A Pragmatic Approach to Inventory Management

Transcription

1 A Pragmatic Approach to Inventory Management By Björn Erlandsson and Jacob Duhan Applied Value s Series on Capital Efficiency Ref: CAP-1011 December 2008 Inventory management is an important area that is not given the proper attention it deserves in many companies. Inventories tend to be regarded as a necessity to protect against something going wrong in production or distribution to the customer. A common approach to inventory management is to make pointwise inventory optimization, when inventories for some reason have increased, or when the company is in need of cash, without a continuous process for improvement and adaptation to new market situations. Traditional inventory management tools are also often very theoretical and are based on a data availability and accuracy that is in many cases not available. A more pragmatic approach with a continuous improvement methodology is therefore a better way to optimize inventories over time. A Plan-Do-Check-Act (PDCA) approach is a powerful tool for ensuring that safety stock levels and order quantities match current requirements. Proficiency in this area can lead to quickly reduced inventories, thereby freeing up significant amounts of cash. The company at which we developed the PDCA approach realized inventory level reductions of close to 40%. Primary Industry: Automotive Other Industries: Relevant for all Process Industries Keywords: Inventory, stock, production, raw material Authors Björn Erlandsson, Senior Manager Jacob Duhan, Consultant Contact valuepapers@appliedvalue.com Phone: +1 (781) Phone: Copyright YEAR, Applied Value LLC; all rights reserved. (Edit year)

2 A Pragmatic Approach to Inventory Management By Björn Erlandsson and Jacob Duhan 1. INTRODUCTION Inventory management is a sometimes undermanaged area within companies. One reason is that traditional management approaches are theoretical and heavily data dependent. This section gives a general introduction to inventory management, and discusses why we have developed a pragmatic approach to manage inventories Why Inventory Management is important The role of inventory management is unclear to many of today s industrial leaders. The challenge is to balance efficient material supply, infrequent stock-outs, and low levels of stock. A common mistake is to overbalance the risk of stock-outs with consequent production losses by building excessively big buffers. Modern production companies that have adopted the principles of lean manufacturing often point at the negative effects excess stock has on production. Excess stock hides inefficiencies that would otherwise be visible. This is often explained with the parable of the Japanese lake: when the water (stock) is high it is easy to sail on it and everything seems fine, but when the water is low, the rocks at the bottom become visible and it is easy to get stranded. Excess stock thus hinders problems to be dealt with and it breaks the continuous improvement efforts that can be such a powerful way of improving productivity. Lowering total stock levels is a challenge to anyone in production since no one wants to be responsible for a stop in production; and it often leads to discoveries of new, innovative ways of working. Apart from being an engine of productivity enhancement, lowering stock means that previously bound capital can be freed and used to make value-creating investments. This means that the return on invested capital will improve, since either the invested capital decreases or the yield increases. Money bound in stock does not create any value; on the contrary, it is money at risk, since the stock could become obsolete and lose its value. Sound inventory management does not necessarily require advanced IT-support or complicated mathematical formulas to work. By implementing the pragmatic approach that is suggested in this Value Paper, organizations can significantly lower their inventory levels and still keep costs and efforts at bay. This pragmatic approach has been developed to take into account that high-quality data is most often not available. It should be easy to use with a low level of complexity. After the introductory section explaining different kinds of inventory, the second section will lay out the text around complexity and uncertainty. Section 3 explores the reasons for choosing a pragmatic instead of a mathematical approach to inventory management. The following section explains step-by-step how to create a continuous process for stock management in order to produce lasting results. The last section concludes with the methods suggested in this paper and summarizes the advantages of them. 1

