Inventory – can’t live with with it, can’t live without it. Let’s talk about how to balance the competing trade-offs of high service levels, the need to control costs, and freeing up cash – and how to make managing your inventory a whole lot easier.

We want to have sufficient inventory on hand to service customers and maximize sales, especially in a make-to-stock environment. Inventory, is of course, necessary. And there are plenty of reasons to hold lots of it.

Why Inventory is Good

By committing to higher levels of inventory we can optimize batch sizing to lower production costs and cost per item. By ordering more stock/materials we can optimize transportation costs. We can get price breaks by ordering higher volumes on a monthly basis vs lower volumes on a weekly basis.

Having inventory on hand limits fines for late delivery in the case of retailers like Walmart and Amazon. And a high level of inventory limits costs for expediting delivery for stock we didn’t have readily available. Having inventory ahead of a sale is cheaper than having to source it last minute.

The flipside is the cost of tying up cash in stock that isn’t selling. Right now we have a perfect storm of rising inflation where inventory/materials are more expensive to source, debt is more expensive to service, and sales are going down. In such an environment your CFO will be on your back to reduce inventory.

Why Inventory is Bad

Beyond the hard cost of dollars being tied up in assets sitting in storage, there is an opportunity cost associated with tying up capital in inventory. With that extra cash your company could shore up the balance sheet, service debt or deploy it for new initiatives. Storage is also a cost, not just in terms of the space but in terms of people and equipment required for warehousing.

Inventory also comes with damage and pilferage. What’s more, companies with short lifecycles face obsolescence, never being able to shift stock for certain items which have to be disposed of (another cost). Insurance is yet another cost, the premium being paid on the total assets your company holds.

So there are advantages to holding inventory and disadvantages to holding inventory. Finding a balance between the two that is right for your company is the holy grail of planning.

Lean Into Your Company’s Priorities

If you’re thinking I want on-time, in-full to be 99%, I want to have 24 turns a year, I want to have less than one week’s worth of inventory in stock, and I want to maximize my margin – wonderful, everybody wants that! One of those objectives, one is going to win, and it is up to you to decide which is most important. What are your company’s objectives? In the Cost-Service-Cash triangle, your company will naturally lean into one dimension more than the others, and it’s up to you make the trade-offs that support enterprise strategy.

Prioritize customer service, and your costs will increase and cash will be tied up. Prioritize cash, and you’ll have to accept that customer service will suffer and costs will increase. Prioritize lower costs, and service will decrease and cash gets tied up. Which dimension you need to prioritize most will inform your safety stock levels.

Stop Self-Inflicted Uncertainty Now!

There are certain things companies do that unintentionally introduce demand uncertainty, making it more difficult to know the required safety stocks. There are certain supply planning actions we can take to make inventory management more effective.

Beware Demand Shaping: IBF research reveals that a 1% reduction in uncertainty equals a 6% reduction in my safety stock. Dynamic pricing and promotions shift demand, causing uncertainty that has makes inventory management more complicated. While promotions may be necessary, there are consequences to adding in that demand variability.

Reduce your lead times: It’s not just about demand forecasting; proper supply management pays dividends when it comes to reducing the cost of holding inventory. On average, every 1% reduction in lead time results in a 0.95% percent reduction of safety stock.

More SKUs equal more inventory: I can’t believe that some people don’t understand that logic. More SKUs serving the same demand adds uncertainty without increasing the top line. A 10% reduction in SKUs represents a  5% reduction in safety stocks.

Lower your service levels for a given customer: Some customers won’t necessarily need the service level you’re providing, meaning that you can afford to carry less inventory. What does customer X really need, and what can you reasonably get away with?

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During my 30-plus years in Supply Chain Management I have participated in many projects as a practitioner, consultant or educator to improve demand forecasting. I have also participated in many projects to improve inventory management. My experience is that most businesses have not done enough to link these two areas from a people-process-technology perspective. This article highlights some lessons I have learned and provides some suggestions to improve the link between these important processes.

New Products & Inventory Control

We all know the story regarding new products. Sales and Marketing are conditioned to over-forecast these in most cases. In their view it is much better to create excess inventory than to risk a loss of revenue. Of course, in most units Sales & Marketing are not held responsible for inventory levels or dysfunctional inventories. Supply Chain is accountable for inventory performance, but this responsibility does not usually address new product introductions in a timely manner. Often excess and aged inventories result from lower than expected demands for new products. Several years ago, a business unit with poor inventory performance got a new leader. When discussing this with the Supply Chain leader, he was told that new product and trial inventory was over $500,000.  He directed the Supply Chain manager to add this inventory performance to the monthly report reviewed in the S&OP meeting. Table 1 depicts an example of the report, along with comments.

• There were no sales for these 10 items in the month.
• Inventory increased on two items which were produced to the original plan although no sales have occurred the past three months.
• The current month-end value of this inventory ($552,000) represents approximately 85% of total new product-trial inventory.

Based on this poor performance, the business implemented a process whereby the sales forecast was used to establish an inventory plan. The actual sales and inventory were compared to plan and a meeting was held every two weeks between the product development, marketing, sales and Supply Chain functions. The result was improved demand-supply balancing in both quantity and timing. Chart 1 explains further sales, production and inventory, and Chart 2, planned and actual inventory.

Chart 1 | New Product Sales & Inventory Plan

Chart 2 | Tracking New Product Sales and Inventory to Plan

Demand versus Stocking Strategy

The textbooks tell us that make-to-stock strategy should be limited to higher volume products that are easy to forecast. This ensures an acceptable inventory investment and that slow-moving and obsolete inventory can be controlled. One mechanism to evaluate the stocking strategy is ABC-XYZ volume variance analysis. Variance is based on the Coefficient of Variation (CV) which is defined as the standard deviation in period sales divided by the average period sales. Most practitioners use a CV threshold of >1.0 to segment those products which will be difficult to forecast. If the product also has low sales volume (C item based on volume), the correct stocking policy is make-to-order (see Table 2).

