• Create a new demand plan (or several) and instantly see how this new plan would impact your supply chain down to the smallest component?
• Drive your supply plan from any forecast stream (Sales, statistical, marketing, customer, pessimistic, optimistic) or combination of streams?
• Change which forecast drove your supply chain and evaluate the impacts?
• Visualize these plans against the same key corporate metrics you use to run your business?
• Compare various plans against each other AND against your annual targets?
• See a problem at the sales and operations level, and were able to drill down until you found the problem, no matter how far down the supply chain the problem exists?

Sounds pretty good right?  Let’s see what it takes to get this kind of functionality:

1. You need a system that allows you to create “what-if” scenarios instantly, and provides the ability to collaborate with these scenarios.
2. You need to have both collaborative demand planning and complete supply planning in the same tool.  The supply planning tool needs to accurately emulate the planning done by your ERP system.
3. You need to be able to drive the supply planning system from the demand plan.
4. Further, you need to be able to configure which forecast stream (or combination of streams) forms the demand plan (and therefore drives the supply planning process). This combined with #1 will allow you to evaluate and compare different demand planning scenarios.
5. You need excellent reporting tools that allow you to understand supply issues, their cause, and potential resolutions.
6. You need excellent reporting tools that allow you to present the recommended S&OP plan, the issues, and alternative resolutions to the executive team.

What does this do for your S&OP process?
Two things:

1. Your S&OP planning process will be faster – supply planning and demand planning are in the same tool, collaboration is enabled between supply planner and demand planner, and resources allow you to quickly identify and resolve issues.
2. Your S&OP plan will be more accurate – powerful collaborative forecasting tools are combined with the ability to understand at a detailed component level how a given demand plan impacts supply.

So, why are speed and accuracy so important?  I think accuracy is self-explanatory, but what about speed? We do S&OP on a monthly cadence, so why should I worry if my S&OP process takes several weeks? There are two key drivers for faster S&OP processes:

1. Timeliness of data – remember that the first step in any S&OP process is data gathering.  If your S&OP process takes three weeks, then the data you are basing your decisions on is at least three weeks old! I talked to one company that had a six week S&OP process to support a monthly cycle. I don’t know how they came to any useful decisions with that data.
2. Ability to respond – Imagine the following scenario: You just finished your S&OP process and have an approved plan. You come in Monday morning to discover that one of your key suppliers has had a major problem and has cut production in half and this has impacted items across multiple product lines. The response is going to require coordination across sales, marketing, procurement, and manufacturing. Sounds like an S&OP level problem right?  If it takes you three weeks to pull the plan together, you may as well not bother…decisions will be made on little or no data because they need to be.  If, however, you can pull a plan together in days or hours, you can base your response on current, accurate data.

When I presented my workshops at the Kinexions user conference, I polled the room asking how many participants had an active S&OP process at their companies. In each case, the vast majority of participants had an active S&OP process. Those that didn’t were planning on implementing one soon. What this means is that S&OP itself is no longer a differentiator. To step above the competition requires that S&OP be a more agile, responsive tool. Traditional S&OP systems simply are not capable of being this tool because the supply plan and demand plan are not connected, they don’t allow easy simulation and they don’t allow you go drill from the high level to the detailed in a single tool. It’s time for a new way of looking at S&OP. What do you think? Comment back and let us know.

This forces companies to then rely on increased safety stock buffer points to reduce lead times to customers, or to avoid missed delivery dates, a key customer metric. All this adds cost or reduces customer satisfaction, leading to a deteriorating competitive position and a reduced bottom line.

