Managing an inventory of service parts
- Publication number
- WO2005122076A2 WO2005122076A2 PCT/IB2005/002418 IB2005002418W WO2005122076A2 WO 2005122076 A2 WO2005122076 A2 WO 2005122076A2 IB 2005002418 W IB2005002418 W IB 2005002418W WO 2005122076 A2 WO2005122076 A2 WO 2005122076A2
- Grant status
- Patent type
- Prior art keywords
- Prior art date
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0631—Resource planning, allocation or scheduling for a business operation
- G06Q10/06315—Needs-based resource requirements planning or analysis
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q10/00—Administration; Management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement, balancing against orders
MANAGING AN INVENTORY OF SERVICE PARTS
BACKGROUND OF THE INVENTION
 This invention relates to inventory systems and specifically to planning and deploying inventory systems for service parts used to service and repair equipment.
[ooo2] Many companies regard post-sale servicing of their products as little more than a distraction. From this perspective, service is only an expense. However, analysis shows that post-sales service can be a significant revenue generator when properly planned and managed. For example, in the highly competitive jet engine business, manufacturers have realized that the value of servicing a product over its life can exceed the original sales price by as much as five times.
[ooo3] The tasks faced by post-sales business groups vary greatly by industry and by customer type. Personal computer manufacturers may have a large client base but only a relatively small number of parts to keep on hand to service perhaps 30 models of PCs. In contrast, manufacturers of construction or land moving equipment may have a smaller client base but may need to service such a wide range of complicated machinery that perhaps 500,000 replacement service parts need to be available to the repair technicians. Tracking and planning for the half million parts is a very challenging task.
[ooo4] Unfortunately, those managing the inventory may not be well qualified. For example, certain OEMs allow their dealers to control part inventories. The planners at these dealerships may treat all parts equally, applying the same forecasting, stocking, lot sizing and reviewing policies— regardless of the demand, supply and profit characteristics of the individual parts. For example, planners may make no distinction between an item with a high-volume demand, stable order patterns and quick replenishment lead times, and another item that rarely fails in the field, is sourced from one supplier and has a six-month lead time. As a result, these dealers may keep excess inventory aging on shelves while lacking the specific parts needed by their service technicians.
[ooo5] In an attempt at a solution, some post-sales business units have implemented software to help their inventory management needs. However, current transactional and advanced planning software fails to identify and integrate the detailed root causes of inventory performance. For example, demand for service parts typically exhibits random, or stochastic demand patterns; this uncertainty must be included in developing deployment and replenishment strategies. Also, enterprise resource planning systems currently available may help OEMs to meet only 40% - 60% of their high-volume post- sales needs. This leaves a 60% - 40% gap that has been difficult to fix.
[ooo6] What is needed is a way to close the gap between the inventory of service parts and the need for the parts. What is needed is a method to prioritize the management of a parts inventory to optimize the process, thereby having the proper mix of inventory to meet agreed upon service levels without overstocking on inventory. What is also needed is a way to perform such management without requiring planners to have an advanced degree in statistics or operations research. What is needed is a methodology that is cost-effective as well as scientifically based rather than only being based on rules of thumb or 'guestimates'. What is needed is a way to determine the drivers of inventory levels for service parts and to control these drivers. In addition, what is needed is a systematic and easy-to-understand methodology and toolset for that will allow the average user to conduct advanced optimization techniques on their service parts inventories.
[ooo7] There are several other factors and issues typically associated with managing service parts inventories. First, supplier performance for service parts is typically very poor; replenishment lead times typically range from 6-18 months, and line fill rates typically are less than 50%. Next, service parts distribution networks are typically fragmented with lots of individual locations: central depots, field depots, customer depots, and mobile stock. The large number of forward deployed inventories makes inventory visibility difficult, thereby making it very difficult to develop and implement stable deployment and replenishment strategies. Next, many service parts are also considered repairables, which are parts that can be fixed when they fail, instead of disposed. Repair operations require reverse flows (from customer to repair depots), forecasting of repairs, and disposition decisions, all which further add complexity to managing service parts.
BRIEF SUMMARY OF THE INVENTION
 In one aspect of the present invention, there is a method for optimizing target stock levels for the inventory of parts, such as those used by asset operators and equipment manufacturers to service their equipment. Under this method, a service strategy may be developed for a set of segments and a service level may be quantified for each segment. The segments and their service levels may be analyzed for identifying at least one logistically distinct business. Each of the segments may be assigned to a "best-fit" planning model for indicating each segment's deployment, replenishment, forecasting and review characteristics. A probability distribution function may be identified for estimating a demand process of each of the segments. In one embodiment, identifying the probability distribution function may include calculating a target stock level for each of the segments. Such target stock levels may be calculated to likely meeting a service level at a desired cost objective.
BRIEF DESCRIPTION OF THE DRAWINGS
[ooo9] Figure 1 is a flowchart of one embodiment of the invention divided into five steps.
[ooιo] Figure 2 illustrates how sampling may represent an ABCD population.
toon] Figure 3 is a flowchart of the one embodiment of the invention's sampling methodology.
[ooi2] Figure 4 is an illustration of pipelines identifying a logistically distinct business.  Figure 5 is a diagram of a planning model continuum.
[ooi4] Figure 6 shows one approach to calculating the distribution function as contemplated by the present invention.
[ooi5] Figure 7 demonstrates how distribution functions may offer insight.
[ooi6] Figure 8 illustrates a structured analysis for the stocking decision.
DETAILED DESCRIPTION OF THE INVENTION
[ooi7] 1. Calculating the Baseline Inventory
[ooi8] Figure 1 shows a flowchart of a general steps in one embodiment of the invention, which includes calculating the current baseline inventory, developing a strategy and a series of segments, assigning each segment to a planning model, matching the demand for service parts with a distribution function and then calculating optimized target stock levels.
[ooi9] The present invention's process may begin by calculating an inventory baseline 110 for understanding information about the currently held inventory. While gathering this baseline is known in the art and there are various techniques that can be used, some of representative tasks are to gather initial inventory data, build the inventory baseline model and validate, modify/customize data requests to operating environment, determine data sampling strategy, identify data sources within the IT infrastructure, and submit detailed data requests.
 A goal of the inventory baseline is to understand the present inventory: such as by answering what is on-hand, where is it, what are its characteristics and how well is it currently operating. While it is not technically difficult to generate the baseline, it can be a difficult task to handle. In many industries, service parts are not tracked once they are distributed to the repair technicians. In such a case, the inventory of service parts may be considered an operating expense. Thus, once perhaps 30 to 50 percent of the inventory is sent ahead to satellite depots or to individual technicians, that inventory becomes invisible and outside of the equation.
 Since tracking down the inventory piece by piece may be unduly challenging, software may be used to create statistically valid samples of the inventory to approximate the inventory baseline. This can create a representation of the inventory while minimizing collection time and effort. In some environments, one may extract transactional data from each network echelon and location to model the entire supply chain. To generate the sample, a planner may determine the total parts population to be sampled and then determine if a sampling strategy is needed (since sometimes analyzing the entire parts population may not be too difficult). If a sample is needed, then the planner may create a statistically valid sample from the population and identify randomized stock keeping units ("SKUs") to be in the sample. Then data for those SKUs may be extracted from various data sources and the statistical analysis performed.
 Figures 2 and 3 illustrate one novel way to create a proper sample. This approach reflects design decisions made about the present invention's optimization approach. Figure 2 shows a parts population as a pie chart 205 that can be represented by sample 210. In figure 2, the parts population is segmented into four classes A through D. Such a classification may be referred to as ABCD. In such a classification, the parts assigned to class A make up 80% of cumulative dollar volume for a company 215, class B has parts representing the parts such that class A and B make up 95% of cumulative dollar volume 220. Similarly, class C includes the parts such that classes A, B and C make up 100% of cumulative dollar value 225. Class D may include those parts that make up 0% dollar volume 230.
