EVERYTHING YOU NEED TO KNOW ABOUT INVENTORY Introduction Inventory is considered the necessary evil of the supply chain. In fact, there has been a whole movement; lean manufacturing that has tried to reduce it to a bare minimum as part of the elimination of waste. Of course, inventory plays many important functions and while it does have a cost, we need to keep inventory to protect against risks. These can include:! Running out of product before a new order arrives! Surges in demand! Late arrivals of shipments! Quality problems! Low service levels! An unexpected disaster In order to make decisions on the amount of inventory required to protect your supply chain, it is worth understanding what kind of inventory you keep and what are its drivers. There are three main types of inventory: 1. Cycle stock, to cover for the reorder process time. 2. Safety stock, which protects against variability in demand and lead-times - a buffer against the ongoing business risks. 3. Strategic inventory, to cover for critical parts in case of an unexpected event - completely unforeseen such as an earthquake, riots, port closing etc. All these types of inventory are needed for raw material, components, finished goods and spare parts. Keeping too little inventory can have a huge impact on the business if something goes wrong, but keeping too much inventory or keeping inventory in the wrong place can be costly. This is a complex tradeoff that requires a deep understanding of the way inventory behaves and the cost of inventory, called carrying cost. The automotive industry tends towards lean; low inventory practices while some industries, notably pharmaceutical companies, prefer to have excess inventory rather than risk losing sales. There are three key drivers for safety stock: demand variability, lead-time variability and service level. When facing inventory problems, many companies blame the forecast or the demand variability part, but we think you should not waste time on improving forecast. Why do we believe this? Because an important concept, related to tradeoff decisions like these, is the efficient frontier, which represents a range of possible strategies each with a corresponding cost and response time or service. End-to-end optimization can create overall better performance on both cost and service by shifting the efficient frontier. By developing a model that enables the firm to analyze and optimize inventory across multiple echelons, we can determine the appropriate inventory levels (cycle stock and safety stock) at different locations.
Another important concept is the Efficient Frontier, which represents a range of possible strategies each with a corresponding cost and response time or service. We will show how optimization can create overall better performance on both cost and service. In this white paper, we will explain the different types of inventory in detail and show how you can take advantage of risk pooling and the efficient frontier to better understand your inventory needs and how to improve overall performance. We will also provide a few examples of companies that implemented these ideas. 2
Cycle Stock The portion of inventory which companies cycle through to satisfy market demand for a specific replenishment period is called cycle stock. Cycle stock level depends on a product s order frequency and demand profile. The equation for calculating average cycle stock is as follows: Where! RP = Replenishment Period! D = Average Demand! RP (D) = Average Demand during Reorder Period It is not difficult to see that cycle stock is driven by demand and replenishment period. Cycle stock will rise when the demand and reorder period increase. For example, if a product has an average demand of 10 units per business day and is replenished every 5 business days, then the average cycle stock level is 25 units, half of the average demand during the reorder period. If product is replenished every business day, then the average cycle stock level is reduced to 5 units. The key to cycle stock reduction is to reduce the replenishment period. 3
Safety Stock Safety stock, as the name suggests, is the buffer inventory kept to prevent stock outs arising due to misalignment of actual and forecasted demand, utilization and delivery time shortfalls. In order to understand the proper levels of safety stock, it is important to understand the drivers. The general equation for safety stock, assuming both lead-time and demand are normally distributed, is as follows: Where D = Average Demand! StdD = Standard Deviation of the Demand! LT=Average Lead-time! StdLT = Standard Deviation of the Lead-time There are three key drivers for safety stock: demand variability, lead-time variability and service level. 1. Demand Variability - occurs when actual demand deviates from forecast demand. Naturally, the way to combat fluctuations is to hold more inventory. The first factor affecting that amount of inventory is demand variability. The amount of inventory needed due to demand variability is the product of square root of the average leadtime and the standard deviation of demand. This portion of the equation covers the demand fluctuations between the order date and receipt date considering the leadtime and the demand that might occur. 2. Lead-time Variability - drives the safety stock similar to the demand variability where in the amount of inventory needed to mitigate the effect of variation of time between order date and ship date is calculated by the product of average demand during that period and lead-time standard variation. 3. Service Level - defined as the probability of not having stock-outs. It is used in the safety stock equation in terms of its Z score. In statistical terms, the Z score refers to the number of standard deviations above mean that a parameter can fluctuate. Here, with respect to safety stock, Z (service level) is the number of standard deviations above mean demand needed to protect you from having stock-outs. 4