The question is, how can this situation be improved? The answer is: forecasting should be viewed as an important tool in meeting corporate goals for growth and profitability, not just as a budget exercise. In order to leverage forecasting as a vital asset to the enterprise, following issues need to be addressed:

1. Forecasts are a dynamic variable, so they can change significantly over even a short period of time. This means the forecast process needs to be much more frequent than annually, preferably monthly. While a baseline is needed (budget) for financial accountability, the demand picture needs to adjust to reality.
2. Forecasts should cover your longest lead time items, in order to properly anticipate demand and not be caught short. This means forecasts need to roll forward, covering the full length of the demand horizon at any point in time.
3. In order to support a more frequent forecasting cycle and improve accuracy, the forecast must be streamlined and easy to use. A forecast which takes a month to prepare is already out of date by the time it is released. This requires good data to base assumptions on, and a tool which can quickly and accurately generate forecasts for analysis, preferably with the ability to quickly compare various scenarios in order to determine the optimal one for the current environment.
4. The forecasting process must be viewed as integral and important tool in the overall functions of the company. The people generating the forecast must be aware of its importance to the strategic interests of the enterprise.

I have heard some people comment that, “Our forecast is always wrong, so we need to look at better safety stock management tools.” While safety stock is an important tool to buffer against unanticipated demand fluctuations, a better strategy would be to invest in instituting a continuous forecast process as a competitive tool.

We are currently going through another tough phase in the business cycle, with consumer demand softening and business spending becoming very conservative. This slowdown is causing even more volatility in the market place, which will require even better and more frequent analysis in order to able to respond to the fluctuations taking place in demand. Having an accurate, regularly updated, long range forecast to provide guidance to the supply chain is more important than ever. A tool which can quickly and easily supply such a forecast is a must, as is a process to provide feedback on forecast assumptions to enable corrective action to keep the enterprise on track.

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

Related articles

• Service Parts Planning 101 – Part 1 (kinaxis.com)

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.

There was an interesting article in Industry Week last month suggesting that the US was becoming a low-cost country for manufacturing.  Well, I guess it all comes down to how you spin it.  Let’s look at what’s happening;

• According to the article, Chinese wages are rising by 17 percent per year.
• The value of the Yuan is increasing.
• Many states are offering incentives to bring manufacturing into the state.
• Unions and workers are more willing to provide concessions in order to get back to work.
• The estimate from the article is that net labor costs in China and in the US will converge by 2015.

The article goes on to point out that several companies including Caterpillar, NCR, and Wham-O are bringing production back into the US from Mexico and China.

Given these factors alone, you might make the argument that the US is becoming a low-cost country for manufacturing.  I don’t think that is a fair assessment because the next logical step in the argument would be that if the US were becoming a low-cost manufacturing center, other countries will start manufacturing their goods in the US. I don’t think that is likely to happen. I think what really is happening is that China is pricing themselves out of the low-cost advantage they’ve had for years. As we start coming close to cost parity, other factors are making local manufacturing more attractive.

The on-shoring or near-shoring movement is gaining speed. This is the idea of bringing manufacturing back to where the demand is. A blog post in Plastics Today points out that according to management consulting firm Accenture, “Companies are beginning to realize that having offshored much of their manufacturing and supply operations away from their demand locations, they hurt their ability to meet their customers’ expectations across a wide spectrum of areas, such as being able to rapidly meeting increasing customer desires for unique products, continuing to maintain rapid delivery/response times, as well as maintaining low inventories and competitive total costs,” and that “managing supply operations that are separated far from where demand occurs has weakened their overall operational planning, forecasting and general flexibility, while in some cases driving up costs with the need for complex network management. In some cases, this situation has limited the companies’ competitive advantage.”

Let’s look at some advantages of putting manufacturing where the demand is;

• Time to market can  be significantly improved
• Less risk to intellectual property
• Lead times are reduced and are more consistent
• Given reduced and more consistent lead times means inventory levels can also be reduced.
• With fuel prices going through the roof, reducing the overall distance traveled for our manufactured goods can only help the bottom line.
• Reduced product travel also impacts the overall carbon footprint for the product, a factor that is starting to become more and more important in the eyes of the consumer and governments.

I’ve always been in favor of manufacturing near where the majority of the demand is (See my blog post from last year here). I think when companies look at the overall costs associated with offshore manufacturing, more will realize that manufacturing where their market is just makes sense (and dollars too).

Are you currently manufacturing offshore? Are you considering moving your manufacturing back to North America? Have you moved already? Comment back and let us know!