 One skilled in the art will recognize that the parts population may be divided in various ways without departing from the scope of the present invention. For example, some in the art commonly divide populations into three classes, known as ABC. One might also divide the classes based on a criteria other than collar volume. Thus various numbers of classes and various class criteria may be chosen. Regardless of how the classifications are chosen, a sample 210 may statistically represent the profile of the parts in the population 205.
 One may use various methods to generate the sample. Figure 3 illustrates one approach that may be used in one embodiment of the present invention. While figure 3 assumes that the population and sample have four segments, namely A, B, C and D, one skilled in the art will recognize that the method may be used for other segmentations.
 At step 310, the total population of the parts to be included [N] may be determined. The scope of the analysis may be defined at this point, such as the number of locations or echelons, the number of internal vs. external sites, active vs. inactive parts, etc. At this point, one may choose to distinguish SKUs from raw product numbers.
 At step 320, whether or not a sampling strategy is needed is determined. This decision may be driven by the analysis tool to be used. For example, Excel may only process approximately 65,000 records while a database tool may process many more than that number. If the population [N] is small enough, sampling may not be needed.
 If a sampling strategy is not needed (step 324), then the entire parts population [N] may be modeled or analyzed without the use of a sample population. Otherwise, the process of figure 3 may continue in order to generate a statistically valid and randomized sample (326). At step 330, ABCD unit volume segmentation may be conducted on the entire parts population to determine the population count for each category, NA, NB, NC and ND. Such segmentation may have already been done. Or, if necessary, the category populations may be estimated by applying percentages to the overall [N].
 At step 340, the sampling error, or level of precision to be used in the analysis [e] may be determined. In some circumstances, the sampling error chosen may be 90% (.10) or 95% (.05). Of course, other sampling errors may be used. In the analysis, [e] may be the probability measure that states how much the sample characteristics )such as the mean and standard deviation, for example) may deviate from the population if [N] had been analyzed instead of [n].
 At step 350, the sample sizes for each of the segments are calculated. For example, ABCD segments would have (ΠA, ΠB, nc and no). One way to calculate these sample sizes is with the formula:
 where e may be 0.05, 0.10 or another preferred value and where x represents a segment, such as A, B, C, or D as discussed above. Thus, if four segments are used, then the formula is applied four times to derive a sample size for each of the segments. One skilled in the art will be aware that this formula is a simplified version of more advanced sampling techniques and that other formulas may also be used.
 At step 360, the product numbers in each segment are sorted or ranked based on some chosen criterion measure. For example, unit cost and item name are two possible criteria. Once the product numbers are sorted, then each part number may be assigned its ranking number. For example, the first product number in segment A may be assigned to 1. If this is not feasible to system limitations or otherwise, then steps 360, 370 and 380 may be skipped.
 At step 370, a random number generator is used to randomly select the parts to include in the sample from each segment. For example, if Excel is being used as the sampling tool, then the formula "=RANDBETWEEN(1 , 333)" may be used to generate the random numbers (where the current segment has 333 elements, for example). If using Excel, one may wish to use the Excel Analysis Toolpak add-in. The quantity of random numbers needed to be generate matches the values of ΠA, ΠB, nc and no.
 At step 380, the randomly selected parts are identified and their data is extracted. For example, if NA JS 2,000 and ΠA JS 333, then one may generate 333 random numbers between 1 and 2,000. Then one may match those random numbers against the sorted, numbered A items. These may then become the sampled parts for that category.  As another way to minimize the effort of calculating the baseline, the present invention offers a statistical analysis tool with data requests preconfigured for different environments. For example, data requests for wholesale distribution, retail aftermarket, airline MRO, telecomm maintenance, high-tech spares, dealer channels, public utility/energy, military logistics, fixed asset maintenance and plant operations may all be installed. The invention contains data requests that collect the data in a method that facilitates the optimization approach in the invention.
 At the end of the baseline inventory procedure, some of the deliverables may be the inventory baseline model, completed data request templates, and a data sampling and management strategy.
 2. Developing a Strategy and Defining Segments
 Developing a service strategy 120 for a plurality of segments is another step of the present invention. While it is shown in figure 1 after step 110, actually it may be done before, during or after that step. Broadly speaking, this step involves viewing the parts inventory from a top-down view and understand a strategy for what kinds of service levels the company wants to offer to its customers. One of goals of this step is to select a segmentation criteria that is based on business requirements and that may create meaningful and unique segments that can be analyzed for operational insights.
 Two examples of segmentation criteria are a supply-focused segmentation and a demand-focused segmentation. In a supply-focused segmentation approach, the chosen segments may be based on criteria such as: supplier lead time variation and duration, supplier delivery performance, standard cost of part, replenishment frequency, economic lot size, supplier relationship type, and part lifecycle phase. In contrast, in a demand-focused segmentation, the segments may be based on: criticality of customer demand, degree of demand perishability, part profitability or contribution margin, economic importance of customer, strategic importance of customer, demand variability, and interrelation with other segment businesses.  Results from segmentation may be used to find "pipelines" within the customer's business to understand how to manage a segment in a more focused manner. Figure 4 shows one example of pipelines and how logistically distinct businesses ("LDBs") may be identified within the broader parts supply chain. In a complex supply chain that is tightly managed, there may only be a single LDB. Other supply chains may indicate the presence of more than one LDB. In figure 4, a "churn and burn" LDB is indicated.
 LDBs are critical to structure the customer service strategy of a service business for three reasons. First, construction of LDBs allows the business to identify the unique physical flows that exist in any distribution network. For example, the LDB modeling may demonstrate that a specific grouping of customers also exhibit a specific ordering profile, such that they are driving the majority of the logistics activity in the network. Second, LDB's also allow the business to identify the unique service and supply chain requirements associated with that LDB. For example, LDB modeling will identify the lead time, fill rate, packaging, delivery, etc. requirements that are associated with a logical grouping of customers. Last, LDBs are important because they determine the value component associated with a logical grouping of customers and parts. For example, LDB modeling may show that a group of customers drive the majority of sales, are the least profitable accounts, have the highest service requirements, but are also strategically important to the business and therefore require new selling and operating capabilities to manage.
 Segmentation may also be described from different viewpoints. For example, high-level customer segmentation models may be built. Part segmentation models may able be constructed. For other companies, it may make sense to identify the service levels by channel, customer grouping, SKU, etc.
 Once the segments are identified, a planner may use empirical data to quantify a service level for each of the segments. Assigning the service level attempts to balance the cost of holding or carrying the inventory for the segment against the cost of a stock- out in which a needed service part must be ordered. Service levels may be defined implicitly or explicitly. With the implicit method, one uses a calculation to imply the optimum service level as a function of stock-out costs and carrying costs. Such an implicit service level may be a best-fit if the values for the input parameters to the function are available, if the business has not existing customer service strategy, or if the business does not understand cost-to-serve concepts. When the service level is implicitly determined, the planning planner considers costs and service to determine the optimum balance for a customer or LDB. Presently, most companies use the implicit method.
 The explicit service level approach uses management expertise to set the acceptable minimum number of planned stock-outs. While in the past a company's sales force may have instructed the parts inventory planner to maintain perhaps a 98% fill rate, there was no tie back to the company in terms of cost. The explicit service level approaches provide such information.
