Driving effective inventory management. Five key insights can increase visibility and reduce costs, all while setting appropriate service levels

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1 Driving effective inventory management Five key insights can increase visibility and reduce costs, all while setting appropriate service levels BY OVIAMATHI K 46 Industrial Engineer

2 Global manufacturing enterprises deal with complex and compelling challenges, including economic volatility, fluctuating commodity prices, supply-chain inefficiencies and increasing customer expectations. Today s fluid business environment demands that manufacturers find new, smarter ways to meet these challenges. That journey begins with thoughtful consideration of how supply chain managers can more effectively design and direct inventory-management processes to help manufacturers grow, improve profitability and increase customer satisfaction. In this dynamic business environment, manufacturing enterprises must think of inventory management effectiveness in terms of their ability to address different markets and to operate from the most optimal location and in the most optimal manner. They need to think about connectivity in terms of how it can provide the visibility and ability to anticipate evolving stakeholder needs. They need to think creatively to develop innovative responses to both opportunity and uncertainty. And they must develop an adaptive mindset to respond to change with agility. These intelligent manufacturing enterprises are realizing that an effective inventory management process plays a pivotal role in minimizing working capital and increasing profitability by releasing cash from excess inventory, increasing customer satisfaction through higher service levels, and providing significant business growth and increased production capacity. Key levers that enable effective inventory management include better visibility of inventory levels, effective inventory segmentation, advanced forecasting methods and service level planning. This article sums up five insights and best practices to consider when trying to move those levers with maximum effectiveness. 1. Inventory visibility management Better visibility of inventory across the supply chain can help reduce overall inventory by 5 percent to 7 percent. Supply chain complexity from an organization s global footprint, variable demand, complex supplier landscape and operational cost pressures invariably adds invisible layers of inventory. Manufacturing organizations that do not have enterprise-level dashboards that provide clear, real-time inventory visibility are at a disadvantage in the global marketplace. The design of dashboards must track key performance indicators in ways that clearly link business outcomes with operational metrics. For example, if the business wants to reduce working capital, then executives and managers have to decide the number of days of inventory outstanding that must be reduced first. Next, officials have to set target inventory levels and inventory turns across locations and product divisions. Targets should be set for various segments. For instance, if the ABC principle (the Pareto rule) is used for segmentation, then targets should be set at each segment level that will have the most minimal impact on service levels. The bottom line is that you must have clear end-to-end visibility of inventory. That means seeing the entire picture, from in-transit inventory to inventory stocked in multiple warehouses across the enterprise, along with forecasts on projected inventory based on the planned demand followed by a systematic approach of distributing the business outcome targets. Without it, expecting to cut inventory even 5 percent without compromising effectiveness is recklessly unrealistic. Case study: A global engineering company was facing challenges in managing raw material inventory. The key metrics were low inventory turns and high inventory levels. These problems were due to lack of a single and comprehensive real-time look at the 20,000 components of inventory the business had spread across 30 product lines in six global assembly shops. The issue was approached in two phases. First, centralized visibility of inventory across locations had to be provided. Then process August

