more than $400,000 annually. From here, managers could try further options, such as giving back some of the $2 million savings to achieve a higher average service level.

Summary

Modern software packages for inventory planning and inventory optimization should offer three kinds of supply chain analytics: Descriptive, Predictive, and Prescriptive. Their combination lets inventory managers track their operations (Descriptive), forecast where their operations will be in the future (Predictive), and optimize their inventory policies in response in anticipation of future conditions (Prescriptive).

Summary

Setting a target service level or fill rate is a strategic decision about inventory risk management. Choosing service levels can be difficult. Relevant factors include current service levels, replenishment lead times, cost constraints, the pain inflicted by shortages on you and your customers, and your competitive position. Target setting is often best approached as a collaboration among operations, sales and finance. Inventory optimization software is an essential tool in the process.

Service Level Choices

Service level is the probability that no shortages occur between when you order more stock and when it arrives on the shelf. The reasonable range of service levels is from about 70% to 99%. Levels below 70% may signal that you don’t care about or can’t handle your customers. Levels of 100% are almost never appropriate and usually indicate a hugely bloated inventory.

Factors Influencing Choice of Service Level

Several factors influence the choice of service level for an inventory item. Here are some of the more important.

Current service levels:
A reasonable place to start is to find out what your current service levels are for each item and overall. If you are already in good shape, then the job becomes the easier one of tweaking an already-good solution. If you are in bad shape now, then setting service levels can be more difficult. Surprisingly few companies have data on this important metric across their whole fleet of inventory items. What often happens is that reorder points grow willy-nilly from choices made in corporate pre-history and are rarely, sometimes never, systematically reviewed and updated. Since reorder points are a major determinant of service levels, it follows that service levels “just happen”. Inventory optimization software can convert your current reorder points and lead times into solid estimates of your current service levels. This analysis often reveals subset of items with service levels either too high or too low, in which case you have guidance about which items to adjust down or up, respectively.

Replenishment lead times:
Some companies adjust service levels to match replenishment lead times. If it takes a long time to make or buy an item, then it takes a long time to recover from a shortage. Accordingly, they bump up service levels on long-lead-time items and reduce them on items for which backlogs will be brief.

Cost constraints:
Inventory optimization software can find the lowest-cost ways to hit high service level targets, but aggressive targets inevitably imply higher costs. You may find that costs constrain your choice of service level targets. Costs come in various flavors. “Inventory investment” is the dollar value of inventory. “Operating costs” include both holding costs and ordering costs. Constraints on inventory investment are often imposed on inventory executives and always imply ceilings on service level targets; software can make these relationships explicit but not take away the necessity of choice. It is less common to hear of ceilings on operating costs, but they are always at least a secondary factor arguing for lower service levels.

Shortage costs:
Shortage costs depend on whether your shortage policy calls for backorders or lost sales. In either case, shortage costs work counter to inventory investment and operating costs by arguing for higher service levels. These costs may not always be expressed in dollar terms, as in the case of medical/surgical supplies, where shortage costs are denominated in morbidity and mortality.

Competition:
The closer your company is to dominating its market, the more you can ease back on service levels to save money. However, easing back too far carries risks: It encourages potential customers to look elsewhere, and it encourages competitors. Conversely, high product availability can go far to bolstering the position of a minor player.

Collaborative Targeting

Inventory executives may be the ones tasked with setting service level targets, but it may be best to collaborate with other functions when making these calls. Finance can share any “red lines” early in the process, and they should be tasked with estimating holding and ordering costs. Sales can help with estimating shortage costs by explaining likely customer reactions to backlogs or lost sales.

The Role of Inventory Optimization and Planning Software

Without inventory optimization software, setting service level targets is pure guesswork: It is impossible to know how any given target will play out in terms of inventory investment, operating costs, shortage costs. The software can compute the detailed, quantitative tradeoff curves required to make informed choices or even recommend the target service level that results in the lowest overall cost considering holding costs, ordering costs, and stock out costs. However, not all software solutions are created equal. You might enter a user defined 99% service level into your inventory planning system or the system could recommend a target service – but it doesn’t mean you will actually hit that stated service level. In fact, you might not even come close to hitting it and achieve a much lower service level. We’ve observed situations where a targeted service level of 99% actually achieved a service level of just 82%! Any decisions made as a result of the target will result in unintended misallocation of inventory, very costly consequences, and lots of explaining to do. So be sure to check out our next blog article on how to measure the accuracy of your service level forecast so you don’t make this costly mistake.