 Such an explicit approach may be valid if the business management is capable of assigning discrete, differentiated service levels to customers. Three such explicit levels are: cycle service level, fill rate level, and ready rate level. In the cycle service level measure, a specified probability of no stock-outs per replenishment cycle is calculated. This is generally known as an availability measure and can include the probability of periods having zero demand. This cycle service level may be a best fit with numerous periods of zero demand.
 The fill rate service level is a specific fraction of demand to be satisfied routinely from the shelf. This is the most common measure and assumes no backorders or lost sales. It may be a best fit for higher-volume parts or for distribution systems where replenishment lead times are fixed or very costly to change.
 The ready rate service level is a specific fraction of time during which net stock is positive. It may be the least common measure, but has application in emergency environments. It is complex to use to determine optimal inventory policy, but may be applicable for end-of-life products.  One embodiment of the present invention combines these approaches and may leverage the implicit method for critical and high-value items while using one of the explicit methods for less-critical or less profitable parts.
 3. Assigning Planning Models
 Referring again to figure 1 , the next step may be to assign each of the segments to a "best-fit" planning model 130, which is a decision driven by the requirements and rules that define each segmentation class. Such a planning model describes the deployment, replenishment, forecasting and review parameters for the segment. Figure 5 is a diagram of a planning model continuum that illustrates the variation in the planning models from the most basic (on the left) to the most advanced approaches (on the right).
 This continuum indicates that rather than attempting to offer the highest level of service for all parts, one can be more surgical by choosing plans along the continuum that best fit each segment. For example, for low churn items, having the deployment plan be a rule of thumb (such as a time-based criteria with 10 days coverage), may be adequate since reordering can be done quickly. Thus, a replenishment strategy for such a plan may be to order every 5 days. Forecasting may be accomplished using historical sales (with a weighted moving average). The review strategy may be for the planners to review such rules of thumb once a quarter.
 At the right end of the spectrum of figure 5, some segments may need a complex plan. While the cost of this type of plan may be greater, it may be justified due to the critical nature of the parts, the high profitability of the parts, etc. This type of planning model has been supported by commercially available software since around the year 2000. Some of the many vendors offering software to support such complex plans are: SAP, Servigistics, Finmatica, i2, Manugistics, MCA Solutions, Baxter Planning Systems, and Xelus.
 One of the goals of this step of choosing a best-fit planning model for each segment is to focus planners on what is important rather than having them try to manage all service parts equally. Planners may now spend the proper time and effort on the proper segments for an optimized planning approach for deployment, replenishment, forecasting and review.
 4. Determining a Probability Distribution Function
 Once the best-fit planning model is assigned, the fourth step in the process may be to identify a probability distribution function ("PDF") 140. In this step, one may use a range of statistical tests to identify (i.e., "fit") the demand process to the most likely probability distribution that represents the demand. This can be a difficult mathematical procedure, but generally one may collect demand data and then find the probability distribution function that underlies the part and model that function to gain insight from it.
loose] As one would expect, if demand is normally distributed (such as a bell curve), then managing the stock fora part is generally easy. Unfortunately, part demand is not always normally distributed. Prior part systems for inventory management did one of two things. The first type of prior part systems skipped this step of determining the distribution. Rather than understand the complex function describing the distribution, a planner calculated target stock levels for the service parts using rules of thumb or other rough approaches.
 The second type of prior part systems used a back door approach to attempt to generate a number associated with the probability distribution function. In such prior part techniques, one used historical demand data for the services parts and used that data to simulate the stocking locations. The simulation would be run to find the first pass fill rate ("FPFR"). Once the FPFR was known, one would run the simulation again on an iterative basis, slowly (and clumsily) backing into the number hopefully associated with the distribution function. This technique was used without understanding the function itself. Such systems had many disadvantages. In addition to not revealing a probability distribution for the demand, distributions with probabilistic functions, stochastic functions or randomized functions were generally missed. By directly calculating the probability distribution function, the present invention overcomes the shortcomings of such prior art systems.
 Another prime disadvantage of using this back door approach is that it is not repeatable or consistent. For example, if this trial-by-error method is used and the optimum stocking level is determined, it is most likely by random chance; the dynamic nature of service parts supply and demand will make this same result impossible to recreate in the next planning cycle. Likewise, if the back door approach is used and over- or under-service conditions results, there is no ability to determine a root cause of the event because there was no formality in determining it in the first place.
 Figure 6 shows one approach to calculating the distribution function as contemplated by the present invention. First, a planner may collect demand data 610. Preferably, data is collected for a part segment or LDB to represent multiple, similar SKUs. In one embodiment, monthly demand data for one to three years may be collected. To minimize the data needs, a manageable set of candidate demand processes may be selected and used as proxies across other, similar demand processes.
 Second, the data may be analyzed for insights 620. Histograms may be generated to visualize the shape and skewness of the distribution. The data may also be analyzed for auto-correlation errors and independence may be assessed.
 Third, distribution fitting tests may be performed on the data 630. In this step, statistical software may be used to perform fitness tests. In one embodiment of the present invention, 22 predefined probability distributions may be compared with the fitness tests. Such tests may generate a relative score (out of 100, for example) based on the distribution parameters.
 Fourth, comparative analytics may be run to select the distribution function 640. Graphical overlays between the histogram and the probability density function may be performed. Fitness tests (such as the Chi Square test, the Kolmogorov-Smirnov test, and the Anderson-Darling test) may be run against specific probability distribution functions. Based on the results of these and other tests, the final distribution may be chosen for use in the deployment algorithm. While the tests involved in figure 6 are well known in the art, such traditional planning approaches for service parts fail because they apply a standard distribution to a non-normally distributed part. The present invention overcomes this deficiency.
 Using the present invention's method for step 140, a planner generates the function associated to demand and can therefore gain insight from it. For example, if the function is stochastic, the planner would realize that the part number would need to be managed more closely and rigorously. To illustrate the insights that may be gained, refer to figure 7. The top distribution function is a normal bell-curve 710. In such a distribution, one may calculate the reorder point and the safety stock 730 that should be kept on hand in inventory. However, if an inventory part is represented by a Poisson function 720, then the mean and variance the are same value, the reorder point is at a different location and the safety stock that should be kept on hand 740 is much greater.
 The approach described here in step 140 may be less costly to apply than the brute force, back door approach of the prior art systems and the findings may be more accurate, which translates to cost savings for inventory management.
 5. Calculating Target Stock Levels
 As a final step shown in figure 1 's general view of the invention, the target stock levels ("TSL") for the segments are calculated 150 based on the deployment algorithm. This step of the process may answer questions for deployment and replenishment. For example, for deployment, the TSLs may answer how much inventory is required to ensure an adequate level of service. For replenishment, the TSLs may answer when inventory should be order or moved, and by how much.
 To calculate the TSLs at this step of the process, specific algorithm policies may be applied that may perform well over a wide range of demand values and types. Such policies are valuable because in the traditional planning approach to parts inventory management, a z-score is calculated for an area under the distribution curve. The shaded area (see figure 5 ) represents the demand that the planning meets while the unshaded portion represents the amount of inventory that will be stock-outs. Since current systems cannot calculate non-normal distributions readily or accurately, a z- score may not be accurate. The policies in this step offered by the present invention convert the probability distribution function into a planning language that is usable by planners. For example, the planner may plug in the monthly demand for a SKU in units as well as the variation in units to calculate an inventory plan for the part. The accuracy of the present invention may double the savings of inventory costs over the prior art.
