Archive for August, 2011

Increase the effectiveness of the executive S&OP meeting – Part 2

Published August 31st, 2011 by John Westerveld 1 Comment

Here’s part 2 on increasing the effectiveness of your executive S&OP meeting. Check out part 1 .

The effectiveness of the executive S&OP meeting is limited by the capabilities of the tool used to present the plan. Many companies use Excel to drive the S&OP meeting, but is that the best tool? Let’s take a quick look at what is needed to effectively present the S&OP plan to the executive team.

1) If there is one thing to remember it’s that the executive team is very busy and don’t have the patience or the time to try to understand mounds of data. They need the issues and resolutions laid out clearly and visually, with supporting data at hand if necessary. As we’ve all seen, a picture is worth a thousand words (or a thousand columns of numbers) so make sure that the presentation software supports multiple charting modes.

2) When we change the demand plan, make supply changes, set constraint levels, we base these changes on a set of assumptions.These assumptions must be captured and presented to the executive S&OP team so that they understand the assumptions that these plans are based on. Hopefully, the executives will confirm the assumptions made, but it is possible that the executive team may disagree with an assumption (or simply have better information).It’s much better to make this discovery before you execute the plan.

3) Often, an executive will need to look at the next level of detail in order to better understand an issue or opportunity. If the information isn’t at hand, the team may not be able to make a decision, or if they do, may be making the decision based on incomplete data. Neither situation is good. Make sure that the tool you use to present the S&OP meeting includes the ability to drill down through multiple levels of detail, so that you have the answers when your executive team asks.

4) In many cases, the pre-S&OP team will present a recommended S&OP plan, but may have alternative resolutions for discussions. This means that the executive team needs to be able to clearly see the differences between the various resolution alternatives against the key corporate metrics so that they can make an informed decision.

How successful has your Executive S&OP meetings been? What tool(s) do you use to present the S&OP plan? How do you keep the executive team engaged? Comment back and let us know!

Posted in Miscellanea


Increase the effectiveness of the executive S&OP meeting – Part 1

Published August 30th, 2011 by John Westerveld 1 Comment

I’ve been working on a presentation about the executive S&OP process that I’ll be giving at our upcoming user conference. Working on this presentation has made me think about the many executive S&OP meetings I’ve observed and what factors contributed to an effective executive S&OP meeting. (And what factors contributed to S&OP failures.)

Let’s start with the goals of the executive S&OP meeting.Sales and operations planning is all about alignment.It’s about getting the entire company pulling in the same direction. To achieve this, the executive team needs to:

  • Clearly understand the current status of the company and have visibility into the issues and opportunities.
  • Review alternative possible resolutions and be clear about the costs and benefits of each solution.
  • Gain agreement on which plan is the best (typically the best plan is proposed by the pre-S&OP team).
  • Approve and publish the plan.

It is critical that the stakeholders are present for the executive S&OP meetings. At a minimum, the following are necessary for a successful executive S&OP meeting; CEO / general manager, Marketing, Sales, Operations, Finance.Depending on the company, you may also have need Engineering and HR. Many S&OP processes fail to get off the ground because the executives stop attending the meetings. This occurs for various reasons, but one of the key reasons is that the executive feels they aren’t getting value out of the process.If you are reading this post, I’m going to assume that you know the value that S&OP brings, however, this value can be lost if the meeting is poorly run.There are many tips out there that cover how to run a good meeting.Let me quickly point out a few of the key ones;

1) Have an agenda with time guidelines and stick to it. If further discussion is needed for a specific topic, book another meeting.This ensures that all topics that need to be covered are covered.

2) Book your S&OP meetings on with a regular cadence (the third Tuesday of every month for example) and do everything in your power to never move them. The further in advance you book a meeting, the more likely that everyone can attend. More capable S&OP systems enable S&OP “on-demand” this is a powerful capability but should only be used for S&OP level decisions that can’t wait for the next cycle.

3) Be prepared.Have the information you know you will need…and the information you think you might need, available before you go into the meeting. You never know where the executive team will take you!

4) Use the right tool to present the S&OP plan (this one is critical).

Stay tuned for part 2 tomorrow where I will cover the best ways to drive your S&OP meeting.

Posted in Miscellanea


CPG companies recognize limitations of planning optimization

Published August 25th, 2011 by Lori Smith 0 Comments

Forgive the self-promotion but we are so proud to have formally announced the RapidResponse deployment at Unicharm today.