“Trillions of records of millions of people…Finding the useful and right information, understanding its quality and producing reliable analyzed data in a timely and cost-effective manner are all critical issues.”

Smart Software Senior Vice President for Research Tom Willemain recently had the opportunity to talk with Dr. Mohsen Hamoudia, President of the International Institute of Forecasters (IIF), to discuss current issues with, and opportunities for, big data analytics. The IIF informs practitioners on trends and research developments in forecasting via print and online publications and the hosting of professional conferences.

Dr. Hamoudia begins, by way of introduction:

In all industries, data availability is exploding in volume, variety and velocity. Big data analytics is playing an important role in identifying the data that is most important to the business.

Let me take the example of the Information and Communication Technology (ICT) sector. We are seeing literally exponential growth in the amount of data available to telecoms, Over-the-top (OTT) independent content distributors, government, regulators and other organizations.

Around the world, we are witnessing petabytes of data: trillions of records of millions of people—all coming from multiple sources. Among these sources: internet connections, sales, customer contact centers, social media, mobile and land lines data. Finding the useful and right information, understanding its quality and producing reliable analyzed data in a timely and cost-effective manner are all critical issues. ICT companies are increasingly looking to find actionable insights in their data. How they can increase their customer base and loyalty programs? How can they improve the quality of service (QoS) and reduce customer turnover? With the right big data analytics platforms in place, they can be more competitive and efficient, improving operations, customer service and risk management. Forecasting and predicting customer trends and directions are key for telecoms.

Forecasting skills, including mathematics, statistics and econometrics, form one of the most important “blocks” of required skills in managing Big Data. Some forecasting activities naturally form part of the big data debate.

In retail industries, forecasting addresses daily demand across thousands of products. Financial forecasting, whether considering customer behavior or financial data series, generates massive on-line data sets. As pointed out by Robert Fildes, Distinguished Professor at Lancaster University, as yet the academic forecasting community is not thoroughly engaged—with only a few exceptions. Hal Varian of Google has looked at some of the work that David Hendry and Jennifer Castle, at Oxford University, have undertaken on searching large data sets for data congruent meaningful models. Stock and Watson have also developed their own approaches to large macro data sets. But despite the attempt, at last year’s symposium on forecasting in Seoul, to explore the theme of big data and its forecasting applications, there remain few convincing applications of using on-line data on real forecasting problems.

Q. One hears a great deal about “predictive analytics” these days, yet the phrase rarely is linked with forecasting. Do you agree that forecasting lies at the heart of predictive analytics? Have you an explanation for why the link has been broken? Have you ideas about how to re-inject forecasting into the conversation?

The results of forecasting (the “what”) are perhaps now perceived as less important than the “how”. Consequently, the trust that users give to traditional forecasting has declined. Who indeed is challenging the accuracy or relevance of forecasting by comparing, a posteriori, the reality vs. forecast—making a case for metholodiges’ effectiveness and therefor building credibility?

With the current perception of “predictive analytics”, there is probably more space in the public imagination allocated to the “how” side of things, and therefor a more credible story to tell to partners, investors or customers.

Q. It appears that there is almost no link between traditional forecasting and mobile technology (smart phones, tablet computers). Is this true, or are some companies migrating forecasting to mobile devices? Do you see a path forward in which traditional forecasting algorithms would routinely reside on mobile devices?

First of all, I am really delighted to invite your readers to have a look at our latest issue of Foresight. An excellent paper on the subject, “Forecasting In the Pocket: Mobile Devices Can Improve Collaboration”, explains that “the increasing popularity of PDAs, smartphones, tablet computers and other mobile devices opens up new opportunities for communication and collaboration on business forecasts.” The authors tell us “mobile forecasting (m-forecasting) applications may streamline approaches to collaboration between retailers and suppliers, thus contributing to the provision and exchange of product information, especially since forecasts are strongly tied to local context knowledge.”

For example, on the ICT & OTT side, a large number of predictive projects, such as those of Google+ and Facebook, are happening thanks to the inclusion of the “user location” data in the OTT IT systems. In my opinion, and what I see in some sectors like retail and logistics, is that traditional forecasting and mobile forecasting (m-forecasting) are complementary. This latter could be seen as a bottom-up forecasting approach that will or will not confirm the top-down forecasting results.