 Two such algorithms that may be used are (S-1 ,S) and (s,Q), where "S" stands for inventory level and the first parameter is the inventory level and the second parameter is the order quantity. In this case, (S-1 , S) is a stocking model where the system will order one unit (S) when one unit is used and falls below the level of S. (S- 1 ,S) is also called "issue one, replace one." This algorithm may perform well for low demand and sporadic demand parts, such as a satellite hub for telecomm networks and parts closets for a utility service. On the other hand, the (s,Q) policy may perform well for moderate demand and high volume demand parts where it is desirable that replenishment lot sizes may vary. For example, dealer parts inventory and plant maintenance stockrooms may make the (s,Q) appropriate. These two models explicitly account for demand variability, supply variability, and service requirements. The policies are easy to understand, easy to implement, and may be automated.
 Once the TSLs are calculated using such models as (S-1 ,S) or (s,Q), a planner may also need to use a structured analysis to make a stocking decision (i.e., to stock or not to stock) for individual SKUs. Figure 8 illustrates one example of a structured analysis for the stocking decision, based on an airplane company.
 The foregoing description addresses embodiments encompassing the principles of the present invention. The embodiments may be changed, modified and/or implemented using various types of arrangements. Those skilled in the art will readily recognize various modifications and changes that may be made to the invention without strictly following the exemplary embodiments and applications illustrated and described herein, and without departing from the scope of the invention, which is set forth in the following claims.
 The appendix section of this provisional patent application is a practice aid (in the form of a slide presentation) for one embodiment of the present invention.
 The Appendix starts on the following page.
For large OEMs, asset operators and resellers of capital goods equipment, service parts inventories typically represent significant financial investments. In addition, the time and location availability of these inventories is the primary driver of customer service. Unfortunately, many service businesses have not rigorously or formally managed these inventories - in other words, taken a scientific approach. As a result, both the financial and service performance of these businesses has suffered. Accenture's offering contains the specialized, hard-to-acquire content a business needs to simultaneously improve the financial and service performance of their parts business. It's Accenture's experience that a pure technology solution is not the answer to optimizing these inventories - the right mix of skills, processes and technology are needed to transform the parts business.
The primary purpose of this document is to provide a market offering to our clients to drive financial and operating value in their service parts operations - Provides the toolkit to optimize their service parts planning capabilities - Presents deliverables, benefits and project approach - Presents Accenture client case studies
The secondary purpose of this document is to act as a training tool and job aid for Service Management personnel - Provides details on our methodology - Provides application-specific content for project support
This document also communicates to a broad Accenture audience the capabilities of the Service Management team in service parts management
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Typical Issues Common Business Responses
Typical Issues Common Business Responses ■ Sales and Marketing plans created without ■ Inventory personnel have to over-provision Operations input or collaboration to avoid missing sates Miliini ll ■ Engineering mandates 100% parts availability at ■ The business builds-in obsolescence beginning of new product launch expectations in its financial projections
Sparse documentation or visibility to configuration Technicians will carry excess truck stock Configuratioi of installed equipment and the business expects extra truck rolls βffir No on-going updates to configuration as compensate for lack of configuration maintenance occurs knowledge
Status of open purchase orders and delivery dates Local personnel over-order and hoard parts
Based on Accenture's experience, a parts business will benefit from Scientific Service Parts Management® if they can answer "yes" to three or more of the questions below.
1. Is customer service dropping (fill rates* delivery times, back order duration, number of split orders) while inventory investment is growing? 2. Does the business struggle with answering questions such as, "what should my service levels be?", "how much inventory do I need to support that service?", and "where should parts be stocked in the network?" 3. Is the business using a simplistic, single-echelon stocking network, that is Incapable of offering
© differentiated service levels to specific customers? 4. Are planners and buyers applying the same broad set of rates for deployment and replenishment for all parts, even though the SKU base is non-homogenous? 5. Does obsolete Inventory account for 5% or more of gross inventory? 6. Is there no integrated planning process between the functional departments that influence inventory performance, such as Sales, Marketing, Engineering, Logistics, and Inventory Management? 7. Has the business adopted a mindset that parts forecasting is too difficult to accomplish, and therefore has given up on a demand planning capability? 8. Does the parts business suffer f om adverse supplier actions, such as poor fill rates, lengthy delivery lead times, and mandated minimum order quantities (MOQs)? 9. Are there forward-deployed inventories that are not under perpetual control, or that the business lacks visibility to, such as technician stock or customer site inventories? 10. Are expediting and non-recoverable costs (premium freight, indirect labor, overhead) too high, from fulfilling too many emergency and split orders?
Broad Issue Area Specific Questions Answered c What unique customer segments does the service business serve?
What service parts-specific capabilities does the business need?
Ul assessment Ul ■ Perform inventory analytics and ■ Continue to build and refine ■ Develop initial hypothesis tracking benchmarking hypothesis tracking document document ■ Perform qualitative best practice ■ Finalize business case ■ Construct evaluation tools - assessment ■ Finalize inventory analytics interview guides, site visits, data requests ■ Construct initial business case ■ Finalize best practice assessment structure and rationale ■ Submit data request and schedule ■ Finalize interviews and site visits site visits and interviews Deliverables Deliverables Deliverables ■ Interview write-ups/summaries ■ Working session documents ■ Assessment scope model ■ Site visit write-ups/summaries ■ Refined hypothesis sheet ■ Initial hypothesis sheet ■ Complete data request B Finalized business case ■ Data request templates, interview ■ Initial set of inventory analytics, β Finalized analytical package with guides benchmark report and best practice detailed quantitative and qualitative assessment Interview and site visit schedule assessments ■ Business case structure and format
The quantitative and qualitative assessment will be driven by the inventory analytics, interview guides, and best practices that are components of our toolkit.
Inventory Management 1 Deployed inventory segmentation Parts movement profiling j SKU growth and proliferation c Network inventory fragmentation
All of these tools are focused on the unique characteristics of the service parts business - not manufacturing operations or the finished goods supply chain. Representative Best Practices and Interview Guides
Process: Inventory Management Sub-process: Deployment Planning and Execution Description of Current Recommended
Using input from key personnel and our analysis, a final set of hypotheses are confirmed; those that cannot be validated are rejected or changed.
Data Requests Our toolkit contains data request templates for different parts environments: Wholesale distribution > Retail aftermarket
Optimizers" • Selectively invest in capabilities to drive volume • Look for low-cost methods of generating new revenue relationships • Reevaluate economic relationship on a regular basis
In addition to customer segmentation models, part segmentation models are also built, which are used to develop customized planning rules. ^ - -- — VMI Decision Framework " Criticality Definition - how a stock-out High VMI program would only be High Poor candidate for VMI; will impact a customer's operation successful with effective use internal DRP system to ," High - causes unit down demand collaboration manage; establish safety Medium - equipment has limited program and time-tested stock parameters and window of operation relationship in place service levels Low - little or no impact Criticality Criticality Excellent candidate for internal replenishment VMI; Business should Unit Usage Definition - a relative approaches such as DRP measure of sales or issuances per time α> establish service are best approach with requirements for vendor to period O Low levels follow Low moderate service Create an index where the fastest CO mover = 100% and index all other High VMI candidate in the long High Poor candidate for VMI; parts to that run after effective supplier optimize single-item / partnership and single-location stocking Usage Variation Definition - a relative confidence has been controls and review measure of how volatile a part's developed performance regularly demand is compared to its volume Criticality Criticality Coefficient of variation (Cv): ratio of Good candidate for ERP the standard deviation and mean Excellent candidate for auto-replenishment; fine- > Cv = (standard deviation / mean) * VMI; establish consensus tune ROP/ROG/S5 100 per time period f replenishment process and parameters over time to > Cv < .25 = smooth demand Low parameters with vendor Low minimize inventories Cv < .50 = erratic demand > Cv > .50 = volatile demand Low Usage Variation High
Multi-echelon Deployment Strategy
Best Fit IMPLICIT METHOD - Using a calculation process to imply the optimum ■ If values for input parameters are available for use service level as a function of stock-out costs and carrying costs. a If business has no existing customer service strategy or doesn't understand cost-to-setve concepts EXPLICIT METHOD - Use management expertise to set the acceptable * Explicit methods are valid if management is capable minimum number of planned stock-outs. of assigning discrete, differentiated service levels to customers r Cycle Service Level- Specified probability [P of no stock-outs per replenishment cycle a For parts with numerous periods of zero demand ■ For a business where back-orders are common - [P<] - Generally known as an availability measure can be viewed as the probability of no back-orders - Includes the probability of periods having zero demand during the year
Fill Rate Level - Specific fraction of demand [P2] to be satisfied routinely from a Valid for higher-volume parts the shelf - Good fit for distribution system where replenishment - Most common of all measures - assumes no backorders or lost sales lead times are fixed or very costly to change
Ready Rate - Specific fraction of time [P3] during which net stock is positive a Complex to use to determine optimal inventory policy - Least common measure - has application in emergency environments < Applicable for end-of-life products
Our approach recommends using an implicit, quantitative method for critical and high-value items, while using an explicit method for less-critical or less profitable parts.
