Understanding the (S, s-1) Inventory Policy


In several articles distributed across different SCM Focus sites, the (S,s-1) inventory policy is discussed. For this reason, it made sense to create an article which explains what it does. The following was written by Wayne Fu.


In Muckstadt’s book, section 1.1.2, it explains briefly about the (s,S) policy.

  1. s is normally the reorder point
  2. S is the order up to level

When inventory position (which is on hand plus on-order minus backorder) falls to or below s, it triggers an order to raise the inventory position to S.

And (S,s-1) is just a specialized form of (s,S).  Basically it is s-1 only. In section 1.2, Muckstadt states the fundamental assumption of his model. He assumes the costs of parts are high enough to be managed by (S, s-1) policy. (S,s-1) is an ordering policy basically says if the inventory level is one below (S-1), place an order to bring inventory level to S.

It is very commonly used in long lead-time environments such as aerospace.

Author Thanks
I wanted to thank Wayne Fu for his contribution. I was not aware of many of the details which are described above, and I think this should be of interest to anyone who practices in this field.
Author Profile
Wayne Fu is a Senior Product Management in Servigistics.With operation management background, Wayne has worked in service part planning domain for more than a decade. In Servigistics, he led the research and development of various areas like install-base (provisioning) forecasting, inventory optimization and distribution planning. Currently, he is focusing on the effectiveness of forecast techniques in Last Time Buy.

Important Service Part Multi Echelon Inventory Optimization Books


It is very interesting to find out about the origins of service parts inventory optimization. It’s rarely discussed, though I thought I would write and article on this topic.

Guest Co-Author

For this post we have a guest co-author. Wayne Fu, a Senior Product Manager at Servigistics provides a perspective on what he considers some of the pioneering works in multi-echelon inventory optimization.


Mult-Echelon Inventory Optimization is interesting but relatively more challenging arena than other popular optimization practice like production scheduling. This is due the fact that the performance measurements in inventory management (fill rate, back order, availability etc…) are largely non-linear. Thus some common methods like linear programming are not easily applied to the problem. And because it is a solution that requires a holistic “hovering-view,” the problem cannot be segregated in smaller scope very easily. This fact places challenges on performance and scalability. These issues are even more severe in the service part environment due to larger volume.

One of the major publish in Service Part Inventory Optimization is the “Optimal Inventory Modeling of System” by Craig C. Sherbrooke. Sherbrooke laid out both the METRIC and Vari-METRIC algorithms in this book. They are generally recognized as the foundation of many heuristic based optimization algorithms today. (for more on “heuristic based algorithms” see this post below.)


METRIC is specifically designed to address the multi-echelon issue while Vari-METRIC is an enhanced version of METRIC to resolve multi-indenture problem.

METRIC and Marginal Analysis

In simplified terms, METRIC is an algorithm based on marginal analysis. This approach still recognized as the most accurate and effective approach, but is very computationally intensive. Several issues merged in practice of METRIC also. Sherbrooke generally assumed the inventory policy to be (S,s-1) and operated under relatively low demand environment. And it is dominated by fill rate (no stock out) measurement. Thus, the application of METRIC is limited to specific industries that fit this model, most notably aerospace and defense. Vari-METRIC, on the other hand, placed more emphasis on availability. Fill rate applies to any supply planning environment, whereas, availability is primarily used in service operations. A service level agreement (SLA) will guarantee an availability of unit such as a plane or piece of industrial equipment. Availability is then used to model this the uptime of this equipment, which is dependent upon the fill rates of a variety of service parts that support that unit, all of which have different failure rates, part costs, etc.. (to read about SLAs, see this post)


METRIC measures the fill rate and other measures at the intermediate location, which ended up being a highly debated aspect of METRIC.











At What Locations to Measure Service Level?

In fact, where to measure service level is an extremely important topic in and differentiation between inventory optimization products. Currently, the vast majority of supply planning organizations measure service level at their internal locations in addition to measuring it at the customer location.