3 driving effective inventory management improvement efforts could be planned based on the results of a root cause analysis that had delivered the needed visibility. First, a centralized Web-based tool was developed that provided a real-time enterprise view of inventory. For this, we needed to segment inventory into the categories of prior, current, ahead and surplus. These were classified based on the Pareto principle. Excess inventory levels, meanwhile, were shown at planner level to the leadership team. Targets for inventory were set based on best-in-class industry inventory turns, and weekly dashboards were reviewed by the leadership team. The various levers identified through root cause analysis for excess inventory were planning (period of supply, minimum maximum, fixed order quantity, order policies), logistics (transit time, ship window, carriers) and configuration changes. In phase II these levers enabled the kick-starting of new projects like implementing electronic kanban for selected parts and using analytical models to recalculate safety stocks and ordering quantity. In a span of one year, reduction in working capital totaled $31 million, and the material inventory cycle increased from 12 to Better visibility in material scheduling Traditionally, organizations face an inventory management challenge during a change in the configuration of their suppliers and parts. The risks involved in such transitions are high. Results can include an inventory stockpile that is redundant or not required, trapping valuable cash. Best-in-class organizations have excellent visibility of material scheduling that allows them to make informed decisions and better manage the interplay between risk and inventory. Overcoming this problem requires a clear-cut planning process for the transition to the new part from the old part or from old suppliers to new suppliers. Careful planning can ensure that the inventory of old parts is depleted by the time the new parts arrive. This can be done with a structured process that allows good collaboration between the engineering, procurement and planning teams. The right communication should be in place with all three departments, with responsibilities assigned to avoid risk of inventory pileup. This can slice inventory buildup by 6 percent to 8 percent. Case study: A heavy equipment manufacturing company reduced raw material inventory by 8 percent by implementing a smarter process to manage supplier/parts configuration and transition. The team analyzed the as-is process, which dealt with supplier/parts configuration. It turned out that inventories piled up because the roles and responsibilities of various departments were not defined clearly. The supply chain team collaborated with technical experts to develop an automated and intelligent workflowbased tool to enable all three department stakeholders (procurement, planning and engineering) to follow a rigorous process that established accountability, along with escalation triggers. The process involves actions to be taken by all three departments, beginning at the time a new part is identified or introduced by engineering, followed by sharing the functional and technical test results of the new part and introduction or production of the part for use. The actions are designed in a way that the old part inventory is depleted before we start introducing the new part to use. The planning team cut in the forecast, and the procurement team procured the new product at the right time. This coordination enabled the reduction of obsolete and nonmoving inventory by $2 million in a period of one year. 3. Inventory classification and segmentation Multicriteria inventory classification can net still more inventory reduction, typically 3 percent to 4 percent. It allows for a more accurate understanding of inventories by segment, and that allows for better planning that lets managers confidently trim their inventories. Unlike the traditional way of using just one dimension, teams can use an analytic hierarchy process to do multicriteria segmentation and classify the overall parts into top priority, medium priority and low priority segments. Customized business policies and strategies should be developed based on effective inventory classification, such as a higher reserve or safety stock for top-ranked products, along with a relatively lower stock for low-ranked products. This helps reduce excess inventory, along with allowing the company to redeploy investment in higher-performing inventory. Case study: A global industrial company reduced its on hand inventory by 27 percent and improved its inventory turns from eight to nine by using a multicriteria approach to conduct inventory classification. An analytical hierarchical process was applied to prioritize spare parts into criteria of top, medium and low importance. The choices were made based on parameters like ABC classification, product criticality to customers and demand hits (fast vs. slow vs. not moving). Low priority parts will run on the minimummaximum replenishment model and be restocked based on replenishment lot 48 Industrial Engineer

4 anticipating the future Figure 1. Supply chain management teams have various tools that can help them forecast demand accurately. Consensus forecasting A blend of various forecasts i.e., statistical, sales, product management, etc. Demand pattern analysis Classify demand pattern based on multiple criteria such as continuous vs. intermittent Forecast (dis) aggregation Aggregate/disaggregate forecast by a variety of dimensions such as time, product family and geography quantity. Medium priority parts will run on the mentioned deterministic models based on cost and removal rate (failure and maintenance). Top priority parts run on either or both deterministic and stochastic models, with more frequent micromanagement to support extensive validation. Service levels were set based on inventory classification ranks to avoid a one-size-fits-all approach. 4. Demand forecasting Proper selection of forecasting models, along with incorporating market intelligence for various demand patterns, can improve forecast accuracy as much as 25 percent. Use of advanced forecasting models to handle parts that have dynamic or intermittent demand patterns plays a key role in reducing forecasting errors, and hence, inventory-level requirements. Incorporating market intelligence in the forecasting models enables organizations to forecast accurately. Successful use of this intelligence can be a key differentiator in a marketplace where the Best-fit modeling Identify right forecast model with the least margin of error Demand volatility analysis Compare forecast accuracy vs. demand variability to assess efficiency of method and prioritize items to validate forecast manually Customized forecasting models must be developed to reflect certain business outcomes and consumption patterns. mathematical sophistication of forecast models is typically more important than supplementing the quantitative results with expert opinions and judgments (e.g., promotional activities and sales targets, budget constraints, changes in business conditions, etc.). Forecasting demand is a key driver in upstream planning activities that ultimately will solve major downstream problems in getting the right inventory. Figure 1 illustrates many of the effective ways to do such forecasting. Case study: A motor manufacturer wanted to improve its demand forecasting process. The practice team assessed the process and identified that many of the parts followed an intermittent demand pattern for which a regular forecasting model will not suffice. So the team implemented a proprietary demand forecasting tool. The tool followed a structured process of demand-pattern analysis, followed by best-fit model selection. For parts that follow an intermittent demand pattern, the Croston method is specifically designed for data sets where the demand for any given period is often zero and the exact timing of the next order is unknown. This method works by combining a smoothed estimate of the average demand for periods that have demand with a smoothed estimate of the average demand interval. The forecasts are not magic. They won t tell you when the next order will be placed. However, they often yield a better forecast for expected demand than other time-series approaches. Fourier series a model based on fitting sine waves with increasing frequencies and phase angles to a time series were used to better predict and forecast. Along with best-fit forecast models, 20 strong exception-management rules (e.g., the current year s forecast is greater than 200 percent of last year or less than 50 percent of last year) were framed to identify parts that had anomalies in their forecasted output. Then they were corrected manually. A demand variability mechanism was created for when the manufacturer needed to identify parts and customers that had a highly variable demand pattern. Strategies were developed to reduce variation where possible. This helped improve forecast accuracy by 23 percent. 5. Service level planning Customized replenishment and service level policies, along with a periodic review of supplier lead-times, can improve service levels up to 20 percent. Success depends on determining how much to order, and when, to meet business needs. Unfortunately, manufacturing organizations usually follow a suboptimal approach that s all about reducing the costs of carrying inventory, often at the expense of increasing the costs of ordering or being out of stock. August

