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How much does inventory really cost to hold?

Inventory holding cost may not seem like a strategic lever. In fact, it is one of the key ways in which Finance departments can help improve working capital.

In 2019, a commonly encountered belief is that holding cost is low because interest rates are low. This is partly true, but misleading. In this white paper we will examine why, and why it matters.

One reason why organisations may hold too much inventory is that they underestimate the true cost of holding inventory. In this white paper we will also examine why this is an issue, what a realistic holding cost is and how it should be used.

What is inventory holding cost?

Holding cost is the cost of holding inventory and is expressed as a percentage of unit costs. If an item costs €100,000 to produce or buy, and it costs €20,000 a year to hold, its holding cost is said to be 20%.

Holding cost is made up of the cost of capital tied up in inventory, plus the operational cost of holding the inventory: warehouse space, material handling, obsolescence, insurance, and so on.

Importantly, holding cost is related to but not the same as the P&L (EBIT) impact of increasing or decreasing inventory. Holding cost is the total cost of holding inventory and is used in inventory management for calculating batch sizes and deciding on advance or discount purchases. It is greater than the P&L impact of changes in inventory for a number of reasons. Firstly, EBIT does not include the cost of capital, but secondly, holding cost includes a number of costs which cannot be switched on and off at will: warehouses, employment, loans, etc. Holding cost includes P&L items, but also balance sheet effects.

It is a mistake to ignore these “fixed” costs. If you believe inventory to be expensive to hold, you will try to hold very little. If you believe inventory to be cheap to hold, you will tend to hold a great deal. But smart organisations should avoid this. Why fill up a building with inventory when you might have better uses for the space, such as adding a production line, or subletting part of the building? And once the building is more or less full, a perception that all that inventory is needed can lead to sub-optimal longer-term decisions, such as adding a warehouse.

Remember there is no such thing as fixed costs, just variable costs with longer or shorter time horizons.

How much is holding cost?

Each organisation will have its own unique holding cost depending on its cost of capital and its operational cost structure. Holding cost can be difficult to estimate with any degree of accuracy, but it is worth putting some effort into generating an approximate figure for your own organisation.(1)

The operational cost can typically be broken down into the following categories:

  1. Storage costs. Both external and internal warehouses, including, for instance
    1. (notional) rental costs
    2. Maintenance
    3. Temperature control
    4. Security
    5. Insurance
  2. Obsolescence. This can be further broken down into sub-categories such as:
    1. Scrap (the total value of the inventory written off)
    2. Rework costs
    3. Discount costs (selling items at below cost)
  3. Internal transfer costs. Moving inventory around between sites
  4. Handling costs including, in particular, stock counting/stock control and especially those causing production stoppages
  5. Management costs. The time spent by planning teams controlling existing inventory, dealing with obsolescence etc.
  6. Overheads. All of the above requires IT, HR, Finance and other corporate time and expense

While building up an estimate, it is important to keep in mind the difference between total inventory holding cost and P&L impact. You may (rightly) feel that you need a storage warehouse, but this does not mean holding inventory in it has no cost. You will also need to make some estimates. If, for instance, you own a facility where half the space is filled with production lines and half the space is storage for inventory, then half the relevant costs for the facility should be included in inventory holding cost. Or if you have a team who are responsible for ordering, planning and controlling inventory, only their time spent controlling existing inventory should be included in holding cost. And so on.

Then consider the cost of capital. This should be the Weighted Average Cost of Capital (WACC). It is true that, writing in 2019, the global cost of debt is at historically low levels (2) and this in turn has led to relatively low WACC for asset-intensive companies, but this can be misleading. For a highly indebted company, the fact that WACC is low does not mean it can easily issue more debt. For such a company it would be undesirable for decisions to be made on the assumption of a low holding cost since it will lead to more capital being tied up in inventory. The opposite should be the case – these companies should do all they can to free cash from inventory and reduce their dependence on debt.

In fact, there is a compelling argument to ignore the calculated holding cost altogether and consider the opportunity cost instead. Look at the Internal Rate of Return (IRR) threshold for internal business cases to be approved. What is the highest IRR project that was rejected in the last 12-24 months? This can be considered the opportunity cost of the cash you have tied up in inventory and is a useful sense check that you are using a reasonable holding cost rate. The opportunity cost can easily be lower than your inventory holding cost, but if it is higher there is a strong strategic case to be made for using it as your holding cost.

Why does it matter – what is holding cost used for?

Holding cost is an important factor in the inventory levels organisations target and ultimately hold. This is because of the decisions which are made on the basis of assumed holding cost. To take the two most important instances:

Note in these two examples we say “can”. In many cases organizations make these decisions with no explicit holding cost. Instead, they might produce “lot for lot”. Or they might simply choose the batch/order sizes that minimize production/acquisition costs within the constraints of what is possible in terms of capacity and reasonable in terms of expected demand. If you find a year’s worth of a particular raw material in your warehouse because Procurement found a great deal on it, this effect is in evidence.

The importance of holding cost for working capital should not be underestimated. In inventory optimization initiatives a lot of focus is put on safety stock levels, but order/batch size is another key lever. Let me illustrate this with a simplified example:

Demand for a product is 100 units per month. It is produced in 400 unit batches every 4 months. This means on average (net of safety stock), 200 units are on stock over the course of a year.

Now halve the batch size to 200 units produced every 2 months. Average stock holding (again, net of safety stock) over the year is now only 100 units.

So what should you do?

Holding cost remains difficult to quantify precisely. Some approximations are necessary and diminishing returns are had from putting too much effort into it. But the final point to consider is the behavioural aspect. Operations departments have a constant focus on EBIT and only an occasional focus on cash. EBIT focus is a good thing, but can lead to an unconscious prioritisation of EBIT over cash. A key way to counter this is to ensure that a realistic measure of holding cost is used in calculations of batch sizes and factored into TCO calculations for procurement.(3)

We recommend you start by looking at how holding cost is used today and ensure that the holding cost used is not too low. 20% is normally a good starting point, or WACC plus 10% as an absolute minimum. We would then recommend trying to calculate your actual holding cost bottom up until you are confident you are in the range. Avoid having individual plants or business units do this themselves, since it leads to divergent results and much duplication of effort. Instead, Finance should establish a standard holding cost centrally and then ensure it is used globally. Multi-national corporations should find that holding cost varies from country to country in line with interest rates and there is a case to be made for high inflation countries to use a higher holding cost than the global standard.

From a behavioural perspective, if Operations has no working capital targets on their dashboard, there will be a strong tendency to hold too much inventory for the reasons we have set out in this paper. Fixing this is also important. But setting holding cost centrally is a strong lever that Finance can exert to free up working capital. Are you getting the most you could out of it?

If you would like to discuss how to take your inventory optimization capabilities to the next level, contact us: information@nventic.com

Notes

1. An internet search for inventory holding cost can quickly generate an approximate percentage for rule of thumb calculations. For instance, Investopedia suggests 20-30% although it does not cite a source for this statistic.

A textbook from 1993 reviews estimates of holding costs in academic studies between 1951 and 1990 (Lambert, D.M. and Stock, J.R., Strategic Logistics Management (third edition), Irwin (1993), page 366). All but one is within the range of 20-29% and while that one exception goes as low as 12%, that is only as part of a range from 12-34%.

