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
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
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