3 1.2. The four components of inventory Inventory can be split into different components to simplify analysis and to reveal the most common pitfalls within inventory management. There are four general components that build- up the average inventory level: consumption stock, transportation stock, safety stock, and seasonal stock, as seen in Figure 1. Stock component: Consumption stock Transportation stock Safety stock Seasonal stock Calculation principle: Production cycle/2 Batch size (Q)/2 Waiting time + Transportation lead time + Loading & unloading lead time K * (standarddeviation of forecasting error during replenishment lead time) The deviation of the seasonal stock from average stock level during regular season Graphical representation: 0 Figure 1. The four components of stock For each of these four components there are different principles to be used for calculating the optimal inventory level. By optimizing each of the different components and adding them together, the optimal total inventory level for a specific product or customer can be obtained. The consumption stock covers the consumption during the replenishment cycle. This component is optimized through accurate and updated forecasting of the demand during coming replenishment cycles. Transportation stock is the amount of stock carried in transit. Reducing the total transportation lead time optimizes this component. Factors that build up the total transportation lead time are idleness, transit time, loading, and unloading. Safety stock is inventory kept on hand to allow for the uncertainty of demand and the uncertainty of supply during the replenishment cycle. Safety stocks are not needed when the future rate of consumption, delivery lead time, and production availability are known with certainty. The level of safety stock is controllable in the sense that this investment is directly related to the desired level of customer service, i.e. how often a stock on-hand can meet demand. The seasonal stock component adjusts for build-up of stock in order to cover seasonal peaks that cannot be met at normal capacity levels. Seasonal stock is optimized through careful analysis of historical variations in seasonal demand, together with input from the forecasting process. A full analysis of inventory levels from a customer or product perspective should be based on a breakdown of the total inventory level into the four categories in Figure 1. In this paper, however, the main focus lies on the two stock types, safety stock and consumption stock. Forecasting and complexity reduction primarily improve safety stocks, while the development 2

4 of the Economic Order Quantity (EOQ) formula or equivalent pragmatic methods are related to optimization of consumption stock. 3

5 2. COMPLEXITY A KEY INVENTORY DRIVER The problem of complexity is at the core of inventory management, since a complex value chain, i.e. a complex product portfolio, supplier base, and customer base, makes inventories more complicated and difficult to manage. This section explains why complexity drives inventory and introduces what can be done to reduce complexity. Product, customer, and supplier complexity are three of the most important drivers of inventory. The latter of these three is usually the least difficult for companies to change, since suppliers are not as politically sensitive as customers and products. Experience shows that supplier consolidation and larger supplier contracts simplify the inventory management process by reducing the points of contact. Naturally, larger contracts also provide opportunities for volume discounts and improved payment terms. The problem of complexity is at the core of inventory management. The ultimate purpose of inventory is to secure delivery at a certain service level to a downstream party in the value chain. Whether the downstream party is internal or external does not matter. Rather, the importance lies in the nature of the inventory and customer service required. The main reason why increasing complexity drives inventory is the fact that each stock-keeping unit (SKU) requires its own safety stock [1]. Thereby, complexity is a barrier to economies of scale in inventories, as each product variant must carry its own safety stock instead of sharing one common safety stock for multiple customers or locations. Increasing complexity also has a negative effect on planning and forecasting processes because it simply becomes impossible to perform these activities at the SKU level when the article range becomes too wide. The fact that planning and forecasting are usually made at an aggregated product group or segment level generally drives inefficiency from two important aspects: Safety stock levels for the segment will be based on SKUs with high volume and volatility resulting in unnecessarily high safety stock levels for a large number of SKUs. Planning and forecasting at the segment level exclude details about demand characteristics, which create an uncertainty that needs to be adjusted for. Since safety stocks exist to manage uncertainty, this will automatically increase the total safety stock level. In industries where customers require earmarked inventory, either because of quality or security requirements or as a result of high demands on service levels and lead times, customer complexity is as important as product complexity as a driver of inventory levels. One of the cornerstones of the PDCA approach for inventory management, which we will present in the coming sections, is to categorize inventory according to the value each SKU ties up in inventory. This approach reduces the negative aspects of complexity, since it allows management to focus on the smaller amount of SKUs, but the ones that are important to optimize in order to minimize total inventory cost. 4