Interest rates are indeed very low in 2019, even within the perspective of the last 100 years, although when Harris (1913) first derived the EOQ model he took holding costs to be 20% at a time when interest rates (in the USA) were at 4.5%, so only 2% more than 2019.

The CSCMP (Council of Supply Chain Management Professionals) commissions a yearly state of logistics report which includes a benchmark of inventory holding rates over the past 10 years in the US. The most recent figure, for 2018, was 18%.

2. The European Central Bank (ECB) at the time of writing in 2019 is actually offering negative interest rates on deposits (and 0% on loans). Of course, most companies cannot borrow at ECB base rates, but costs of borrowing are historically low, as is WACC, which factors in the cost of equity. Some very big companies, based on what they report in their annual reports, have WACC in the low single digits.

A benchmark of US firms from the start of 2019 found the average WACC for 6000 US non-financial firms was 8.22%. A KPMG study from 2018 focusing just on 276 firms in the DACH region, including 26 of the DAX30, is more or less identical (7% average WACC when including financial firms). Note that operational costs can easily add another 10-20% on top of WACC.

3. This often adds additional impetus to reducing EBIT effects too. An organisation that complacently uses large batch sizes with an assumption of low holding cost (or no sight of holding cost), when confronted with a requirement to factor in a holding cost of 20% or more, will often urgently have to address topics like SMED to avoid productivity measures being negatively impacted.

If you asked most supply chain professionals what super power they would most like, the ability to see the future would probably come fairly high up the list. Uncertainty – of demand, but also of lead times, failure rates, pricing, exchange rates, and many other variables – creates a high percentage of the headaches which supply chain professionals have to deal with on a daily basis.

But how much would 20-20 foresight really help? Ask Oedipus’s father! He got 100% forecast accuracy from the Oracle at Delphi, but little good it did him. (1) Amongst the many forecasting methodologies used by modern professionals, consulting high priestesses in Greece doesn’t normally feature too highly. But the Oedipus story illustrates an important limitation of forecasts: when you don’t have all the details, knowing what the future holds is not as useful as you think. This principle is very important in inventory management.

For the non-specialist, it can be tempting to think that forecasting is the challenge with inventory optimization. If only we knew what demand is going to be, goes the thinking, we could always have just enough inventory. There are two major dangers with this argument:

  1. Forecasts are almost always wrong due to the inherent unpredictability of the future
  2. Too much emphasis on forecasts can blind you to the importance of other factors. Even with a perfect forecast, you can easily get your inventory levels wrong

Let me illustrate this second point.

Product A Daily demand: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2

Product B Daily demand: 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 60

In both instances the monthly demand is 60 units, with an average daily demand of 2 units. If your forecast was for monthly demand of 60 units, well done, your forecast accuracy is 100%!

Now think about how much stock you need to fulfil all orders in each case. Daily demand for product A is uniform – you need no safety stock. Each day you ship 2 units and produce 2 units for the next day. On average you hold 2 units. Product B is very different. In order to meet orders, assuming you don’t know which day the order(s) might fall, you would need to carry 60 units every day. That’s 30 times as much stock as for product A. Quite a difference, considering forecast accuracy was perfect and total demand was identical in both instances. (2)

It is said that forecasts are always wrong. The further into the future they look and the more detailed they are, the more wrong they are. Knowing this, forecasters normally refresh their forecasts regularly and avoid too much granularity – grouping demand into weekly or monthly “buckets”. But as our example has shown, even a perfect forecast of aggregate demand has limited usefulness without an equally good understanding of what variability to expect.

Rather than focussing too much on forecasting, inventory managers need to give adequate consideration to other factors, such as demand variability, lead time, target service levels, and so on. However, this is not to say that you don’t need to give proper attention to demand. It remains one of the most important inputs to inventory calculations and is probably the most likely to change. But to consider demand you don’t have to turn to forecasts.

Forecasting is a substantial discipline in its own right and one which we’re not going to explore in any detail here. At nVentic, our approach when calculating optimal inventory levels is to start by using actual daily demand instead of any forecast. There are a number of benefits to doing this:

  1. You already know your actuals, so you don’t need to put effort into generating a forecast
  2. Actuals are in their natural form and not aggregated into buckets that hide the actuals
  3. You can use actuals to measure variability as well as average demand
  4. Actuals are incontrovertible – you don’t need to spend time convincing other people how realistic they are
  5. Actuals are immutable – as time progresses more actual data become available, but the actual data for any given time period will not change. Forecasts, however, are constantly overwritten and it is easy to lose track of what they were at the time plans were put together, especially since lead times vary by product

There are certainly cases where actuals are inadequate: for instance, with new product launches, where there are no actuals; or sporadic spare parts, where mean time between failures (MTBF) models may well be more useful. And there are events that planners need to be aware of and allow for that may not be visible in the actuals (sales promotions, patent cliffs, seasonality, etc.) as well as longer term product cycles which may be evident in the actuals (sales growth, decline, etc.).

We are not suggesting that using actuals is always better than using forecasts, or that you shouldn’t put efforts into improving forecast accuracy. Our approach is to start by using actual demand to calculate optimal inventory levels and then factor in the forecast if (and only if) you are confident it will be more accurate or more beneficial than the actuals. (3)

Conclusions

In inventory management, it is very important to understand and accept the limitations of any forecast and not to neglect the other key levers. Properly factoring demand variability into the approach is often a largely untapped area of potential. Shortening lead times takes some effort to achieve, but allows organisations to be more responsive and to carry less stock. And developing explicit service level targets and building them into your inventory approach is a very valuable way of balancing the opposing requirements of high service levels and low cost in an objective way.

If you follow this approach, we predict you will be pleasantly surprised by the results.

Notes

  1. King Laius consulted the oracle at Delphi as to whether his wife, Queen Jocasta, would ever have children. He was told that any boy born from their union would end up killing his father. Therefore, when a boy, Oedipus, was born, they left him out on a hillside to die. Oedipus was spared but due to the sequence of events grew up ignorant of who his true parents were. In a classic example of a self-fulfilling prophecy, Oedipus ends up killing Laius precisely because he doesn’t know who he is – which one can assume would not have happened had Laius not heard the prophecy in the first place!
  2. This example is deliberately simplistic. In reality a majority of items are likely to display more normally distributed demand, but these two extreme examples illustrate the point with a minimum of statistics.
  3. How do you determine if the forecast you have been using is better or worse than actuals? In statistical terms, if the Mean Absolute Deviation (MAD) of your forecast vs your actuals is greater than the MAD of your actuals, you would be mad to use the forecast!

10 steps to supply chain heaven

Along with shortages, excess and obsolete (E&O) inventory is a good indicator of how well your supply chain is performing. Some waste is inevitable in most supply chains, since demand is unpredictable and shelf life is not infinite, but organisations can do a lot to minimise E&O, with both financial and environmental benefits.

Here we present a guide to the 10 steps organisations can take to reduce E&O, organised into 3 levels of increasing sophistication. As far as E&O is concerned, prevention is better than cure, but dealing with existing E&O should not be neglected, and is included here.