As we will see in the following section, another researcher who followed Sherbrook’s work changed this location service level measurement assumption.

Measuring at the Intermediate Location

The second most influential publication in service parts inventory optimization“Analysis and Algorithms for Service Parts Supply Chain” by John A. Muckstadt. Dr. Muckstadt’s model could be said as an updated version of Sherbrooke’s. It is an availability based algorithm and avoids the need of approximations required in METRIC due to convexity problem. Muckstadt also proposed some novel approaches to reduce the performance and scalability issues during implementation. Perhaps most importantly, Muckstadt moved always from measuring the satisfaction at intermediate level location, measuring the customer facing demand only. However, Muckstadt is still based on the (S,s-1) order policy and assumed in low demand environment. This means that Muckstadt’s model faces some of the similar challenges as do Sherbrooke’s in broader application. (for a description of the (S,s-1) order policy, see the link below:



The previous paragraphs were an overview of two of the most important publications in service part inventory optimization and distinct from finished goods inventory optimization. Sherbrooke’s and Muckstadt’s algorithms are used in service parts planning products to this day, with alterations here and there.

Author Thanks

I wanted to thank Wayne Fu for his contribution. I was not aware of many of the details which are described above, and I think this should be of interest to anyone who practices in this field.

Author Profile




Wayne Fu is a Senior Product Management in Servigistics.With operation management background, Wayne has worked in service part planning domain for more than a decade. In Servigistics, he led the research and development of various areas like install-base (provisioning) forecasting, inventory optimization and distribution planning. Currently, he is focusing on the effectiveness of forecast techniques in Last Time Buy.







Heuristic Based Algorithms Explained


This post documents an email discussion between myself and Wayne Fu regarding heuristic based algorithms.

Question for Wayne Fu

What is a heuristic based optimization algorithm? I thought that heuristics were one form of problem solving, and optimization was another. How is a heuristic based algorithm is different from a non-heuristic based algorithm? That would help me and readers out a lot. – Shaun Snapp



Optimization can be classified as deterministic and stochastic, while all inputs are a constant in deterministic optimization. Inventory related optimization is definitely stochastic, since the demand is never been a constant, but a given distribution. The most classic optimization method in deterministic is linear programming.

Another name for stochastic is meta-heuristic. Meta-heuristic is a vast topic and used very broadly, because it is much more flexible, contingent, and even could yield better result than deterministic methods while inputs are deterministic.

Heuristics in Major Solvers

Like ILOG’s CPlex, they are very powerful linear programming solvers, but eventually when it tries to determine a solution, it uses heuristics. i2 Technologies used to use CPlex in master planning to provide draft outcomes, and then MAP as the heuristics solver to fine-tune the solution.

A Metaphor for Comparing Heuristic Versus Optimization

One extremely simplified way to see the deterministic and heuristics is like searching for a house. Using a deterministic approach would be like zooming out to a couple thousand miles always from earth, and then picking a location you think is best by giving all the criteria you can check at that distance. Then heuristics would be like standing in front of a train station, start asking the people around or checking local newspaper to figure out where is the better place to live. Then you move over there, check around again and narrow the scope further down or even jump out to next place.


So, inventory optimization is meta-heuristic. In METRIC, it is basically using margin analysis as the criteria of heuristic. (for more on METRIC see this post)


It starts by searching the for the part which provides best value to increase its inventory, then next one, then next one in the believe that we will stop at some point and that will be the optimal inventory position overall.


Follow Up Comment from Shaun: I think one of the complicating factors in understanding the difference between heuristics and optimization is that they are often taught as separate methods. A generalization is that an optimizer has an objective function, while a heuristic does not. However, in practice and in many important foundational research papers, in fact heuristics are combined with optimization. I think you provided a very good explanation of meta-heuristics. It enables a person who reads METRIC (an acronym for Sherbrooke’s foundational Multi-Echelon Technique for Recoverable Item Control), to understand it much better.


