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Inventory optimization is a much-used phrase, but what exactly is it, how do you achieve it and do you in fact even need it? As our mission as a company is to help organisations get value from inventory optimization, this is a topic very close to our heart.

In this article we consider inventory optimization to be the application of mathematical techniques to define ideal inventory levels. Such theoretical models are not easy to apply in practice. The idea that a piece of software can allow you to optimize your inventories simply and automatically is attractive but at present far from possible.

We believe that the biggest value to be had from inventory optimization techniques is in showing you how much better your inventories could be, while the correct interpretation and application of that information is likely to require changes to how you manage your inventory.

The benefits of overcoming these challenges are substantial. Inventory optimization allows you to free up working capital, reduce shortages and obsolescence, reduce your environmental footprint and increase flow through your supply chain, usually by significant amounts.

You will often see any improvement to inventory management described as inventory optimization, but this is not strictly speaking accurate.

In mathematics, optimization means selecting the best option from a set of alternatives. When talking about inventory, this comes down to determining the best amount of inventory to hold to meet your objectives. Inventory optimization mathematically seeks to find the sweet spot between having too much and not enough.

This is a simple concept to grasp in principle, but not to apply in practise. In order to calculate an optimum level mathematically, you need to factor in a number of different considerations, some of which are themselves difficult to quantify.

Let’s say that you want to optimize on purely economic principles – what will make you the most profit? To do this you need to be able to quantify, on the one hand, the true holding cost of inventory and, on the other hand, the cost of shortages, which might include backorder costs, expediting costs, the cost of lost sales and lost market share, even the cost of damage to your brand. Noting that these costs are entwined with each other. It’s not that this is impossible to do, but it is laborious and somewhat imprecise.

In fact, it would be fair to say that few people, if any, actually try to calculate this. Instead, normal practice is to set a target service level, either implicitly or explicitly, and then try to minimize inventories while staying at or above that service level. This is a good, pragmatic move, that ties down one of the more difficult parameters to set. However, even with a pre-defined target service level, there is still a lot of work to do to calculate optimum inventory levels.

When it comes to improving inventories, we like to differentiate between optimizing, which involves working with the givens, and improvements based on changing the givens. What are the givens? They are the parameters you need as inputs to your inventory optimization calculations. We have already looked at target service level as a given, but this is just one of a range of such parameters.

When you work with the givens, you don’t challenge them, you accept them as fixed parameters and then try to optimize your inventory levels on that basis. Which is not to say that the givens can’t be improved, but you strictly speaking need to have such parameters defined to be able even to attempt an optimization calculation.

How can you optimize your inventories?

Inventory optimization comes down to defining two things for each item: the optimal order quantity/batch size (Q) and the optimal safety stock (SS), noting that the latter might be zero or even negative. But there is no one way to do this for all items you stock, since the method chosen will itself vary depending on a number of factors. Let us start by taking a couple of simple examples.

1. You have a product that is made to order. Whenever a customer orders one, you have 10 weeks to make it. It takes one week to make and 4 weeks to source the parts. Everything you need is at or above your minimum order quantities (MOQ’s) and you have no inbound supply variability or capacity constraints.
2. You have a product that is made to stock. Demand is relatively stable, with a limited amount of variability, as is supply. You check inventory levels of finished goods continuously; you schedule production to avoid stock outs and you try to maintain raw materials inventory to be able to produce when needed.

Note that if anything in bold changes, the change will need to be factored into your optimization calculation, as will many of the other factors shown in Figure 1 above.

Different types of inventory require not just different optimization calculations, but also different planning methodologies.

For the first example above, we can consider a deterministic planning model. This is appropriate when you know with a high degree of certainty what demand is going to be, or at least that it is going to be significantly different to the past. In this instance, your order and batch sizes can be set to exactly what is required and in principle you shouldn’t need safety stock, although you might need to have some safety stock to protect against variation in supply, such as late deliveries or quality problems in production.

For the second example, we can consider a replenishment model. This is appropriate for make-to-stock finished goods and buy-to-stock raw materials where demand is not too intermittent but displays variability. In this case, the EOQ model or one of its variants should be used to calculate optimal order/batch size. Safety stock can be calculated using the target service level, the standard deviations of demand and lead time, and the lead time itself.

However, this replenishment model, which is based on probably the most widely known text-book set of equations, has important sensitivities. The most commonly found version of the safety stock equation, for instance, assumes that demand is normally distributed, that average demand does not vary, and it also breaks down when the variability is very high. You will sometimes find people saying that the equations “don’t work”, but this is often just their understanding of the equations running into some of these barriers which they either don’t understand or at any rate don’t know how to work around.

Note that different models might work best at different points in your supply chain. For instance, you might, depending on your exact circumstances, choose a replenishment model for finished goods and a deterministic model for raw materials. To really optimize your inventories, you are likely to need to employ different strategies for different types of inventory.