For those in a hurry, here are the 10 steps summarised:

  1. Develop good basic inventory hygiene and visibility
  2. Be sensitive to product lifecycles
  3. Examine root causes and address them
  4. Eliminate naïve desire for “100%” service level and apply better segmentation
  5. Stop buying/making to a poor forecast
  6. Shorten stock cycles
  7. Reduce complexity
  8. Reduce your need for safety stock
  9. Use a richer blend of inventory management approaches to optimize by item
  10. Leverage S&OP to drive inventory optimization


Level 1: The basics

Step 1 – Develop good basic inventory hygiene and visibility

There are a number of things which contribute to basic inventory “hygiene”. They are not necessarily pre-requisites to the more advanced steps below, but they are simpler to implement and will add significant accuracy and clarity to further steps.

First, ensure good booking processes. Inventory needs booking in and out accurately, and in a timely manner. Failure to do this can both distort demand and supply signals further down the line and give planners an inaccurate sense of how much inventory is held in the first place. It can also lead to inventory getting lost. Automated warehouses, barcodes and RFID can help minimise this type of error, although not eliminate it entirely. A good lagging indicator of booking issues is when systems show negative stock balances!

Then make sure accurate stock counts take place. However good you believe your booking processes to be, it is important to verify the actual figures regularly. Depending on the scale and complexity of your facilities, cycle stock counting might be a useful enhancement to, or substitute for, an annual stock check. Physical counts, even if randomly carried out, will show up both missing inventory and forgotten inventory.

And finally, in terms of hygiene, a sturdy broom is essential. There may be a natural aversion to loss, but holding on to obsolete stock in the hope that it eventually finds a buyer or another use is unwise. Accounting rules and financial considerations will determine what needs to be devalued when, but even fully devalued stock takes up space, effort and management time. Anecdotes of something generating value well after it should have been used are legion, but it is overwhelmingly better to clear such stock out. The first consideration should be the use of discounts to promote sales, followed by options to rework or recycle, before finally scrapping.

All of the hygiene factors above should help improve the quality of inventory data at your disposal. Beyond this, especially where you are coordinating inventories across multiple facilities, visibility is also critical. You need to know how much you have, where and when. “Blind spots” or inadequate coordination can lead to shortages or excesses when considering the network as a whole.

While good booking processes should deliver accurate data in ERP systems, those systems can be laborious and inflexible when it comes to visualising the data in user-friendly ways. Various ERP add-ons, as well as third party software solutions, exist to enhance inventory visibility. Increasingly, these visibility tools allow organisations to see inventories in their wider network, such as at suppliers or customers.

Step 2 – Be sensitive to product lifecycles

A common cause of E&O inventory relates to product lifecycles. Where historical data exists, supply chain teams can relatively easily ignore the more optimistic forecasts of their sales organisations, but with new product introductions a very optimistic forecast can often be king, leading to overstocks in the introductory phase. In extreme cases the initial supply can be larger than the all-time demand.

Similarly, as products reach the end of their lifecycle, E&O is in danger of being generated, precisely at the worst point in the cycle. This could be caused by a failure to recognize declining demand, a failure to communicate that sales focus is switching to new products, or simply a failure to plan for the switch.

While operational silos and the natural tension between sales and supply chain teams can seem one of the most intractable problems to solve – and we return to Sales and Operations Planning (S&OP) in step 10 below – sensitivity to product lifecycles and basic communication is sufficient to avoid the worst E&O effects in this area.

Level 2: Good practice

Step 3 – Examine root causes and address them

With the basics (steps 1 and 2) hopefully in place, the first step towards good practice in E&O reduction is creating a culture of continuous improvement relative to inventories. As with any business process, sustainable improvement comes from measuring outcomes, digging into root causes, addressing them and then measuring the improvement.

The key challenge with inventory is often quantifying the impact of different root causes, since inventory sits right at the heart of your supply chain and is influenced by many things simultaneously. In the absence of good data there is a danger of particular root causes being singled out and blinding the organisation to other contributory factors. This can be as much on the basis of individual perception as fact.

As you move through the more advanced steps below, you should along the way develop more sophisticated analytics to better understand the relative importance of different inventory levers. However, it is rarely a good idea to wait for perfect data before starting improvement efforts. A lot of your instincts as to what root causes are driving E&O will be correct. The important thing at this stage is to identify and take actions, and to measure the results.

A critical success factor here is the implementation of short interval controls and KPI’s to focus attention on E&O and inventory levels more generally. You can’t improve what you don’t control and you can’t control what you don’t measure.

Step 4 – Eliminate naïve desire for “100%” service level and apply better segmentation

Even before getting into the realms of inventory science (see below), you can reduce E&O by doing two simple things. The first is to make some conscious and differentiated decisions about the service levels to provide your customers – both internal and external. Consider if a lower service level would be more profitable. The second is to segment your inventory items in ways that allow you to prioritise what is most important and deprioritise the rest.

You may use different metrics to measure service level, such as OTIF, fill rate, out of stock time, back order quantities, waiting time, and so on. Whatever measure you use, in the face of uncertain demand and supply you can never entirely guarantee availability. The role of inventory is not to prevent all shortages but to keep shortages to an acceptable – in most cases the most profitable – minimum. This is an important principle to grasp. Depending on your market, you may be delivering an unnecessarily high service level.

Start by baselining your current service level performance (using whatever service level metric is already in use and familiar to you) and on that basis set some service level targets depending on how important something is for your business. There may be some items where you do need to have very high availability on demand, but this is not necessarily the case for everything. Moving to a more segmented model where you have different service levels for different items, rather than the same for all, will help reduce E&O.

In the absence of other criticality criteria (for instance medication that is essential to the preservation of life), use profitability or simply revenue to segment your products. An ABC analysis would be a good starting place. List your items by total value (value * annual volume) and then segment the top 80% (A), the next 15% (B) and the bottom 5% (C). An improvement would be to group by finished goods product, since a low value raw material might be essential to a very high value finished product.

ABC analysis is very widely used. Less so is XYZ analysis, where variability is a second segmentation dimension. XYZ analysis is more complex than ABC analysis to do, since it involves statistics, but it should be a bigger driver of inventory levels, since variability is precisely what inventory is buffering against. At this stage do not worry too much about the mathematics of XYZ but find a segmentation that works for you, is clear to all in your organisation, and separates your most variable demand products from your least variable.

Various other segmentations exist, such as VED (Vital, Essential, Desirable), or FSN (Fast, Slow, Non-moving), to name just two. All such segmentations can be useful methods to focus the prioritisation of management attention, depending on your individual circumstances.

At this stage don’t worry too much about the exact segmentation rules. Ensure that items you know to be the most critical to your organisation are in the top priority category.

Then set some target service levels, bearing in mind the nature of your business. If you stock items where it is critical to have very high availability on demand, for instance foodstuffs or critical medical supplies, then a target service level of 99% or higher might be appropriate. Whereas if your customers are used to waiting a few days or weeks then you can make do with a lower service level.

It is important to baseline your service levels and track their development. Inventory reduction programmes frequently meet resistance and then it is important to show that service levels have not suffered unless a conscious decision was made to lower them.

The most important principle here is differentiation – that not all products need the same levels of inventory. If you struggle to equate service level with required inventory levels then try to differentiate on the basis of cover, i.e. how many days’ or weeks’ average demand you aim to hold in inventory, although note that cover is far from optimal as a concept and will be addressed further in step 9 below.

By segmenting your products, by setting explicitly different target service levels and different target inventory levels by segment, however approximately, you are starting to familiarise your organisation with some of the most important principles in inventory optimization and already delivering benefits relative to a simplistic “one size fits all” approach.