6 3. DECIDING ON STOCK MANAGEMENT MODEL Traditional inventory management approaches are heavily dependent on available and accurate data. This section explains why the traditional approaches are not always viable options, and thus a different approach is required The importance of accurate data The importance of accurate data in traditional inventory management approaches is very high. Correct data enables a mathematical approach where safety stock and order quantity can be optimized to ensure lowest possible stock levels and yield the desired stock availability. As will be shown below, the required data is not always available and in those cases a more pragmatic approach must be chosen[2]. Such an approach builds on a Plan-Do-Check-Act (PDCA) approach and an ABC classification of all items. Approach to Setting Inventory Levels Bad data Perfect data Pragmatic Safety stock set with ABC approach Order quantity set with ABC approach Mathematical Safety stock calculation Economic Order Quantity calculation Figure 2. Inventory management approach 3.2. Why EOQ and mathematically calculated safety stock levels are difficult to use in practice Mathematical Safety Stock Calculation Lead-time deviation not measured Demand data may be inaccurate Safety Stock = k Lσ D2 + D 2 σ L 2 k = Service level factor L = Supply lead time (weeks) D = Demand per period (units) σ D = Demand standard deviation per period (units) σ L = Lead time standard deviation per period (weeks) The mathematical calculation of safety stock level is dependent on the desired service level, average demand and supply lead times, and standard deviations of demand and supply lead times. However, the standard deviations of demand and lead time are often hard to measure. Consider the difficulties in measuring the standard deviation of demand in the following example: A large manufacturer in the aeronautic industry keeps raw material in a separate stock and puts it in the production cells when the material is needed. This transition from raw material to work in process is logged in the ERP system. Some of the pieces (O-rings for example) are so small that containers of hundreds of pieces are checked out from the raw material stock at once. The demand data logged in the ERP system will show big, but very few outtakes 5

7 (which yields very high standard deviation) although real demand is evenly spread over time. The parts cannot easily be regarded as standard-sized lots since the outtakes (lot sizes) vary between the different production cells. Introduction of a policy compelling the workers to log each part individually is not a viable option in the example above, and, since lot sizes vary the items cannot be grouped. The mathematical approach is simply not fit in this case and a more pragmatic approach should be chosen. Mathematical calculation of safety stock cannot be done until lead time is properly measured and recorded. This can be the case if a supplier has agreed to deliver an annual volume without a precise commitment of when, and there is no clear connection between the actual volumes delivered and the call-offs. If this is the case in your organization, the supplier agreement should be renegotiated so that deliveries correspond exactly to orders placed, and lead times are tightly monitored. The Economic Order Quantity (EOQ) is (in its basic formulation) the order quantity that minimizes ordering costs and capital costs for holding inventory. The formula is dependent on ordering cost, demand, purchasing cost, and cost of capital. The two parameters that can make EOQ unsuitable are ordering cost and demand[3]. EOQ works best when demand is fairly constant. Companies with volatile production levels often find that the lot size calculated with EOQ is irrelevant; it is either too big or small, does not satisfy the demand, and still leads to stock surplus. Mathematical Order Quantity Calculation Ordering cost not necessarily identical for all orders Q Great variations in demand 2KD VW Q = Economic order quantity K = Ordering cost/order D = Units required/period V = Value (purchasing cost) per unit W = Cost of capital (%) Ordering cost has fairly big impact on EOQ, yet the confusion is great when it comes to the calculation of it. The main difficulty is that different goods do not necessarily drive the same ordering cost. A company purchasing a limited number of items can calculate individual ordering costs for each item. This is, however, not realistic for a company buying thousands of different items. The choice is then to either do the calculation with only one value for all items, or group the items and use a few different values. This will lead to imperfections, and in some cases they are so great that an alternative method must be chosen. Mathematically calculated safety stock and EOQ require good data, homogeneous products, and fairly constant demand. A pragmatic approach will not fully optimize the stock levels, but blind faith in a complicated analysis based on poor input data could definitely be worse. 6