And these two examples are far from representing the only types of stock you might carry. For instance, you may have make-to-order items where the customer promise time is shorter than the total supply lead time. You may have spare parts with highly sporadic demand but which need to be available on demand. And so on. Each different type of inventory needs an appropriate optimization approach.

In order to fully optimize your inventories, all of the factors above need to be taken into consideration for every single item that you stock. And so far, we’re just talking about mathematical optimization. In addition – and no less a challenge – you have to consider what processes, systems and people are in place and how they would need to change in order to deliver and maintain the optimization.

In our experience, the biggest gap is often in the people themselves and how they are used to working. The knowledge of optimization techniques and how to take advantage of them is a rare commodity amongst material planners, whereas data scientists do not necessarily have the requisite understanding of the practicalities of the business to enable practical application of their models. Technology is sometimes seen as a way around this – remove people from the equation as far as possible – but we are a long way from this being a satisfactory solution yet.

Do you need inventory optimization?

From the brief summary above, it should be evident that inventory optimization is not simple. In fact, it would be closer to the truth to call it impossible than to call it simple, but this is not an all or nothing situation. Given the complexities outlined, as well as the fact that your optimal inventory levels are constantly changing, it should be admitted from the start that fully optimized inventory is not an achievable goal. But the application of optimization techniques is incredibly valuable. You don’t have to win an Olympic gold medal to get value from learning how to swim. Moreover, many of the biggest benefits from inventory optimization come from the first steps.

In the absence of inventory optimization, inventory management is flying blind. You are searching for the sweet spot between too little and too much inventory without knowing where that sweet spot is. Think of it as trying to find a black mouse in a completely dark room. With optimization techniques, you are switching the light on. It may still be difficult to catch the mouse, but at least you can get closer to it because you can see where it is.

It is of course possible to manage your inventories without trying to optimize them, and especially in times of abundant cheap cash this is what many organisations choose to do. But without the analytics of inventory optimization you have no real way of knowing how well you’re doing. Or rather, your view becomes lopsided. Your service level performance will tell you if you have enough, but what is telling you if you have too much? Top-down aggregate measures like cover, inventory turns or DIO give you ratios that will tell you whether you’re getting leaner or not over time, and can be used for quick benchmarking exercises, but strictly speaking tell you nothing about how much you ideally should have.

Where organisations do not use optimization techniques, there is a strong tendency to apply “one size fits all” approaches to different types of inventory: for instance, setting target safety stock levels for all raw materials at 2 weeks’ average demand. This could only possibly be optimal if all of your raw materials had identical variability of demand and supply as well as identical lead times. This one size fits all approach means there is a strong correlation between shortages and inventory levels long before you reach an optimized level. Because inventory optimization by necessity works at an individual item level, it allows you in aggregate to reduce inventories and shortages simultaneously.

From our own empirical experience of working with multiple organisations, it is normally the case that inventory optimization alone (so taking the givens as given) allows net inventory reductions of 20-50% while maintaining or improving service levels. This improvement takes time and effort to achieve, but double-digit improvements are normally achievable in the first year. We can think of no other option that will deliver such a large improvement so quickly in such a risk-free fashion.

How do you take advantage of inventory optimization?

The good news is that not all of the technical problems need to be solved at once and that some of the biggest benefits come from the simplest steps.

To take advantage of inventory optimization you need two things. Firstly, the ability to run the optimization calculations. Secondly, and no less importantly, the ability to operationalize the findings of your analytics. You will only sustainably improve inventory levels if your organisation can use the insights generated by the analytics in its day to day work.

The biggest value in inventory optimization analytics comes from the visibility it gives you into where the biggest opportunities for improvement are. It highlights things that are not necessarily obvious or intuitive. In the first instance, a calculation using a number of approximations is normally good enough. Once this is well understood, you can go further and seek to remove or refine the input approximations one at a time, noting that you will never remove all of them entirely.

Because the analytical challenge is high, specialist skills are required to do it properly. But those skills are not necessarily the same ones required to make changes to how your business actually works. Too much emphasis on analytics, or a siloed mentality, can leave a large gap between theory and practice. The analytics itself is only one part of the picture. In addition, you need to build the people, processes and systems to take advantage of it.

Inventory optimization is not something you can implement and then forget about. Rather, it is an ongoing process which needs constant attention. All of the input parameters to your optimization calculations are in a state of almost constant change and the right process controls need to be in place to stay on top of your inventories, especially the closer you get to optimal levels.

If done well, inventory optimization can transform how well your supply chain functions, but this requires a clear understanding of what it can do and how to use it. Year one of a well-designed programme can easily deliver double digit improvements from some of the easiest steps, while those who want to get much closer to optimization can deliver that type of improvement for many years in succession.

