There are many requirements involved in the supply chain management (SCM) of service and replacement parts that make the process different from traditional, "new parts" SCM (see Part One). As a result, some specialist SCM solutions have been developed to address these challenges. Some might resemble conventional SCM solutions, but feature different approaches. The requirements of service and replacement parts SCM solutions also vary given the wide range of members that exist across multi-node supply chains. Each of these members can be grouped into a few major solution functional categories.
Part Two of the Lucrative but Risky "Aftermarket" Business: Service and Replacement Parts SCM series.
Service and replacement parts resource management, which is the main focus of this article, consists of a variety of solutions that are comparable to supply chain planning (SCP) components in conventional SCM suites. Service and replacement parts management has inventory optimization at its core that determines the best way to stock inventory across the supply chain to maximize service levels while minimizing investment. In other words, the basic goal is to maintain the optimal placement of resources, including parts, tools, and service technicians, across service regions to meet service level agreement (SLA) commitments at the lowest possible cost.
These spare parts planning systems provide the means to define and implement a spare parts inventory strategy that meets enterprise objectives. In other words, they tend to help enterprises understand the relationship between a customer service target level and the value of the inventory required to support it. To that end, they combine forecasting with replenishment logic to determine the optimal level and mix of parts to carry at each stocking tier, given certain capital investment targets and customer service level goals. Unlike finished goods, where nearly 100 percent customer service levels are desirable, here only certain classes of spare parts need to be available all the time, at all supply chain nodes.
Spare parts planning systems might also improve user productivity, since by automating the basic forecasting and replenishment process, planners and inventory managers can focus on exceptions and more-strategic planning activities, such as how to handle expensive, slow-moving items or how to use substitute parts to reduce costs or obsolescence.
Achieving this goal requires a mix of tools. These range from strategic tools identifying demand profiles, service objectives, and the best way to position resources to meet demand, to tactical tools determining what orders need to be placed to meet strategic objectives. Such goals include managing the risk inherent in allocations and transships; repair or new purchase orders; new product introductions (NPI) or discontinuations; and the replenishment and redeployment decisions.
Tactical refinements of inventory optimization entail setting minimum and maximum inventory levels, which recognizing stochastic, changing demand and lead-time. The algorithms required to provide this support are significantly different from those found in conventional, new parts production SCM, and justify the use of focused, point solutions, including dynamic programming, simulation, mixed integer optimization, etc. In the case of inventory optimization, two parts may be present:
1. Multi-echelon optimization determines optimal stocking levels of an item at a particular location, based on the item's possible investment levels. In this case, an echelon is the level of supply chain nodes, or disintermediation. For example, a supply chain with two independent factory warehouses and nine wholesale warehouses delivering product to 350 retail stores is a supply chain with three echelons between the factory and the end customer. One echelon consists of the two independent factory warehouses, the other echelon consists of the nine wholesale warehouses, and the third echelon consists of the 350 retail stores. Each echelon adds operating expenses, holds inventory, adds to the cycle time, and expects to make a profit.
2. Multi-item optimization determines the optimal allocation of inventory investment across items in a product group.
Even fundamental concepts like customer service level are different in the service and replacement parts milieu. Namely, in new parts production, the customer service level (synonymous with customer service ratio, fill rate, order-fill ratio, and percent of fill) is a measure of the delivery performance of finished goods, usually expressed as a percentage. In a make-to-stock (MTS) company, this percentage usually represents the number of items or dollars (on one or more customer orders) that were shipped on schedule for a specific time period, compared with the total that were supposed to be shipped in that time period. Likewise, in a make-to-order (MTO) company, the customer service level is usually a comparison between the number of jobs or dollars shipped in a given time period and the number of jobs or dollars that were supposed to be shipped in the same period. Yet, in the service and replacement parts world, with a high level of unpredictability, how can one forecast the dollar amount of service or repair parts that were supposed to be shipped during a particular period?
Thus, given the random nature of service and breakdown events, it is clear that demand uncertainty (which can be measured by the standard deviation, mean absolute deviation [MAD], or variance of forecast errors) cannot be eliminated through traditional forecasting methods. Hence, trade-offs must be evaluated on the basis of captured future risk assessments; estimates of demand probability distribution, relevant to specific customer products; and locations at future points in time. The decisions made across the planning horizon thus constitutes an exercise in risk management
source
http://www.technologyevaluation.com/research/articles/supply-chain-management-morphing-the-functional-scope-of-service-parts-18086/
Part Two of the Lucrative but Risky "Aftermarket" Business: Service and Replacement Parts SCM series.