Characteristics of Cost-Service Model *■ Graph is primarily illustrative - increasing service (e.g., number of parts) is described by a geometric cost function >lf demand is highly fragmented (e.g., widely dispersed asset base) then the cost function becomes exponential at increasing service levels -Rationale for higher service levels is driven by higher stock-out costs (internal or external) ' For feasibility, the model can be applied to groups of similar parts instead of each end item Executing this model requires stochastic
Annual Holding Cost = Holding Cost Rate (h) x Cost per Unit x Number of Units
Scientifically quantifying the k factor is the most important factor in enabling the business to meet service levels while maintaining minimum inventory levels.
Determining [k] for a P2 service measure (fill rate level):
Another category of planning models includes multi-echelon optimization techniques. Applying these models is only possible through advanced planning systems capable of this type of decision support. Advanced Optimization Techniques Representative Vendor 1
A third category of planning models is time-phased replenishment planning (TPRP). This approach can combine transactional replenishment with deployment and lot sizing techniques across a high-volume, multi-echelon parts network. TPRP Structure
Atlanta Central Stocking PDC San Diego Customer Item ID: C01140039 Depot Item Descrip: SS Coupling Ratio Tap 90/10 1x2 Delivery Time: 40 days Internal move Jan Feb Mar Apr May Jun and external 2003 2003 2003 2003 2003 2003 purchase
TPRP will integrate service demand across multiple field locations, and can be used to synchronize production operations for internal parts The effectiveness of single-item, single-location models is limited due to their "stable demand" assumption - More basic models require updating their parameters (s,S,Q,R) if demand is fundamentally shifting - TPRP can dynamically account for shifting demand and adjust safety stocks TPRP can be used to show planned shipments, allowing for outbound shipment planning and freight optimization TPRP can incorporate demand from all sources: parts forecasts, internal orders, customer orders, and field demand for failed parts
In addition to assigning the best fit planning model, creating the LDBs and part segments also allows the business to align the best fit forecasting model to a part or customer segment. Classes of Forecasting Models
When should inventory be ordered or moved, and by how much?
When calculating the TSL, we will use specific policies, such as the single-item, single- location models (S-1 , S) and (s,Q). Both of these models perform well over a wide range of demand volumes and types. Example Control Models Advantages > Both of these models can support a wide range of parts environments - (S-1 ,S) performs very well for low- and sporadic-demand parts - (s,Q) performs very well for moderate- to high-volume parts Demand variability, supply variability and service requirements are all explicitly accounted for >• The policies are easy to understand and implement, and can be automated in a TPRP system Appropriate Applications These models can be used in either customer-driven demand businesses, or in true maintenance environments - (S-1 ,S) is appropriate for deploying slow-moving items to forward stocking locations (e.g., MRO line station, satellite hub for telecomm network, parts closet for utility service) - (s,Q) is appropriate for higher-volume parts where its desirable that replenishment lot sizes can vary (e.g., wholesale PDC, dealer parts inventory, plant maintenance stockroom)
Input Variables Required 1) « Demand or failure rate [?] • Number of parts in service [N] • Supply replenishment time [T]
-4 ■ Variation in replenishment time [s2] • Probability a new part is defective [p]
Policy Description ■ Optimal policy for slow moving, high value inventory ■ Will determine the TSL needed to meet a desired ■ Order whenever a unit is used or sold from stock service level ■ Often called "issue one, replace one," or lot-for-lot ■ Easy to understand and implement (L4L) B Can be combined with TPRP for automation ■ Order an amount that returns the inventory level to the order-up-to-level of [S] Policy Applications ■ Order amount is fixed and is typically has a value ■ Where the optimal order quantity is 1 or close to 1 of 1 or near one a Where high carrying costs outweigh ordering and transportation costs
Input Variable Name Description Demand or Field ■ For maintenance environment: percentage of installed units that fail per time Failure Rate period due to the specific part m For distribution business: percentage of total demand that the business will experience per time period for the specific part N Number in For maintenance environment: number of installed units supported by the Service specific spare part For distribution business: number of customers with equipment supported by the specific service part Supply ■ Average time for the location to be replenished from a supply source - this is Replenishment location-dependent <• Supply source could be internal or external Time
Variation in Supply " Should be calculated using empirical data for the supply source of the specific ST - R.eplenishment ■ ζ C?ortmputed , as t .he variance, wh , .ic ,h . is th _.e sum of the squared deviations part used for a repair event will be defective out of the package P Probability a New ■ Probability that a Part is Defective ■ Should be calculated using empirical data
Review the overall (S-1 ,S) deployment algorithm
Calculate the [k] value
Calculate the [S] value
rical, handling, and [D]
Input Variable Name Description Same as (S-1 ,S) ■ See previous slides s*,P
-4 Unit Replacement * '* purchased, the acquisition cost of the part used by the business in their COGS oe OS calculation ■ If manufactured internally, the fully-loaded standard unit cost of the part
Inventory Carrying * See previous slides Cost Percentage B Used as a Pθrcenta9e input
Replenishment * All of the direct and indirect labor, materials, and allocated overhead consumed Order Costs in fulfilling a replenishment order ■ Freight, warehouse labor, clerical labor, etc.
Annual Unit a Estimate annual unit demand (sold or consumed) Demand
For environments where supplier lead times are very lengthy and/or volatile, the safety stock equation can be modified to account for that duration and variation. This is most commonly applied to external replenishment orders, not internal orders.