What impact would a 25% reduction in inventory have on your business? Well:

• It would make a nice difference to your balance sheet. It would reduce working capital and overall capital employed. It would reduce the amount written off for obsolescence each year
• It would make a nice difference to your income statement. Typically inventory holding costs (including finance costs) can add up to 25% of the value of inventory held each year
• You would have some cash available for more interesting uses than sitting in a warehouse: capital investment, acquisitions or equity buy-back for instance
• And it would make a nice difference to the ratios your investors care about. Both Return on capital employed and return on equity will improve, as will Earnings per share (EPS), especially if you choose to use the liberated cash for a share buyback

Suggesting an improvement in working capital to most CFO’s is a case of preaching to the choir. To illustrate the impact of a 25% reduction in inventory on key financials we have included an example in an appendix to this paper. For a €2.5bn turnover company with €400m in inventory such as the one we take in our example it improves ROCE by 2%, EPS by 3% and ROE by over 4%. These percentages will heavily depend on your circumstances; the point is that the difference is enough to care about.

The case for holding inventory

There cannot be too many CFO’s who wouldn’t sign up for the scale of improvement above if they believed it was achievable. But is less inventory automatically a good thing? Well not if it is at the expense of customer service or sales. The whole purpose of inventory after all is to provide a buffer between demand and your ability to supply. If profit margins are high, and raw materials markets unpredictable, the temptation to hold plenty of inventory can be strong. But our contention is that most firms have significantly more than they need, whilst still managing to run short of some items.

A hundred years ago, inventory was considered a good thing. The attitude was “pile them high, sell them cheap”. Assets were a source of pride. But increasingly, leanness is considered a virtue. In times of seemingly constant innovation and change, the risk of obsolescence is high and almost all products can seem ephemeral. Increases in on-demand production and the ease of finding alternatives via online services is only intensifying this trend. Online retailers can actually achieve negative working capital. Whilst this isn’t the case in most other industries, we do see that the ability to be agile and to hold inventories much closer to ideal levels is increasingly going to be a source of competitive advantage.

To get a very approximate sense of how close to the mark you are, look at your Days Inventory Outstanding (DIO). This measure takes your total inventory value and divides it by cost of sales days (total cost of sales/365). The number you get will not tell you very much per se – different businesses will have significantly different inventory needs – but comparison with your peers should be informative, and the figure will give you the ability to sense check it yourself. If your DIO is equivalent to 80 days, for instance, as in the simplified example in the appendix below, that means on average you are holding over 2.6 months of stock. Does it take you that long to source, produce and ship your products, even allowing some spare for demand fluctuation? It might do, if you have long lead times, a lengthy production process and high demand variability, but these are the types of question to ask your Operations teams. Bear in mind as well that with an average of 2.6 months of stock, you will have significantly more of some items. Reducing DIO to 60 days, again following our example, still gives you 2 months’ inventory on average.

This is one of the headaches for a finance executive. Supply chains are different. Depending on your production strategy (make to order vs make to stock), the seasonality and variability of your demand, the lead times in your supply chains and the economics of your target service levels, the “right” amount of inventory for you can vary enormously. Appropriate policies will also vary depending on your industry and the nature of your supply chain.

Plus there are of course other levers to improve working capital, which may well seem much simpler to pull. Reducing accounts receivable is mostly about internal process discipline. Even increasing accounts payable, whilst often labour intensive, is usually just a case of lengthening payment terms with your suppliers. And increasingly, supply chain financing options exist to help improve this balance depending on your own particular working capital needs. By comparison, inventory can seem a very complex undertaking, and one which we know many firms have tried and failed to address in the past.

Between having an intuition that you might be holding excess inventory and actually being able to see exactly what you should have, let alone do something about it, there is quite a gap. Indeed, we find that between firms who have tried and failed to seriously get a handle on inventory and those who have consistently put off doing something about it, there are very few who have tight control of inventories and a strong understanding of the different levers which contribute to it. But why is this so?

In our experience, at the heart of the problem lies data. A reasonably large firm might easily have thousands of different items between finished goods, work in progress and raw materials. Each transaction should be captured in your ERP systems, and stocks should be reconciled with physical stocks at least once per year. With this data, you have everything you need to analyse your inventory and identify optimal levels BUT the data is hard to extract, manipulate and then calculate, whilst inventory optimization software, whether built into your ERP or integrated with it, is highly sensitive to relatively minor input adjustments which are frequently poorly understood. As a result, even mathematically sound planning tools fail to do the job of optimizing inventory and don’t really give you any insight into how you are performing overall and what can be done to improve the situation.