Table 2 | Sample ABC-XYZ Analysis with CZ Item

Notice Article 551 in Table 2 has both high volume and high variance in demand. Collaboration with the customer resulted in a stocking policy to ensure product availability at the point of order. Here the customer provided an order schedule. In this case Article 552 was being forecasted using a 3-month moving average. The result was being out of stock in some months and having excess inventory in others. Chart 3 depicts the actual demand for this article.

Chart 3 | Actual Sales for Article 552 (CZ Article)

As indicated by the quantitative analysis, this article should not be made-to-stock based on a forecast. Some firms use re-order point planning based on average demand for articles with erratic sales patterns. As such, it would not yield acceptable inventory and service.

After collaboration between Supply Chain, Sales and Marketing, it was decided to place the article on make-to-order status. In addition to the erratic demand pattern this article requires a unique raw material in production and the risk of producing this article to stock is high.

Best practice is to perform the volume-variance analysis periodically. The frequency (monthly, quarterly, twice per year) depends on how dynamic demand is within the business. The Demand Planner should perform the data analysis and the Supply Chain leader should facilitate a review with Sales & Marketing in which decisions must be made. There is a strong link between forecastability and inventory control.

Forecast Error & Safety Stock Inventory

Another key link in planning is the use of the demand variance or forecast error to plan safety stock levels. The use of subjective safety stocks by ordering early is common in industry. This judgment model for determining safety stock quantity leads to high inventory. Below is an example of using forecast error to set safety stock levels.

Chart 4 | Use of Statistical Safety Stock Based on Forecast Error

As shown in Chart 4, the resulting statistical safety stock performs well based on historical sales. Statistical safety stocks assume the sales data is normally distributed (bell curve). In this example, the average sales are 498 units and the standard deviation of sales is 57. This yields a Coefficient of Variance of 0.12 (57/498). As mentioned before, CoV’s of less than 1.0 indicate that variation approximates normal distribution and use of statistics based on normal distribution is a valid approach. Next we will look at an example of erratic demand versus safety stock level.

Erratic Demand & Safety Stock Inventory

For some businesses, many items have demand patterns that are difficult, if not impossible, to forecast. Chart 5 gives such an example.

Chart 5 | Erratic Demand & Safety Stock

The CoV test indicates bell curve statistical techniques are not valid in this case.  CoV is greater than 1.0. If statistical safety stock is used, both over-stocks and stock-outs will occur. In that case, the best solution is to place it on a make-to-order policy. However, if the customer insists on the supplier keeping some stock, the best solution would be to maintain a minimum safety stock of 1,500 units. This covers demand for ten of the twelve months. For the two months when demand far exceeds average, the options are advance warning from the customer, expediting by the supplier or longer delivery lead time.

A key point is that the actual demand pattern can help in developing a stocking policy. So often I have been told by planners they just use re-order point based on average demand for those items with erratic sales. Of course, re-order point was created to handle items with stable demand and random variation and does not work well for patterns like this one.

The use of forecast error or demand variance in safety stock calculations often gets a bad reputation because the tool is applied to items with erratic, non-normal demand patterns. Use of the CoV data and inspection of the demand pattern can help to ensure statistics is used but not abused.

Using Exception Reports to Link Demand Planning & Inventory

A good practice is to link forecast performance to inventory and service. The “Top Ten” report below is reviewed in the S&OP Meeting each month.

The red numbers represent how much inventory was planned due to over-forecasting these products. The inventory totals over $1.8M for this month. The other items were under-forecasted resulting in schedule changes or stock-outs. In this case, the stock-outs did result in customer backorders. This business offers dozens of finished goods line items and exception reports are used to prioritize improvement efforts.

Summary

The Supply Chain function is usually accountable for inventory management. They should ensure this includes new product inventory management and work with Marketing & Sales to get inventory plans in place. One aspect of product portfolio management is the evaluation of demand volume and variance. Supply Chain should ensure this analysis is performed periodically and that Marketing and Sales participate in review of the data. Decisions regarding whether to stock the product and how to ensure availability if stocked need to be a team effort.

For items with reasonably normal demand patterns, Supply Chain should link safety stock quantities to demand variance or forecast error. Excess and slow-moving inventories should be monitored routinely; this can help to identify when stocking policies and safety stocks need a review. If items with erratic demand patterns must be made-to-stock, Supply Chain must work with Marketing and Sales to decide what stocking policy is required. It is best not to provide an invalid statistical forecast but rather identify items which require decisions based on informed judgment. The use of exception reports such as “top ten” forecast errors, or “worst ten” over stocks each month is a better practice. Also, the use of the ABC principle to ensure resources are focused on those items that significantly impact the bottom line is very important. The “C” items provide more challenges for both demand planning and inventory control but usually represent a small percentage of revenue.

In closing, Supply Chain must take the lead to integrate new product planning and product portfolio review in the S&OP process. Seldom are marketing and sales held accountable for the inventory and cost issues for mistakes in these processes. Demand planning and Supply Chain are accountable for linking actual demand patterns to safety stock decisions. They must also routinely perform inventory analyses (e.g., slow moving, excess, obsolete) to identify priorities and the need for change. Supply Chain serves as the link between Marketing, Sales, Finance and Operations to better balance service-cost-inventory.

This article originally appeared in the Spring 2020 issue of the Journal of Business Forecasting. Click here to become an IBF member and get the journal delivered to your door quarterly, as well discounted access to IBF training events and conferences, members only workshops and tutorials, access to the entire IBF knowledge library, and more.

FMCG distributors are critical actors in the global supply chain. They manage a large variety of products and high levels of inventories and assets, and help ensure retailers deliver to their customers. To be profitable, we must control inventory costs, have effective facilities allocation, and have extremely well-organized logistics. Let’s look at how the insights from demand planning help us in our capacity planning, providing effective inventory management and order fulfillment.

What We Must Achieve In Capacity Planning

Facilities allocation: Deciding the required facilities, i.e. the space of any facility or zone needed to satisfy the anticipated flow of customer orders. It must support any projected higher inventory levels for each product.

Avoiding stockouts: Supplying warehouses to avoid stockouts with optimal frequency to ensure our products are available in our customers’ stores. Supplying these warehouses should take into account anticipated customer demand for each product, promotional campaigns, urgent orders and returns.