Author Thanks:

I wanted to thank Wayne Fu for his contribution.

Interviewee Profile

Wayne Fu is a Senior Product Management in Servigistics. With operation management background, Wayne has worked in service part planning domain for more than a decade. In Servigistics, he led the research and development of various areas like install-base (provisioning) forecasting, inventory optimization and distribution planning. Currently, he is focusing on the effectiveness of forecast techniques in Last Time Buy.


Deloitte Writes “Ok” Paper on Service Parts But Would You Want to Hire Them?


The paper The Service Revolution by Deloitte “research” is an average white paper which has some interesting numbers about service parts along with quite a lot of fluff to reach its 13 pages. I would recommend it to skim rather than read. It reminded me that I recently wrote an article on the low quality white papers that seem to fill the internet that are primarily focused on gaining business rather than imparting any knowledge.


However, while Deloitte was able to put together a middling white paper, are they the right consulting company to use for your service part solution needs? This is Doubtful. Aside from some strategy consultants who “dabble” in service parts and (can give good presentation), and a number of consultants who have been working on Cat and Ford on SPP, it’s very difficult to see how Deloitte, a company that fakes an aftermarket presence with a few white papers every few years, should be selected over consulting firms that really focus on the aftermarket. There are quite a few reasons why I came to this conclusion.

How Many Times Can a Company Bomb?

The answer for large monopoly consulting companies like Deloitte is unlimited. Deloitte has bombed on 100% of the projects that I have followed them on, which is now around five, including one where I worked with them while they were still on the project and getting close to rolling off. Not only do they bomb, but after they leave, the workers at the client tend to have developed a number of terms for them that include one swear word or another, followed by the name “Deloitte.” Another client had basically banned the use of the work Deloitte, and would insert some other word to describe them.

I used to have to deal with this animosity when I worked for them, and its nice to no longer have to deal with it now that I am independent. Often I was put in the position of having to  compensate for the fact that the Deloitte had been failing to meet expectations for quite some time before I would show up on an account. I noticed the higher-ups at Deloitte never thought very much about this, but would usually tell me that the client “was their own worst enemy.”

But at Deloitte, there is a simple rule, everything rolls downhill. The partner is never to blame, they blame the Sr. Managers, the Sr. Managers blame the Managers, and so on. The vast majority of the Sr. Managers and Partners at Deloitte have these tremendous egos and extreme type A personalities, however, what they can’t explain is if they are so talented and know how to staff and manage projects well, why is it that the majority of their projects are in the ditch? The Sr. Managers and Partners are also deeply deluded about the lack of corruption that exists in the major accounting/consulting firms. I was once told by two Sr. Managers out of the Cleveland office that the Andersen Consulting involvement with Enron was “built up” by the media, and what Andersen did there was “not a big deal.”

How to Misconfigure the Wrong Solution

At the client where I worked with them Deloitte had chosen the wrong solution for them, and then had not taken down requirements properly, and right before go-live the Deloitte consultant’s answer to the problem was to leave the project and then to leave Deloitte. Interestingly, the software selected never had a chance of supporting the business requirement, but Deloitte recommended it anyway.

Obvious Failures with SPP

Deloitte is associated with not only failing on projects generally, but has failed specifically on at least three SPP implementations that both Deloitte, SAP and the clients are hiding from the public. These are at Cat Logistics, Ford, and the US Navy. Part of the reason is that Deloitte is the implementation partner. However another reason is that the SPP solution is not yet ready to implement. There are many obvious things that Deloitte could have done to bring those projects live. One would have been to understand the weaknesses of SPP, that it was a beta product with functionality that did not work, and to blend it with a best of breed solution. However, they did not do this because they have no independence from SAP. I discuss SPP’s implementation mistakes in this post.