From a lack of clear and accessible data stems a number of other challenges: inability to correctly identify the root causes of sub-optimal stock levels, and a politicisation of inventory. In the absence of good data there are many competing voices with different agendas and no obvious objective mechanism to decide between them. Other KPI’s, which should be balanced through inventory management, are given priority: Supply Chain focus on On Time In Full (OTIF) deliveries, whilst Sales provide inflated forecasts because they are measured on total sales not forecast accuracy. Unions like inventory as it reduces pressure on workers. All of these factors will tend towards excessive stock being accumulated.

Cutting through the data complexity

How nice it would be to have the following picture available for every item you stock, as well as a roll-up of overall optimization potential:

A = actual historical stock on hand. B = average of A. C = target stock on hand. D = average of C. E = excessive stock, or difference between actuals and target.

This graph shows a familiar basic supply chain concept. The blue “sawtooth” line (C) represents what you should have had on stock. The green line floating above it (A) represents what you actually had. Fairly simple to plot a course of action from this picture.

Of course, the simplified model upon which this diagram is based needs to be enhanced with probabilistic considerations to account for all kinds of variability. You also need to understand demand patterns, which is a science in itself. This raises the bar somewhat on the mathematical front. It also demands segmentation along ABCXYZ lines to identify appropriate policies for different types of stock. But with this type of granularity you have a perfect springboard to drive root cause analysis item by item.

We meet many executives who understand the principles at play here, but precious few who have access to the ability to extract large volumes of data (many millions of transactions) and perform the appropriate mathematical analysis on them. We have personal experience of consultants being brought in to do the heavy lifting. A team can be buried away for 2 to 3 months crunching the numbers, which drives cost and is highly prone to human error. Or high-level assumptions are made to avoid going into this level of detail, but then the business case is less robust and the roadmap of what to attack is less clear and usually focuses on macro-processes such as planning and forecasting. Consultant-led projects normally do succeed in taking out inventory in the short term – if nothing else the executive focus their presence guarantees will deliver this – but it is less clear that this benefit is sustainable, and this engenders reluctance to try this route again.

We also find that inventory optimization software has a tendency not to deliver on its promise. Whilst the better of these tools are built on similar mathematical principles to the more basic of our own tools, they are mostly focused on deriving target inventory levels and they are highly sensitive to inaccuracies in input data such as lead times or demand forecasts. We find many instances where companies are simply overwriting the targets generated by the software since this is an easier fix than understanding and addressing the underlying issues. It is easy to spend a lot of money on inventory optimization software without getting much value from it.

What we have done at nVentic is to automate the data analytics required to truly understand what’s going wrong in your inventory. What others will take weeks if not months to do, we can do in a matter of hours or days. But we are not selling software. We find that a rather high level of expertise is needed to understand how inventory optimization software really works, and we find it more beneficial to our customers to provide them with the outputs of the analysis rather than the tools themselves. Plus our customers don’t need to train people or buy a licence for an analysis they only need to carry out once or twice a year.

In addition, we firmly believe that the only way firms will develop sustainably optimized inventories is by building capability in their internal teams. Our preferred way of working is therefore to partner with a client team to deliver change. nVentic provides the analytics and the expertise, and we help guide your team through a process of understanding the root causes and addressing them over a period of months and in some cases years. For if the truth be told, we know that many companies out there, particularly in the manufacturing sector, have the potential to reduce inventories by much more than 25% whilst actually improving service levels. Getting there is likely to take more than one year though – your teams need to learn to walk before they can run, and whilst you most likely already have a number of strong inventory people, they need to be able to bring the rest of the organization with them. 25% is typically a reasonable target for year 1 however.

Is it time for you to get your inventories in shape?

For more information about nVentic, please see our website www.nventic.com or contact us for an initial discussion: information@nventic.com

Appendix

Illustration of the impact of a reduction in inventory on key financials*

(All of the figures in this very simplified example are set out below and on the next page.)

Let’s say your business turns over €2.5bn each year, with cost of sales of €1.85bn and net income of €250m. On the balance sheet you have €400m inventory, €600m in receivables and €700m in cash. In addition, you have non-current assets of €1.2bn, giving total assets of €2.9bn. On the liabilities side you have €900m in current liabilities and €800m in non-current liabilities. With €1.7bn liabilities you therefore have €1.2bn in equity.

Your capital employed is €2bn. Using this highly simplified model, you therefore have ROCE of 12.5% and ROE of 20.8%.

Let’s say you reduce your inventory from €400m to €300m, a 25% reduction. Since studies have shown the holding costs of inventory to be approximately 25% of the value of inventory each year, you will at least in theory increase net income by €25m. (In fact, the P&L impact will almost certainly be less than this, since holding costs include both variable and fixed costs, and absorption can be a challenge, especially in the first year of a programme, but for simplicity here we will use the full 25%. See also our article on holding costs.) Your ROCE will thus improve to 13.8% and your ROE to 22.9%. If you just transform the reduced inventory into cash you will not have affected your net working capital overall, although your acid-test ratio will have improved. You will also most likely need to write down less inventory at the end of the year. Maybe an improvement from €20m to €15m written down. But with the example we have taken, liquidity ratios are not a concern and cash reserves are generous. More likely that with a return on equity of 20%+ there is a strong case for further investment or a share buyback to boost earnings per share (EPS). If price per share is €20 and there were 150m shares on average to begin with, 5m shares could be bought back and earnings per share increase from 1.833 to 1.896, an improvement of nearly 3.5%.