Performance of the order-to-cash process: Optimizing resources and reducing the order cycle time to satisfy the actual and future customer demand – in full, on time and without errors. Variables which must be monitored here are the product demand and the characteristics of the daily orders flow, either weekly or monthly.

Understand Demand Variables To Better Plan Capacity

The secret of capacity optimization for distributors is to know customer demand, seasonality, promotions, supply constraints, financial aspects (assets, cash etc.) and delivery regulation (e.g. OTIF), from a short-term horizon to a long-term horizon. This comes from the demand plan.

Inventory management requires planning for warehousing space, resources, manpower and logistics. For this, we need demand planning to know likely future sales.

How We Use Demand Planners For Better Inventory Management

FMCG products are characterized by a high demand variability: seasonality, promotions, new product demand inaccuracy, and urgent and specific orders. So, the order point method is recommended in this situation to manage transfer to the picking warehouse. It has three parameters: Safety Stock, Order Point and Maximum Quantity.

Demand planners give us the following to assist us with this:

• Products families based on demand volume and demand variability,
• The Demand plan which contains quantities of each products per month,
• Other information including shelving measures and zone spaces.

The maximum quantity is defined as the maximum storage capacity of the shelves satisfying the demand plan. In order to determine the maximum quantity more precisely, an optimal distribution of the products in the shelves can be determined, taking into account the relative ratios between the products (importance in terms of demand and volume of the box) and the maximum volume of the shelves. Linear programming can be a good way to resolve this problem, by using the Solver tool in MS Excel.

The order point policy aims at initiating replenishment as soon as an order point is reached (including the safety stock), with a quantity that cannot exceed a predefined maximum. ERP systems can simplify this policy and provide the following complementary functions:

• Real-time tracking of product volumes in their locations, based on the calculation of the updated inventory after each internal move.
• Alert notifications will be sent in case of reaching or exceeding a pre-set order point.

Scenario Planning For Order Cycle Process Optimization

In the order cycle process, orders can have significant waiting times during the picking operations. These long waiting times mean that operators will lose the opportunity to serve other customers (downtime) and often have to work overtime. In general, the problem is perceived as a lack of facilities and workers, which makes sense as managers have to control the costs associated with these. Therefore, managers should perform scenario planning in order to find the strategy that will optimize the order cycle time and overcome the problem of overtime while increasing logistics capacity.

Demand planners should know when customer orders are likely to come in for a certain period of time, and the demand we will experience for products in each zone in the warehouse. The what-if Analysis can use different performance levers: the inter-arrival time of customer orders, the waiting time for picking, and the number of resources.

Analytics can have a significant role in optimizing the process. The following information helps us translate the demand plan to a supply and capacity plan (as well order arrival policy): Picking Time in each zone of the warehouse, number of lines per order in each zone, and probability that Picking-List visits each zone.

Demand Planning Process Has A Great Impact On Capacity Optimization

As we see, the demand planning process has a great impact in optimizing capacity for FMCG distributors. Accurate demand forecasts and a deep knowledge of customers’ behaviors, and even demand forecasts at the zone level for the warehouse, are key factors in optimizing the supply side, along the with the support of an ERP system and analytics.

Last week we looked at cutting inventory through improving inventory quality (getting rid of obsolete or low value stock) and reclassifying mislabelled inventory. Once you’ve done those those 2 quick fixes, the next inventory cutting initiative is testing for forecast bias and reviewing products in transition. Like last week’s Quick Hits, these will yield fast results without much in the way of research, tools, or infrastructure changes. Removing forecast bias should yield measurable inventory reduction results in just two to three months.

Testing for Forecast Bias

Forecast bias is a problem in many organizations. The perpetual challenge in demand planning is to balance the aspirations of Sales and Marketing with reality as reflected in consumer sales and factory shipment trends. When aspirations consistently exceed the reality, bias is the result.

Supply chain planning typically begins with a forecast, and if it is overstated it creates excess inventory. Conversely, consistently understating a forecast (or “sandbagging”) causes a bias as well. This can lead to expediting, schedule cuts, and other detrimental supply chain behaviors. While targeting bias seems to be the realm of a longer-term forecast improvement project, measuring forecast bias often drives fast and significant change in both inventory reduction and forecast error levels.

Measuring bias requires only a modest effort, usually focused on report building, and can be implemented in a relatively short period of time. It’s a perfect quick-hit opportunity. For those not already familiar with measuring forecast bias, there are a number of different ways to calculate this metric; our experience suggests that simple rules are the best. One quick test is to track any item that has been over or under forecasted for three months in a row (in the same direction).

A directionally biased item becomes a ‘problem item’ if the average deviation or error over the same three months is greater than 25%.

Identifying Problem Items

If this is your first venture into measuring bias, you should expect 60%-70% of all items to have some level of directional bias. Of course, it may be impossible to track bias for a potentially large number of items, so you may want to manage the bias measure by exception, using a secondary filter to reduce the list of bias items to those where the directional bias is most significant. The typical secondary filter is aggregate error – the summing of forecasts and actuals to arrive at an error percentage over the course of three months. In one of the simplest implementations, a directionally biased item becomes a ‘problem item’ if the average deviation or error over the same three months is greater than 25%. Again, this is a simple rule of thumb that may not apply well to all businesses.

As you might expect, the number or percentage of biased items is a meaningful S&OP measure. Figure 1 shows the number of biased items (problem items) above a 25% error threshold on a two month lagged forecast with three months of consecutive directional error. Making biased items a key measurable in the forecasting process improves the quality of a forecast. In turn, a better quality forecast is more likely to help drive down inventory levels.

Once you work through some of these questions, you may present the bias information in your demand consensus process for root cause analysis and correction

Figure 2 shows how the total inventory level (and specifically the finished goods inventory) dropped significantly over the same time as the number of bias items was lowered. This works, and it works well. So, how do you get started? First, you need to create a bias measure that works for your company. Looking at historical data can help you determine the number of directional forecast misses over time. You will need to determine a relevant level at which to test for bias in an item, brand, product grouping, or category, and/or what percentage of a miss represents bias for your organization. Most business leaders start with 25% as a measure, and then shift the percentage up or down depending on what they think is best for their organization. Once you work through some of these questions, you may present the bias information in your demand consensus process for root cause analysis and correction. Implementing a simple test for forecast bias can pinpoint unmet aspirations within a forecast and help you quickly reduce inventory levels within 2 to 4 months. Again, this is one of those cases where measurement can help change business behaviors.