Service and replacement parts resource management, which is the main focus of this article, consists of a variety of solutions that are comparable to supply chain planning (SCP) components in conventional SCM suites. Service and replacement parts management has inventory optimization at its core that determines the best way to stock inventory across the supply chain to maximize service levels while minimizing investment. In other words, the basic goal is to maintain the optimal placement of resources, including parts, tools, and service technicians, across service regions to meet service level agreement (SLA) commitments at the lowest possible cost.
These spare parts planning systems provide the means to define and implement a spare parts inventory strategy that meets enterprise objectives. In other words, they tend to help enterprises understand the relationship between a customer service target level and the value of the inventory required to support it. To that end, they combine forecasting with replenishment logic to determine the optimal level and mix of parts to carry at each stocking tier, given certain capital investment targets and customer service level goals. Unlike finished goods, where nearly 100 percent customer service levels are desirable, here only certain classes of spare parts need to be available all the time, at all supply chain nodes.
Spare parts planning systems might also improve user productivity, since by automating the basic forecasting and replenishment process, planners and inventory managers can focus on exceptions and more-strategic planning activities, such as how to handle expensive, slow-moving items or how to use substitute parts to reduce costs or obsolescence.
Achieving this goal requires a mix of tools. These range from strategic tools identifying demand profiles, service objectives, and the best way to position resources to meet demand, to tactical tools determining what orders need to be placed to meet strategic objectives. Such goals include managing the risk inherent in allocations and transships; repair or new purchase orders; new product introductions (NPI) or discontinuations; and the replenishment and redeployment decisions.
Tactical refinements of inventory optimization entail setting minimum and maximum inventory levels, which recognizing stochastic, changing demand and lead-time. The algorithms required to provide this support are significantly different from those found in conventional, new parts production SCM, and justify the use of focused, point solutions, including dynamic programming, simulation, mixed integer optimization, etc. In the case of inventory optimization, two parts may be present:
1. Multi-echelon optimization determines optimal stocking levels of an item at a particular location, based on the item's possible investment levels. In this case, an echelon is the level of supply chain nodes, or disintermediation. For example, a supply chain with two independent factory warehouses and nine wholesale warehouses delivering product to 350 retail stores is a supply chain with three echelons between the factory and the end customer. One echelon consists of the two independent factory warehouses, the other echelon consists of the nine wholesale warehouses, and the third echelon consists of the 350 retail stores. Each echelon adds operating expenses, holds inventory, adds to the cycle time, and expects to make a profit.
2. Multi-item optimization determines the optimal allocation of inventory investment across items in a product group.
Even fundamental concepts like customer service level are different in the service and replacement parts milieu. Namely, in new parts production, the customer service level (synonymous with customer service ratio, fill rate, order-fill ratio, and percent of fill) is a measure of the delivery performance of finished goods, usually expressed as a percentage. In a make-to-stock (MTS) company, this percentage usually represents the number of items or dollars (on one or more customer orders) that were shipped on schedule for a specific time period, compared with the total that were supposed to be shipped in that time period. Likewise, in a make-to-order (MTO) company, the customer service level is usually a comparison between the number of jobs or dollars shipped in a given time period and the number of jobs or dollars that were supposed to be shipped in the same period. Yet, in the service and replacement parts world, with a high level of unpredictability, how can one forecast the dollar amount of service or repair parts that were supposed to be shipped during a particular period?
Thus, given the random nature of service and breakdown events, it is clear that demand uncertainty (which can be measured by the standard deviation, mean absolute deviation [MAD], or variance of forecast errors) cannot be eliminated through traditional forecasting methods. Hence, trade-offs must be evaluated on the basis of captured future risk assessments; estimates of demand probability distribution, relevant to specific customer products; and locations at future points in time. The decisions made across the planning horizon thus constitutes an exercise in risk management
source
http://www.technologyevaluation.com/research/articles/supply-chain-management-morphing-the-functional-scope-of-service-parts-18086/
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