We can summarize the numbers as follows (assuming the share buyback):

* The “Before” figures for this illustration are all taken (approximately) from a real company, the one major change we made being to reduce their actual starting inventory from ~€700m to €400m.

Inventory science is a field well represented in the academic world. From at least Harris (1913) (1) onwards, work has been done to explore how to calculate optimum inventory levels: more than a hundred years, therefore, of research.

And yet if you look at what is going on in organizations today from a practical perspective, you will find surprisingly little of that research being applied. In this white paper we’re going to explore why this is, what impact the failure to apply inventory optimization principles has, and finally what we’re doing at nVentic to help bridge the gap. (2)

Mind the gap

So why is inventory science so popular with academics but under-developed in practice? In a word, complexity. Academics love a good challenge. And there is sufficient complexity in inventory to make it an attractive discipline.

Outside of academia, however, complexity is usually not so welcome. If something is difficult or onerous to do, and one can get by without doing it, there is a strong temptation to leave it alone. Let us have a look at why inventory science is so complex.

The image below is familiar to most students of supply chain:

On the basis of a few key parameters, we can determine how much should be ordered, and when, to maintain optimum inventory levels. We can also determine, based on a few other parameters, how much safety stock to keep.

This is a simple model, and not too difficult to follow, but in our experience, there are few organizations using it to any great extent, if at all. There are a number of barriers to using it in practice:

1. Some of the parameters are hard to quantify. To build the model above for any SKU we need to be able to quantify re-order costs, holding costs and shortage costs (the forth cost parameter, unit cost, is usually easier to quantify)
2. We need to layer variability into the model. Demand is not uniform or constant, and it may not even demonstrate normal distribution. The same may well be true of lead time. This demands some more advanced mathematics
3. Everything in this model needs to be done SKU by SKU. Organizations often have thousands of SKU’s, so the data burden alone is often a roadblock
4. The mathematical model is too precise. The underlying equations will deliver results in fractions, but in reality, organizations need to deal with minimum order quantities, stock controls that aren’t instant, and so on

We will come onto what nVentic does to counter these problems below, but first let’s consider what impact a failure to apply inventory optimization principles will have.

Pile them high, sell them cheap

When Harris was writing, in the early 20th century, inventory was considered a good thing. But over the last 50 years in particular, leanness has come to be considered a virtue. In a world where customers expect greater choice, and constant product innovation, and are only a click away from finding it online, holding too much inventory can be very risky. Holding inventory costs money and ties up working capital.

Of course, you can improve your inventory situation without applying optimization techniques. Measures such as shortening lead times and using consignment stock will make it easier to operate with less inventory on your books. But you still have to make decisions in terms of how much stock to hold, how much to order and how often. If organizations are not using optimization models, how do they do this?

If an optimization model is not used at all, the alternative is often a simplified concept of how many days’ or weeks’ stock you need to hold. Some ready reckoning will factor in average demand, demand peaks, perishability if appropriate, lead times and convenient (for logistics, production or purchasing) lot quantities. Although even doing this requires quite a lot of manual effort and data manipulation which isn’t always easy.

Accordingly, some organizations help themselves by using planning software which will leverage at least some of the mathematics we describe above. However, as we have already noted, these models are very sensitive to the input data, so using tools can be prone to error. We have seen many instances of software being overwritten or ignored because early experience with it led to undesirable outcomes.

Our view is that most organizations are not doing much in terms of inventory optimization. The 2017 REL Europe Working Capital Survey suggests €350bn is tied up in excess inventory in the biggest 1000 companies in Europe alone. And what is harder to quantify because not publicly reported, but no doubt an even bigger concern to many organizations, are the sales lost due to shortages.

That £350bn of tied up working capital is a wasted opportunity, since it could be put to more interesting uses, such as acquisitions, paying down debt, or equity buy-backs. Moreover, as the holding cost of inventory is typically in the region of 20%, that’s also €70bn of unnecessary cost. And that’s before factoring in the impact of lost sales. If companies haven’t addressed this sooner it is surely not only because of the difficulty, but also because most of their competitors haven’t either. Yet it represents a major, largely untapped, opportunity to gain competitive advantage.

Build a bridge and get over it

At nVentic, our mission is to close the gap between the academic theory and practical application of inventory science. So what do we do to enable this?

