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).

No, not that kind of regime change: Nothing here about cruise missiles and stealth bombers. And no, we’re not talking about the other kind of regime change that hits closer to home: Shuffling the C-Suite at your company.

“Regime change” has a third meaning that is relevant to your profession as a demand planner or inventory manager. To researchers in economics and finance, regime change means sudden shifts in the very character of a time series of random observations. The random time series in question here is the sequence of daily (or weekly or monthly) demand counts for your products and inventory items.

Most forecasting software uses statistical algorithms to process historical demand. It may add additional steps, such as incorporating field intelligence from sales people, but everything starts with the demand history of whatever item you must manage.

The question raised by regime change is, which data do you use? The simple answer is “All of it”, because that leads to the most accurate forecasts — but only if your data world is stable. If your data world is turbulent, then using all the data means you are basing forecasts on bye-gone conditions. In turn, inputting obsolete data into your forecasting algorithms inevitably leads to reduced forecast accuracy.

Note that dealing with regime change is not the same as dealing with outliers. Outliers are usually one-off exceptions caused by transient events, such as a kink in your supply chain caused by a huge blizzard choking off all transit paths. In contrast, regime change persists over a longer period and is therefore capable of doing more damage to your forecasts. Here’s an analogy: Outliers are about weather, and regime change is about climate.

The most drastic forms of regime change are existential. Figure 1 shows an example of an existential change: There was no demand at all for a long time, then suddenly there was demand. If you had no demand for an item because it didn’t exist but you retain zero demand values in your database, and then the item goes live and you do have sales, the transition from nothing to something is an extreme regime change. Including all those zero demand values from before “Day One” is sure to bias statistical forecasts down below where they should be. The same thing happens if you kill off a product but keep recording zero demand: Including all those recent zeros degrades your demand forecasts.

In principle, careful record keeping should eliminate these problems. You should record only meaningful zero values. If you have a new item, start recording when it goes live. If you no longer have any demand for an item and expect none, purge it from your database, or at least forecast zero demand.

Unfortunately, there is a difference between principle and practice. We see many instances in which the data records for both new and dormant items are not properly kept, with “fake zeros” confounded with “real zeros”. This problem is not necessarily the result of incompetence: Usually, it is a byproduct of the scale of the problem, with too few people trying to keep track of too many items.

These existential regime changes are relatively easy to deal with compared to more subtle forms, which appear to afflict more items. Figure 2 shows two examples of regime changes in a pattern of ongoing sales. There are any number of factors that can change the demand for an item: salesforce performance, marketing and advertising efforts, competitor and supplier actions, new customers arising or old customers disappearing, etc. If demand for an item has been chugging along at a steady 1 unit per day but suddenly doubles (or vice versa), that’s a regime change. In the new world order, demand is 2 units/day and forecasts should reflect that. Instead, statistical forecasting algorithms will forecast too little demand if fed all the data, including that from before the regime change.

How do you protect yourself from regime change? The answer is the same for the cruelest dictator or the most innocent demand planner: Intelligence. And because threats are many, the intelligence is best automated. Modern software systems have the capability to screen tens of thousands of items for signs of regime change. Then the software can call your attention to the problematic items and prompt you to designate which recent data to use in calculations. Or the software can automatically detect and correct for regime change, working quickly at a scale that would easily defeat any busy person working “by hand”.

Service level is a key performance indicator for companies that put a premium on satisfying customer demand. Service level is defined as the probability of surviving a replenishment lead time without stocking out.

Inventory management best practice begins with setting service level targets, then calculates reorder points (also called Mins) to achieve those targets. These calculations should account for variability in both demand and replenishment lead time. There are many software systems available for doing these calculations. If everything works out, the achieved service level ends up very close to the target service level. Unfortunately, there is often a painful gap between the two.

One reason for the gap is unrealistic models of demand. In many cases, software for calculating reorder points uses textbook formulas based on mathematical assumptions that make analysis simple at the expense of realism.  Many “Inventory 101” textbooks use formulas that assume demand has a Normal distribution (a.k.a. the “bell-shaped curve”) for finished goods and the Poisson distribution for spare parts. Fortunately, there are now inventory optimization and forecasting systems that process the actual demand histories of the inventory items using probabilistic forecasting.  These solutions calculate an accurate estimate of the distribution – not some idealized version.  To learn more check out this past blog on probabilistic forecasting:

But there is a second source of error in textbooks that operates invisibly in many inventory software package:  “undershoot”.

Calculations of reorder points almost always assume that stockouts arise when the total demand during a replenishment interval exceeds the reorder point. For example, assume that demand averages 1 unit per day. If lead time is 5 days, then on average lead time demand is 5 units. Setting the reorder point at 5 units would yield a laughable service level somewhere in the vicinity of 50%. Adding safety stock to the calculation might result in a reorder point of, say, 11 units, which might correspond to a service level of 95%. Another way to say this is, starting at a reorder point of 11 units, there should be a 95% chance of surviving the 5 day lead time without experiencing cumulative demand of more than 11 units. Theoretically!