Step 5 – Stop buying/making to a poor forecast

We frequently hear the complaint that poor forecasts are to blame for E&O inventory. Most of the time this is at least partly true, but not necessarily in the expected way. It is frequently the use of the poor forecast, not the poorness of the forecast, that is the biggest problem.

If you have a forecast demand for 100 units of a product and you produce 100 units, but then only have demand for 10, it is easy to blame the forecast. If you have a forecast demand of 100 units every month and produce 100 units every month, but only ever have demand for 10 units per month, the blame should be with you for using the forecast.

Of course, in most real-life situations it is less clear cut. Forecasts are not usually so consistently wrong, nor demand so uniform. In reality, there is a lot of “noise” to deal with – demand goes up and down, forecasts are frequently revised. It is not necessarily so obvious that forecasts should be ignored. But what do we mean by forecasts should be ignored? Surely some forecast is required for any inventory planning exercise.

A useful starting point is to consider actual demand for each product. There is incontrovertible evidence of what has already been sold. If you compare this with your forecast, you can calculate if the forecast is adding value or destroying value relative to just using actual sales as the forecast. While superior methods to measure forecast value add exist, here is a relatively quick method to check if your forecast is better or worse than using actuals as a forecast: consider whether the mean absolute deviation (MAD) of your forecasts vs your actuals is greater or less than the MAD of your actuals. To take a simple example:

Forecast demand for 10 periods: 10, 10, 10, 10, 10, 10, 10, 10, 10, 10

Actual demand for those periods: 7, 8, 8, 11, 6, 5, 7, 13, 6, 6

The MAD of the forecast vs actuals is (3 + 2 + 2 + 1 + 4 + 5 + 3 + 3 + 4 + 4) / 10 = 3.1

The MAD of the actuals (deviations from mean of 7.1) is (0.1 + 0.9 + 0.9 + 3.9 + 1.1 + 2.1 + 0.1 + 5.9 + 1.1 + 1.1) / 10 = 1.72

In this example, you would be better using your actuals as a forecast than the forecast. Or at least you probably would! A key thing to understand about any forecast is that it is almost certainly going to be wrong.

When doing this exercise, it is important to look at the forecast at the time of planning. For instance, if orders or production plans are confirmed 10 days in advance, the forecast on that day (i.e. actuals minus 10) is the one to compare with the actuals. Because forecasts are frequently revised, it is all too easy to lose track of what the forecast was at the critical planning horizon.

Before we go into more advanced approaches below, the important thing to retain here is that it is very valuable to understand where your forecast is destroying value and to decouple the forecast from the plan in those cases.

There is also a behavioural aspect to this step. We have worked with numerous organisations where the sales team were responsible for creating and maintaining forecasts in the system and where planning teams planned to those forecasts even though they knew them to be consistently wrong. There can be a culture of “we know the forecast is wrong, but if it is right and we haven’t got the stock then it’s our fault, whereas the forecast being wrong isn’t our fault”. This silo mentality needs breaking down.

It is also important to understand that forecasting is not the only game in town when it comes to reducing E&O. A lot of demand is inherently unforecastable and setting up inventory practices to mitigate this uncertainty is a more reliable approach than trying to second guess it.

Level 3: Leading practice

Step 6 – Shorten stock cycles

Large production batch and purchase lot sizes drive high cycle inventory. The more inventory you have, the more likely it is to be excessive and at risk of becoming obsolete. By making or purchasing fewer items at a time, more frequently, E&O can be reduced. The challenge of achieving this can be approached from two directions.

Firstly, by minimizing set up and reorder costs. Techniques like single minute exchange of die (SMED) drive down the cost of production setups. Procurement efficiencies, such as the use of automated purchasing tools, make it less costly to reorder.

Secondly, by ensuring that inventory holding costs are properly factored in. All other things being equal, production teams like large batch sizes because it reduces the number of changeovers necessary and procurement teams like large lot sizes if it allows them to take advantage of supplier volume discounts. But the “savings” of these high volumes need to be offset against the holding cost of the inventory they generate and this is where mistakes can be made. The cost of capital, especially as expressed by public interest rates, is much lower than inventory holding costs.

The true holding cost of inventory is made up of not just your organisation’s weighted average cost of capital (WACC) but also all of the operational costs involved in holding inventory, including not least the cost of obsolescence! By making your organisation aware of the true cost of holding inventory, in other words, you can help reduce E&O.

Where production and procurement are forced to use a higher holding cost, batch/lot sizes should decrease, assuming that they are using some form of total cost calculation to define their batch/lot sizes in the first place.

Step 7 – Reduce complexity

Complexity is the enemy of inventory efficiency for a variety of reasons.

First is the impact on forecast accuracy. Forecasts are more accurate at an aggregate level than at granular levels. It is much easier to accurately forecast demand for a whole class of product than for each individual variant of that product. Simply by having a high number of alternatives, you are increasing your chances of having E&O inventory. Of course, you still need to convince your marketing department of the benefits of standardisation!

The second is the sheer proliferation of items. We frequently work with clients with many tens of thousands of different items in inventory. The very volume of items makes it harder to stay on top of optimum inventory levels for each.

Thirdly, a lack of standardisation makes items much less flexible. By having a high degree of standardisation at a sub-component level you allow yourself to re-purpose or even re-use items in a variety of situations.

And fourthly, the number of stocking points you have drives how much inventory you need. The square root law is an approximation which states that the amount of inventory required by a system is proportionate to the square root of the number of locations in which it is stored. So, for example, if you move from 1 warehouse to 4 warehouses, your inventory requirements will double. Since demand volatility at each individual warehouse will be higher than the aggregate demand overall, risks of E&O increase. Of course, there can be good, even necessary reasons to increase your number of stocking locations – retail premises being a prime example – but if you are adding a new warehouse just due to capacity constraints at your existing one, the E&O effect is one to bear in mind.

Inventory is of course only one consideration in network optimization and technology exists to help optimize network design, but as a basic rule of thumb the square root law is a useful heuristic to facilitate E&O reduction.

Complexity quickly asserts itself in large organisations, and simplification is often a long-term lever to reduce E&O but it is a very significant root cause and should not be ignored.

Step 8 – reduce your need for safety stock

There are a number of important parameters which affect how much safety stock you need. Safety stock exists to ensure the ability to continue supply in the face of variability on both the demand and the supply side. Where demand is variable and lead times are long, you can feel squeezed in the middle. Both sides can be influenced to reduce E&O.

On the demand side, the most important step is trying to reduce variability. We have already (step 7) discussed the benefits of product standardisation to reduce demand variability, but price stability is also beneficial – discounts and other promotions introduce additional variability into the supply chain. Even when prices are being discounted to dispose of obsolete or close-to-obsolete stock (step 1) it should be remembered that this is liable to have a knock-on effect on other product lines.

On the supply side, there are benefits both to reducing the variability of lead times and to reducing the absolute lead times themselves. Reducing the variability of lead times sounds easier said than done, since, at least for raw materials, it usually comes down to external factors, but this is not to say that improvements cannot be made, especially within production.

A more straightforward lever may be reducing lead times. Within a factory, this comes down to reducing cycle times while maximising throughput. With third party suppliers, the key lever is finding more local sources of supply. Of course, there may be cost benefits to sourcing from further afield but this needs putting in the proper context of the holding cost of inventory (see step 6 above).