It makes little sense to hire a consulting company that is simply controlled by a major vendor. The entire concept behind hiring a consulting firm is that you are buying independent advice in addition to the bodies. The fact that SPP, a beta product, has been recommended by the large consulting firms without describing SPP’s limitations to their “clients,” is a clear demonstration that Deloitte puts itself before its clients. To see how SAP remotely controls the advice given by the major consulting companies see this post.


Inability to Partner with Best of Breed Vendors

The problem for clients with bringing in Deloitte to implement even a best of breed solution is that you will end up paying for Deloitte consultants that the vendor is then required to train and have on the project. Secondly, no best of breed service parts planning vendors requires or wants Deloitte or any other consulting firm for that matter to implement their solution. They all maintain consulting practices and they can implement far better independently. The main things a consultant can do is perform a software selection, and other activities during the project such as business process work, training and integration to ERP applications. However, neither Deloitte nor the other major consulting firms will be satisfied with this role. Vendors would always prefer a direction relationship with clients rather than being controlled by some corrupt major consulting firm. Unless it is a major vendor like SAP or Oracle, major consulting companies will strongly tend to abuse the relationship with any best of breed vendor to benefit the consulting company over the client or the vendor. Secondly, this control will take place behind the scenes and will not be apparent to the client. As all of the real service parts planning solutions are best of breed, this of course is a serious problem for selecting any major consulting, including Deloitte company to manage your service parts project.


So do yourself a favor if the Deloitte white paper on the service business, skim the paper for the data that is presented. The rest of the paper is mostly filler, designed to get business. It has some useful statistics, although Deloitte is not above faking statistics to make a case, so I am not sure how reliable they are. However, skip contacting them, because they are not suitable to help you select or implement service parts solutions. There are however plenty of good boutique firms that are better choices.

How the Bill of Material, MTBF and the Product Structure All Tie Together

MTBF and the Product Structure

In our previous post we discussed the different vendors and services offered for reliability testing and prediction. One of the important issues with relation to MTBF management is the product structure. The product structure is the hierarchy (or at least at first glance) of materials that make up an overall product. This has different names depending upon the application. In SAP ECC it is referred to as a Material BOM or an Equipment BOM. In MCA it is referred to as the product indenture network. This survey conducted by Arena Solutions on this topic is quite interesting.

BOM vs. PLM Software

Being able to deal with the BOM in a flexible and distributed manner is increasingly a capability with what is referred to as PLM software. However, that is not right. BOM management is actually a subcategory of the broader term PLM or life-cycle management. Lifecycle management exists in a number of applications in supply chain, as the article below explains.


Eric Larkin, Chief Technology Office at Arena Solutions, has some interesting things to say about PLM vs. BOM management.


Having powerful and collaborative BOM management software is important for many reasons that include improving the efficiency of product development and building quality into products as well as product costing for contract development. However, it is also important for service parts planning and MTBF. MTBF calculation integrates with the BOM.

ERP for BOM Management

There is increasing evidence that BOM management greatly benefits from specialized software. ERP software manages how the BOM relates to execution and planning, but does not tend to have advanced capabilities with regards to BOM management. (of course Oracle purchased Agile in 2007, a leader in PLM, however, software mergers often kill the aquired company’s innovation and product. Look how little Oracle has done with the PeopleSoft functionality). Here is an interesting quotes regarding ERP for PLM from Arena Solutions.