To actually reduce the inventory, the best strategy is to create a report of biased items, review them in a demand consensus meeting, and then report them in the Executive S&OP meeting for an appropriate action. Clearly explain that the data reveals bias and is causing excess inventory and a drag on profitability, using the hard data in the report to support your argument.

All too often, product portfolio decisions are made without facts or are based on simplistic rules or metrics such as annual revenues.

Keep an Eye on Products in Transition

As items progress through the normal product life cycle of introduction, growth, maturity, and decline, there is a need to constantly evaluate how these products are planned. Each phase brings its own expectations. A product at launch needs a great deal of inventory and flexible capacity because of the uncertainty of demand. Items at maturity or in decline need their inventory to be optimized as demand is much more certain and reserve stock is not needed. Inventory is at greatest risk during the early introduction phase and in the later phases of decline— often called the long tail of the product life cycle—where sales volumes have dropped so low as to be considered for discontinuation, either through a voluntary market withdrawal or by the loss of a key customer or two.

Product Rationalization To Cut Low Value Items

There are two challenges in managing inventory throughout the long tail. The first is correctly determining product rationalization, that is, deciding which products should stay in your portfolio and which should be discontinued. The second challenge is managing the exit of those products slated for removal. In some industries, products move through life cycle phases seemingly in the blink of an eye. Yet even in industries that specialize in introducing new and improved products each year, it is not uncommon to find poorly defined processes for anticipating/ determining and managing product discontinuation. Likewise, the processes for managing planning parameters that determine inventory utilization through the end of the product life cycle are often equally deficient.

Portfolio management meetings address items as they transition through all phases of the product life cycle—from the ideation to discontinuation

Portfolio Management Review Meeting Should Control Excess Inventory

To meet these challenges, some S&OP process models include a step called the portfolio management review meeting. Within these meetings, the product portfolio is discussed in detail, including discussion regarding current ideas for products as well as those that are in the launch phase. If your firm’s S&OP process doesn’t include such a meeting, you should consider instituting one. Portfolio management meetings address items as they transition through all phases of the product life cycle—from the ideation to discontinuation. A monthly dialogue on the product portfolio can be an enormous leap forward in providing a forum for tracking product strategy and execution. Within portfolio management review meetings, many firms are beginning to use detailed analytics to properly target items for deletion off the long tail. All too often, product portfolio decisions are made without facts or are based on simplistic rules or metrics such as annual revenues. Using a rigorous approach to identify products for discontinuation on an ongoing basis enables the company to shorten the supply while facilitating effective management of obsolete inventory. Once a product is identified for potential discontinuation, supply planning assumptions such as fill rates and batch sizes can be tuned downward to reduce raw and packaging inventory exposure and risk during the final run-out.

Reviewing Stock Levels For New Products Is Crucial

Managing the supply planning parameters during the course of a product life cycle is particularly important. As an example, at one consumer products firm it was commonplace for supply chain planning to carry an additional 35 days’ worth of forward coverage inventory for all new product introductions after the initial pipe fill. The company intentionally carried excess inventory at product launch because of the uncertainty about demand early on. The company was more concerned about having available inventory to support a successful launch rather than the holding cost.

As you might expect, there were multiple examples where this supplemental-coverage inventory was never adjusted downward during the post-launch because there was no process in place to routinely review planning parameters. Under normal circumstances inventory targets are reduced to standard, steady state coverage levels, but with personnel changes and focus shifting toward the next new product launch life cycle, these planning parameters are overlooked, resulting in excess inventory.

Keeping An Eye on Products in Transition requires at least a quarterly review of planning parameters for all products less than two years past the launch phase while also conducting a similar examination of those items nearing the end of their run. Special attention should be reserved for products on the long tail of their life cycles, since these items need focused corrections in their planning parameters as their demand recedes. The adjustments in these parameters should be made with a goal of limiting inventory exposure in the event the market collapses for a product or if key customers drop the product completely.

This is where a portfolio management review meeting can add tremendous value in anticipating and managing slow and no-turn inventories. Many companies review each and every planning parameter, at least once a year. This should be the minimum review. If your company does this, great! The quick hit opportunity in this case is a very detailed one time review of all inventory policy settings for both finished goods and components.

These two initiatives are quick and highly effective. Despite minimal effort, they can resolve systemic problems that place cost burdens on your company. Next week, we’ll be looking at Waste and Yield Percentages and QC Hold Time. Stay tuned!

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This article has been adapted from the Journal of Business Forecasting and Planning, Fall 2011 issue. To receive the Journal of Business Forecasting and other benefits, become an IBF member today.

In the first of this mini-series, Pat Bower of Combe Inc. reveals the quickest and easiest ways to reduce inventory. Detailing the preferred methods used by leading S&OP professionals and consultants, he discusses how to cut poor quality or obsolete inventory for significant cost savings. Read on for quick improvements that can be leveraged easily in any supply chain focused organization. This week Pat discusses the two easiest inventory reduction initiatives: improving inventory quality and inventory classification.

Identifying Easy Targets For Inventory Reduction

As a consultant hired looking to improve supply chain efficiency in many different businesses, I would look for inventory reduction opportunities that could be implemented without much in the way of research, tool, or infrastructure changes, with the intent of yielding measurable inventory reduction results within a two-to three-month time frame. I call these Inventory Quick Hits. But where do these opportunities lie? In most cases, Quick Hits are a subset of the below inventory reduction solutions, but are narrower in scope and focus:

1. Improve inventory quality by reclassifying R&D inventory and removing junk inventory.
2. Testing and get control over forecast bias.
3. Keep an eye on products in transition so you can manage supply chain parameters reflective of their phase in the product life cycle.
4. Examine waste and yield percentages as well as QC (Quality Control) hold time.
5. Examine “actual” buy-side tolerances.
6. Investigate size, component, and formula consolidation opportunities. Look for easy-to-implement product and component.