8 4. THE PLAN-DO-CHECK-ACT APPROACH TO INVENTORY MANAGEMENT As we have explained in the preceding sections, traditional inventory management approaches are sometimes not the best option, for example, due to data constraints. This section introduces our more pragmatic approach, which builds on continuous monitoring and improvement of stock levels PDCA overview Plan-Do-Check-Act (PDCA) refers to a model for constant improvement. Stock management is not constant and is therefore suitable for the PDCA approach. The first step, Plan, will lay out and set the ambition level. The service level target should be set once a year by the production manager. In the Do-step, all items should be classified and the safety stock and order quantities set. The Check-step aims at monitoring actual stock levels. Follow-up and adjustment of stock levels are done in the Act-step. Plan Do Check Act Task Frequency Responsibility Set overall service level target at least equal to Production system Figure 3. PDCA overview Once a year Production manager Do product classification Once a year Purchasing manager Set safety stock and order quantities for all items Once a year Purchasers Weekly forwardlooking stock monitoring Once a week Purchasers Monthly backward-looking stock monitoring Once a month Purchasers Follow-up on inventory levels Weekly for all products Monthly for biggest A products Purchasers 4.2. Set overall service level target As introduced in the PDCA overview, the stock service level is the ambition level and it should be set in accordance with the overall production strategy. Setting of the service level will determine the stock level; in fact, all parts of the PDCA are dependent on and try to reflect the desired service level. The idea is to set an initial stock level that corresponds to the desired service level, implement it in practice, monitor the actual levels, and fine-tune them. This procedure should be iterated at a regular interval so that changes in demand are mirrored with changes in stock levels. Setting of initial stock levels is not done mathematically; it is based on the experience of the production manager. Companies often have stated service level targets as a part of their strategy. This target is normally a good one that can be used for the stock. A prerequisite for effective stock management is tight supplier control; in fact, without satisfactory supplier control, all other efforts will only be fine-tuning the results. Therefore, the same service level targets (or higher) must be set for the suppliers as for the company itself. 7

9 Implement stock levels Set overall service level Set initial stock levels based on experience Monitor stock levels Figure 4. Setting of service level Fine-tune stock levels 4.3. Do product classification One of the keys to efficient inventory management is to differentiate between products and customers that require different degrees and methods of inventory control. Such differentiation will help managers both to focus their efforts on the relevant areas and to identify proper actions for each improvement area. One of the basic methods for categorizing inventory is to create a classification of products and customers by aggregated inventory value. Table 1 illustrates how inventory units are sorted by aggregated inventory value as an initial step of categorization. Such categorization could be performed both by product and by customer, depending on the characteristics of the company s demand and product range. Table 1. Sample listing of inventory units by descending dollar usage Sequential Number Volume Value (Q x V) % of Total (Q x V) Cumulative % 1 1,800,000 18% 18% 2 1,600,000 16% 35% 3 1,300,000 13% 48% % 100% % 100% % 100% A list of inventory units by descending value as shown in Table 1 is a simple but valuable tool for inventory management, since it helps to identify which products and customers to focus on. Naturally, customers and products that represent a large part of the accumulated inventory value will be assigned a higher priority in the allocation of management time and resources. Generally, a three -level priority rating is sufficient to determine the order of priority: A (most important), B (intermediate), and C (least important). Class A items should receive most attention from management. The units constituting the first 80% of the aggregate volume value (quantity times volume) are usually designated for this most important class of items. Generally, these items account for around 10% of the total number of items. 8

10 Class B items are of secondary importance in relation to class A. These items should receive moderate attention. This category includes items with an aggregate volume value ranging from 80% to 95% of the total, which typically corresponds to 15% of the items. Class C items are the made up of the tail of the relatively numerous remaining units, but typically account for only a minor part of the total inventory value (5%). These items should receive less management time and tie up fewer resources. Figure 5 shows an example of how ABC categorization is graphically illustrated by a traditional Pareto curve. 100% 90% Quantity x Value 80% 70% 60% 50% 40% Cumulative % 30% 20% 10% $ millions 2,000 1,800 1,600 1,400 1,200 1, % Class A Class B Class C Figure 5. Graphical representation of ABC categorization Analyzing customers and products contribution to turnover is a step in completing the picture of how to focus inventory management efforts. A simple calculation of Days of Sales in Inventory (DSI) reveals which items, regardless of their value, have a poor turnover and are natural targets for immediate improvements. When combined with the ABC classification, the DSI analysis provides further guidance on where to focus the effort. A products, or customers with high DSI, are clearly an area of high priority, and management should focus heavily on how to improve the turnover rate of these items. Many companies tend to have a very long tail of class C items in inventory that also suffers from a low turnover rate (high DSI). Such products or customers are targets for complexity reduction as described in section 2 of this paper Set safety stock and order quantities Once all items have been classified into ABC, corresponding stock levels and order quantities should be set. The basic idea is to give high priority to class A items (products with high volume value, i.e. quantity x unit value is high) and lower the inventory levels of these. The safety stock level should be kept low and the stock should be replenished often, since this will have a great impact on the total stock value. In order to free resources that can be allocated to the high runners, products with low volume value should be paid little attention. This is done through high safety stock levels and infrequent replenishment. 9