There is a misconception that Enterprise Resource Planning (ERP) systems can be used to manage all product information after design, including changes and communication. Unfortunately, even though the final production BOMs, the Item Master, and costing information are ultimately loaded into ERP systems, these systems do not have integral processes for ECOs or file management. Therefore they cannot be used to control BOM or item changes or manage associated files. Furthermore, as a tool primarily for internal groups, ERP systems cannot be used by external partners and suppliers to obtain product information. – http://www.arenasolutions.com/images/pdf/rc_docs/whitepapers/Arena_Turning_Great_Designs_Into_Great_Products_Whitepaper.pdf

ERP systems are not designed to be change control or file managementtools, and must be manually updated to reflect approved productchanges. To update and change product information across electrical andmechanical CAD tools and ERP systems, many companies employ spreadsheetsoftware, such as Microsoft® Excel, to manage part changes, SOPs andBOMs and to communicate them to project teams.” – http://www.arenasolutions.com/images/pdf/rc_docs/whitepapers/Arena_WP_Med_Device_Doc_Control.pdf

Reinforcing this statement is the poor track record of SAP PLM. We personally analyzed this “solution” several times only to find that it did not involve new software as much as simply leveraging the old structures with a few bells and whistles added in.


(in the past several years, SAP product management and marketing is increasingly following the Oracle model of presenting vapor or stretching pre-existing functionality to fit new solutions)

Spreadsheets for BOM Management

Exporting BOM information to a spreadsheet and managing it there for MTBF and other purposes is not a very competitive solution with the other alternatives that are present. In fact, even using an on line spreadsheet like Google Spreadsheets, while better than using Excel with its isolated files, is still not really capable of managing the complexity of BOMs. Furthermore with the rise of contract manufacturing and distributed product development and manufacturing, islands of data created by Excel are even less useful. Amazingly PLM software is still lightly implemented out in the marketplace.

Graphic from Arena Solutions – taken from an online webinar – not a formal study.

As far as ERP systems, while ERP systems have BOM functionality, it is not the functionality offered by Arena. Rather ERP BOM management was developed in order to support transaction processing. This is quite a bit different from what specialized BOM management software does.

Arena Solutions

Arena Solutions’ website is quite good and for anyone interested in PLM and BOM management we recommend a visit. It is of course selling a service, however it is also very educational and most the statements made on the site are reinforced by our consulting experiences.


In one of their white papers we found a very good explanation of the needs of modern BOMs.

“As the design progresses toward production, the part-list-like engineering BOM must transition into a detailed manufacturing BOM that includes all the items required to make sub-assemblies and the final product. During this process, numerous project teams contribute to the BOM and item changes (Figure 2). The resulting manufacturing BOM is highly relational and includes various associated data and files, such as design drawings, software files, item files, costing information, compliance status, specification data, and supplier information.” –


The Relational Model for BOM Management

One easy way of understanding this is that one sub-component often is part of more than one parent component. Therefore, by using a relational BOM configuration (which is different from a relational database, you can use a relational database, but still follow a restricted hierarchical model in your BOM configuration.), when the sub-component is changed once in one location it affects all parent components immediately. This is the desired end state, that all parent products be instantly updated when a change to a sub-component is rolled out. This relates to all life-stages of a product’s existence. This updated part data is then sent over the planning system where a flag is changed that tells the planning sytem this part should no longer be planned. Having this data updated is as important as the algorithms you use to produce a forecast.

This complexity really requires a software specialized software solution. Furthermore, this is perfect application for a hosted application. (we increasingly wonder why companies continue to ask for software they have to install and manage, particularly when the application is shared.) With hosted applications, as long as the software provides a standardized feed of some type (such as RSS), application integration can be managed completely on line, so a BOM Management – PLM service provider like Arena could be integrated with an on line version of a transaction processing system and the service parts planning system.

Application Screen Shots

Arena has a nice interactive demo on their website, so we decided take a few screen shots. This screen shows the different status of notifications.

Below we have a listing of notifications for particular BOM numbers. We also see the people (users) that have the ability to view or edit or comment on the BOMs.

Below we see the view for Monica Williams, and the materials for which she has notifications. You can see that each of the materials has an event code attached to it.

When we select one of them we get taken into the detail.

Here we can see who is part of the notification distribution list.

Here we have a flowchart of the process status.

Here we can see that suppliers are involved in this process and can log in.

Also, the individual products that make up the BOM are listed as well.