Q. Some people argue that big data will facilitate the replacement of forecasting with “sense and react” systems. Practically speaking, how would you explain “sense and react”, and are there application areas where you think it is or is not likely to take hold?

It seems to me that “sense and react” is fully oriented to the short-term perspective. Forecasting extends this by addressing needs for a variable horizon: short-term and medium-term.

As a side effect of ATAWAD (Anytime, Anywhere, Any Device), the decision-making criteria are, more than ever, “short term”. Big data is a “weak signals” sensing system, which enable the near-real-time detection of business opportunities that would go unnoticed with traditional IT systems. There are not really preferred or non-priority applications for this, the question is more on the “when” side.

Big data is relevant when looking below the surface in difficult economic times, but I am less sure whether it is worth the effort in “normal” economic period. To conclude on this point: I will be happy to see an example on how accurate are forecasts which are based on “sense and react” versus forecast based on traditional models.

Q. I’m asking some big questions. To what extent do you see the IIF community shaping these discussions and outcomes? How can readers join in the dialogue?

We are expecting an increasing availability, and increasing usage, of huge amount of data in many industries—such as energy, transportation, health care, finance, telecommunications and tourism.

Many of the IIF’s members are engaged in different aspects of the big data “movement.” The IIF is doing some work in the forecasting activities that naturally form part of the big data debate. More generally, the IIF is actively participating in, and providing a forum for, the discussion of forecasting in the wider world.

The theme of our last International Symposium on Forecasting (ISF) held in Seoul was “Forecasting with Big Data” and a few presentations were related to health care and telecommunications. A relevant workshop has just been run by the European Central Bank (ECB). If these models are capitalized on, they have the potential to impact the economic policy of Europe quite quickly.

Readers can join in the dialogue by contributing papers to the IIF’s publications (The International Journal of Forecasting, Foresight and The Oracle). Foresight, for one, is an invaluable voice in bringing academics and practitioners together in an ongoing discussion.

Readers also can present papers at the annual conference (the aforementioned ISF). They also can suggest and organize specific workshops for specific applications of big data, like the one that was just organized by the ECB in Frankfurt. Another opportunity is to invite IIF’s members to attend any meeting related to forecasting with big data. All these opportunities form good platforms for networking and working together.

Mohsen Hamoudia, PhD, is the President of the International Institute of Forecasters. He also serves as Head of Strategy for Large Projects (Paris) for Orange Business Services (the former France Telecom).

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.

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.

Recall the definition: Turns = Annual dollar cost of goods sold / Average dollar value of inventory. If you sell $1 million of stuff in a year and have an average of $100,000 of stuff on the shelf each day, you are running at an impressive 10 Turns (Walmart runs at around 8). Supposedly, having high Turns signals efficient management, and keeping your Turns higher than competitors’ signals competitive advantage.

But as happens with most performance metrics, there is more to the story. Turns may be very salient to the CFO, but they can be a straightjacket to the COO. This is because Turns are not directly related to customer service; in fact, high Turns can be synonymous with low service levels and fill rates. S&OP consultant Darrin Oliver calls Turns his “pet peeve metric” because “the customer doesn’t care about Turns.”

Suppose you are unhappy with your current Turns value. What can you do to boost the number? Since Turns is a ratio, you can increase it by either increasing the numerator (goods sold) or decreasing the denominator (inventory). Increasing sales is more difficult because it requires the cooperation of the customer. Decreasing inventory is easier because it’s completely under your control: just make smaller replenishment orders, which also saves money in the short run. Indeed, you can get very enthusiastic and cut inventory to the bone. You end up with a better looking number for Turns—and a serious problem with stockouts, backorders, lost sales, lost customer good will and lost market share. Who’s sorry now?

Here’s a lightly edited version of a story on this topic told by a very wise practitioner. “Back in my other life they were all about improving Turns. Why, I have no idea. So I pointed out the risks that you run. And they really weren’t interested. So we took our global inventories down to [a lower level], and then were breaking on stock left and right on a daily basis. Our turns were great, but we weren’t making any money, because we couldn’t get anything out the door, because we didn’t own it. The higher your turns, the lower your inventory’s going to have to be, or you’re just going to have really good flow. And in our industry that’s a very, very difficult thing to achieve. So if we can have reasonable Turns but still be in stock, I think that’s what we want to do. It can be very frustrating in an operations world to try to explain what we do every day and what the risks to the business are when the financial people are just looking at one or two metrics. They’re basically trying to plan the business in a vacuum, and it’s very difficult and very risky to do that.”