11 Table 2. Example of safety stock levels and order quantities Class A Class B Class C Volume Value 80% 15% 5% Safety Stock 2 weeks consumption 4 weeks consumption 12 weeks consumption Order Quantity 1 week s consumption 2 weeks consumption 4 weeks consumption Resource Attention High Moderate Low Table 2 shows an example of product classification with associated safety stock levels and order quantities. The example refers to a company with high demand volatility; and this is the reason why safety stock needs to be higher than order quantity. Safety stock and order quantities are expressed in weeks consumption, which is dynamic; if total consumption increases, then the safety stock and order quantity will increase as well. Setting targets relative to consumption is preferable to setting targets in terms of number of articles, since targets in the latter case have to be reset frequently to adjust for changes in consumption. The C-products in Table 2 may seem to have a disproportionally large safety stock, but this is nothing exceptional; many companies have a stock of C-products corresponding to more than half a year s consumption. It makes it possible to bulk-purchase small products, and the time spent on this product class is limited. Setting the initial levels is most easily done by gathering all purchasers who deal with the daily procurement. They are experienced in what levels could work and what would cause unacceptable disruptions. Once the initial levels have been set, these need to be adjusted for minimum order quantities, batch sizes, pallet sizes, and other restraints. The importance of this rounding should not be underestimated, as it might constitute the difference between practically useable safety stock levels and order quantities, and irrelevant numbers that cannot be applied Stock monitoring The purpose of monitoring the stock is to make sure that stock-outs do not occur, and at the same time eliminate unmotivated stock. In a dynamic world with changing demand, monitoring also plays an important role in continuously giving signals to adjust safety stock levels and ordering quantities. Stock monitoring can be done in different ways. The longer the replenishment lead time, the more important it is to use forward-looking monitoring. Such monitoring presupposes reliable forecasting to work. It is better to monitor the class A-parts on a weekly basis than to try to monitor all parts less frequently. For parts with low volume value, the safety stock should be big enough so that little monitoring is needed. The forward-looking monitoring should match future demand with placed orders and current stock level to warn for critical stock-outs as well as surplus stock. Backward-looking monitoring can be used once a month to evaluate historical stock levels and adjust targets for groups and individual items. Such monitoring is based on historical data, and could take into account the peaks in stock levels, trends, and service level. 10

12 4.6. Inventory level follow up As described in section 4.5, stock levels can be monitored in two ways: forward-looking and backward-looking monitoring. Forward-looking monitoring is done frequently (typically weekly) and the follow-up is done to manage the supply that is needed for production. The actions involve placing, adjusting, or cancelling orders so that the stock is kept on a desired level on short term. It could for example mean making contact with the supplier to arrange for last-minute deliveries. An effective short-term follow up enables tight stock levels to be set. The backward-looking monitoring aims at having an impact on longer term. Parts that chronically seem to have too low or too high stock levels should be adjusted. Adjustments can be done in three ways. Change of parameters per item category is the most general adjustment. For example, all class-a items could be given an increased safety stock of three weeks consumption instead of two, or two weeks order quantity instead of one. Change of parameters for individual items is another possibility. The third option is to reclassify items, e.g. reclassifying a class-a item as a class-b item, with the motivation that it would fit better into that group. The adjustments are normally done in conjunction with the monitoring, which typically takes place once a month. 11