Demand Uncertainty Supply Uncertainty
For environments where supplier lead times are very lengthy and/or volatile, the safety stock equation can be modified to account for that duration and variation. This is most commonly applied to external replenishment orders, not internal orders, (continued) ROP and Safety Stock Formula
Example Lot Sizing Algorithms
New tools for daily execution and process management icfua/ c//'e/7r model Working software models to make the new used by buyers and planning approaches sustainable planners to plan New analytics and metrics to monitor financial internal and external and operational performance replenishment orders, and to manage slow- Fact-based methods for setting performance moving SKUs targets
Scientific Service Parts Management® contains Accenture's proprietary tools and methodology for parts inventory optimization; it has delivered hard-dollar benefits for multiple clients. Asset Definition Asset Inventory Accenture's proprietary methodology and tools for Accenture's proprietary methodology and tools optimizing the planning infrastructure in a service Data requests, interview guides parts business Best practices, industry benchmarks Provides specialized analytical approaches that customize planning processes and parameters to Statistical analysis tools for sampling, parts support service levels and operational goals segmentation, inventory analysis, and probabilistic modeling Takes a lifecycle approach to designing, testing and implementing new capabilities to deliver Comprehensive survey of parts planning breakthrough levels of cost and service performance models in the parts business Deployment algorithms to meet a variety of parts demand processes Client Applications (Internal Use Only) Test and deployment procedures Case New Holland (CNH) GE Parts Corning Photonics Technology Division (PTD) Pratt & Whitney Cingular US Airways Pacific Bell
Asset Definition Asset Inventory A software toolkit used to assess the strategic Creates a comprehensive business process, alignment of a service business technology and organization assessment for a Detailed strategic best practices on service strategy, service business. oe
-4 field logistics, parts management, and customer Driven by an in-depth "strategic best practices" interaction framework across the enterprise. Cross-industry financial benchmark database and Provides detailed financial and shareholder proprietary economic profit calculator value analytics. Business case engine integrated with automated Includes both a financial and operational assessment scorecards benchmark database for OEMs and service businesses. Client Applications (Internal Use Only) Culligan Water Ricoh Family Group Case New Holland (CNH)
To aid our practitioners and clients, Accenture has developed and used several implementation acceleration tools to facilitate better, faster and lower-cost implementations. These assets are part of our Service Parts Management offering. Asset Definition Asset Inventory Delivers a comprehensive application architecture Business case tool set and scorecard and integration requirements for a best-in-class Industry-specific service parts management parts planning capability process model oe oe Contains our program management and project Deliverable templates and samples delivery methodology for design and deployment of the solution Preconfigured methods delivery manager (MDM), fast-start implementation jump-start kit, Contains detailed case study on similar and configuration guidelines and demo implementations Interface design and data mapping for SAP Reduces the time, design, and implementation risk integration of complex projects Training material for templates for service parts Client Applications (Internal Use Only) management processes (i2 Service Parts Planning, Service and Budget Optimizer, and Daimler Chrysler Demand Fulfillment) Applied Materials Siemens Power Generation - Westinghouse Direct TV Harley Davidson Motorcoach
Asset Definition Asset Inventory Accenture's comprehensive suite of offerings all MRO 3-day Financial Assessment - A developed to optimize and reengineer the MRO structured, in-depth analysis of an MRO organization business used to identify value targets Has been developed and implemented at multiple MRO Process Model - Uses cross-industry clients across different maintenance environments experience and commercial best practices to Contains specialized, client-tested assets across the present a comprehensive process model for dimensions of technology, process and organization design and assessment purposes. Provides a point-of-view on MRO-specific MRO Supply Chain Assessment - A value requirements for CRM, supply chain, back-office, targeting exercise focused on MRO supply strategic management, product management, etc. chain areas, supported by a range of assets such as business cases, analytics, metrics, Client Applications (Internal Use Only) interview guides, and assessment tools
Accenture has developed extensive parts-specific thought leadership through our client work and participation in industry events and committees.
Asset Definition Asset Inventory (sample) Accenture's global supply chain practice has "Making Collaborative Planning Work in the developed thought leadership and specialized Real World: A Case Study of Harley-Davidson assets that are focused on service parts and Its Dealers." APICS Conference, Doug management Derrick, 2002. The thought leadership has been developed as "Service Management: Building Profits After the published articles, white papers, and conference Sale." Accenture Supply Chain Perspectives presentations White Paper. Mike Dennis, Dr. Ajit Kambil, Currently, we have Accenture executives that are 2003. members of the APICS Special Industry Group (SIG) "The Forgotten Supply Chain: How OEMs Can on Service Parts and Remanufacturing Escape the Value Squeeze by Optimizing the Our Knowledge Xchange® is a global repository of Aftermarket Business." APICS The thought leadership and client work that contains our Performance Advantage, Doug Derrick and best thinking on service parts management Robert Jacoby, 2003. "Extending the Service Efficiency Frontier with New Service Management Models." Accenture Outlook and ASCET Volume 2, Mike Dennis and Adam Wolf, 2002. "Increasing Customer Loyalty Through Service Parts Management." ASCET Volume 1 , Tom Jenkins, 1999.
Optimizing the aftermarket parts supply chain by reengineering supply, demand, and inventory core processes
© © annually through better planning processes 80% improvement of spare parts forecasting accuracy -J $20M reduction of purchasing/material costs by converting to blanket PO's based on forecast rather than spot buys Approach and Solution j Improved customer service levels: m The Spares division is responsible for supplying consumables and capability to deliver 'downs' parts 93% of spare parts for the repair, maintenance an overhaul of their the time within 4 hours, 95% within 24 customer's wafer fabrication systems hours and 98.5% within 48 hours c Organization is a reactive culture focusing on expediting to fulfill Improved fill rates due to having the right demand and perceived demand part at the right time, in the right place Program was focused on delivering a comprehensive solution to 5 Automated customer order sourcing to improve order management, forecasting, inventory management quickly locate spare parts - Develop regional DCs for fast moving parts and one NDC for slow Accenture Contact moving materials ~> Duane H. Laun
-,! * 1 ji. —
I -i. *
Overview Founded in 1972, SAP is the largest enterprise solutions vendor in the market. They have provided business solutions for all types of industries and for every major market for the last 30 years.
-4 Headquartered in Walldorf, Germany, SAP is the world's largest inter-enterprise software company, and the world's third-largest independent software supplier overall. SAP employs over 28,900 people in more than 50 countries. The company boasts 12 million users, 60,100 installations, 1,500 partners, and 23 industry solutions. SAP's primary product offering is the mySAP Business Suite, which is the broad suite of business applications they sell across enterprise functions. The SAP APO 4.0 module (advanced planning and optimization) is the planning component to the SAP SCM 4.0 solution. The APO solution has been deployed for service parts environments.
Selected Customers (parts solutions): ■ Porsche ■ Pratt & Whitney ■ VW/ Audi
Application Summary With SAP APO, SAP has combined the ERP execution power of the SAP The SAP Advanced Planner and Optimizer builds on the SAP Business R/3 System with advanced data analysis and supply chain management Framework to improve information flow by incorporating real-time
© oe tools. Because SAP has built a robust integration layer between SAP APO collaborative decision support, advanced planning, and optimization into and the underlying execution system, SAP APO can gain immediate and the SAP R/3 System. SAP APO uses a powerful memory resident seamless access to Online Transaction Processing (OLTP) business data. analytical engine and highly specialized, highly configurable data objects While the data objects contained within SAP APO are, in most instances, that offer major new components: structurally optimized instances of OLTP data, they remain synchronized through a series of real-time triggers and messaging, a task that is - Supply Chain Cockpit seamlessly accomplished through the integration services of the Business - Demand Planning Framework. SAP refers to this technique as "semantic synchronization". - Supply Network Planning - Production Planning and Detailed Scheduling The SAP APO server also integrates with the SAP Business Information - Global Available-to-Promise Warehouse using this same mechanism providing access to vital business - Supply Chain Cockpit decision data. Advanced Optimization Techniques and Technology In addition to highly specialized data objects, SAP APO uses a library of The Demand Planning component is a toolkit of statistical forecasting advanced optimization algorithms and a high performance, memory techniques and demand planning features that helps you create accurate resident data processor to perform planning and optimization. You can forecasts and plans. Demand Planning gives you a sound understanding configure SAP APO to provide task-specific, industry-specific, and of all the factors that affect demand, delivering context-based demand company-specific optimization, automated decision, and real-time event planning, which raises forecasting to a new level of sophistication and notification to the under-lying business processes. accuracy. You can analyze actual demand using a variety of tools, such as multiple linear reqression, and incorporate causal factors like orice.