Firstly, we have built analytical tools which will do all of the calculations for you automatically based on historical data, so the opportunity for manual error is removed. Secondly, we work with clients to actually optimize their inventories. We believe that the tools in isolation have limited value but by working collaboratively with our clients we help them not just to realize the benefits identified, but also to build internal capability for sustainable improvement.

We come to inventory optimization with a strong command of the mathematics, which we have embedded in our own analytical tools, but moreover with many years of helping organizations make concrete improvements in inventory management.

In terms of dealing with the 4 barriers to applying the mathematical model in practice, we apply the following approach:

1. (Parameters which are hard to quantify.) Here the key is approximation. Sensitivity analysis shows that modest changes in these parameters will not make significant differences to the outcome. We can advise clients on what to use here based on experience
2. (Factoring in variability.) This is vital and we use advanced algorithms to deal with it. However, there are also practical ways to simplify it if necessary in the short term by looking at maximum values
3. (Dealing with large data sets.) We have developed tools which take raw data from your ERP system(s), and then automatically transform them and perform over 100 mathematical routines on them. The output is an overall quantification of potential, and the segmentation of your SKU’s by value/volume and variability (ABCXYZ analysis). In addition, our tools allow you to drill down by SKU: historical stock evolution, key parameters and deltas (how much stock did you have compared to how much you optimally should have had?) We can perform this analysis in a matter of days even on very large data sets
4. (The mathematical model is too precise.) We deal with this in two ways. Firstly, we automatically round values up depending on known constraints such as packing sizes or minimum order quantities. Secondly, we recommend an incremental approach to making improvements. Rather than jumping straight to the calculated target, aim to build in a little spare buffer in the first instance and work towards optimal levels incrementally, focusing on the most valuable SKU’s

We find that even taking a conservative approach, significant double-digit improvements are possible for most organizations in the first year, with plenty more to come in subsequent years. The transparency that our tools provide typically enables a certain number of quick wins – identifying excess stock holdings which can safely be drained. And then we work with clients to interpret the data and implement the underlying changes which are necessary to embed optimized inventory management. In this way they build internal capability to make year on year improvements with an increasing command of the subject.

Bridging the gap between theory and practice in inventory management is something we believe in passionately. To truly optimize inventory takes time and effort, but significant improvements are possible quickly and, thanks to the technology we have developed, easily. If you would like to build a bridge in your organization, reach out to us for a conversation today.

Notes

1. Ford W. Harris, “How Many Parts to Make at Once.” Factory, The Magazine of Management 10 (2), February 1913, 135-136, 152. And “How Much Stock to Keep on Hand.” Factory, The Magazine of Management 10 (3), March 1913, 240-241, 281-284.
2. Another topic that we’re not considering here is why Finance departments tend to abandon the topic of inventory management to Operations given how strategic working capital is. This in itself leads to inventory being neglected as a strategic asset, with more emphasis on the tactical topic of inventory control. See also Linda G. Sprague and Marc J. Sardy, “Inventory Management: Some Surprising news about classical views on inventory and some non-classical responses to traditional practice”, Inventory Management – non-classical views, ed. M.Y. Jaber, 2009, Chapter 2.

The opportunity to improve working capital is large across the economy. Macro studies from consultancies like The Hackett Group and PwC calculate the opportunity in the trillions of Euros. (1) There are many interesting points to note in these reports, of which we will highlight three:

1. There is a wide spread in performance between companies. Hackett finds that upper quartile companies convert cash 7 times faster than the median
2. Sustainability is a challenge, with very few companies demonstrating consistent improvements year on year. Both studies highlight this
3. The only working capital lever consistently applied over recent years is Days Payable Outstanding (DPO). PwC finds that companies have increased pressure on their suppliers rather than managing their own assets better. But new payment terms legislation in several countries is limiting how much more can be achieved through DPO

All of this resonates with what we at nVentic observe as inventory optimization specialists. The opportunity to improve working capital through inventory optimization is great, but few organizations have achieved sustainably high performance in this area.

Why should this be so? Hackett highlights what it believes to be a reason why inventory has been neglected as a lever:

“Inventories can often be a… complex area to drive working capital optimization due to competing cross-functional objectives (cash/cost/service) and the ability to easily identify core improvement drivers from within. […] Generally, no one function can drive change without the participation and collaboration of the others, and the process re-engineering and optimization requirements needed to release cash internally can sometimes be politically overwhelming if the exact causes of excess inventories have not been determined.” (Hackett, 2018 US Working Capital Survey)

Inventory can indeed be a complex area to improve, and not just due to the cross-functional challenges involved. Inventory optimization even as a paper exercise requires a strong understanding of the key levers, some of which are complex in themselves and most of which are in a state of flux: the data challenge alone can be daunting. And even once you’ve determined the best course of action you still have to take the rest of the organization with you.