What’s missing from this analysis is the undershoot phenomenon. Undershoot means that the lead time begins not at the reorder point but below it. Undershoot happens every time the demand that breached the reorder point took the stock down below (not down to) the reorder point. The figure below shows replenishment cycles with and without undershoot.  Undershoot picks your pocket before you even begin to roll the dice. It deludes the inventory professional into thinking his or her reorder points are sufficient to achieve their targets, whereas actual performance will not make the grade.

There is only one situation in which undershoot is not a worry: when demand is always either zero or one unit. In that case, undershoot is impossible. But in all other cases, undershoot is sure to happen to some extent, and it can seriously undercut the service level actually achieved by a given choice of reorder point. Our analyses show that the conditions most vulnerable to undershoot involve highly intermittent and skewed demand with very short lead times – the very conditions being made most common by market trends.

What can be done to protect yourself from the effect of undershoot on reorder point calculations?  Use inventory optimization and forecasting software that isn’t tied to the old textbook assumptions and instead automatically accounts for undershoot when calculating the service level produced by any choice of reorder point.

To see Smart Software’s Inventory Optimization solution in action, register to see a recorded demo below:

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.

In our last blog we posed the question:  How can you be sure that you really have a policy for inventory planning and demand forecasting? We explained how an organization’s lack of understanding on the basics (how a forecast is created, how safety stock buffers are determined, and how/why these values are adjusted) contributes to poor forecast accuracy, misallocated inventory, and lack of trust in the whole process.

In this blog, we review 10 specific questions you can ask to uncover what’s really happening at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

Always start with a simple hypothetical example. Focusing on a specific problem you just experienced is bound to provoke defensive answers that hide the full story. The goal is to uncover the actual approach used to plan inventory and forecasts that has been baked into the mental math or spreadsheets.   Here is an example:

Suppose you have 100 units on hand, the lead time to replenish is 3 months, and the average monthly demand is 20 units?   When should you order more?  How much would you order? How will your answer change if expected receipts of 10 per month were scheduled to arrive?  How will your answer change if the item is the item is an A, B, or C item, the cost of the item is high or low, lead time of the item is long or short?  Simply put, when you schedule a production job or place a new order with a supplier, why did you do it? What triggered the decision to get more?  What planning inputs were considered?

When getting answers to the above question, focus on uncovering answers to the following questions:

1. What is the underlying replenishment approach? This will typically be one of Min/Max, forecast/safety stock, Reorder Point/Order Quantity, Periodic Review/Order Up To or even some odd combination

2. How are the planning parameters, such as demand forecasts, reorder points, or Min/Max, actually calculated? It’s not enough to know that you use Min/Max.  You have to know exactly how these values are calculated. Answers such as “We use history” or “We use an average” are not specific enough.   You’ll need answers that clearly outline how history is used.  For example, “We take an average of the last 6 months, divide that by 30 to get a daily average, and then multiply that by the lead time in days.  For ‘A’ items we then multiply the lead time average by 2 and for ‘B’ items we use a multiplier of 1.5.” (While that is not an especially good technical approach, at least it has a clear logic.)

Once you have a policy well-defined, you can identify its weaknesses in order to improve it.  But if the answer provided doesn’t get much further past “We use history”, then you don’t have a policy to start with.   Answers will often reveal that different planners use history in different ways.  Some may only consider the most recent demand, others might stock according to the average of the highest demand periods, etc.  In other words, you may find that you actually have multiple ill-conceived “policies”.

3. Are forecasts used to drive replenishment planning and if so, how? Many companies will say they forecast, but their forecasts are calculated and used differently. Is the forecast used to predict what on hand inventory will be in the future, resulting in an order being triggered?  Or is it used to derive a reorder point but not to predict when to order (i.e. I predict we’ll sell 10 a week so to help protect against stock out, I’ll order more when on hand gets to 15)? Is it used as a guide for the planner to help subjectively determine when they should order more?  Is it used to set up blanket orders with suppliers?  Some use it to drive MRP. You’ll need to know these specifics.  A thorough answer to this question might look like this: “My forecast is 10 per week and my lead time is 3 weeks so I make my reorder point a multiple of that forecast, typically 2 x lead time demand or 60 unit for important items and I use a smaller multiple for less important items.  (Again, not a great technical approach, but clear.)

4.  What technique is actually used to generate the forecast? Is it an average, a trending model such as double exponential smoothing, a seasonal model? Does the choice of technique change depend on the type of demand data or when new demand data is available? (Spare parts and high-volume items have very different demand patterns.) How do you go about selecting the forecast model? Is this process automated?  How often is the choice of model reconsidered?  How often are the model parameters recomputed? What is the process used to reconsider your approach?  The answer here documents how the baseline forecasts are produced.  Once determined, you can conduct an analysis to identify whether other forecasting methods would improve forecast accuracy.  If you aren’t documenting forecast accuracy and conducting “forecast value add” analysis then you aren’t in a position to properly assess whether the forecasts being produced are the best that they can be.  You’ll miss out on opportunities to improve the process, increase forecast accuracy, and educate the business on what type of forecast error is normal and should be expected.