Unicharm is the largest manufacturer and distributor of diapers and other consumer sanitary products in Asia. They implemented RapidResponse (replacing existing supply chain planning solutions) so they could move away from the limitations of statistical-based planning systems. With RapidResponse, Unicharm can complete planning calculations in five minutes—a process that previously took 12 hours.

In a news release distributed today, Unicharm said:

“We chose RapidResponse for its unparalleled ability to allow us to effectively manage our supply chain in today’s environment given the urgent and explicit need for supply chain visibility, simulation, and collaboration capabilities.”

It’s so humbling hearing it directly from the customer! Check out what other customers are saying about us in the Executive Perspectives section on Kinaxis TV.

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Posted in General News, Supply chain management


The importance of Response Management – Part 2

Published August 24th, 2011 by Trevor Miles @milesahead 2 Comments

Here is part 2 on the important of Response Management. Make sure to check out part 1!

Kerry Zuber, one of my colleagues who is a Lean/Six Sigma black belt, has introduced me to two great terms that are crucial to understanding the value and key capabilities required of effective response management:

  • Time to detect
    This is all about knowing sooner that some event has occurred that is creating risk or harm to your organization or supply chain.  This may be that a customer has changed their mind about an order, or a supplier has de-committed on a delivery date or quantity, or that a tsunami has occurred in Japan that has wiped out a large part of world-wide semiconductor manufacturing capacity. But not only knowing about the event sooner, but also knowing about the impact sooner (which lines will go down, which orders will be impacted), and, perhaps as importantly, knowing who is impacted sooner.  Without knowing all of these three things you cannot act.
  • Time to correct
    Once you know what went wrong, you need to act quickly to find a solution through compromise across multiple functions, even multiple tiers, often with competing objectives.  The timeliness of resolution is a key measure of the quality of the solution. We still hear of companies that have not fully understood the impact of the Japanese tsunami on their operations and their ability to satisfy demand, let alone put into place a recovery plan. And yet it is often the day-to-day events, such as when a customer changes their mind on an order, where most margin or customer satisfaction is lost because either the response to the customer is too slow or actions are taking with little understanding of the financial and operational impact.

The significance of these two terms is that for the most part the physical supply chain is a constraint in that your ability to change it in the short term is very limited so every minute lost in detecting something ‘wrong’ and, once the event has been detected, every minute used to determine what to do to reduce or eliminate the risk or harm, is a minute taken away from being able to use the physical supply chain in some manner to reduce the risk.  Even in situation where there is next to nothing that can be done to eliminate the risk, if you know sooner you can at least tell your customers who are impacted about the impact sooner so that they have more time to react.

There are three core capabilities required that impact the time to detect and the time to correct:

  • Being able to represent several versions of a multi-tier supply chain in a single data model so that there is a single version of the truth, and being able to represent several states – historical, present, and future – of the supply chain in order to determine what of significance has changed that is causing future harm to the organization, as well as several scenarios that represent alternative ways of solving the problems.
  • Being able to determine who is impacted by the change and therefore who needs to be alerted, not only in your own function, but in other function within your own organization and people in your customers, contract manufacturers, or component suppliers.
  • Being able to bring those people impacted by other the original threat or proposed actions together in order to collaborate on the resolution of a problem by testing several ways of resolving the issue and being able to reach compromise through sharing of the impact of possible courses of action on financial and operational metrics.

These concepts and capabilities can be applied to a broad range of business problems, not just the supply chain.  In the diagram on the left we can substitute other function’s analytics for the supply chain analytics and still retain the core capabilities described above to solve response management problems in a wide range of business activity.  Within manufacturing industries the supply chain is absolutely central to being able to get product to market.  But within manufacturing companies response management issues are not constrained to the supply chain.  Workforce management is an easy one to describe in this context because it is (or rather it should be) tightly coupled to the longer term revenue forecast. If a company anticipates their demand or sale revenue growing they need to ramp up recruitment because of the long lead time in recruiting and training people, but they also need to be very quick in shutting off the recruitment process if they see that their revenue is not growing as anticipated. In other words workforce management also requires the ability to plan for the future based upon anticipated demand, monitor actual performance against anticipated performance, and a quick response to correct the misalignments.

What drives the need for response management is the fundamental fact that we cannot predict the future with a great deal of accuracy and our success or failure is dependent on how effectively we respond to the present, how we manage in the “now”.  What varies across industries, across functions, and across business processes is how far into the future we need to predict what we think is going to happen, and how quickly we can affect change.  But what does not change is that the time it takes us to detect that something of significance has changed and decide on the best course of action determines both the time we have available to carry out the course of action and the feasibility of the course of action, which is of course a key measure of the quality of the decision.