Will this problem ever go away? It is argued that if you have brand loyalty then the risk of perishable demand and worried investors is low. This argument holds for Apple where customers are willing to wait. Does the same hold true for BMW when their entire supply of 5 Series was consumed in one month in 2010? Some people just need to get a car. Brand loyalty may not be as influential. Which companies plan for limited supply versus the risk of excess inventory? The article talks about the Kentucky bourbon called Rip Van Winkle. Have you heard of it? Probably not as it is usually hidden behind the counter of the liquor store. They would rather keep production low than risk having inventory. Other companies with short life cycles may do the same. They must get their product to market as soon as possible but cannot risk the bottom line impact of scrapping product when demand is not meeting forecast. There is also the case where limited supply is not planned and can have serious consequences. Canadian company Lululemon faced shortages when their apparel line exceeded expectations and were forced to pay premium freight to accelerate supply. Margin erosion is often a result of demand exceeding supply.

So what does this really tell us? For the most part, forecasts are inaccurate. It has been proven that improving forecast accuracy results in higher customer service with the same inventory or the same service level with less inventory. How do you improve forecast accuracy?  Companies are finding more innovative ways to address this. Many are recognizing that improving demand response will reduce the cost and error of forecast error. Improved collaboration with trading partners; customers and suppliers also improves forecast accuracy. Duncan Klett has written an interesting white paper http://www.kinaxis.com/campaign/demand-planning-reduce-risk-and-impact on Demand Planning where he is really talking about the value of response management in the demand planning arena. He statistically proves that by focusing on customers with high demand variability, reducing cycle time (more frequent demand updates) will improve service and reduce inventory. Collaboration is another component where the sharing of up-to-date forecast information between trusted partners results in improved accuracy and reduced latency.

What are your thoughts?

Lora Cecere has been writing about forecasting, principally within the CPG industry for many years. She has worked in industry, for a software vendor, and most recently as a highly respected analyst. In a recent blog Lora states that

Mean Absolute Percentage Error (MAPE) for a one month lag was 31 percent + 12 percent.  Data eight years ago for the same companies was an average of 36 percent + 10 percent MAPE.

This made me sit up and listen.  Especially when she went on to quote from her research while at AMR Research that

Based on AMR Research correlations, a six percent forecast improvement could improve the perfect order by 10 percent and deliver a 10-15 percent reduction in inventory.

In other words, there is a lot of benefit to getting the forecast right.  But a range of highly respected CPG companies cannot do better than 31 percent MAPE, with a range of 19 percent to 43 percent?  That caught my attention.  Mostly because I am more familiar with the High-Tech/Electronics industry which has much shorter product life cycles than CPG and therefore more volatile or variable demand patterns. Of course it is difficult to be precise with industry classifications. Does Consumer Electronics fall into CPG, High-Tech/Electronics, or both?  However we slice it, things like cell phones, tablets, cameras, etc have shorter product life cycles, greater seasonal variations in demand, and greater demand variability than do nearly all categories of CPG such as soap, washing powder, etc.  In Consumer Electronics, and more generally High-Tech/Electronics I hear from companies that they seldom get their forecast accuracy, as measured by MAPE, above 50 percent, which is consistent with my observations about the characteristic differences with CPG.  Higher demand variability/volatility would imply a lower forecast accuracy.

Before anyone jumps down my throat, especially Lora, let my state unequivocally that everyone MUST forecast and that all companies should be demand driven.  But …

But where is the discussion about how best to satisfy the missing 31 percent demand in the case of CPG and 50 percent in the case of High-Tech/Electronics?  Where is the discussion about the profitable response to the demand that is not anticipated? I feel as we are only having half the conversation.  The half about forecasting.  But if the best we can do is improve forecast accuracy from 64 percent to 69 percent over eight years in an industry segment with relative stable demand, I think we should be talking about supply chain agility and responsiveness.  What amazes me is that since the early 1990’s we have been applying optimization engines, typically Linear Programming (LP), to the supply side.  Ignoring for the moment the inherent issue of using linear models to represent highly non-linear systems, if you are basing your optimizations on inputs that are best 69 percent correct, are you not focusing on the wrong problem?  Should you not be focusing on systems that enable you to detect true demand early and determine the best way to satisfy the unanticipated demand using the competing requirements of profitability and customer service?  Of course you will need a supply chain that can execute in an agile and responsive manner consistent with your decision.