It should also be remembered that long lead times not only increase the safety stock you need, but also increase what we call pipeline stock: inventory that is not yet showing on your books but which is already in transit from your suppliers. Such inventory makes you less agile and less able to respond to changing demand signals. A similar phenomenon is caused by long production frozen periods. While frozen periods can be good for production efficiency, they reduce agility and will tend to drive higher E&O.

In summary, reducing lead times, delays, lags and variability throughout your supply chain will reduce your need for safety stock and so reduce E&O.

Step 9 – Use a richer blend of inventory management approaches to optimise by item

While standardisation helps reduce E&O if applied to product design (see step 7), differentiation is what holds the key to unlocking substantial benefits when it comes to the management of inventories. No two items that you stock are the same. Segmentation (considered in step 4) is a useful improvement on “one size fits all”, but flat policies across segments, such as setting cover targets by segment, ignore differing variability at an item level. In terms of the individual parameters that will allow you to reduce E&O while maintaining service levels, both safety stock and cycle stock need to be defined at an individual item level.

To calculate optimum stock levels by item, scientific approaches exist. Fortunately, given the data challenge involved, tools exist to calculate these parameters for you. Unfortunately, all such tools by necessity work on a number of assumptions, which means that their recommended parameters do not work for many items. Thus, while the optimum parameters can be calculated for each and every item, it is currently somewhat difficult and laborious to do so with a reasonable degree of accuracy for all items.

Safety stock and cycle stock are essential concepts when it comes to replenishment models. Here, by replenishment models, we mean an approach where target safety stocks and order quantities are set, along with a re-order level. When stock on hand drops to a given level, more is ordered or made. In replenishment models, target safety stock can be calculated to handle expected variability in demand up to a defined service level. In general, replenishment is an underutilised resource, since many people find variability a difficult concept to factor in well.

The main alternative to replenishment is deterministic planning. This seems to be more widely used since it requires less understanding of statistics. In deterministic models, stock is bought or produced in direct response to a demand signal, whether confirmed orders or a forecast.

Deterministic planning works well when demand is known (such as through confirmed customer orders), or at least is known to be different to the past (such as for a promotion, or the phasing out of a product). In other words, to go back to the theme of step 5, if you are certain that your forecast is better than actuals for predicting future demand, then deterministic planning should be superior.

While this is a simplification, and care needs to be taken with terminology, deterministic planning is often championed by proponents of Material Requirements Planning (MRP) systems, where “MRP” is selected as the production planning method. The principle here is that requirements for all components can be established through the explosion of the Bill of Materials (BOM). While finished goods might be managed using a replenishment approach, since end-customer demand is unknown, raw materials, WIP and semi-finished goods are often managed through a deterministic MRP model that reflects the production plan (which itself reflects the sales forecast).

The problem here is that if customer demand is unknown and MRP is being used to plan deterministically, you are just passing your variability, and stock imbalances, up the supply chain. Alternative approaches like Demand Driven Material Requirements Planning (DDMRP), CONWIP or Kanban, seek to counter this by introducing decoupled replenishment points in the supply chain.

The debate for and against MRP is sometimes presented as a binary choice. In reality, there are situations in which MRP works very well, and situations in which it doesn’t. This is where differentiation is essential. Enterprise Resource Planning (ERP) systems offer a choice of strategies, including not only MRP and replenishment as set out here, but multiple variants too.

The very number of options available, along with all of the essential parameters which go with them, can initially seem overwhelming, but restricting yourself to one or two means that an opportunity is being missed. Differentiating your approach can bring enormous benefits. We recommend an incremental approach. Do not go from 1 stock policy to 15 overnight. Do not trial a new stock policy with all of your items. Instead, identify classes of item suitable for a different approach and then test it with a restricted number of items before scaling up.

Similarly, where you are using a new tool or method to define target stock levels, do not implement them in full immediately, but work to them gradually. Let us say your new tool has suggested you can reduce your safety stock for an item by 50%. You may or may not understand exactly how it has made that calculation, and it may or may not be very precisely correct, depending on what assumptions the tool is making and how good they are.

The point is not that the tool is weak and should be discarded – it has suggested something that you had not thought of and it may well be right – the point is that if you drop safety stocks by 50% and that is too much, you will end up with shortages. This will damage the credibility of you, the tools, and any further optimization initiatives. It is much better in the example given to try reducing safety stocks by, say, 20% at first, watching what happens carefully, and then reducing them further. Don’t go for the full 50% until you are confident.

This step 9, which embraces differentiation in inventory management approaches, has enormous potential. nVentic’s extensive experience and diagnostic tools show that the application of this step 9 alone can typically deliver reductions in inventory levels of 20-50%, all but eliminating E&O, without sacrificing service levels. However, this approach also involves substantial complexities and numerous pitfalls. To be successful, we believe it is essential to develop capability in your supply chain teams so that you have a better understanding of the science rather than just trusting in “black box” solutions which you know from personal experience to have limitations. Here too, a phased approach with incremental improvements year over year is appropriate.

Scientific approaches to inventory optimization hold enormous potential, despite the complexities involved. Existing technology, whether driven by formulas, artificial intelligence or modelling, is currently unable to take account of all variables. The human mind still has a vital role to play. But advanced scientific methods and tools can help you to go a lot further in reducing E&O without service level suffering. Where you have a large number of items to manage then you will benefit greatly from using technology to optimize at an individual item level. The limitations of the technology do not mean you shouldn’t use it, they just mean you should seek to understand those limitations and work with them.

Step 10 – Leverage S&OP to drive inventory optimization

It may surprise you to find Sales and Operations Planning (S&OP) this far down the list. After all, S&OP is surely one of the most important planning processes at your disposal and you certainly wouldn’t want to exhaust all of the scientific approaches to inventory optimization (step 9) before turning to S&OP. Indeed, as we said at the start, these 10 steps are not to be thought of as a strict sequence, where each step needs to be completed before progressing to the next.

The reason we have placed S&OP at the end is due to its strategic nature. Done well, S&OP is the way your organisation makes its most important supply chain decisions – how much should be bought, manufactured and stored in order to maximise your corporate goals. Various strategic decisions can and should flow from S&OP. Achieving the right balance between conflicting management incentives. Deciding what should be made to order and made to stock. How to find the best balance between demand and supply lead times. (Where customer promise times are short and supply/manufacturing lead times are long then E&O will thrive.)

S&OP is also a vital counterweight to silo behaviour. In simplistic terms, sales organisations like high inventories, since they never have to worry about stock outs. Having everything available on demand makes their vital revenue-generating role easier. Production and supply chain organisations, on the contrary, look to control costs and maximise efficiency. This natural and healthy tension comes for resolution to the S&OP process.

However good your S&OP process, you will still generate some E&O inventory. Inventory is itself a strategic lever. You choose how much you will hold to buffer yourselves against variability. It is only one possible buffer, the other main ones being time and capacity: you can deliberately retain spare capacity to be able to react to changes in demand, or you can use time – asking customers to wait in the event of demand spikes.