5 driving effective inventory management a european opportunity If business success is a matter of supply chain vs. supply chain, then European organizations are gearing up for the future. According to Supply Management magazine, the number of supply chain and logistics vacancies has risen by 26 percent overall in Europe and 38 percent in the United Kingdom. In fact, there were more new supply chain and logistics positions than in accounting or financial services. The figures came from the recruitment firm Robert Walters, which cited companies desire to cut costs, become more efficient and take advantage of the improving economy. Instead, organizations should pursue a holistic approach that minimizes total inventory-relevant costs, such as carrying, stock-out and ordering costs, while setting service levels. Instead of using single-replenishment policies for all products, design a hybrid approach to customize replenishment policies. This leads to superior results by accounting for inventory classification, demand pattern and product criticality to customers, along with other businessspecific parameters. Likewise, the process of balancing service levels with inventory investments is a key step in achieving targeted service at a reduced cost. Realizing this balance means targeting parts where you can increase service levels with small incremental investments and reduce service for a few parts with high demand and supply variations. This targeted balancing has to be done carefully so it won t affect overall aggregate service levels. And the standardized assumption of normal distribution fails for the majority of SKUs, as only 40 percent will follow normal distribution. To set safety stocks right, one might need to look at non-normal distribution, such as Poisson or gamma. This could be particularly important for parts that have a low volume or that follow intermittent demand patterns. Case study: A global provider of waterprocessing equipment wanted to assess the reason for low service levels in one of its product divisions. This company had a firmly established rule of thumb that set safety stocks and determined order quantities. The key drivers for low service levels were more random demand variation, lead-time variations from the supplier and suboptimal inventory levels, a result of the current stocking policies that were based on days of supply. The company s rule of thumb increased the inventory by stocking more for the parts that had minimal variation. The supply chain team developed service groups based on ABC analysis, demand variations and lead-times. For each service group, the team devised a simulation tool that incorporated demand and supply variation to identify the right inventory to ensure minimum service levels. Lead-time optimization projects also corrected lead-times. For 10 percent of the SKUs, lead-time was less than the current metric, so lead-time was increased for those sets of parts. For 15 percent of parts, the analysis led the team to reduce lead-times. A small buffer was added for a set of parts where operations could not control demand variations. This right-sizing of inventory, along with correcting the existing rule of thumb by using a statistical analysis model, helped improve service levels from 80 percent to 90 percent. The agile adapters will win When all is said and done, thinking for a moment that you can rely on what the aggregate inventory figure (or worse, your gut) may be telling you about how to manage your supply picture is a dangerous game. The world is changing fast. If you don t develop an agile and adaptive strategy for effectively responding to whatever comes next, you ll end up being neither efficient nor effective. d Oviamathi K is an inventory management expert with Genpact and a senior member of Genpact s supply chain management practice. He has worked with companies in the chemical manufacturing and transportation sectors and has a wealth of experience in inventory management, demand planning, fulfillment and logistics. He has earned a bachelor s degree in mechanical engineering and an MBA in operations management. He is certified in production and inventory management (CPIM) as well as being a certified supply chain professional (CSCP) and a Six Sigma green belt. 50 Industrial Engineer