So where do you think the next breakthrough will come from?  Better forecasting or better response management?

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Posted in Milesahead, Response Management


The importance of Response Management – Part 1

Published August 23rd, 2011 by Trevor Miles @milesahead 0 Comments

Response Management is all about what you do when what you planned to do (forecast) does not match what is happening (actuals). This can be applied to any forecast, whether that is the traditional sales forecast of the number of units sold in a region, the projected cash position of a company, the expected completion of a new factory, or the commercial availability of a new product.  For this discussion I will focus on traditional supply chain forecasting because that is the underlying data I have available to support my description.

First let’s examine why companies create a revenue or sales forecast.  It is intuitively obvious that they do so to determine how much of their product they believe the market will purchase. But this is only the 10 percent of the iceberg that floats above the water.  The other 90 percent of the story  is that the forecast is used to drive investment plans in marketing, engineering, manufacturing capacity, inventory planning, and strategic sourcing.  In other words, if the forecast is inaccurate there are a whole bunch of consequences beyond just getting the revenue forecast incorrect.  Of course here I am referring to the longer term forecast used to drive the S&OP process or even the annual operating plan/budget and the principal forecast is investment.

The medium term forecast is used to drive how an existing infrastructure, particularly the supply chain infrastructure, can be used to meet market demand.  This is the period in which investments are not going to make a difference.  As one colleague told me, nine woman are not going to be able to produce a baby in one month, no matter how hard they try.  But it is important to realize that even within this period the significance of forecasting varies by industry, as is reflected in the diagram to the left from an Aberdeen report titled “Demand Management – Bridging External Market Inputs with Internal Statistical Forecasting” published in June 2011.  In this context it is important to understand how the difference between demand lead time and supply lead time varies across industries.  Airplane manufacturers typically have a 3-5 year lead time from a firm order to delivery date, with a manufacturing lead time of about six months.  A diaper manufacturer has between a one day to one week demand lead time, and about a three week supply lead time.  On the other hand, an airplane is highly customized to a specific customer’s order, whereas  a diaper is a diaper, even though there are variations.  The complexity in a plane is in how bought materials are coordinated to assembly a customer specific product.  The complexity in a diaper is in how it is distributed with hundreds, in some cases, thousands, of distribution locations through a multi-tier distribution network.

The combination of the difference between demand and supply lead time and degree of final product configurability determines the extent to which a company can use postponement as a strategy to mitigate the risks associated with forecasting incorrectly.  CPG companies, such as diaper manufacturers, typically have little opportunity to postpone at the manufacturing stage, and therefore will use a make-to-stock supply model. However, CPG companies can postpone distribution through their multi-level distribution network. On the other had an airplane manufacturer will seldom order components before they have a firm order, which is a “build-to-order “ postponement strategy.  Imagine the financial risk that an airplane manufacturer would be taking on if they built an airplane and then tried to find someone to buy it.  In reality a diaper manufacturer is taking on similar risk, but the risk is distributed over millions of diapers and many thousands of consumers, so their risk per item is much smaller.

And yet a semiconductor company takes on the same level of risk when they commit $3B-$5B to build a new factory over the next 24-36 months that an airplane manufacturer would be taking on if they used a “build-to-stock” supply chain model.  It is an equivalent risk when a company decides to invest in penetrating a new market and needs to invest in establishing a local legal entity, office rentals, marketing, and hiring and training local staff.  These are big commitments of funds that are based upon the anticipated behavior of the market, a forecast, and once in execution they take a lot unwind.

Terra Technology, one of the leading forecasting technology companies focusing on the CPG space, where statistical forecasting is very prevalent, published a study on forecast accuracy titled “2011Forecasting Performance Benchmark Study” in which they study best practice demand forecasting in leading CPG companies.  The reason that statistical forecasting is so prevalent in CPG is that demand is relatively stable – when compared with other industries – and products have fairly long life cycles so there is a lot of history to rely on.  And yet in the introduction the authors note that:

  • Promotional volume jumped about 75 percent in 2010 as companies looked to drive sales by offering consumers additional value. Contrary to conventional wisdom, promotional periods are actually forecast as accurately as non-promotional periods for the same items. Perhaps this is due to the extra time demand planners spend on promotions. Not surprisingly, the bias is considerably higher during promotions.
  • New products remain hard to forecast with weekly item/location error rates of 65 percent, compared to 46 percent for existing products
  • Demand Sensing continues to provide a consistent step change in forecast accuracy for all scenarios, including promotions and new products. For the combined 2009-2010 period, Demand Sensing reduces average weekly error by 40 percent.
  • Outdated mathematics and optimistic marketing departments continue to undermine the performance of Demand Planning. This highlights the opportunity for a structured approach to forecasting based on additional demand signals and new mathematics.
  • MAPE is the correct measure for supply chain performance since it is the error that Product Supply contends with. However, insight from the report raises questions regarding MAPE as the proper metric to evaluate the performance of Demand Planning because it may not properly reflect the value add by planners. Using the dataset as a resource, Terra plans to evaluate a number of different metrics in the future editions of the study.

Before analyzing the numbers, let me reiterate that CPG, when compared with industrial equipment manufacturing for example, has stable demand and long product life cycles, which, in theory, means that CPG companies should be able to use statistical forecasting to predict demand fairly accurately, but, as the Terra Technology study shows, they can’t. When we consider the three month digital camera lifecycles and six months cell phone lifecycles, the relevance of the second bullet about the forecast accuracy of new products becomes very apparent.  New product launch is typically the time when the most margin is captured, an yet it is also the time when the forecast is most inaccurate, meaning that a lot of margin is not captured.  And therefore anything Terra describes in the report is significantly worse in industries with shorter product life cycles, which automatically leads to more volatile demand. In fact our anecdotal evidence from speaking to companies in consumer electronics is that their forecast accuracy is very seldom above 50 percent regardless of the life cycle stage of the product largely because the short life cycle means that the product is either being introduced or being phased out.

In the Aberdeen study referred to above, the author notes that Best in Class performance

  • Average percent forecast accuracy at product family level (across a three-month time period) is 87.1 percent
  • Average percent forecast accuracy at individual SKU item level (across a three-month time period) is 70.8 percent

In the Terra Technology study the author notes that

  • During 2010, the average weekly error was 48 percent with a slight difference by top performers, who came in six points lower at 42 percent.
  • Meanwhile, monthly error averaged 33 percent with a five point spread between top performers and the average.

With leading CPG companies struggling to get forecast error below 30-40 percent after years of trying, with product life cycles shrinking every year, and with market differentiation leading to product proliferation, what is the likelihood that forecast accuracy will improve dramatically over the next five years?  In my opinion very little.  So where do you think your next breakthrough in supply chain performance will come from?

  • Learning to forecast and plan better?
  • Learning to respond profitably to actual demand, or plan variance?

The third bullet from the Terra Technology report introduction states that “Demand Sensing continues to provide a consistent step change in forecast accuracy for all scenarios, including promotions and new products. For the combined 2009-2010 period, Demand Sensing reduces average weekly error by 40 percent.” hints at the importance of response management, but does not go far enough since it only indicates how to get a better understanding of demand in the short term. Response management is about satisfying that demand in the most profitable manner.  Demand sensing is about knowing sooner about demand shifts. Actually I find this standard definition of demand sensing a bit funny and it is a good illustration of how planning is seen from the wrong perspective. The term demand shift implies that the forecast is correct and somehow demand has shifted in time or location, which is of course completely wrong. The customer didn’t buy the ‘wrong’ stuff in the ‘wrong’ amount at the ‘wrong’ time. What actually happened is that we predicted incorrectly what they wanted, how much they wanted, and when they wanted it, even where they wanted it.  Nevertheless demand shift captures the concept that actual demand occurs at a time, quantity, price, and/or location that was not expected.  While demand sensing is really important, even perhaps the most important aspect of supply chain execution, it only describes part of the response management story. Response management is about knowing sooner about a broad range of supply chain disruptions and acting faster to provide a profitable response.

Stay tuned for part two of “The importance of Response Management” tomorrow!

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Posted in Milesahead, Response Management


Kinaxis life sciences newsletter: Featuring complimentary analyst case study report on Amgen

Published August 17th, 2011 by Lori Smith 0 Comments

We recently produced a newsletter which includes complimentary access to a 6-page Gartner report titled Case Study: How Amgen Reinvigorated Its Supply Planning Process (Barry Blake, Hussain Mooraj: May 18, 2011)

This Case Study provides insights on how Amgen transformed its supply planning processes and enabled rapid planning capabilities, with advanced planning capabilities and systems.