Quick Hit 1: Improve Inventory Quality

Inventory has many classifications: active, slow-moving, obsolete, and inactive or run-out mode. Inventory that turns quickly and is actively sold is thought to be of high quality, while inventory that is slow moving, has no turn over, is obsolete, or residual is considered low quality. This “junk” or low-quality inventory is a favorite target of inventory consultants trying to reduce inventory. Slow-moving inventory has low volume sales or consumption relative to on-hand inventory. It simply does not turn over that fast or frequently. There are countless potential reasons why inventory is slow moving.

It’s possible that inventory turns slowly because of batch sizing, where each production run makes a large portion of an annual forecast, or because the product’s demand is slowly eroding, or for other reasons. Reducing slow-moving inventory requires a reassessment of supply chain parameters such as batch sizes, EOQs (Economic Order Quantity), and the like.

By contrast, obsolete inventory has no demand, and is often a combination of residual finished goods and leftover components. Obsolete inventory has lost the primary customer base, and the only sales opportunities it has are typically in distress or closeout channels. Run-out inventory is typically a combination of finished goods and components that are still being sold, but clearly are at the end of the product life cycle. Inventory is often placed in a run-out mode to help reduce the final inventory liability and may be kept as an active finished good, even with a greatly reduced customer base as a placeholder for a new product introduction. Run-out inventory usually requires a greater understanding of what the total inventory liabilities are as well as the possible disposal options. Inactive inventory generally has no raw material or packaging consumption for some period of time, typically six months to a year. It is not uncommon for ongoing finished good sales to be maintained for a variety of reasons, including trying to exhaust raw and packaging liabilities, or to cater to a niche market, or a special customer.

Analyse Inventory Holding Costs Vs. Potential Liability Savings

Eventually, when the finished goods or components are exhausted, the product ceases to exist, and the raw and pack components will be inactivated. In one company, for example, we observed there was a considerable inactive inventory for components sold into foreign affiliate markets. Despite the fact that components were ordered in the smallest possible increment, a single production run yielded enough finished goods inventory to cover demand for a couple of years. These unused components sat idle for a number of years waiting for the next production run. An analysis of inventory holding costs vs. potential liability savings are typically a first step in determining whether the inventory should be held. Most planning systems (ERP or SCM) have some variation of reporting tools for slow or not-moving, obsolete and excess inventory (often referred to as SLOB) that planners can run to examine any inventory that has not been used in the last 12 months or more, or that has exceeded coverage tolerances. Such reporting is very helpful in targeting inventory that falls into the junk classification, yet in our experience it’s often under-utilized. The quick-hit opportunity for inventory reduction is really focused on becoming very familiar with this reporting.

Understand Why Inventory Is Not Turning

Certainly, finished goods inventories should be examined, but the real opportunity is usually with the raw, pack, and component inventory left unused for 12 months or with excess coverage. There are many reasons why this excess inventory exists, including discontinuations, poor EOQs, packaging changes, and formula changes. The real magic in this suggestion is not to simply toss out the old inventory, but to get to the root cause of why inventory is not turning, and see if there are ways to correct the problem from a planning perspective. To get a better grasp of this “junk” and to create a unified metric for inventory quality, you may want to use tools that provide an Inventory Quality Ratio, (IQR) in which active inventory (net of excess, slow-moving, inactive, or obsolete) is divided by the total inventory.

In a newly developed S&OP process, the inventory quality ratio (IQR) can be an important metric used to track overall inventory values. Some companies have a very high percentage of slow-moving, inactive, or obsolete inventory. When I was consulting, it was not uncommon to see clients with low-quality inventory in the range of 25% to 50%. Periodic reviews and measurement of inventory quality, followed by purging the warehouses and plants of useless products, is among the simplest ways to reduce inventory.

Quick Hit 2: Reclassify R&D Inventory

This is a bit of a variation on the “cleanup of junk inventory” theme. Inventory should be properly classified from an accounting perspective and yet it is not uncommon for companies to accidently place inventory used for research and development purposes into an active (everyday use ) status. For example, a company that makes mixes, compounds, or blends chemistry may have a slew of random R&D chemicals, materials, or components that have been mistakenly classified as active production inventory. The list of companies with misclassified inventory includes more than just big industrial/commodity chemical companies— OTC pharma, cosmetics firms, health, beauty, and even food manufacturers blend chemistry. How does inventory get misclassified?

Cut 2% Of Misclassified Inventory Right Away

Sometimes inventory is ordered using the wrong accounting code, and sometimes-unused production inventory (mostly chemical compounds) of obsolete products are saved for a future R&D use. Either way, this inventory is often sitting on the books in an active status yet has no consumption or use against it, historically or planned. A full review of inventory may reveal a number of these odd ducks; one such review at a client revealed that over half of the inventory saved for future R&D use was expired and unusable, even for research purposes. While the percentage of this type of misclassified inventory is relatively small (usually within 1%- 2%), experience has taught us that most of it serves little useful purpose and occupies valuable storage space, making it a relatively easy target for reducing your total inventory tally. A full review of all active items with little to no consumption over the last year or more is likely to turn up in some of these stray inventory exceptions.

These two quick hits are easy targets for inventory reduction, and should be your first port of call when attempting to streamline inventory. They often provide significant cost savings in a relatively short amount of time, and with relatively little effort. Next weeks Quick Hits are Testing for Forecast Bias and Keeping An Eye On Product Transition. Stay tuned!

This post has been adapted from an article that appeared in The Journal of Business Forecasting, Fall 2011 issue. To receive the Journal of Business Forecasting, and wide range of other benefits, become an IBF member today.

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If you have any knowledge of Demand Planning, I am sure that you have heard the following: “Demand Planners are like meteorologists, they rarely get credit for doing the job correctly and they’re only noticed when they get it wrong.” Even so, the bottom line is that there are serious and costly ramifications which can occur if these decisions are wrong. For this reason, the demand planning position can be one of the most important and visible in the company. It is a great place to impact many areas of business and gain corporate approval. It is best to take a positive approach and be an agent for fact based decision making. Using this approach, along with good communication skills, is a great avenue to gain the knowledge and build relationships that will prepare you for success and lead you along a career path with much variety.