For each product, there is a coding for the items compliance requirements as well whether the prase of the item (if its in production, obsolete, etc..)

If we select the files, we can see all the attachments to each product.

In conclusion, we find this software very compelling. Furthermore they offer a fully hosted solution which they call on-demand. In our consulting experience, Arena is providing answers for a lot of problems that plague BOM management at many a company.

Open Question

One of the questions we do have is where an MTBF value is located. For the purposes of service parts planning, Arena just needs to feedone number per part. Both SAP SPP and MCA can perform their forecasting(if the option is selected) from a simple MTBF value associated withevery product record. This is called leading indicator forecasting inSPP and causal forecasting in MCA. At least MCA has some involved ways of calculating the overall service level, and one ofthe inputs is the MTBF of the underlying items – related to theinventory coverage for each item.This is something that should naturally be maintained in Arena. How this value is obtained is a different topic and is covered here.


However as far as how Arena holds the MTBF, we will update this post when we find out.


Wikipedia on PLM Arena Solutions


Determining MTBF and ReliaTech

Service Parts Forecasting

The main way service parts are currently forecasted is through the development of a MTBF. The MTBF is often developed from using similar parts and can be derived mathematically. However, there are also companies that perform physical testing to develop the MTBF number. One such company is called Reliatech. http://reliateck.com/new/index.php?option=com_content&task=view&id=32&Itemid=95 You send your products to them, and they perform the reliability testing. This type of testing goes beyond simply testing an overall component. This is explained below.

Reliability testing may be performed at several levels. Complex systems may be tested at component, circuit board, unit, assembly, subsystem and system levels. (The test level nomenclature varies among applications.) For example, performing environmental stress screening tests at lower levels, such as piece parts or small assemblies, catches problems before they cause failures at higher levels. – Wikipedia

Specialty Area

These vendors work in what is called the “reliability prediction” area or sub-industry. This page gives a good overview of how this is done.


A few other MTBF services out there provide you with an MTBF when you provide your BOM to them. Optionally, instead of having them do the work, there is also reliability software. In either case you correlate the MTBF to your BOM.(This actually brings up the topic of PLM which we will discuss in our next post.) See the MTBF service below.


Generally, there is a high level of frustration at clients we have seen in developing and managing their MTBF. Reliability testing and prediction is a difficult area and one should not shy away from bringing in expertise in this area to get the MTBFs as accurate as possible.


Article on MTBF

We found this to be an informative article on the topic, in terms of why MTBF is used as well as different MTBF options.


The Service Parts Software Under Investment

Where is they money for service parts software? Companies will end up spending one way or another, either on solutions that can help the rather weak state of service parts management improve, or by continuing to incur high obsolescence and other costs.

The Current State of Investment

It is well known by those that work and follow the service parts industry that it is tremendously underinvestment in. Most companies, with the finished goods focus place their resources in the latest and newest thing. This is illogical, as service parts are a very good business and most often one of the most profitable parts of any product oriented business. A typical quote in the industry is the following: That’s bound to change as the success stories multiply. At corporate phone network operator Avaya Inc. (AV ), Servigistics software has helped slash the number of raging e-mails the company’s CEO would get when big clients such as General Electric Co. (GE ) had trouble with their Avaya phone systems. With 2,000 stocking locations worldwide processing 8,000 phone and network parts, Avaya’s service department managed a huge logistics spiderweb. Until three years ago, however, parts were tracked on a simple Excel spreadsheet — which meant that Avaya technicians had the right ones in stock just 39% of the time. “It was an absolute disaster,” says Jeffrey S. Gardner, Avaya’s director of global service operations.”(August 01, 2005 BusinessWeek Magazine – Yes, Ma’am, That Part Is In Stock)

However, we are not so sure. This is why we are proposing that service parts software vendors lower the cost of software by offering an integrated service platform which is described in the article below.