We have placed S&OP as the last step of our 10 for two interrelated reasons. In the absence of robust analytics, strategic transparency is limited. Decisions are made on the basis of incomplete information and organisational politics can become the guiding force. Only once you have full transparency of the relative costs and benefits of alternative approaches can you be confident of making the best decisions. And at the same time, it is of limited value to have the best inventory analytics in the world if you cannot bring the rest of the organisation with you. S&OP needs inventory analytics, just as inventory analytics needs S&OP.

Reducing E&O inventory may be seen as a desirable tactic, but at a certain level it needs to be weighed in the strategic balance, since fully eliminating E&O is likely to be impossible without service levels suffering, although we have never seen any organisation reach that level in practice. Rather, the application of a full range of inventory optimization approaches will allow you to reduce E&O even while you maintain or improve service levels and this win-win scenario is vital to sustainability. You know that you have truly achieved mastery of E&O when all stakeholders see inventory optimization as something which helps them, rather than something which endangers the objectives of sales to further the objectives of operations.

Honourable mentions:

We are coming towards the end of our guide to reducing E&O inventory, and you may feel that some things have been missed from our ten steps. Two notable ones we chose to leave out are vendor managed inventory (VMI)/consignment stock and multi-echelon inventory optimization (MEIO). Let’s briefly consider them here:

VMI is where you outsource the management of some of your inventory to your supplier. Consignment stock is where inventory in your warehouse belongs to your supplier until you use it. You might use a combination of VMI and consignment stock or just have one or the other. Consignment stock is a tempting quick win when you want to reduce inventory levels, since you are getting inventory off your books without it leaving your warehouse. VMI might seem a good option if you believe your supplier to be better at managing inventory than you are.

There can be benefits to both VMI and consignment, especially in terms of vertical supply chain visibility – your supplier will have granular visibility of your actual consumption of their products, rather than relying on your forecasts. However, we chose to leave both out of our guide. VMI and/or consignment might be good for you, especially at lower ends of your own inventory management maturity curve, but it is essentially outsourcing a problem rather than solving it, and it has downsides as well as benefits: A lot of work needs to go into the service level agreement if it is to work well, the relationship needs managing closely, it can be difficult to have good visibility of what your supplier is doing or if you’re getting value for money. With consignment stock, you still have a number of the holding costs, such as storage and material handling, even if your cash is no longer tied up in the stock itself.

In short, while VMI/consignment stock doesn’t preclude good inventory management, neither does it automatically deliver it. Use it judiciously and, if you use it, put effort into doing it well.

MEIO we left out for a different reason. In theory, MEIO will be better than single echelon inventory optimization, but in practice it is fraught with difficulties. Here we need to be very clear with terminology. It is always good to work out the best place in your supply chain to hold inventory. This is often referred to as Strategic Inventory Positioning (SIP). It is also good to optimize all echelons in your supply chain. With single echelon optimization you decouple each site in your network from the others and treat demand signals between them as independent. So safety stock in your manufacturing facility might be calculated on the basis of the variability of the demand coming from your distribution centre. With MEIO you treat your whole network as one: you optimize what is held at each point on the basis of the whole.

This is good in principle, but there are currently a number of challenges with applying it in practice. Firstly, there is the computational challenge – effort is exponentially driven by permutations. Secondly there is an issue with fuzziness. MEIO is highly dependent on a number of parameters like waiting times, which are normally hard to quantify accurately. Finally, and perhaps most importantly, there are challenges with implementation. To realise the benefits of MEIO, you need a differentiated range of service levels. You might want a given location to deliver a service level of 50% for one item, 80% for another, 98% for a third. Where you have managers used to targeting, say, 95% for all items, on the understanding that 96% is even better, this is immensely complex.

In short, while MEIO has potential, especially for reducing E&O since you are lowering overall levels of inventory across the network, we wouldn’t recommend anyone to try it unless they are already very advanced in all of the other approaches. Implementing MEIO successfully demands high degrees of competency in our steps 9 and 10 as a prerequisite. And even then, it is probably only worth the effort beyond heuristics for a relatively small number of items. For the vast majority of organisations, it will be better to focus on our 10 steps.

Conclusions:

So there you have it. The ultimate guide to reducing excess and obsolete inventory. Did we miss anything out? If we did, or if you have any questions on the content of this guide, contact us and let us know.

This guide has deliberately been technical in nature and is intended as a checklist of things to try when aiming to reduce E&O. It does not pretend to be comprehensive on all topics. Rather, we hope it might act as a jumping off point for new approaches. Organisations that have very low E&O invest in their people, systems and processes to deliver sustainable benefits. Getting whole organisations to perform strongly on this measure takes time, effort and commitment.

And yet, as we head into the 2020’s, it is to be hoped that more organisations prioritise E&O reduction. E&O has a negative financial effect, but it is also bad for the environment. Every product that is surplus to requirements represents resources and energy wasted, from the extraction of raw materials, via multiple stages of processing, transportation, packaging and so on. Wherever you are in your E&O reduction journey, it is always worth striving for the next level.

When it comes to inventory optimization, for many people the first and sometimes only focus is safety stock. This is not unnatural, but actually one of the first things to understand about safety stock is that it is not the only show in town. Neglecting other types of stock, such as cycle stock, pipeline stock, anticipation stock, congestion stock, and so on, is a mistake.

But our topic for this article is safety stock. Let us start with a definition:

Safety stock is inventory kept to buffer against uncertainty on both the demand and supply side.

The value of safety stock is in allowing supply to continue uninterrupted most of the time. The downside of safety stock is that it can lead to excesses and eventually obsolescence.

So far so simple, but there are different ways of defining the amount of safety stock that you need. We will look at some of the most common ones in this article. We will also consider how often you should revise safety stock levels, where in your supply chain you should keep it and what alternatives to safety stock exist.

To start, though, we will differentiate safety stock from contingency stock and discuss how different types of inventory require different safety stock models.

Safety stock and contingency stock

First of all, let us differentiate between safety stock, which is designed to buffer against typical uncertainty, and what we shall call contingency stock, which is designed to buffer against unusual events. This is particularly relevant at the time of writing, since the impact of the Covid pandemic has been much more substantial than the regular uncertainty you get from the occasional supplier shipment being delayed or the usual fluctuation in customer demand.

Do you need contingency stock?

Contingency stock is appropriate in two scenarios.

The first is where your inventory is absolutely essential, i.e. related to the preservation or protection of life. Hospitals and the military are examples of organisations that routinely hold contingency stock: they need to be prepared to withstand substantial disruption on the supply side as well as major swings on the demand side.

The second is where you are trying to assure business continuity in the face of a specific supply-side risk. For instance, if there is only one possible source of supply for a key input material to your products and you have concerns about your supplier’s reliability, you might choose to increase your inventories of that material in case there is a disruption.

The right-sizing of contingency stock is a type of risk management and closely related to business continuity planning. You have to assess the level of protection you want to have, for instance, the ability to continue without any supply for 6 months, or the ability to handle a sudden doubling in demand (eg for flu vaccines), and so on.

You can never protect yourself against all possible scenarios (what happens if supply is disrupted for 12 months? For 24 months? What happens if demand triples or quadruples?) and you also have to weigh the risk of running short against the waste involved if you never need your contingency stock. This is why contingency stock is only appropriate in very specific circumstances, in a lot of which a public or governmental organisation will directly or indirectly be picking up the bill.