Demand Planning is Transferable To Any Industry

Demand Planning touches every aspect of the business and the impact can make this person a valuable asset very quickly. It requires broad business knowledge and detailed customer interaction. Also, it is a functional area that has the ability to transfer these skills to any industry. It involves working with several areas of the business simultaneously and provides an excellent opportunity to tap into the  knowledge of others. Also, working in cross functional teams can be very rewarding by providing a lot of variability to the job and making it more pleasurable.
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Demand Planning Is A Collaborative Process That Provides Visibility, And Opens Doors

It is a collaborative process which aids in developing many relationships through the internal organization, as well as, customers and other suppliers. It is a highly visible position which can lead to new and exciting projects. Along with the knowledge to be gained from these groups, the relationships become an asset for your forecasting success and in turn your career path. Also, it can be very rewarding to work with other people to help them attain their goals and reach a collaborative decision that will benefit the entire company. A successful demand planner must become a leader in fact based decision making and a champion for change.

The Required Leadership In The Role Is a Challenge

Along with business knowledge and relationship building, leadership skills are also an asset to a successful demand planner. A successful demand planner uses the knowledge gained and is able to interact with customers, managers, sales representative, marketing, pricing and supply chain colleagues. Becoming a good communicator is imperative to collaboration among internal and external customers. This will enable the demand planner to guide various groups in terms that make sense to them and to reach consensus among the group. All of these things together help the demand planner to provide the best forecast possible which in turn will become a huge advantage for both the company and the demand planner.

Ultimately, a bad forecast leads to bad corporate decisions and the loss of career possibilities. Take the positive approach using business knowledge, building relationships and leading your colleagues to collaboration. Pave the way for fact based decisions that will benefit you and your company. Don’t become a victim and fall for the curse. I have learned over the years to approach my career and my life with gratitude and a “can I help you” attitude. This will take you farther than any expertise on any day. Curse or Blessing, well maybe it was best defined by the Beatles, “I get by with a little help from my friends.”

Sylvia Starnes
Demand Planning Leader
Continental Tire

Globalization has intensified competition. Every day, thousands and thousands of new products enter the market, but their window of opportunity is very narrow because of shorter life cycles. Plus, too much uncertainty is associated with new products. Their success rate is from poor to dismal—25% according to one estimate. Despite that, they are vital for fueling growth. Big box retailers are putting more pressure on suppliers to provide differentiated products. Consumers want more choices and better products. All these factors contribute to the greater than ever number of products and product lines, making management of their demand more complex, increasing working capital to maintain safety stock, raising liability of slow-moving and obsolete inventory, and increasing cost of production because of smaller lots and frequent change overs. Product portfolio optimization deals with these matters.

Product portfolio optimization includes the following: one, how to rationalize products and product lines and, two, how to manage most effectively their demand. Product rationalization includes deciding which products and product lines to keep and which ones to kill, based on the company’s policy. Demand management, on the other hand, is leveraging what Larry Lapide from University of Massachusetts and an MIT Research affiliate calls 4Ps (Product, Promotion, Price, and Place) to maximize sales and pro‑t. The sales of low-performing product lines may be bumped up with a price discount, promotion, line extensions, or by finding new markets.

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Although the S&OP process has a component of product portfolio optimization, its team members pay nothing more than lip service to it. Pat Bower from Combe Incorporated discusses in detail the process of product portfolio optimization in the framework of new products. How new products should be filtered from ideation to development and, after launch, how they should be leveraged. Their window of opportunity is very small; most CPG products flame out within the first year of their existence, says Pat.

Mark Covas from Coca-Cola describes in detail 10 rules for product portfolio optimization. He suggests companies should divest low margin brands, no matter how big they are. Many companies such as ConAgra Foods, General Mills, Procter & Gamble, and Estée Lauder are doing it. This makes the allocation of marketing dollars more productive—taking funds away from low performing brands and giving to high performing ones.

Charles Chase from SAS and Michael Moore from DuPont recommend the Pareto principle of 80/20 to determine which products or product lines to concentrate on in their portfolio optimization e­fforts. Greg Schlegel from SherTrack LLC. Goes even further and proposes that this principle should be extended even to customers. He categorizes customers into four: 1) Champions, 2) Demanders, 3) Acquaintances, and 4) Losers. He then describes a strategy for dealing with each one of them. Greg Gorbos from BASF points out hurdles, political and others, that stand in the way of implementing the optimization policy, and how to deal with them. Clash occurs among different functions because of difference in their objectives. Sales looks to achieve revenue targets, while Marketing looks to hold market share and increase profit. Finance also looks at profit, but seeks to reduce cost and increase capital flow, while Supply Chain looks at cost savings. Communication is another issue Greg points out. The company may decide to deactivate a product, but information about it is not communicated to all the functions. Je­ff Marthins from Tastykake talks, among other things, about the exit strategy, which he believes is equally important. He says that we cannot deactivate a product without knowing its inventory position, as well as holding of raw and packaging materials for it.

For survival and growth in today’s atmosphere, it is essential to streamline the product portfolio to reduce costs, and increase revenue, profit, and market share. This issue shows how.

I encourage you to email your feedback on this issue, as well as on ideas and suggested topics for future JBF special issues and articles.

Happy Forecasting!

Chaman L. Jain
Chief Editor, Journal of Business Forecasting (JBF)
Professor, St. John’s University
EMAIL:  jainc [at] stjohns.edu

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While I don’t obsessively worry about every possible risk, I am aware of the possibilities and I take measures to lower both the odds and severity of a mishap. I keep my vehicle well maintained, I buckle up and I pay my auto insurance. Similarly, today’s supply chain professionals must be more conscientious and proactive in their efforts to mitigate the risk of a supply chain disruption and to minimize the impact when the inevitable does occur.

As much as we may feel at the mercy of disruptions from severe weather, natural disasters, economic instability or political and social unrest, members of today’s high tech supply chain have never been better equipped to minimize the risks and capitalize on the opportunities that may arise from a supply chain disturbance.