Our mission at nVentic is to help companies cut a path through the complexity. We have developed advanced diagnostic tools which automatically identify optimum inventory levels based on real data. But equally importantly we know how to handle multiple variables simultaneously and produce a clear and pragmatic way forward. For the truth of the matter is that the vast majority of companies can deliver significant double-digit improvements in their inventory position even before they address cross-functional issues.

Let us dig into why you (probably) haven’t optimized your inventory:

1. Do you know what good looks like?

How many organizations really know much inventory they should have? While it is practically impossible, given all of the variables in play, to say how much inventory an organization should have precisely, and recognizing that this theoretical number changes all the time, there is still enormous benefit in calculating what you need as accurately as possible. In the absence of a statistically derived target, organizations may rely purely on top-down or operationally-derived targets. Each has its weaknesses.

Top-down, an organization may simply aim to increase its inventory turns each year. All very well, but how do you set a target? For instance, your inventory might currently be turning over 12 times each year. What is your target for next year going to be? 13 times? 14 times? The point is, without calculating an optimized inventory policy bottom-up, item by item, you can’t know what is possible or desirable. It could be that 12 is already optimal and that by targeting 13 or 14 you are simply going to increase shortages. Or, much more likely, it could be that optimal is more like 20, in which case a target of 13 or 14 is undershooting.

You could derive a target by benchmarking your peers, and this is generally not a bad starting place, but even here care must be taken since optimal inventory levels are very heavily influenced by variables such as lead times, capacity utilization and target service levels, which may well differ greatly even between peers. And how do you know if even your best performing peer is that close to optimal?

At an operational, planning level, organizations are most likely already deriving their own targets as they try to ensure they have enough to meet expected demand. Because this is done on an ongoing basis the implied targets may never be formalised as such. This type of target is the province of Operations and rarely gets visibility at a board level. Or put another way, the target is used for planning and not for performance management. It is used to drive immediate action (order more, produce less, etc.). It is not a goal to work systematically towards.

Related to this question of what good looks like is what you measure:

2. What KPI’s do you use to assess inventory performance?

KPI’s drive behaviours, so the KPI’s you use to measure inventory performance will have an important impact on your overall performance. We can divide inventory KPI’s into two broad groups:

1. Measures of whether you have enough inventory
2. Measures of whether you have too much inventory

You can judge whether you have enough inventory by measuring how successfully you meet customer demand. The vast majority of organizations use one or more measures of service level, whether it be fill rate, cycle service level, shortages, back orders, lost sales, ready rate, out of stock time, or similar.

Having enough inventory is usually a higher priority for most organizations than avoiding having too much. We find that most organizations lack KPI’s to avoid overstocking, unless it is a high level one like inventory turns. And this is already a driver of underperformance. If you don’t have some kind of balance in your KPI set – so that your teams are trying to keep inventories within a range, rather than just one side of a minimum – you will almost inevitably carry too much inventory across the board. The occasional, or not so occasional, shortage only heightens the perception that shortages are the real risk.

Knowing what good looks like is a prerequisite to having a good set of balanced inventory KPI’s.

Also important is how those KPI’s are used – who is measured by them, who sees them and how targets are set and balanced. If different parts of your organization (say Sales and Operations) are using KPI’s in direct conflict with each other without a robust Sales, Inventory & Operations Planning (SI&OP) process in place, it can quickly turn into a turf war rather than a collaborative effort to optimize overall business outcomes. Remember that KPI’s drive behaviours!

3. Who has inventory targets as part of their personal goals?

This flows from the KPI discussion. Mostly likely your inventory planners do. But how about your general managers? A planner can only do so much without the alignment of the rest of the organization. Of course, you don’t want everyone bogged down with the minutiae of inventory metrics, but how about charging 20% of the inventory value (a reasonable approximation of holding costs) back to the P&L of each general manager? See what difference that makes to inventory levels!

4. Do you consider inventory optimization to be risky?

If you hear people talk about reducing your inventory by 25% do you immediately think of shortages?

We have encountered few executives who wouldn’t welcome reduced working capital in principle. But if organizations consistently fail to take full advantage of this opportunity, one of the main reasons is surely the perceived risk. The whole purpose of inventory after all is to enable you to supply your customers with as much of your product as economically desirable. Especially when the cost of capital is low and profitability is strong, why risk running short when you can just carry a little more inventory? Yet inventory optimization and inventory reduction are not synonymous. Good inventory optimization initiatives simultaneously reduce inventory levels and shortages.

This counter-intuitive phenomenon is due to the individuality of every single item you hold in inventory. Each one has its own unique profile in terms of demand, demand variability, lead time variability, perishability, normality of distribution, and so on and so forth. What we invariably find when we analyse organizational data sets is that while some (and usually most) items are overstocked, a smaller number are understocked. Inventory optimization addresses both of these issues. This is why you can simultaneously reduce your overall inventory levels while increasing inventories of some items and therefore reducing shortages. This is one of the few occasions in life where you really can have your cake and eat it!