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.

There’s a stale old joke: “There are two types of people – those who believe there are two types of people, and those who don’t.” We can modify that joke: “There are two types of people – those who know there are three types of supply chain analytics, and those who haven’t yet read this blog.”

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.

Descriptive Analytics

Descriptive Analytics are the stuff of dashboards. They tell you “what’s happenin’ now.” Included in this category are such summary numbers as dollars currently invested in inventory, current customer service level and fill rate, and average supplier lead times. These statistics are useful for keeping track of your operations, especially when you track changes in them from month to month. You will rely on them every day. They require accurate corporate databases, processed statistically.

Predictive Analytics

Predictive Analytics most commonly manifest as forecasts of demand, often broken down by product and location and sometimes also by customer. These statistics provide early warning so you can gear up production, staffing and raw material procurement to satisfy demand. They also provide predictions of the effect of changes in operating policies, e.g., what happens if we increase our order quantity for Product X from 20 to 25 units? You might rely on Predictive Analytics periodically, perhaps weekly or monthly, when you look up from what’s happening now to see what will happen next. Predictive Analytics uses Descriptive Analytics as a foundation but adds more capability. Predictive Analytics for demand forecasting requires advanced statistical processing to detect and estimate such features of product demand as trend, seasonality and regime change.  Predictive Analytics for inventory management uses forecasts of demand as inputs into models of the operation of inventory policies, which in turn provide estimates of key performance metrics such as service levels, fill rates, and operating costs.

Prescriptive Analytics

Prescriptive Analytics are not about what is happening now, or what will happen next, but about what you should do next, i.e., they recommend decisions aimed at maximizing inventory system performance. You might rely on Prescriptive Analytics to best posture your entire inventory policy. Prescriptive Analytics uses Predictive Analytics as a foundation then adds optimization capability. For instance, Prescriptive Analytics software can automatically work out the best choices for future values of Min’s and Max’s for thousands of inventory items. Here, “best” might mean the values of Min and Max for each item that minimize operating cost (the sum of holding, ordering, and shortage costs) while maintaining a 90% floor on item fill rate.

Example

The figure below shows how supply chain analytics can help the inventory manager. The columns show three predicted Key Performance Indicators (KPI’s): service level, inventory investment, and operating costs (holding costs + ordering costs + shortage costs).

 Figure 1: The three types of analytics used to evaluate planning scenarios

The rows show four alternative inventory policies, expressed as scenarios. The “Live” scenario reports on the values of the KPI’s on July 1, 2018. The “99% All” scenario changes the current policy by raising the service level of all items to 99%. The “75 floor/99 ceiling” scenario raises service levels that are too low up to 75% and lowers very high (i.e., expensive) service levels down to 95%. The “Optimization” scenario prescribes item specific service levels that minimizes total operating costs.

The “Live 07-01-2018” scenario is an example of Descriptive Analytics. It shows the current baseline performance. The software then allows the user to try out changes in inventory policy by creating new “What If” scenarios that might then be converted to named scenarios for further consideration. The next two scenarios are examples of Predictive Analytics. They both assess the consequences of their recommended inventory control policies, i.e., recommended values of Min and Max for all items. The “Optimization” scenario is an example of Prescriptive Analytics because it recommends a best compromise policy.

Consider how the three alternative scenarios compare to the baseline “Live” scenario. The “99% All” scenario raises the item availability metrics, increasing service level from 88% to 99%. However, doing so increases the total inventory investment from $3 million to about $4 million. In contrast, the “75 floor/99 ceiling” scenario increases both service level and reduces the cash tied up in inventory by about $300,000. Finally, the “Optimization” scenario achieves an 80% service level, a reduction from the current 88%, but it cuts more than $2 million from the inventory value and reduces operating costs by more than $400,000 annually. From here, managers could try further options, such as giving back some of the $2 million savings to achieve a higher average service level.

Summary

Modern software packages for inventory planning and inventory optimization should offer three kinds of supply chain analytics: Descriptive, Predictive, and Prescriptive. Their combination lets inventory managers track their operations (Descriptive), forecast where their operations will be in the future (Predictive), and optimize their inventory policies in response in anticipation of future conditions (Prescriptive).