One of the most simple, but powerful, tools at our disposal is information. Twenty-four hour news stations, social media and cellular communications give us literally instant access to events occurring in the most remote reaches of the world.

More tactically, mapping the physical network of the supply base, including manufacturing facilities, warehouses and distribution hubs, is an important part of any risk management strategy. The key here is mapping the entire supply chain network, not just top-spend suppliers or first-tier contract manufacturers. Most of this information is relatively accessible through supplier audits and, with the help of Google maps, you can create a pretty comprehensive picture of your physical supply chain.

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Remember, though, supply chains are much more fluid than they have ever been. Today’s multinationals are likely to rely on three to five different contract manufacturers (CMs) and original design manufacturers (ODMs), and scores of other suppliers around the world for the tens of thousands of parts needed to build and maintain their products. With outsourced production so commonplace, production lines can be shifted between locations within a matter of weeks, so frequent monitoring and updating of supply chain shifts is critical.

IoT technology such as sensors and RFID tracking can also provide meaningful intelligence that may be used to identify and mitigate risk throughout the end-to-end supply chain process. The ability to gather and analyze these constant data inputs is a recognized challenge throughout the supply chain profession. Those who master the digital supply chain sooner, will enjoy a substantial competitive advantage.

Once these various vehicles are used to create a composite picture of the risk landscape, then risk mitigation strategies take center stage. These efforts can range from traditional techniques such as the assignment of a cache of safety stock to more intricate maneuvering of storage facilities and full network design. Deployment of these mitigation strategies requires a detailed recovery and communications plan.

In my upcoming presentation at IBF’s Supply Chain Forecasting & Planning Conference at the DoubleTree Resort by Hilton in Scottsdale, AZ, February 22-23, 2016, I will delve deeper into the growing range of potential disruptors in the high tech supply chain. I will outline the core elements of a comprehensive supply chain risk management strategy, including how to define and map the physical supply chain, the landscape around supply chain risks and their impact on financial metrics, and how to proactively assess potential risk. I hope to see you there.

Forecast Error

The first session I attended on Tuesday was “How to Measure & Reduce Error, and the Cost of Being Wrong” an advanced session presented by Dr. Chaman Jain from St. John’s University.  Dr. Jain reviewed the basic methods and mechanics of how to compute forecast error and the pros and cons of each technique. It was interesting that IBF has found that more and more companies are moving from MAPE (Mean Absolute Percentage Error) to a Weighted MAPE (WMAPE) to focus their attention on errors that have a relatively larger impact or little to no impact at all.  Standard MAPE treats all errors “equally”, while WMAPE places greater significance on errors associated with the “larger” items. The weighting mechanism can vary, typically unit sales are used, but I was intrigued by the notion of using sales revenue and profit margin as well.  If a company has low volume items but they are big revenue and profit items, they would not want to miss an opportunity to focus attention on why they have significant errors on these items.

Another interesting concept that Dr. Jain discussed was the use of confidence intervals around error measurements.  Many companies report their error measurement as a single number and rarely present the error measure in terms of a range of potential errors that are likely. Having a view into the potential range of errors can allow firms to exercise scenario planning to understand the impact to supply chain operations and the associated sales based upon multiple forecast errors instead of a single number.

My last takeaway is related to the question of how much history should be used to support time series analysis. Dr. Jain stated, and I believe rightly so, that it depends. Are there potential seasonality, trend, business cycles, or one-time events? How much does one need to see these? What if the past is really not a good indicator anymore of the future? What if the drivers of demand for a product have substantially shifted? One technique suggested that seems sound is to test the forecasting model’s performance using different periods of historical data. Use a portion of the history to build the model, and the remaining portion to test the accuracy of the forecast against the actuals held out of model construction. Try different lengths until you find the one that has the lowest error and also allow the process to have different history lengths for each time series forecast.

Lean Forecasting & Planning

Next I attended another advanced session led by Jeff Marthins from Tasty Baking Company/Flowers Foods on “Lean Forecasting & Planning: Preparing Forecasts Faster with Less Resources”. The session focused on doing more with less, a common theme that has permeated the business world these last several years. Marthins’ session was really about how to focus on what matters in demand planning: looking at the overall process, agreeing to and sticking with the various roles and responsibilities in the process, and understanding how the resulting forecasts and plans are to be used by various consumers in the business which drives the level of detail, accuracy and frequency of updates.

To gain an understanding of the demand planning process, Marthins asked the participants to look at a picture of his refrigerator and answer “Do I have enough milk?” This relatively simple, fun question elicited numerous inquiries from the participants around consumption patterns, replenishment policies and practices, sourcing rules, supplier capacity and financial constraints that illustrated the various types and sources of information that are required to develop a solid, well-thought-out demand plan. It was a very effective approach that can be applied to any product in any company.

To illustrate the need to understand the level of accuracy required of a forecast, Marthins used the weather forecast. How accurate is the weather forecast? How often is it right? How precise does it need to be? Once we know the temperate is going to be above 90 degrees fahrenheit, does it matter if is 91 or 94 degrees?  Is there a big difference between at 70% chance of rain or an 85% chance of rain?  What will you do differently in these situations with a more precise weather forecast? Should I plan to grill tonight? Will I need to wear a sweater this evening? Can we go swimming?  If the answer is nothing, then the precision does not really matter and spending time and effort creating or searching for greater forecast accuracy is a “waste” and wastes should be eliminated or reduced in Lean thinking. Marthins also stressed the value of designing your demand planning process with the usage of information in mind. Adopting a Forecast Value Add (FVA) mentality to assess whether each step in your forecasting and demand planning process is adding value will help to accomplish this. Start by asking if the first step in your forecasting process results in greater accuracy than a naïve forecast such as using the same number as last time you forecasted, or a simple moving average? When your accuracy improves with each step in the process, is it worth the effort or time it takes? Can I be less accurate and more responsive and still not have a negative impact? If I can update my forecast every day with 90% accuracy versus once a week with 92% accuracy, or once a month with 96%, which is better? How responsive can I be to the market by making daily adjustments that are nearly as accurate as weekly ones?

