Who Are The Players?

The differences between new parts production supply chain and service and replacement parts supply chain are significant. Companies using conventional supply chain management (SCM) methods to track their service and replacement parts supply are failing to grasp the special needs of the aftermarket. Further , one can even differentiate between the inventory optimization approaches of new production parts. Pure distribution parts include finished consumer goods (not including fashion/apparel items due to their seasonality idiosyncrasies, see Intentia: Stepping Out With Fashion and Style; Part One: Characteristics and Trends of the Fashion Industry), with a large number of items, large number of locations (whereby store levels can get out of hand), and with a desire for very high customer service levels (98 percent or more). The vendors that cater to these customers would be the likes of ToolsGroup. Mixed manufacturing and distribution for new parts require the exact positioning of parts. Exact positioning is highly important for manufacturing and configuring (postponement) purposes, because bill of materials (BOM) logic is heavily leveraged for inventory planning and optimization. Leading vendors in this market include Optiant, LogicTools, SmartOps, or i2 Technologies.

Part Four of the Lucrative but "Risky" Aftermarket Business—Service and Replacement Parts for SCM series.

These solutions typically leverage stochastic optimization using nonlinear modeling techniques to analyze input data for randomness. This knowledge is applied to determine an optimal inventory policy at a particular node in a multi-tier supply network. Namely, as supply chain variability has increased, the data has become more random. Consequently, user companies need to not only look at the nominal values per se, but also at the probability of the value. For example, they need to know what the probability is of the forecast value, the purchase/transportation lead time, the manufacturing run time, the supplier quality, etc.

Contrary to these new parts production segments, aftermarket service and replacement parts are typically "slow movers," but may be critical for the operation of expensive equipment, often containing associated service level agreement (SLA) penalties for inadequate service. Thus, for the reasons of repair and indenture level and SLA considerations, one has to optimize inventories for required service levels and end-equipment availability. Varied service and customer entitlements complicate things, since aftermarket service must support warranty commitments; contract extensions, which might include same-day or next-day service; and direct or through distributor part sales. These entitlements may have different service objectives, which may include fill rates, response times, or system uptime maintenance.

How is risk factored into decision-making for service parts? In this case, forecasting might use demand history, but perhaps more importantly, mean time between failure (MTBF) data and an analysis of causal factors can provide item- and location-specific estimates of usage. This data can also be used to calculate the probability of demand occurring during the planning period in question. For example, a forecast might state that there is a 12 percent chance that the user will need a specific part in the next thirty days at a specific location. However, this forecast is risk-based, rather than consumption-based, as it is in new parts production supply chain planning (SCP).

Moreover, the design of the distribution network including which parts and how many of each are positioned at which depot(s) is another risk based evaluation. Typically, this dictates a multi-tier or multi-echelon depot strategy, where tactical planning involves risk-based decision-making that considers the probability of demand and therefore, the probability of a stockout. To refresh our memory, stockout costs may include lost sales, backorder costs, expediting, and additional manufacturing and purchasing costs (not to mention lost face before the customer and hurting SLA penalties). Thus, the strategy include issues like, if we have a 20 percent chance of needing a single unit of a specific part in the next thirty days, what are the odds that we will need two? Moreover, given the part delivery lead time, what are the odds that the demand for two will create a stockout?

Given the random, sporadic nature of service events, forecasting approaches cannot eliminate the uncertainty of demand. Hence, inventory decisions must be evaluated on the basis of risk, whereby the considerations should include MTBF; the number of a particular asset type to be maintained; the product life cycle stage; the locations of assets and available spare parts; SLA commitments; the cost of downtime and of the service or replacement part, etc. To deal with these variables effectively companies must address the complexity and the need to manage risk directly. Some vendors, as will be described later, have developed approaches incorporating these factors into the proprietary models and algorithms.