Here is the rub: All our resources are limited. Time. Cash. People. So in this zero-sum game, where are you going to apply your energies?  Spending eight years to improve the forecast by five percent, or working on the manner in which you satisfy the unanticipated demand in the most timely and profitable manner?

Typically, a forecast is developed first by applying statistical model(s) to sales history with the result referred to as the ‘statistical’ forecast. The technical statistical forecast is then adjusted by various other fundamental factors such as product life cycle phase, seasonality, or marketing or sales promotions, usually as part of the Sales and Operations Planning (S&OP) process. The resulting forecast is typically known as the ‘consensus’ version of the forecast. However, related to planning, and to account for potential upsides in sales and to maximize customer service, some buffer or uplift may be added to the forecast. Moreover, this uplift can be driven in the planning system by being added to the forecast or as safety stock.

From my perspective, the basic difference between adding a buffer quantity to the forecast versus driving safety stock in the planning system is that a buffer quantity in the forecast could be consumed by actual customer orders if the buffer is realized by actual customer orders. On the other hand, safety stock (depending on the approach utilized) will plan to keep a certain level of inventory regardless of customer order levels. Conceptually, I am wondering what the best approach is for this. To summarize it seems that there are three options:

1. Forecast should inherently account for potential upsides in customer demand based on the statistics used and the fundamental factors or adjustments applied.
2. Additional ‘buffer’ should be added to the forecast – advantage is that the forecast can be consumed by actual sales orders and the disadvantage is that this buffer needs to be separately maintained.
3. Safety stock should be planned in addition to the forecast – advantage is that the buffer does not need to maintained and can be calculated by the planning system, whereas the primary disadvantage is that additional or excess inventory may result.

Intuitively, to me it seems that the option 1 above should be used – let the forecast drive demand in the planning system and not attempt to plan any buffer or safety stock. However, as we know, the first rule regarding forecast is that they will be wrong. Therefore, to complement, a way to get early signals that there will be customer demand greater than the forecast and have a system to rapidly respond to potential shortages in supply needs to be present.
Do you have any experience or insights into this? Please let me know your thoughts.

Note: I posted an excerpt of this piece on the Supply Chain Expert Community – Join the discussion!

I read a lot of books.    I typically have a few books “in the queue” but occasionally I’ll run out of reading material and have to wait for my next trip to the city to buy more.   When I travel, I’ll bring the book I’m currently reading and one or two more just in case I run out during the trip.  I’ll often re-read books I’ve enjoyed so I tend not to sell or give away books I’ve already read. As a result, my book shelves are stuffed way beyond their capacity.

So, when the Kindle came out a few years ago, I watched with interest.  There were still a few bugs to work out, so I waited.  The Kindle was also kind of expensive and…oh yeah…it wasn’t available to us cave-dwelling Canadians.  The Kindle 3 was announced in July.  It had some nice new features and a great price and was available north of the border, so I decided that I would treat myself for my birthday and ordered one on August 17th.

When I placed my order, Amazon couldn’t tell me when my new Kindle would ship.  Hmmm.   Yesterday, a couple of days into September, Amazon STILL couldn’t tell me when my Kindle will ship…not online anyway.  What’s going on?  Do they honestly not know when they can ship my product?  Is the date so bad that they are afraid I’ll go elsewhere?   Those among you that have been involved in supply chain, customer service or sales, know that the only thing worse than not shipping a product on the date a customer wants it is not being able to tell the customer when they can expect it.  