In yet another session, the topic of scenario analysis was raised. The team at IBF are getting this one right making sure it is discussed in multiple sessions. What I wonder is how many companies are adopting scenario analysis in the demand planning and S&OP processes? From my experience it is not the norm.  Marthins suggested testing the impact of various forecasts, and hence forecast accuracies, would have on supply chain performance and even using scenario analysis to understand if a systematic bias, either high or low, might make sense. I have known companies that have employed the policy of allowing overestimating to ensure their resulting demand plan was on the high side. Carrying more inventory even with all the associated costs was of greater benefit to the company than a lost sale or backorder. Bias is not a bad thing if you understand how it is used and its resulting impact, just like inventory is not an evil when used in a planned and methodical manner.
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Data Cleansing

After lunch I attended my second session delivered by Mark Lawless from IBF “Data Cleansing: How to Select, Clean, and Manage Data for Greater Forecasting Performance”. As in any analytical process, the quality of the inputs are crucial to delivering quality results. Unfortunately I had another commitment during the session and I could not stay for all of it.

Lawless discussed a variety of ways to look at the data available, decide if it should be used, update or modify it, fill in missing values and apply various forecasting techniques.  Simple reminders and tips such as consideration and awareness for how data is provided in time periods, e.g., fiscal months (4/4/5) or calendar months, and how they should be reported was a good reminder to make sure the data inputs are clearly understood as well as how the output from the forecasting process will be used.

While most of what I heard was related to the data going into the forecasting process, Lawless did spend time talking about various analytics associated with assessing the output of the process. You might be expecting me to talk about various error and bias metrics again but that is not the case. Rather, the idea is to look at the error measurement over time.  What is the distribution of errors? Do they have a pattern or random? If there is a pattern, there is likely something “wrong” with the forecasting process. It made me think about the application of Statistical Process Control (SPC) techniques that are most often applied to manufacturing processes but can be applied to any process. SPC control charts can be applied to check for patterns such as trends, systematic sustained increases, extend periods of time at unexpected very high or very low errors, randomness of errors, and many more. It gets back to the notion that in order to improve the quality of the demand planning process it must be evaluated on a regular basis and causes for its underperformance understood and corrected as much as possible or warranted.

Regression Analysis/ Causal Modeling

The final advanced session of the Academy was delivered by Charles Chase from the SAS Institute on “Analytics for Predicting Sales on Promotional Activities, Events, Demand Signals, and More”.  This session was about regression modeling on steroids.  As someone who has used regression models throughout my career I could easily relate to and appreciate what Chase was discussing.  In two hours Chase did a great job exposing attendees to the concepts, proper use, and mechanics of multivariate regression modeling that would typically be taught as an entire course over weeks.

While time series models are a staple used to forecast future demand, they provide little to no understanding of what can be done to influence the demand to be higher or lower. They can be used to decompose the demand into components such as trend, seasonality and cycles which are important to understand and respond to.  They are focused on the “accuracy” of the predicted future.  Regression models however describe how inputs effect output. They are an excellent tool for shaping demand. Regression models can help us understand the effect internal factors such as price, promotional activity, and lead-times, as well as external factors such as weather, currency fluctuations, and inflation rates have on demand. The more we can create predictive models of demand based on internal factors the more we can influence the resulting demand as these factors are ones we control/influence as a firm. If external factors are included, forecasts for the future values of these inputs will be needed and we become more reliant on the accuracy of the input forecasts to drive our model demand.

In case you missed it, you can see pictures from the 2015 IBF Academy HERE.

I trust I have brought some insight into IBF’s recent Academy in Las Vegas and perhaps offered a nugget or two for you to improve your forecasting and demand planning activities. If only I would have learned something to apply forecasting success at the gaming tables :).

The key question becomes what do we do with all this data? In the past, companies have always struggled with managing and analyzing large sets of data and could seldom generate any insights.

However, what’s different today vis-à-vis five years ago, is that we now have the ability to cleanse, transform and analyze this data to generate actionable insights. Moreover, today’s retail consumers are extremely demanding and want choices on “When”, “Where” and “How” to purchase product. Whether it is a traditional stand-alone retail store, shop-in-shop, website or mobile app; consumers want the flexibility to research, purchase and return product across multiple channels.

Today, many retailers and wholesalers have a vast amount of POS data available. However, many of them still don’t use the data at the lowest level of detail in their demand planning cycle. The result is significant out of stocks and inability of consumer to find product at the stores.

For a company to be successful in today’s Omni-channel environment, three key steps are needed:

1) Use Point-of-Sale (POS) data as a key input into demand plans: POS is the data that is closest to the consumer and is the purest form of demand- it is critical to leverage this data at the right level of detail into a product’s demand plans. Information available at stock-keeping-unit (SKU) level- should be aggregated and disaggregated to ensure that all attributes of a product are factored into the planned forecast.
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2) Link Point-of-Sale (POS) data to your Allocation & Inventory Management Systems: Today’s allocation systems have the ability to read sell-thru at POS and react and replenish based on what product is selling and what is not. It is critical to make sure that these systems are linked together so that the process is automated and seamless. Linking these systems will allow retailers to send the right product to the right store at the right time- thereby maximizing the chances of making a sale. This will not only contribute to top-line, but will also make our inventory investments more productive.

3) Collaboration with Value Chain Partners to share Point-of-Sale (POS) data: Today’s retail world is complex, many companies have multi-channel operations and work with a number of channel partners to distribute their products. In such a scenario, it is not always easy to gain access to POS data. However, it is important for companies to invest in a CPFR program (Collaborative Planning, Forecasting and Replenishment) that can give them access to downstream POS data which can be used to build better forecasts. It is critical to emphasize a “Win-Win” relationship for both companies and channel partners to bring everyone along on the collaboration journey

Along with Rene Saroukhanoff, CPF, Senior Director at Levi’s Strauss & Co, we’ll be talking about the above, as well as how to use size forecasting, optimized allocation, and visual analytics at IBF’s Business Planning & Forecasting: Best Practices Conference in Orlando USA, October 18-21, 2015.  I look forward to hopefully meeting you at the conference!  Your comments and questions are welcomed.