Key findings of the report include:

  • Rapid demand and supply planning processes are foundational capabilities required for a multitier sales and operations planning (S&OP) process that can propel companies beyond simple supply and demand matching to conscious, value-driven business decisions.
  • Life science manufacturers don’t often incorporate into their planning processes “what-if” scenario analysis to optimize supply. Additionally, many companies have disconnected their short-term planning from long-term capacity and supply commitment processes.
  • By centralizing elements of its planning process and deploying the appropriate advanced planning tool, Amgen can now quickly model the impacts of various “what-if” scenarios and extend these analyses to multiple planning levels across the entire supply network.
  • The company is now able to rapidly develop more-accurate supply plans that optimize capacity, inventory and product shelf life, decreasing the total planning cycle from 21 to 12 days. These consolidated, synchronized views of demand and supply across the entire product supply network are generated by the tool in minutes.

Download the newsletter here: http://www.kinaxis.com/campaign/kinaxis-gartner-pharma-newsletter/

Posted in General News


Service Parts Planning 101 – Part 2

Published August 15th, 2011 by Pradeep Chadha 0 Comments

Here is part two of my posting on service parts planning. Check out part one here.

On Friday I started explaining how in regards to service parts, supply chain planning is different from planning for manufacturing. The first functional area I covered is Master Data. Here are the differences with Demand Management and Supply Management.

Demand management: For accurate demand determination of service parts, which are those that have independent demand, several techniques and data streams may come into play. A good system should be able to aggregate demands from various locations (multi-echelon) and should also provide drill downs from the top level. Some of the demand determination techniques are outlined below:

  1. Reliability Data/Failure rates: If the total population of install base product is known and the data on failure rate or the mean time between failure is known, it can be used to calculate a baseline service parts demand plan for the service organization.  When a New Product Introduction (NPI) happens in the absence of historical data to do statistical forecasting, the failure rates are used to make the base plan. But often this data needs to be monitored, as with time, there are several product revisions and engineering changes on high failure parts; leaving a mix of parts in the install base to serviced.
  2. Statistical Techniques: Several statistical techniques may be used to determine demand pattern of service parts. Forecasting algorithms like; Weighted/Moving average, Single/Double/Adaptive smoothing, Winters/Croston forecasting algorithms may be used. The intent is to minimize forecast tracking errors and a relevant method may be picked for it. Apart from forecast, these statistical methods may also be used for calculating repair BOMS.
  3. Service Level contracts: Typically service organizations have contracts to maintain service levels for different products with customers. A higher the service level, in most cases equates to a higher investment in inventory to support the service level (In the teaser, what happens if you want to have the bulb replacement available 90 percent of the time?).
  4. Product Life Cycle Curves: Provides indication on volume ramp up/ramp down over the period of time. They act as multipliers on calculated forecast to calculate the increasing or decreasing volumes.
  5. End of Life Planning: Very typical in electronics manufacturing where a supplier declares that he is doing last production run; The 60GB/5400rpm drive is getting obsolete- so supplier informs the service organization that they want to do a last production run (the service organization should have the tools to calculate the final demand) to figure out how many it should order to honor all the open service contracts.
  6. Understanding what is in the channel:  It is critical to understand what product has made it all the way through the channel and is installed at the end user.

Supply Management: The main focus area for managing supplies for operational effectiveness in service parts planning are:

  1. Managing Returns: A service organization receives defective parts or units. These are primary source of supply post repair/refurbishment. As soon as returns are received, it typically goes through triage to determine its proper disposition. E.g.no fault found, defective within OEM warranty, defective out of OEM warranty, etc. Based on the triage results, returns should be available for future fulfillment with relevant rules –like defective but within OEM warranty needs to be sent back for credit, not to be stocked, etc. There may be lead-time associated with repairs which should be taken into account.
  2. Order Priority: Service organization should always try to minimize the new buy parts. The preference should always be given to using similar repaired part, or repaired parts which are valid alternates.  So when the planning engine runs, it should be able to generate supply recommendations accordingly. New buys should happen only when repaired supplies are not available.
  3. Obsolescence Management: To decrease the risk of obsolescence, when service organization buys a part, it wants to buy the part which is very flexible and may be used as an alternate on several BOMS even if it is slightly expensive. If a purchase is done of unique component, even if it is priced less, the risk of obsolescence may erode all the cost savings.  The system should be able to run analytics on cost savings vs risk of obsolescence for better purchasing decisions.
  4. Inventory Management: Since the service organization is multi-echelon, visibility into location level inventory is very important. Inventory could be at manufacturing site, supplier, depot, third party logistics company, a service contractor’s truck, an onsite storage locker, etc. and at each level it has its cost benefit equation. Having all that information available in the system and to do cost of service/benefits analytics, can be vital in decision making.  For example,  what is the benefit of keeping inventory with Fedex/UPS  vs a local warehouse, which and how many of those sku’s will provide good balance on investment/service.