Safety stock is not needed when the fluctuations are happening below the mean demand. That is because the fluctuations will be taken care of by cycle stock when the demand moves between zero and mean demand. After the mean demand, safety stock is needed to combat fluctuations. To understand this, we need to look at how service level based on the Z-Score drives inventory. Service levels and Z-scores have a non-linear relationship. Hence, higher service levels incur highly disproportionate safety stock. Imagine that no safety stock is carried. In this situation, the Z score is zero. Even then based on the Safety Stock equation, there will be enough inventory to meet demand 50 percent of the time. If the Z-score equals 1, the safety stock will protect against one standard deviation; there will be enough inventory 84 percent of the time. But if you want to have a service level of 98%, then you have to hold two times the safety stock as in the case of 84% Service Level. It is always a managerial decision to balance between prevention of stock outs and customer service. Based on the organization's culture, optimum safety stock is calculated using the above-mentioned equation by choosing a proper service level so as to balance inventory costs and customer service. However, it is also possible using advanced optimization techniques to move the tradeoff curve and improve both inventory levels and service levels. 5
Strategic Inventory From Superstorms to Factory Fires describes a method developed by David Simchi-Levi to manage unpredictable supply chain disruptions. It helps prioritize the financial impact of risk through the Risk Exposure Index (REI). This enables companies to focus their mitigation efforts on the most important suppliers and risk areas instead of ignoring them or using an exhaustive approach. This method was successfully applied at Ford Motor Company. The process as described in the article describes the implementation of the concept of Time to Recovery (TTR) - defined as the time it takes for a supply chain node to fully recover after a disruption. This information is integrated with a multi-tiered supplier, bill of material (BOM), operational and financial measures, inventory levels and demand forecasts for each product. By removing one node at a time and calculating the response (draw down from inventory, alternate suppliers etc.) and from there, the financial impact of the loss of the node. The financial impact on all the nodes is then used to create the Risk Exposure Index. But how do you know what is the TTR, such as key element of this analysis? This information, which we expect will soon become standard in supplier negotiations, is not easy to find as it requires a comprehensive analysis of an item s Bill-Of-Material (BOM), first, second and third tier supplier data, and transportation routes. In the case of Ford, this was done using a questionnaire that is described in the paper and certain assumptions that were used in order to run the models. Therefore the process for TTR calculations is: 1) Work with suppliers to receive BOM and second tier supplier data of the components 2) Identify potential risks - single source supplier/ locations, specialized components and etc. 3) Repeat process for Tier 3 4) Complete questionnaire 5) Evaluate the time for the disrupted supplier to recover or line up alternative suppliers This is not a very reliable way to understand TTR since this relies on a commitment that in case of a disaster may not be accurate enough or sufficient to cover the company s needs. We also still need to answer the questions: What is adequate strategic inventory? Do we need to look at every single node and supplier? How do know what are the most critical issues when we are not sure of the TTRs? In order to figure this out, you need to calculate the amount of time the current usage levels can continue to be fulfilled from existing inventory, assuming the supply chain is cut off. This will help determine whether the current strategic inventory is sufficient to withstand facility and supplier disruptions and can determine the bottlenecks in the supply chain and the areas where you need to focus. You need to narrow the analysis and focus only on items that have the following three attributes: High Impact Percentage of customers potentially affected or high financial burden 6
Critical to the System An item that will impact service levels if not available Low Stock An item that doesn t have enough strategic inventory to carry for the short fall. It is important to notice that cost of the item is a significantly less important factor in the risk analysis in comparison to the three mentioned above. Furthermore, any item that is either High Impact or Critical to the System and has Low Strategic Stock presents a considerable potential risk and should be further explored. 7
Risk Pooling If there is only one theoretical concept you need to understand to make better supply chain decisions, it is risk pooling. First introduced in the supply chain context in Designing and Managing the Supply Chain, risk pooling is a statistical concept that suggests that demand variability is reduced if one can aggregate demand, for example, across locations, across products or even across time. This is really a statistical concept that suggests that aggregation reduces variability and uncertainty. For example, if demand is aggregated across different locations, it becomes more likely that high demand from one customer will be offset by low demand from another. This reduction in variability allows a decrease in safety stock and therefore reduces average inventory. Operations Rules Rule 3.1 - Aggregate forecasts are always more accurate than individual forecasts is a useful guideline to think about the impact