5. Are you spending money on inventory optimization tools but are still unsure what good looks like or what value you have really got from them?

We are not opposed to the many inventory optimization tools on the market. On the contrary, we believe there are several good ones out there. But here is the rub. Are they being used, and are they being used to their full potential? Plus do the people using them understand their limitations and sensitivities? We come across many organizations with licenses to advanced inventory optimization tools that they barely use, if at all. In many cases trials were made with the tools but problems were encountered so they are switched off and old and trusted manual methods are reinstated. The possible causes of confusion are numerous, but to list a few common examples:

• Do your tools require a single lead time, and do you know what to do if your lead time varies?
• Are your tools dependent on you feeding them a forecast and are you using unconstrainted sales forecasts which invariably fail to materialize?
• Are your tools reliant on data in your ERP system? Do you know which data and how accurate and up-to-date they are?
• Do your tools offer you alternative settings that you find hard to choose between because you don’t really understand what each means or what difference it will make?
• Do your tools produce values that you know from all practical experience to be incorrect, but you can’t tell why?
• Do your tools assume normal distribution of demand? If so, do you know how to test for normality and what to do with other types of statistical distribution?

In short, inventory optimization tools and statistical models more generally can be immensely valuable, but only if used correctly. Used incorrectly they can be positively detrimental. Because the expertise to get the most out of these tools is rare, they tend not to deliver on their promise. This is (often) not so much a fault in the tools themselves as in how they are used.

6. Do you manage to reduce inventories from time to time when you have a big push on working capital, but find the levels always seem to rise again?

It is true of most things in life that given attention they improve, but when neglected they soon revert to a lower level. Yet there seems to be something about inventory which makes it particularly prone to this phenomenon. Why does sustainability seem to be such a challenge? We believe there are two main reasons.

Firstly, inventory reduction can be quick and simple to deliver. You just need to buy and/or produce less! Thus, when companies have a sudden need to free up working capital a strong top-down mandate is usually sufficient. But done crudely, this creates problems, since shortages usually follow. After the initial squeeze, therefore, inventories return to their previous levels as operations revert to their normal way of functioning. Nothing really changed other than a temporary prioritisation.

Secondly, thorough approaches require a certain level of expertise and a concerted effort. When there is a big push on inventories, consultants may be brought in and additional internal efforts also allocated. But this increase in resource is temporary and this in itself creates problems with sustainability, while any deep statistical analysis conducted has the disadvantage of being static.

Rather like inventory optimization tools, one-off projects are not a silver bullet. To deliver sustainable improvements in inventory you have to invest in internal capabilities, frequently refresh analysis and maintain top-down pressure.

7. Do you find inventory a difficult topic? Would you rather focus on less complex things?

It makes sense to do easy things that will make a positive difference before turning to more difficult ones. The business case for optimizing inventories is normally extremely strong – a reduction in working capital and holding costs, combined with an improvement in service level, all delivered at a very strong return on investment. So the fact that most organizations have not done more inventory optimization comes down, above all, to this one consideration: that it is difficult.

While not underestimating the human or political difficulties involved, we believe that what differentiates inventory optimization from many other business change initiatives is the difficulty working out how much inventory you really need, which needs to be calculated on a regular basis. But without this essential first step, done really thoroughly, all of your other efforts will be sub-optimal. You won’t be able to set the right targets, using the right KPI’s, allocated to the right people. You will be at risk of running short and potentially spending money on initiatives without being able to tell how successful they really are.

Yet we believe this difficulty is also one of the reasons why inventory optimization is a great topic to address. Precisely because inventory optimization is a frequently neglected opportunity, significant double-digit improvements are possible in most cases.

To return to one of the Hackett metrics with which we opened: top quartile companies convert cash 7 times faster than median companies. On how many metrics do you have this kind of advantage over your competitors? Surely this is worth overcoming some difficulties to achieve?!

Conclusions

There are many reasons why organizations fail to maintain optimal inventories. We have explored 7 of the most common here, of which we consider the ability to know what good looks like to be the single most important. Based on this insight, nVentic has developed diagnostic tools which quickly and accurately show clients how much inventory they should hold. We then work with them to embed sustainable inventory capabilities in their teams.

Until the macro reports start showing a marked and sustained decrease in inventories, our work will not be complete.

If you would like to discuss how to define what good looks like for your organization and take your inventory optimization capabilities to the next level, contact us: information@nventic.com

Notes

1. For the most recent editions of these reports at the time of publication, see The Hackett Group, 2018 Europe Working Capital Survey and 2018 US Working Capital Survey, and PwC, Navigating Uncertainty, PwC’s annual global Working Capital Study 2018/19. Each of these documents looks at a different selection of companies and each finds a potential of at least 1 trillion Euros or Dollars for the selection they look at.