Overview The company (formerly LPA Software) provides software that companies use to manage enterprise service operations. Its products track contracts and parts, schedule workers, and manage repair center, maintenance, and field service operations, including applications for asset management and recovery. Xelus also provides consulting, training, and other services. Customers include 3Com Cisco Systems, and Dell. Xelus has expanded beyond its roots in the telecommunications industry into fields including aerospace and defense, transportation, and utilities. Founded in 1972 the company's investors include MMC Capital and Oak Investment Partners
Selected Customers (parts solutions): Siemens SBC Cisco Systems Verizon IBM Honeywell Samsung Dell Xerox 3Com BAE Systems British Airways Subaru Compaq Sun Microsystems TRW Aeronautical Systems Hewlett Packard Lockheed Martin
Sales: €125.7 million (2002) Company Type: Private
Overview Finmatica acquired Mercia Software in June 2002 FINMATICA is an Independent Software Vendor (ISV) which develops products and solutions for Supply Chain Management, Finance, Information & Internet Security, Document Management and Transportation Management.
FINMATICA has recently acquired Mercia, a leading provider of advanced planning systems for service parts businesses.
Selected Customers (parts solutions): ■ Saturn ■ Canon ■ GM ■ Honeywell ■ Daewoo ■ Rolls Royce ■ TRW
Key Modules and Solutions: Potential Differentiators:
■ Demand Planning ■ Develop leading-edge practices for the forecasting of slow-moving
■ Strategic Inventory Planning and sporadic demand parts
■ Distribution Requirements Planning ■ Positions itself as the leader in forecasting for stochastic service These modules are part of MerciaLincs parts
Application Summary Finmatica acquired Mercia Software in June 2002
MerciaLincs allows organizations to take control of demand in order to plan inventory, distribution and requirements in the most efficient manner. By using a combination of sophisticated, low intervention statistics and subjective input, MerciaLincs demand planning easily handles difficult situations such as promotions, new product launches, obsolescence, etc. By balancing and optimizing inventory throughout the network, maximum customer service can be delivered for the minimum investment in stock.
Overview Servigistics is a provider of global service parts management solutions specifically designed to decrease costs, increase profitability and improve customer loyalty by streamlining and automating complex service parts operations. Servigistics solutions have been tested and proven by leading global companies, including Axcelis, Cray, Dell, EMC, IKON Office Solutions, Subaru, Toshiba Medical Systems and UPS.
Servigistics mission is to help leading companies rapidly improve customer loyalty and profitability through superior service parts management.
Selected Customers (parts solutions): ■ AGFA ■ Dell Toshiba Medical Systems ■ Akibia ■ EMC UPS ■ AUSPEX ■ Ikon ■ Axcelis ■ Patterson Dental Company ■ CNT ■ River Stone Networks ■ Cray ■ Subaru
Application Summary Servigistics 7.1 is the optimal solution to deliver your service Easy to use, the global solution delivers the key functionality parts strategy, making certain you have the right part, at the required to support your core businesses processes and right place, at the right time. complex service parts requirements, including: Our proven and tested Service Parts Management solution Sophisticated Optimization Logic - plans the right stock, was built from the ground up with one goal in mind - to deliver at every echelon and every location, from central to field beyond expectations and help leading companies like yours rapidly improve customer loyalty and profitability through: What-if Analysis - proactively estimates the impact and cost of various service strategies and decisions before they Information Consolidation - seamlessly integrates are made. information from multiple, world-wide transaction systems into a single decision support system Demand & Lead Time Forecasting - considers seasonality, multiple demand streams and leading indicators Inventory Optimization - advanced optimization to deliver highly accurate forecast science replaces manual processes to deliver optimal inventory plans to transaction systems Complex Parts Chaining & Alternate Parts Planning - incorporated into all processing and decision support to Event Management - configurable logic enables users to maximize inventory options proactively identify important events before they turn into critical problems Exception Analysis - focuses users on critical business issues Global Visibility & Analytics - powerful functionality provides complete parts visibility and analytics to discover, adjust and Executive Dashboard - delivers enhanced management manage key processes and exceptions on a local or global and control via hierarchical views, detailed drill downs and basis performance analyses
Overview Manugistics Service & Parts Management describes its value propositions as: manage your service business as a profit center; provide highest levels of customer service at the lowest costs, while optimizing asset uptime; improve utilization and capacity of operational and production assets; cut service parts inventories, service costs, and purchasing overhead costs; improve forecasting and deployment of service parts and resources; provide inventory visibility across service parts stocking locations; profitably manage part allocations between production and service; and, optimize the complete asset repair loop
Manugistics Service & Parts Management solution provides the integrated set of capabilities needed to effectively manage service and parts operations. Features such as statistical forecasting, inventory optimization, and multi-site finite capacity scheduling help ensure your ability to optimally deploy spares, consumables, and repair resources to meet the needs of both planned and unplanned activities.
Selected Customers (parts solutions): ■ Boeing ■ Defense Logistics Agency ■ Harley Davidson ■ Mitsubishi Motors
Application Summary Service network planning: The service network is designed to determine Service delivery management: To ensure on-time deliveries, parts plans can be locations of repair parts, field technicians, repair depots, central inventory executed, purchase orders created, and shipments monitored. At the same time, locations, and field stocking locations, in order to balance customer service fulfillment of service calls is based on optimizing service technician dispatch goals and asset uptime with associated costs. queues, technician skill sets, customer priority, contractual response time commitments, and customer location. Service and parts pricing: Pricing for service technician labor, service parts, and service agreements is determined in order to help maximize profitability Reverse logistics: Inbound assets must be forecast and tracked, whether they are for each market segment being serviced. from planned or unplanned repairs, overhauls, customer returns, warranty services, damage, recalls, or other reasons. Repair demand and parts planning: Many factors, including usage patterns, buy versus repair estimates, failure rates, maintenance and repair Collaboration and integration: Service part procurement planning and execution schedules, mean time between failure (MTBF), and mean time between requires supplier collaboration; contract management and service support requires unplanned repair (MTBUR) are used to create an optimal plan for preventive customer collaboration. Manugistics provides an open architecture to support such and overhaul maintenance. internal and external integration. Allocation management: To meet customer priorities and improve overall Event management and analysis: Time-phased event management and critical margins, parts must be allocated between service and production areas, path resolution pre-empt problems. Analytical reporting by part, asset, and while parts and labor must be allocated between the factory service shop commodity and network-wide visibility of parts, materials, and repair and overhaul and field service. schedules proactively alerts maintenance and service personnel about disconnects in capacity, parts, tools, and materials demand and supply — and provides Resource scheduling: Planned work and forecasted non-planned work, resolution paths. scheduled according to simultaneous consideration of resource availability and parts availability, helps ensure alignment of asset planned downtime, Sourcing, content, and knowledge management: Management of service parts capacity, repair and overhaul time needed, labor availability, technician lists, specifications, bulletins, illustrated parts catalogs, and supply sources to capability, parts, materials, tools, customer priorities, and contractual service support service technicians and purchasing allows companies to capture asset 'as- obligations. maintained' histories, planned resource and parts needs for asset repair types, and
Overview i2 Service and Parts Management is designed to enable enterprises to maximize the utilization of parts, people, budgets and facilities so that they can attain key performance objectives such as high customer service, market leadership, low operating costs and profitability Capabilities include: - Optimize inventory levels using proven parts criticality methodology. - Optimize the tradeoffs between inventory levels, budget and customer service level targets - Determine the optimum mix of parts while considering storage constraints. - Enable service organizations to successfully and profitably service more customers per day. - Respond more effectively to customer demand with the one solution that integrates planning, order execution, and logistics i2 differentiators include: • Solutions deployed in a modular, independent fashion • Functionality tailored for service parts planning • Forecasting techniques • Replenishment planning algorithms • Optimization of stocking levels and re-order points using mixed integer linear programming • Proven installations realizing quantifiable value Selected Customers (parts solutions): ■ DirectTV ■ Southwest Airlines ■ Toyota Motor Sales
Application Summary i2's service parts product offerings are supported by two applications: i2 Service Budget Optimizer can help companies: Service Parts Planner (SPP) and Service Budget Optimizer (SBO). - Determine optimized inventory levels for customer service targets i2 Service Parts Planner can help companies: - Rebalance inventories - Recommend replenishment policies at a part location level - Accurately model complex service chains - Improve forecast accuracy Service Budget Optimizer maximizes return on service assets, provides a - Minimize inventory required to meet customer service targets quantitative basis for determining budget and inventory deployment across - Efficiently manage large volumes of parts a service parts network, and can be integrated with tactical customer interaction management planning tools. Many planning tools available today can only support the simple situation of a single stocking location, which is inadequate for large Using Service Budget Optimizer planners can view existing inventory at all service organizations with multiple stocking locations and a complex locations and make inventory decisions based on budget and customer supply network. constraints. Service Parts Planner forecasts demand for spare parts based on historical data and equipment usage and failure rates, sets parts target inventory levels, and generates replenishment plans that consider the forecast for repairable and returned parts while considering alternate parts.