Service and replacement parts inventory optimization is a big issue for a wide gamut of manufacturers. Aeronautical and defense (A&D) companies that design products for high reliability figure most prominently, but they still have to maintain stocks of complex and expensive spare and replacement parts, since the impact of any type of failure is large and requires the widespread and global stocks of parts for rapid replacement. The situation becomes even more complicated with rotable parts, such as the interchangeable elements of an aircraft that are removed, rebuilt, or reinstalled, which, almost as a rule, are always on a different aircraft. In an industry where every nut and bolt is important for safe operation, immense amounts of attention and effort are used to track interchangeable components and subassemblies for costing, replacement scheduling, and mean time-for-failure (MTFF) prediction.

A&D companies design low-volume, high-cost products for high reliability, but still maintain stocks of complex and expensive spares, since the impact of any failure in this industry, is large and requires adequate stocks of parts at several locations for rapid replacement in case of repair. On one hand, minimizing the number of new parts introduced into the market (and subsequently into inventory) should be a major aim, particularly because parts face obsolescence as new finished product are introduced. Yet, on the other hand, rotable parts and reusing ("harvesting") repaired components only adds to the complexity and likely impaired the efficiency of this process. Further, lot and serial tracking capabilities, the so-called tail effectivity, permits users to tie every part (within part lists and diagrams) on a plane back to that one entity. For more information, see MRO and Spare Parts Management Considerations.

Similar low-volume, high-cost, high-impact concerns are applicable to a range of other manufacturers, such as automotive and high-tech/electronics makers of complex medical equipment, large industrial systems, and mining equipment. All have immense, installed bases and complex, multi-echelon supply chains with high occurrences of slow-moving parts. Manufacturers of durable goods, like household appliances, have an additional issue with the need for highly mobile service van stocks. In addition to original equipment manufacturers (OEM), asset-intensive manufacturers, and service organizations, like refineries, chemical plants, primary metals producers, telecommunications, utilities, and municipalities, have to maintain large stores of spare parts to minimize the impact of failures on their revenue generating activities.

SOURCE:http://www.technologyevaluation.com/research/articles/supply-chain-management-systems-for-service-and-replacement-parts-players-benefits-and-user-recommendations-18090/

Who Are The Players?

The differences between new parts production supply chain and service and replacement parts supply chain are significant. Companies using conventional supply chain management (SCM) methods to track their service and replacement parts supply are failing to grasp the special needs of the aftermarket. Further , one can even differentiate between the inventory optimization approaches of new production parts. Pure distribution parts include finished consumer goods (not including fashion/apparel items due to their seasonality idiosyncrasies, see Intentia: Stepping Out With Fashion and Style; Part One: Characteristics and Trends of the Fashion Industry), with a large number of items, large number of locations (whereby store levels can get out of hand), and with a desire for very high customer service levels (98 percent or more). The vendors that cater to these customers would be the likes of ToolsGroup. Mixed manufacturing and distribution for new parts require the exact positioning of parts. Exact positioning is highly important for manufacturing and configuring (postponement) purposes, because bill of materials (BOM) logic is heavily leveraged for inventory planning and optimization. Leading vendors in this market include Optiant, LogicTools, SmartOps, or i2 Technologies.

Part Four of the Lucrative but "Risky" Aftermarket Business—Service and Replacement Parts for SCM series.

These solutions typically leverage stochastic optimization using nonlinear modeling techniques to analyze input data for randomness. This knowledge is applied to determine an optimal inventory policy at a particular node in a multi-tier supply network. Namely, as supply chain variability has increased, the data has become more random. Consequently, user companies need to not only look at the nominal values per se, but also at the probability of the value. For example, they need to know what the probability is of the forecast value, the purchase/transportation lead time, the manufacturing run time, the supplier quality, etc.

Contrary to these new parts production segments, aftermarket service and replacement parts are typically "slow movers," but may be critical for the operation of expensive equipment, often containing associated service level agreement (SLA) penalties for inadequate service. Thus, for the reasons of repair and indenture level and SLA considerations, one has to optimize inventories for required service levels and end-equipment availability. Varied service and customer entitlements complicate things, since aftermarket service must support warranty commitments; contract extensions, which might include same-day or next-day service; and direct or through distributor part sales. These entitlements may have different service objectives, which may include fill rates, response times, or system uptime maintenance.