5. How do you use safety stock? Notice the question was not “Do you use safety stock?” In this context, and to keep it simple, the term “safety stock” means stock used to buffer inventory against supply and demand variability.  All companies use buffering approaches in some way.  There are some exceptions though.  Maybe you are a job shop manufacturer that procures all parts to order and your customers are completely fine waiting weeks or months for you to source material, manufacture, QA, and ship.  Or maybe you are high-volume manufacturer with tons of buying power so your suppliers set up local warehouses that are stocked full and ready to provide inventory to you almost immediately.  If these descriptions don’t describe your company, you will definitely have some sort of buffer to protect against demand and supply variability.  You may not use the “safety stock” field in your ERP but you are definitely buffering.

Answers might be provided such as “We don’t use safety stock because we forecast.”  Unfortunately, a good forecast will have a 50/50 chance of being over/under the actual demand.  This means you’ll incur a stock out 50% of the time without a safety stock buffer added to the forecast.  Forecasts are only perfect when there is no randomness. Since there is always randomness, you’ll need to buffer if you don’t want to have abysmal service levels.

If the answer isn’t revealed, you can probe a bit more into how the varying replenishment levers are used to add possible buffers which leads to questions 6 & 7.

6. Do you ever increase the lead time or order earlier than you truly need to?
In our hypothetical example, your supplier typically takes 4 weeks to deliver and is pretty consistent. But to protect against stockouts your buyer routinely orders 6 weeks out instead of 4 weeks.  The safety stock field in your ERP system might be set to zero because “we don’t use safety stock”, but in reality, the buyer’s ordering approach just added 2 weeks of buffer stock.

7. Do you pad the demand forecast?
In our example, the planner expects to consume 10 units per month but “just in case” enters a forecast of 20 per month.  The safety stock field in the MRP system is left blank but the now disguised buffer stock has been smuggled into the demand forecast.  This is a mistake that introduces “forecast bias.”  Not only will your forecasts be less accurate but if the bias isn’t accounted for and safety stock is added by other departments, you will overstock.

The ad-hoc nature of the above approaches compounds the problems by not considering the actual demand or supply variability of the item. For example, the planner might simply make a rule of thumb that doubles the lead time forecast for important items.  One-size doesn’t fit all when it comes to inventory management.  This approach will substantially overstock the predictable items while substantially understocking the intermittently demanded items. You can read “Beware of Simple Rules of Thumb for Managing Inventory” to learn more about why this type of approach is so costly.

The ad-hoc nature of the approaches also ignores what happens the company is faced with a huge overstock or stock out. When trying to understand what happened, the stated policies will be examined. In the case of an overstock, the system will show zero safety stock.  The business leaders will assume they aren’t carrying any safety stock, scratch their heads, and eventually just blame the forecast, declare “Our business can’t be forecasted” and stumble on. They may even blame the supplier for shipping too early and making them hold more than needed. In the case of a stock out, they will think they aren’t carrying enough and arbitrarily add more stock across many items not realizing there is in fact lots of extra safety stock baked into process.  This makes it more likely inventory will need to be written off in the future.

8. What is the exact inventory terminology used? Define what you mean by safety stock, Min, reorder point, EOQ, etc.  While there are standard technical definitions it’s possible that something differs, and miscommunication here will be problematic.  For example, some companies refer to Min as the amount of inventory needed to satisfy lead time demand while some may define Min as inclusive of both lead time demand and safety stock to buffer against demand variability. Others may mean the minimum order quantity.

9. Is on hand inventory consistent with the policy? When your detective work is done and everything is documented, open your spreadsheet or ERP system and look at the on-hand quantity. It should be more or less in line with your planning parameters (i.e. if Min/Max is 20/40 and typical lead time demand is 10, then you should have roughly 10 to 40 units on hand at any given point in time.  Surprisingly, for many companies there is often a huge inconsistency. We have observed situations where the Min/Max setting is 20/40 but the on-hand inventory is 300+.  This indicates that whatever policy has been prescribed just isn’t being followed.   That’s a bigger problem.

10. What are you going to do next?

Demand forecasting and inventory stocking policy need to be well-defined processes that are understood and accepted by everybody involved.  There should be zero mystery.

To do this right, the demand and supply variability must be analyzed and used to compute the proper levels of safety stock.   Adding buffers without an implicit understanding of what each additional unit of buffer stock is buying you in terms of service is like arbitrarily throwing a handful of ingredients into a cake recipe.  A small change in ingredients can have a huge impact on what comes out of the oven – one bite too sweet but the next too sour.  It is the same with inventory management.  A little extra here, a little less there, and pretty soon you find yourself with costly excess inventory in some areas, painful shortages in others, no idea how you got there, and with little guidance on how to make things better.

Modern inventory optimization and demand planning software with its advanced analytics and strong basis in forecast analysis can help a good deal with this problem. But even the best software won’t help if it is used inconsistently.

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.