1 1 IV- Sales: Unknown Company Type: Private Employees: Unknown Headquarters: Philadelphia, PA http://www.mcasolutions.com
Overview MCA's Service Planning & Optimization solution has earned a prestigious National Science Foundation (NSF) Innovation Award. The NSF has described MCA's technology as "one of the best examples of research that has influenced practice." Recently the NSF further substantiated MCA's breakthrough developments by awarding MCA its second NSF Innovation Award.
Service Planning & Optimization (SPO™) Product Suite: MCA Solutions' SPO suite is the only solution on the market that enables you to optimize resources in a multi-echelon service supply chain network. Its integrated, web-centric modules were designed with 25 years of deep industry expertise to address all of the challenges you face every day.
Selected Customers (parts solutions): ■ IBM ■ Cisco ■ Boeing
Overview Baxter Planning Systems, Inc. was founded in 1993 to fill the gap for planning and logistical solutions specifically targeted to the service parts industry. The key is Baxter Planning System's aftermarket service parts planning and logistics software. Our flagship product, Prophet by Baxter, is a suite of applications that quickly helps companies turn their service parts organization from a cost driver into a profit center.
Baxter's solution has the following exclusive features: • Cost-Based Planning: Our solution calculates the true cost of stocking each part, including "hidden costs" like the cost of stockouts. • Profit-Centered Allocation: Prophet by Baxter gives you the tools to replenish stock based on the actual profitability of each part in each location. • Advanced Product Forecasting: Baxter allows you to map stocking levels to product lifecycles so you don't stock parts that are past their useful life. • Lower Cost of Ownership: Because Baxter offers a hosted solution there are no upgrades to buy, no software to maintain and no drain on IT resources Selected Customers (parts solutions): ■ Agilent ■ Enterasys Philips Medical ■ Lucent Technologies ■ Hewlett-Packard Silicon Graphics ■ Radiant ■ Network Appliance Sony
Application Summary Prophet by Baxter™, an integrated software suite, is a planning Distribution Planning solution focusing on the unique complexities of the after-market, ■ International Site Planning optimizes placement of warehouses and service, repair, and spare parts industries. With increased customer stocking locations. demands for a quick response, often within hours, manufacturers and ■ Optimizations between stockouts and inventory ensure the highest service organizations must have planning tools that enable a level of customer satisfaction. proactive response to changes in customer requirements and/or ■ Extensive modeling of the part, product, customer and location material availability. Prophet by Baxter translates forecasts, demand environment ensures inventory is in the optimal location. and material availability into what to buy or repair, when to buy it or ■ Optimized replenishment/ redeployment ensures response times are repair it, and where to have it in order to meet demand. Prophet by covered. Baxter also optimizes where actual warehouses and stocking locations should be located, based on service contracts and required Supply Planning response times. ■ End of support life planning predicts material requirements. ■ Lifetime buy analysis ensures future requirements are met. Forecasting ■ Supply sourcing decisions such as buy, internal repair, vendor repair, ■ Product-based forecasting, utilizing product life curves, minimizes and/or whole unit teardown provide for least cost sourcing. backlog and excess. ■ Extensive trending and adjustment logic increases forecast accuracy. ■ New product introduction ensures no lapse in coverage.
Our best practices package is one component of our capability assessment, and is a comprehensive tool used to examine the entire parts supply chain. Best Practices Framework
1. "An Introduction to Statistical Methods and Data Analysis tøngrtecker, Michael T.; Ott, Lyrnan. Brooks Cole, 5th Edition. December, 2000. 1 ,184 pgs. 2. "Factory Physics." Hopp, Wallace J; Spearman, Mark L Irwin McGraw-Hill, 2nd Edition. 2000. 720 gs. 3. "Foundations of Inventory Management" Zipkin, Paul H. Irwin McGraw-Hill. 2000. 514 pgs.
-4 4. "Integrated Inventory Management." Bernard, Paul, Oliver Wight Manufacturing Series, John Wiley & Sons, Inc. 1999, 611 pgs, 5. "Inventory Management and Production Planning and Scheduling Peterson, Rein; Pyke, David F.; Silver, Edward A. John Wiley & Sons, Inc., 3rd Edition, 1998, 754 pgs. 6. "Manufacturing Planning and Control Systems." Berry, William L; Vollmann, Thomas E.; Whybark, David C. McGraw-Hill Publishing, 4th Edition. 1997, 896 pgs. 7. Operations Management: Strategy and Analysis." rajewski, Lee J.; Ritzman, Larry P. Addison- Wesley Publishing Company, 2nd Edition. 1990. 871 pgs. 8. Operations Research in Production Planning, Scheduling, and Inventory Control." Johnson, Lynwood A.; Montgomery, Douglas C. Georgia Institute of Technology. John Wiley & Sons, Inc. 1974. 525 pgs.
Priority Applications (4)
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|WO2005122076A8 true WO2005122076A8 (en)||2006-04-20|
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|PCT/IB2005/002418 WO2005122076A8 (en)||2004-06-07||2005-06-07||Managing an inventory of service parts|
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|WO (1)||WO2005122076A8 (en)|
Families Citing this family (23)
|Publication number||Priority date||Publication date||Assignee||Title|
|US7647255B2 (en) *||2004-01-23||2010-01-12||United Technologies Corporation||Rotable inventory calculation method|
|US8224717B2 (en) *||2004-06-07||2012-07-17||Accenture Global Services Limited||Managing an inventory of service parts|
|US20070016496A1 (en) *||2005-07-11||2007-01-18||Bar Hena M||Spare plug management system|
|US7996284B2 (en) *||2005-07-11||2011-08-09||At&T Intellectual Property I, L.P.||Spare plug management system|
|US8200521B2 (en) *||2005-08-24||2012-06-12||Sap Aktiengeselleschaft||System and method for determining demand distribution for safety stock planning|
|US8165914B2 (en) *||2006-06-06||2012-04-24||Logistics Management Institute||Method of determining inventory levels|
|US8600843B2 (en) *||2007-05-18||2013-12-03||Logistics Management Institute (Lmi)||Method and computer system for setting inventory control levels from demand inter-arrival time, demand size statistics|
|US20080027835A1 (en) *||2006-07-31||2008-01-31||Caterpillar Inc.||Methods for maintaining inventory records between audit periods|
|US20080046344A1 (en) *||2006-07-31||2008-02-21||Caterpillar Inc.||System and method for managing inventory control processes|
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