Several electronic manufacturing service (EMS) providers have started to provide after sales services as a part of their end-to-end service offerings. For them to be successful, the top four things they should focus on are:

  • Accurate Forecasting – This leads to:
    • Improved service level
    • Improved fulfillment metrics
    • Reduced liability
    • Decreased costs due to order expediting
    • Reduced excess and obsolete (E&O) inventory at end-of-life (EOL)
  • Lower transaction fees:
    • Promotes competitiveness
    • Value proposition that is passed down to customer
  • Global process with visibility throughout the network:
    • Ease of process adherence and process monitoring company-wide
    • Ease of NPI
    • Supports the communication of NPI and EOL throughout the network
    • Overall inventory reduction
  • Ability to Share Data:
    • Ability to access the EMS data
    • Allows for purchasing power for commodity items, as well as other items (E.g.:  transportation)
    • Consistent data format on all data elements allows for easier communication exchange
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Posted in Demand management


Service Parts Planning 101 – Part 1

Published August 12th, 2011 by Pradeep Chadha 1 Comment

Let me start this post with a teaser:

You have 20 light bulbs in your house and they are all the same kind, made by the same manufacturer. The bulbs are used for eight hours a day. The light bulb packaging indicates that each bulb has a burning life of 1000 hours and the fine print states that the burning life is 70 percent accurate.

How many spare light bulbs should you keep for replacement of blown bulbs?  Assuming you go to local hardware store only once a year to buy the spare bulbs, and you are happy to have a 60 percent chance of spare bulbs available when needed.

Now you find out there is another manufacturer who charges $2.00 extra, but the fine print on the box states 1000 burning hours with 97 percent accuracy rather than 70 percent. What happens to the calculation?

Situation: Mom-in-law is visiting and now you want to have 95 percent chance of a spare bulb available rather than 60 percent. What happens to the calculation?

Can you minimize your total spending by opting for higher priced manufacturer bulb and still have replacement 95 percent of the time?

Lots of math :) .  If you got this teaser you understand the very basic math behind the service parts planning.

In the case of service parts, supply chain planning is vastly different from planning for manufacturing.  I will try to put the key differences in the three functional areas: Master Data, Demand Management, and Supply Management.

Master Data: A good system should be able to maintain key data elements and run analytics based on them. Some of the data elements important for service planning are:

  1. Service BOM Data: A manufacturing BOM could have 100’s of component and several levels, but a service BOM is much simpler. Think of copier machine; if the roller fails to pick up paper, the service part that is shipped to the end user is the entire roller assembly, which the end user can pull and replace. So the service BOM will typically stop at that level.
  2. Alternate Service Parts Data: This element is very interesting and if an organization is able to manage it well, it can reap huge rewards on the inventory metrics.  It is fairly complex when compared to the typical alternate parts management in the manufacturing. Think of a failed hard drive in the end user’s laptop with specifications as 5400rpm/60GB. The service provider can ship equivalent or better replacement. Specifications permitting, the end user will gladly accept a replacement of 7200rpm/80GB, and it may also be easier and cheaper for the service provider to do so if the 5400rpm/60Gb is obsolete and hard to procure. This kind of alternate replacement typically does not happen in the manufacturing planning.
  3. Sourcing Data: Sourcing data needs to be maintained for repair partners apart from new buy partners. Repair lead times and repair yields should be maintained and considered in analytics.
  4. Multi-Echelon Data: Service organization set ups are more multi-echelon as compared to manufacturing. Customers use products everywhere in the world and may have service contracts of next day service or as quick as onsite four hours service. The data on the service level identified in these contracts should be available for analytics.
  5. Logistics Partners/Service Contractors Data: Several service organizations typically work very closely with logistics partners like Fedex/DHL to stock and deliver service parts. There may be field contractors/agents (the guy who came to fix my washing machine, which was still under warranty, when it broke down) who are tasked to fix the unit at the end user. The system should maintain information on these service partners.

Stay tuned for part two on Monday where I’ll be discussing Demand Management and Supply Management.

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Posted in Best practices, Demand management