A Robust Strategy

The opportunities in service parts are clearly tremendous. However, if customers are less willing to spend in this area (irrationally or not) vendors should develop a solution that charges incrementally in order to meet the need. Our view is once companies try these solutions, they will be less reticent to invest in them.

Understanding Service Parts Planning



Generally service parts planning differs from final product planning in the following ways:

  1. A high number of low volume irregular demand items in the planning database
  2. Inapplicability of traditional measures of forecast error (service parts management has much higher month by month forecast error than finished good forecasting. A service part can go many months with no demand at a product/location)
  3. A higher necessity to manage many locations as a single inventory “pool” – this means more transshipments between various inventory holding locations
  4. More complexity in procurement (end of life, lifetime buy, phased out suppliers) leading to different inventory holding strategies
  5. High variability in repair lead times (due to un-serviceable items which require inspection prior to repair lead time estimation)
  6. Inapplicability of common high volume forecasting and inventory management techniques (such as high volume forecasting algorithms and EOQ or POQ)
  7. Higher emphasis and need to handle obsolescence supersession and substitutability.

Service Parts by Industry

Industry Differences

Different industries have very different focuses in terms of service parts. Much of it has to do with the attributes of the products themselves. Some of these attributes are listed below:

  1. The length of life of the supported product (i.e. heavy equipment has much longer service part lifetimes over consumer electronics)
  2. The expense of the supported product
  3. The complexity of the supported product

Generally the larger, longer lived, more complex and more expensive the product the large percentage of overall revenues are derived from service parts. Airplanes are an example of where revenues in the form of service parts can come 25 years or more after the initial sale. The major industries for service parts are the following:

  1. Automotive:
  2. Aerospace:
  3. High Tech: (primarily for the manufacturing machines, such as semiconductor equipment)
  4. Construction and Industrial Earth Moving Equipment: (the largest service part organization and now service part third party, Cat Logistics, was originally an internal organization to Caterpillar, which was setup to service Caterpillar tractors and other earthmoving equipment)
  5. Consumer Durable Goods: Many consumer items are long lived such as washers, heaters and refrigerators.

The Challenging Environment of Service Parts

These features combine to create a very challenging environment for service parts planning. The emphasis here is planning. The execution of the plan is not very much different from the execution of original production items. This is why the requirements (planned production, transport and new buy orders) can be effectively executed through and ERP system, but can not be effective planned by most ERP vendor planning systems. However, regardless of this fact, most companies are still planning their service parts with a combination of ERP system with external planning spreadsheets.

One of the best articles on the importance of service parts planning is (Winning in the Aftermarket, HBR May 2006, Cohen, Agrawal, Agrawal.). In this article the following are mentioned.

According to a 1999 AMR Research report, businesses earn 45% of gross profits from the aftermarket, although it accounts for only 24% of revenues.”

“On average whopping 23% of parts become obsolete every year.”

“Indeed, third-party vendors have become so price competitive that OEMs lose most of the aftermarket the moment the initial warranty period ends.””Each generation has different parts and vendors, so the service network often has to cope with 20 times the number of SKUs that the manufacturing function deals with.”

“Aircraft manufacturers, for instance, can reap additional revenues for as long as 25 years after a sale. The longer the life of the asset, the more opportunities companies will find down the line. Each generation has different parts and vendors, so the service network often has to cope with 20 times the number of SKUs that the manufacturing function deals with.” – AMR

Chicken or Egg?

The question of whether purchasing companies or software companies were what retarded the market is an interesting question and could be the topic of a separate article. This shortage of systems for service parts management has left the majority of service parts networks managed by manual means. Paradoxically, due to the more complex nature of service part planning, it is even more difficult for a human planner to compete with a computerized solution than for a human planning to approach the quality of original product planning. While more complex planning systems are required for service planning, the desire of companies to spend money on service parts infrastructure and software is lower. What results is a seriously sub-optimized sector of the economy. This is why we have proposed a 4PL model for service parts management, which is described in this post.