One size does not fit all

For the rest of this article, let us focus on safety stock itself. When working out how much safety stock to hold, there are 3 main considerations to factor in before you even start calculating. The first is the nature of your demand, the second is the service level you are targeting, and the third is the planning methodology you use.

  1. The nature of demand. Demand can be uniform or variable, flat or nonstationary (i.e. growing or declining), regular or sporadic, predictable or unpredictable, and in terms of probability distributions it can demonstrate normal distribution or other types of distribution such as gamma, poisson, or negative binomial. Noting that whatever it is at the moment, it can change at any time. There is a lot of complexity here. The key point to understand in a first instance is that tools and models designed to define ideal safety stock levels are highly unlikely to factor all of these dimensions in and so will work better for some items than others
  2. Service level. Service level is often poorly understood in this context, but is a very important input to a lot of safety stock models. It represents the likelihood that stock is available when required. Mathematically, only infinite stock will deliver a fill rate of 100%, but organisations can target a specific fill rate (frequently in the 95-99% range) and build safety stock to try to assure that level. Read our article on supply chain service levels for a deeper dive
  3. Planning methodology. There is an important difference between deterministic and replenishment methods. In a replenishment model, you define the required safety stock to deliver a given service level for expected average demand and variability. With deterministic models you are planning on the basis of given demand. As such, the concept of safety stock is in theory redundant, although in practice deterministic methods (such as MRP) still have to deal with some level of variability. A common cause of overstocks is when organisations size safety stock as if they were using a replenishment model when in practice they are planning deterministically, often to a forecast

The three considerations above are important factors in segmenting your inventories. Before trying to optimize your safety stocks it is important to understand what different types of inventory you are dealing with. Trying to use the same model for calculating safety stock across all items is highly likely to lead to sub-optimal outcomes.

Different methods for calculating safety stock

To define what level of safety stocks you need, a number of methods are possible:

This is a high-level overview of the main methods. We will now look a little deeper at the third method, defining safety stock based on service level. While not perfect, we find optimization based on service level most commonly to be the best option, especially when applied at scale. This is also no doubt the reason why it is the approach used by almost all inventory optimization software. We will also look at some of the major exceptions where it may not work well.

Defining safety stock using service level

To define optimal safety stock using service level, you can use one of a number of equations which use well-established mathematics. Let us break down one of the most commonly found equations to understand how it works and what some of its important sensitivities are.

SS Equation for defining safety stock using service level.

Where:

SS = Safety stock
Z = A measure of how many standard deviations above or below the mean a value is
σ = (Sigma) a standard deviation from the mean
Vol = Average volume of demand in units
LT = Lead time (time between ordering and receiving an item)

What the equation does is define the amount of safety stock you need relative to three things:

  1. The lead time. This is intuitive. If you can get something the next day you will need less safety stock than if it will take 3 months to arrive
  2. The absolute variance in demand. So if demand fluctuates between 100 and 200 units you will need more safety stock than if it fluctuates between 100 and 120 units
  3. The target service level you want to achieve. This is done by considering all of the likely values based on the observed distribution of demand and then defining what percentage of those you want to cover

The example given above only allows for variability on the demand side. If you also want to factor in supply-side variability, you can use a modified version of the equation:

SS Equation for defining safety stock using service level, factoring-in supply-side variability.

This isn’t a maths lecture. The thing to retain here is that this version is also factoring in how much the lead time itself varies.

So in summary, if demand, variation in demand, lead time, variation in lead time or your desired service level increase, you will need more safety stock. If any of them decrease you will need less. So much you probably instinctively know. What the equation does is help you to quantify by how much. Other equations exist, this one was chosen as it is fairly commonly found and relatively easy to understand.

Exceptions and special cases

However, there are some important sensitivities to this equation. Firstly, it uses cycle service levels. (See our article on supply chain service levels on this point, especially the section at the end on calculating safety stocks using service level.) Secondly, it assumes normally distributed demand. This is frequently good enough, but especially where demand is very variable and/or sporadic it may not be robust. Thirdly, it assumes average demand to be flat (stationary), not increasing or decreasing. There are further sensitivities, but already you can see how using a well-known equation has a number of pitfalls which could lead to inaccuracy in your modelled safety stock levels, and that it is likely to be problematic for certain types of inventory, especially those with sporadic demand.

In short, the equation given works reasonably well in a replenishment model for items which have moderate variability. If you are managing items with very sporadic demand (eg spare parts that are only infrequently needed) then it is unlikely to work as well. More complex models exist for sporadics, but in a first instance a maximum lead time demand model might be a good safe place to start.

Similarly, care needs to be taken with demand which is clearly growing or declining. This too is a more complex area, but in short, two workarounds are either to factor in the smoothing constant from your forecast or to make a series of calculations at the projected future levels of demand.

Optimizing your safety stocks – a systematic approach

So what is the best approach to optimize your safety stocks? Well maybe a good place to start would be in measuring how much you have, compared to what you think you should have. Another way of defining safety stock is the average stock on hand when a new order arrives.

Safety stock is the average stock you have on hand when a new order arrives

Since actual demand rarely matches forecast demand, there will usually be a difference between your actual safety stock at the end of each cycle and your target safety stock. (The whole purpose of safety stock is, after all, to buffer against this variability! If in your mind safety stock should never be used then you are thinking about sediment, which is surplus, or the level of inventory never used.)

However, over time and on average the actual should be the same as target unless you have forecast bias. If you consistently have less safety stock than planned then there is a case to be made for increasing it, and if you consistently have more then you should consider decreasing it. (Or, if you never run out, a case can be made for reducing your planned safety stock!)

This doesn’t address how much safety stock is optimal, but it does at least allow you to cancel out any forecast bias, is simple to compute and may allow you to make some improvements quickly, by increasing or decreasing your target safety stocks where you observe systematic understocks or overstocks.

Another step to consider is what we call changing the givens. We saw above how lead time, demand variability and service level are all key inputs to any optimization calculation. Anything you can do to reduce lead times and demand variability, even to relax service levels in some situations, will reduce your need of safety stock. You still need to calculate how much you can reduce it, but all of these steps certainly help and in some instances are addressed in advance of any attempts to optimize safety stocks since they bring other benefits.

The next step is to segment your inventories. When implementing a new approach to safety stocks it is unadvisable to switch everything over in one go. Much better to trial with a smaller number of items and watch carefully what happens before rolling out further.

There are many ways to segment your inventory. A good starting place is an ABC analysis, whereby A items account for the top 80% of your inventory turnover (value * volume in a year), B items the next 15% and C items the remaining 5%. As a general rule, set C items aside. By definition they tend to be rarely used, but this doesn’t mean they can’t be critical when required. As such, it is often best to set a conservative safety stock for C items to avoid stock outs, which are likely to be more costly than the value of any benefit from optimizing them.

Amongst the A and B items you then want to identify items that have regular demand (i.e. demand in all or most time periods) that is not growing or declining markedly, and with moderate variability. (For the statisticians out there, variance coefficient is a good basis for carrying out an XYZ analysis on variability. Moderate variability would be X and Y items.)

A and B items with moderate variability are the best ones to start experimenting with. You can calculate optimal safety stock levels for these items and compare them to what you are using. What you are likely to find (based on our experience) is that you need to increase safety stock for a small number of items and can reduce it for a much larger number of items.