on various operations and supply chain decisions. Several examples where risk pooling should be considered when making decisions: 1) Inventory Management as mentioned above the less variability in demand the less safety stock is required to buffer against fluctuations. In addition, the more consolidated the inventory, the easier it is to manage overall and the less risk of obsolescence. Apple has very few products and options therefore it comes as no surprise that according to Gartner they have the highest inventory turnover in the electronics industry - 74, which means Apple turns its entire inventory every five days. 2) Warehouse location and product flow - the decisions on whether to have many warehouses close to the customers or more centralized locations should consider the risk pooling effects. By centralizing a product in one location, you can take advantage of the aggregated demand. On the other hand, you need to consider proximity to customers and other factors that may push towards maintaining more warehouses. The characteristics of each product also comes into play here as high demand products with low variability are not impacted as much by the risk pooling effect while low volume high variability products are highly vulnerable. 3) Transportation - the more consolidated the products and the warehouses are, the cheaper the transportation costs as shipments can be sent in larger batches. Therefore considering the transportation impact on these decisions is important. 4) Push-pull strategy in a push-pull strategy the initial stages of the supply chain are operated on push while the final stages are operated on pull. So for instance, parts could be manufactured but assembled only after there is a good demand signal. The extreme case of this is Dell Direct where the components are ready and assembled only after the order is received from the customer. 5) Postponement - Delayed differentiation in product design by creating a more generic product and adding some of the details once demand is revealed. This allows the use of aggregated demand for the generic product which is much more accurate than the demand for the differentiated products. Benetton is famous for using postponement tactics at the actual sequencing point of the production process, whereby dying of the garments is not 8
completed until the agent network have provided market intelligence on what particular products are in demand in which locations. 6) Product design decisions on the number of choices and complexity in products can benefit from risk pooling considerations the less color choices or other options the simpler the demand forecast and many other aspects of the supply chain since the aggregated demand is easier to determine. A famous example is HP, which created a universal power cord for its LaserJet printers so that it did not need to differentiate between the ones shipped to different parts of the world. How do you take risk pooling into account in practice? Your customer value and business needs are the main drivers of your product offering, procurement and manufacturing strategy and delivery methods. You also need to balance the tradeoffs of various strategic and tactical decisions using the appropriate analytics software. End-to-end Optimization is one approach that can lead you to the efficient frontier where balancing inventory and service level can reduce the overall costs. 9
The Efficient Frontier INVENTORY WHITEPAPER Creating a supply chain strategy is fraught with tradeoffs between responsiveness and efficiency, flexibility and cost, inventory and service levels, risk and cost, quality and price and many more. In addition, different products and channels can have different characteristics requiring different solutions that all need to be integrated. Analytics can help companies understand these trade-offs and how they are performing relative to the opportunity. For example, a typical tradeoff is between responsiveness and efficiency as companies try to do both at the same time as best they can. Often the focus is on reducing costs without understanding how that impacts service to customers. But what if you could improve both at the same time? What if you could quantify the savings or expense of providing a certain level of service? What if you have new opportunities and you need to understand the cost of the decisions made in order to take them on? The trade-off chart below from Operations Rules shows the relationship between efficiency and responsiveness. The current strategy moves on the high curve so increase in responsiveness increase cost along the curve. However, through use of supply chain optimization the whole curve can move down to provide overall better performance. This curve is called the Efficient Frontier and it represents a range of possible strategies each with a corresponding cost and response time. The current strategy moves on the high curve so it increases in responsiveness and increases cost along the curve. However, through use of supply chain optimization the whole curve can move down to provide overall better performance. 10