How is risk factored into decision-making for service parts? In this case, forecasting might use demand history, but perhaps more importantly, mean time between failure (MTBF) data and an analysis of causal factors can provide item- and location-specific estimates of usage. This data can also be used to calculate the probability of demand occurring during the planning period in question. For example, a forecast might state that there is a 12 percent chance that the user will need a specific part in the next thirty days at a specific location. However, this forecast is risk-based, rather than consumption-based, as it is in new parts production supply chain planning (SCP).

Moreover, the design of the distribution network including which parts and how many of each are positioned at which depot(s) is another risk based evaluation. Typically, this dictates a multi-tier or multi-echelon depot strategy, where tactical planning involves risk-based decision-making that considers the probability of demand and therefore, the probability of a stockout. To refresh our memory, stockout costs may include lost sales, backorder costs, expediting, and additional manufacturing and purchasing costs (not to mention lost face before the customer and hurting SLA penalties). Thus, the strategy include issues like, if we have a 20 percent chance of needing a single unit of a specific part in the next thirty days, what are the odds that we will need two? Moreover, given the part delivery lead time, what are the odds that the demand for two will create a stockout?

Given the random, sporadic nature of service events, forecasting approaches cannot eliminate the uncertainty of demand. Hence, inventory decisions must be evaluated on the basis of risk, whereby the considerations should include MTBF; the number of a particular asset type to be maintained; the product life cycle stage; the locations of assets and available spare parts; SLA commitments; the cost of downtime and of the service or replacement part, etc. To deal with these variables effectively companies must address the complexity and the need to manage risk directly. Some vendors, as will be described later, have developed approaches incorporating these factors into the proprietary models and algorithms.

Service and replacement parts inventory optimization is a big issue for a wide gamut of manufacturers. Aeronautical and defense (A&D) companies that design products for high reliability figure most prominently, but they still have to maintain stocks of complex and expensive spare and replacement parts, since the impact of any type of failure is large and requires the widespread and global stocks of parts for rapid replacement. The situation becomes even more complicated with rotable parts, such as the interchangeable elements of an aircraft that are removed, rebuilt, or reinstalled, which, almost as a rule, are always on a different aircraft. In an industry where every nut and bolt is important for safe operation, immense amounts of attention and effort are used to track interchangeable components and subassemblies for costing, replacement scheduling, and mean time-for-failure (MTFF) prediction.

A&D companies design low-volume, high-cost products for high reliability, but still maintain stocks of complex and expensive spares, since the impact of any failure in this industry, is large and requires adequate stocks of parts at several locations for rapid replacement in case of repair. On one hand, minimizing the number of new parts introduced into the market (and subsequently into inventory) should be a major aim, particularly because parts face obsolescence as new finished product are introduced. Yet, on the other hand, rotable parts and reusing ("harvesting") repaired components only adds to the complexity and likely impaired the efficiency of this process. Further, lot and serial tracking capabilities, the so-called tail effectivity, permits users to tie every part (within part lists and diagrams) on a plane back to that one entity. For more information, see MRO and Spare Parts Management Considerations.

Similar low-volume, high-cost, high-impact concerns are applicable to a range of other manufacturers, such as automotive and high-tech/electronics makers of complex medical equipment, large industrial systems, and mining equipment. All have immense, installed bases and complex, multi-echelon supply chains with high occurrences of slow-moving parts. Manufacturers of durable goods, like household appliances, have an additional issue with the need for highly mobile service van stocks. In addition to original equipment manufacturers (OEM), asset-intensive manufacturers, and service organizations, like refineries, chemical plants, primary metals producers, telecommunications, utilities, and municipalities, have to maintain large stores of spare parts to minimize the impact of failures on their revenue generating activities.

SOURCE:http://www.technologyevaluation.com/research/articles/supply-chain-management-systems-for-service-and-replacement-parts-players-benefits-and-user-recommendations-18090/