Before you rush in and change anything, however, there are a number of further steps to take. Maybe start with the items which show the biggest potential to change. Take the top 20 to 50 items by usage or potential, for instance. Then double check the input data to your calculations. Important parameters like lead time can frequently be wildly inaccurate (or even missing) in source data.

Once you’ve satisfied yourself that the data is as accurate as it can be, then try moving towards the suggested safety stocks for those items incrementally (i.e. bridge the gap between actual and target a little at a time, don’t jump straight to the calculated value). All models have a certain level of inherent inaccuracy, plus demand is never quite what you think it is going to be.

Once you have successfully tested this with your top items, gradually expand it to the rest of the stable ABXY items. Then you might want to consider more complex approaches for the rest of your inventories.

Doing this exercise may require you to try a replenishment model for the first time. Remember that deterministic planning in theory shouldn’t require safety stock. And in practise, if you’re using one of the deterministic MRP methods and planning to a forecast, then the safety stock calculations will not apply in the same way.

Safety stock and MRP

We sometimes see it said that replenishment isn’t even a planning method. Opinion can be very polarised on this topic. It really comes down to how predictable future demand is. With replenishment models, the way of thinking is “we don’t know what demand is going to be, but we expect it to carry on broadly speaking as it has been”. You then look at the variability in your demand and use that to set a re-order point which allows for enough safety stock to buffer against that variability. With deterministic models, the way of thinking is “we know that demand is going to be different from the past and we are going to plan for what we expect”.

What this means is that if you genuinely know future demand is going to be different to past demand (for instance, if you are planning for a promotion or a seasonal uplift, or if you are planning materials for a frozen production schedule) then deterministic planning should in theory be better than replenishment based on historical data. (Although of course, you can base your replenishment model on a forecast too, or temporarily add some anticipation stock to it.)

How do you choose between the two? Is your forecast consistently better than a naïve forecast? If not, you should see benefit from a replenishment model. What replenishment is good at is buffering the actual variability you have. However, because people are perhaps uncomfortable with probabilistic models, there is a strong perseverance of deterministic models, where you base your plan directly on what you expect. Variability can get lost in this model.

How often should you check and update safety stock levels?

As segmenting and calculating ideal safety stock levels is somewhat laborious to do well, the temptation is to do it as rarely as possible, and the good news is that this might not be a bad thing. One of the main issues with deterministic models that are not protected from variability is that they are “nervous”, i.e. they are constantly trying to readjust.

Part of the benefit of a replenishment model and the safety stocks they use is that variability is automatically absorbed by them. So does this mean you can set your re-order points and then head to the beach? Unfortunately not.

In reality, of course, change is a constant factor. The fact that variability was within a certain range in the data you used to define your safety stocks does not guarantee that it will remain in that range. While major events, like a delayed shipment, will no doubt come to your attention anyway, and be dealt with as part of the normal day-to-day inventory management work, a subtle but important change in demand patterns might escape you. Statistical process controls like the Western Electric rules are very useful where the volume of items managed is high.

And even setting sudden or substantial changes aside, most things are undergoing a gradual process of change. A lot will come down to your specific situation. If you have mostly mature products with limited change, once a year might be enough to check and update target safety stock levels. In a more dynamic environment, you will probably need to do it more often. If you find you need to constantly revise the levels, it is questionable whether you really need safety stock at all and some other approach, such as reserving spare capacity, might be preferable.

Where should you build safety stock?

The question also arises, where in your supply chain you should keep safety stock? This is partly a matter of business strategy (how quickly do you want to be able to fulfil customer demand) and constraints (how fast can you source materials and manufacture your products). As such, there is no “right” answer to this question. But as a generalisation for manufacturing industries, most keep some level of safety stock in finished goods since end demand is usually variable and make to order is not so prevalent. A replenishment model is frequently appropriate here.

At the other end of the supply chain, safety stock in raw materials can help protect you against delays or quality issues on the supply side. However, where there is considerable variability on the demand side and MRP has propagated it all the way through to raw materials orders, there is often a marked bullwhip effect and buying raw materials to a forecast that has negative forecast value added (FVA) leads to overstocks.

The most complex area for safety stock is WIP (understanding WIP here to include semi-finished goods) since it is heavily tied to capacity utilisation. In an ideal world, demand from the end customer would flow through and you would need no WIP except for the amount of time it takes to produce what is required. (And indeed, leading proponents of “Lean” manufacturing achieve impressively low WIP inventory levels.)

In reality, however, a lot of factories run at or close to capacity, which means there are frequent bottlenecks and waiting times for machinery to become available. Safety stock may usefully be built at relevant points in the manufacturing process such as at bottlenecks, at processes with very variable yields, or for semi-finished goods that are used for multiple end products. You will often find such safety stock in WIP referred to as decoupling stock (to distinguish it from the safety stock which is buffering against external uncertainties).

Such decoupling stock can be managed in a variety of ways, such as Kanban, CONWIP or DDMRP. It is a valid approach to define safety stocks at decoupling points based on the replenishment model, and the judicious positioning of replenishment points within an end-to-end production flow can mitigate some of the worst effects of MRP. A well-designed approach might build decoupling inventories with a replenishment model, optimizing safety stock for target service levels, and avoid safety stocks altogether for the parts of the process that are genuinely deterministic.

Are there any alternatives to safety stock?

While safety stock is perhaps the most common way to buffer variability, it is not the only alternative.

Another option is safety time. This is where you have quite a good idea of the size of demand, just not when exactly it will come. An example of this might be if you know that Production is planning to manufacture a fixed amount of a product, but the schedule hasn’t yet been fixed. Instead of getting additional raw materials, you just bring them in early.

Another option is reserving spare capacity and/or expediting products. Where you have a very limited ability to predict what demand is going to be, it might be preferable to be able to produce and despatch quickly in response to firm orders rather than building speculative safety stocks. An example for this might be for a new product launch where it is unclear how high demand will be.

A third option is waiting time. While in our modern world we have perhaps become used to having everything on demand and without delay, in certain circumstances it is possible just to make customers wait longer. This comes about through necessity in instances of major shortages and is also the option of choice in many instances of high value, low volume make-to-order products

Conclusions

Like many things in supply chain, a few simple safety stock principles can rapidly become extremely complex once you dig a little deeper and try to find optimum levels in the real world.

Supply chain professionals need to find solutions that are practical, proportionate and easy enough to understand to take people with them. However, safety stock is one of the most important levers you control and the benefits of getting it right, such as reducing production stoppages, late customer deliveries, or the need for expedited deliveries, are substantial.

There is perhaps a tendency to feel that having too much is better than having not enough, but having too much safety stock also has a real downside. Warehouses and production areas can get clogged up, production actually slows down because of the excess, working capital is stuck in unproductive surplus inventory and the risk of waste through obsolescence increases.

Many organisations are already taking advantage of various optimization models, either built in-house using spreadsheets or in tools from software vendors. This can deliver real benefits where it takes people away from cover targets. However, for some of the reasons explored in this article, a lot more is usually possible once you go beyond “one size fits all”.

Due to the complexity involved in optimizing safety stock, even companies already using advanced optimization technology usually have an opportunity to further improve their levels, reducing shortages and excesses simultaneously, often by 20% or more.

nVentic has automated the evaluation of inventory data to help clients identify and act on the improvement opportunity quickly and systematically. If you would like to discuss your safety stocks or other aspects of inventory optimization, please contact us.