Inventory Optimization and The Efficient Frontier INVENTORY WHITEPAPER End-to-end inventory optimization is a way to reach the efficient frontier. By developing a model that enables the firm to analyze and optimize inventory across multiple echelons, we can determine the appropriate inventory levels (cycle stock, safety stock, intransient stock) at different locations. The key drivers of inventory are:! Demand: average and variability! Lead-time: average and variability! Fill rates objectives! Order frequency, order size, minimum order quantity! Supplier performance The idea of building a model that globally optimizes is illustrated in the figure below. The Y- axis is inventory cost and the X-axis is committed lead-time to customer. The blue line is the traditional tradeoff between inventory levels and committed lead-time. As you increase lead-time, you can reduce inventory as you respond faster, you need more inventory. These tactical changes mean moving along the blue tradeoff line with no structural change in strategy. However, when you focus on global optimization, the end-to-end model that represents your entire business and we are able to move from the blue line to the pink one. This implies that for the same lead-time you can significantly reduce inventory or for the same inventory level you can reduce response time to the customer. There are many strategies where you can cut inventory and reduce time to market and still improve supply chain performance. To understand the true inventory drivers, inventory is not the problem it s a symptom. The ability to change your strategy and move from local optimization to global optimization (from the blue line to the pink one) associated with global optimization allows companies to improve on multiple performance indicators. It allows you to reduce inventory significantly and at the same time increase service level and fill rate significantly. 11
Case Study 1: Uncovering Inventory Drivers - PepsiCo Worldwide Flavors A good example for what this type of analysis involves is the work we did with PepsiCo Worldwide Flavors (PWF) on end-to-end inventory optimization. PWF went through a reorganization that led to reassessment of inventory in the manufacturing plants. With a multi-tier network of three plants, four Distribution Centers (DCs) in the US serving both the US and Canadian markets, about 450 finished goods and 2000 components and raw materials, this was not something that could be done easily. Management realized that this complex multi-level supply chain network could not be fully optimized using single-echelon optimization methods. Is there an effective strategy to cut inventory and improve supply chain performance? There were different ideas focus on reducing complexity, change production plans, improve forecasting. End-to-end inventory optimization helped determine the important drivers in their supply chain. This allowed us to understand what drives inventory and service level in this specific supply chain and in this case it turned out that positioning inventory correctly was the most critical factor. In fact, in this supply chain, positioning inventory correctly allowed PepsiCo to cut inventory by between 24-29 percent depending on service level. This process involved moving inventory from raw material coming into some of the manufacturing facilities, to semifinished products outside the production facilities. Other factors had very little impact as you can see in the chart below based on the different scenarios tested. 12
Case Study 2: End-to-end Optimization: Schneider Electric INVENTORY WHITEPAPER Companies are often skeptical about how much improvement can be gained from using end-to-end optimization also called Multi-echelon inventory optimization (MEIO). Schneider Electric, the Global Specialist in Energy Management decided to test this out. We worked with them on building a model for the Miniature Breaker Product Line, a product to control and distribute electric power. The team believed there were inventory opportunities based on Single-Echelon Inventory Modeling techniques currently in use. Recent fluctuations in demand had led to extensive spend in terms of expedites and employee overtime to meet customer requests. The results were quite significant with single echelon inventory optimization, Schneider Electric could reduce costs by 11%, but if they were to employ end-to-end inventory optimization they could achieve a 30% reduction while maintaining the same service levels. 13
Case Study 3: Supplier Risk Management: Ford In order to understand the impact of supply chain disruptions, it is necessary to model the supply chain and assess the cost and recovery time from various closures and other scenarios. From Superstorms to Factory Fires, describes a method developed to manage unpredictable supply chain disruptions. It helps prioritize the financial impact of risk through the Risk Exposure Index (REI). This enables companies to focus their mitigation efforts on the most important suppliers and risk areas instead of ignoring them or using an exhaustive approach. This method was successfully applied at Ford Motor Company. What we found as you can see in the graph below that only 2 percent of Ford suppliers pose a high risk and they are often low spend suppliers that do not receive much attention. The power of this method is that you can focus efforts on the most critical parts instead of treating all suppliers the same. Conclusion Inventory management is an extremely complex topic but understanding the drivers and characteristics of how it behaves can help improve performance and protect the business from risk. Modeling the supply chain and using analytics to optimize and understand the key drivers can help in three important ways. First, it can help you focus on the important problems which are not always apparent or easily accessible through rule of thumb analysis. Second, it can allow you to reduce cost and at the same time improve performance by exploring innovative ways to position inventory. Third, is can help reduce unnecessary risk in the supply chain. About OPS Rules OPS Rules is a leading operations strategy consulting company with capable partners and associates who provide expertise, distinctive viewpoints and a reputation for developing and implementing new ideas with leading companies. We take an analytical approach that is fact-based and data-driven that leads to recommendations and innovations that are followed through to successful implementations. We employ a flexible transformation approach that ensures successful adoption of essential new skills. analyze.innovate.transform 14