13 Case Study: A global automotive tier 1 supplier needed assistance introducing the ABC classification and the PDCA approach to manage its inventories. Six sites across Europe and North America were analyzed in detail to build a platform for global roll out of a common model. What was striking was the diversity of systems and approaches to how inventories were managed. The lack of a common approach made site coordination difficult and the inventory situation nontransparent. Four out of the six sites had an old ABC-classification in the ERP system, but no site used it to set safety stocks and order quantities as described in this Value Paper. Strikingly, often purchasers placed orders with the same interval, regardless of classification or volume value. This had the effect of overloading the purchase department and creating surplus stock of the high volume-value items, which no one had enough time to care about. The project team mapped current stock management practices and developed strategies for a transition to a PDCA based management. Demonstrating the resulting impact on total inventory levels of different simulated combinations of safety stock levels and order quantities enabled the site management team to advocate certain stock policies that affected both the internal operations and the external suppliers. Based on facts, they could now argue that increasing order frequency by X% would save Y euros. The team set target values for safety stock and order quantities in a dialogue with site management, with the striking result that is exhibited in the graph below. A potential average total stock reduction of 36% demonstrates the remarkable impact that a relatively non-complex inventory management model can have. A major part of the savings was found in the A-class. Two of the sites expressed concerns about the rising costs of transportation. This was an upper boundary for the delivery frequency; to order A-class items more frequently than once a week was not desirable because it would mean that the goods had to be delivered in half-full truckloads, and thus drive additional costs. The ABC classification does not generate extra logistics cost per se, but if volumes are small then it might be hard fill up a truck. Therefore, a bigger total volume provides more freedom when setting the order quantity /frequency parameter. At the time of the writing of this paper, Site 3, which was the first site to implement the inventory management model, had achieved an actual reduction of 36% out of the identified 59%. That level was reached only five months after starting the inventory reduction program, which clearly demonstrates the swift impact of the pragmatic approach laid out in this paper. The successful implementation of the PDCA gives good reasons to believe that the new total stock will come down even further in the future. Inventory Value ($k) 60,000 50,000 40,000 30,000 20,000 10,000 0 Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Initial Inventory Level Improved Inventory Level Figure 7. Potential savings by using the ABC-methodology 12

14 5. CONCLUSION A reasonably low level of complexity with few stock keeping units, short supply lead times in relation to customer lead time, and firm supplier control are the most important prerequisites for low inventory levels, regardless of management model or approach. Predictability of supply is highly important; and key for achieving this are well-defined supplier contracts, clearly stating terms of supply and KPIs. But, even with these factors in place, a traditional inventory management approach built on EOQ may not be the most efficient model. There are two main reasons for this: lack of good data to input in the model, and lack of process focus to continuously monitor and improve inventory levels. Mathematically calculated safety stock levels and EOQ are theoretically good, but it is not always a viable option to calculate them in a stringent manner. This is typically caused by poor data availability and accuracy. An iterative PDCA approach, with stock levels set according to ABC classification, offers satisfactory inventory management in such cases. It will not yield the exact results that mathematically calculated safety stock and EOQ can give (provided that data available is excellent), but it has a higher focus on continuous improvement, which in many cases is more important than a perfect first shot at the inventory levels without monitoring over time. The method requires continuous monitoring and follow-up so that the desired service level is fulfilled and the stock minimized at the same time. One of the biggest challenges with this approach is the translation of the service level targets into initial stock and ordering values. This will be a guesstimate and the quality of it will never be better than the experience of the people doing it. Updating parameters like safety stock and order quantity is at the heart of the PDCA approach and is absolutely necessary for it to work. The advantage of using a pragmatic ABC classification is the obvious need for doing this. All inventory management systems require regular updating, but highly automated systems tend to give the user a false sense of security everything is fine and nothing needs to be done. A system that requires the user to continuously update parameters, on the other hand, fosters good habits and ensures awareness of the current inventory situation in the organization. As shown in the case study in section 4.6, implementation of the pragmatic approach that this paper advocates yields fast and convincing results (36% in actual reduction of the total stock at one of the sites in the case study). The successful implementation of the PDCA gives reason to believe that stock levels could come down even further. Finally, it is the continuous improvements in the PDCA that will yield a lasting impact and guarantee sustained low inventory levels. As pointed out in the introduction, implementation of the pragmatic approach to inventory management presented in this Value Paper does not necessarily require extensive resources. The simpler the approach, the easier it is to keep the continuous PDCA under control, i.e. to update parameters, which will sustain the low inventory levels. These methods offer great return on the effort invested and, therefore, they are recommended to any manufacturing company with limited stock data wishing to reduce inventories. 13

15 6. REFERENCES [1] M. Muller, Essentials of Inventory Management, AMACOM, 2002 [2] E. C. Mercado, Hands-On Inventory Management, Auerbach Publications, [3] S. M. Bragg, Inventory Best Practices, Wiley, John & Sons, Incorporated,