So what is the cause of the delay?  Bad Forecast?  Supply issues?  We just don’t know and likely won’t know until Amazon tells us (if they ever do). Regardless of the cause, what is stopping Amazon with providing me with a delivery date?  We all know that there are supply chain analytics available that can be used to identify an expected ship date.  Perhaps, being new to the brand owner /  manufacturing side of things, Amazon might not yet be aware of what is possible with good supply chain software.

Desperate for Amazon to tell me where the heck my Kindle is, I broke down and called customer service and finally got an answer that it should ship likely on September 10th.  Why that date wasn’t or couldn’t be provided online when I checked the status, I don’t know.

As someone who understands the complexity of the supply chain, I know that sometimes we can’t ship when a customer wants a product.  However, as a customer, I want to know when you CAN ship my product.  Amazon, you gotta give your customers any insight you can….and don’t make them work for it!

The WSJ article, From Snowmobiles to Cellphones, a Scramble for Parts, (paid subscription or preview sign-up may be required) further notes that companies have had to reconfigure offered products due to persistent supply shortages. It notes that shortages of transistors, capacitors and integrated circuits became pronounced in the first quarter, and persist in the second quarter. Telefon AB L.M. Ericsson indicated that shortages cost the company $400-$550 million in sales and delayed shipments, and Royal Phillips Electronics, Polaris Industries Inc. Motorola and Whirlpool are also mentioned as being impacted. Motorola CEO Sanjay Jha  summed it best noting that his company is scrambling in a “constrained environment.” Companies utilizing current hard-to-find components are seeking their own fixes which include offering customers different features or alternative components and/or technologies.

While it is obvious why Ericsson, Philips, and Motorola are affected by electronics component shortages, what is interesting is Bob’s inclusion of Polaris and Whirlpool in the list of companies. The original WSJ article clearly focuses on electronic parts shortages. Polaris makes snowmobiles and ATV’s. Whirlpool makes dishwashers, dryers, etc.  What does this have to do with electronic parts?  Well, a lot actually. I remember working on a project at Volkswagen in Germany in the late 1990’s shortly after they had just come out with the Golf Mark IV.  I don’t remember the exact details but the number of chips in the Golf had gone up from about 5 in the Mark III to about 50 in the Mark IV. Yet the demand that this represented for the chip manufacturer was still relatively low compared when compared to the demand from electronics companies such as Ericsson. The difference in the relationship with the more traditional Volkswagen suppliers, who made mechanical components for the Golf, was profound.  Whereas the chip demand represented a lot less than 10% for the chip manufacturer, often the demand from Volkswagen for the mechanical component suppliers represented well in excess of 30%-40%, sometimes in excess of 75% when factoring in aftermarket sales. Clearly Volkswagen had a great deal more leverage with the mechanical component suppliers.

Other industries, such as the white goods industry, will often design different washer or dryer models using virtually the same mechanical components, but use different chips to provide differentiation. Electronic components are everywhere. Delays in component deliveries affect many industries. The original WSJ article contains a graphic showing how the lead time for a common type of transistor has increased from 10 weeks in July 2009 to 20 weeks by February 2010.

Initially I set out to try to estimate the effect of electronic component shortages on G20 gross domestic product, but the more I looked into the data, the more it seems that electronic component shortages might already be “old” news. Clearly there is still a lot of caution in the industry and the effects are very real otherwise neither the WSJ nor Bob would have written about component shortages. It is also something we have been hearing from customers and prospects for the past 6-9 months. Yet some really good results over the past four quarters, such as those from Intel, seem to indicate that the situation may be easing. Since the financial results of semiconductor companies are a leading indicator of how other companies are investing in raw materials, it is encouraging to see the upward trend in both revenue and gross margin reported by Intel. But this blog is about component shortages. While Intel has shown better revenue numbers, the increase in the margin would still indicate a shortage situation. Looking at Intel’s inventory numbers indicates that these have been rising sharply too, especially finished goods.

The question is whether Intel is the proverbial “swallow that does not a summer make.” Looking at public semiconductor companies around the globe would indicate that the same is true throughout the industry. The figure below shows averaged financial results for all semiconductor companies.

This effect is most pronounced when considering the semiconductor companies in Asia, though a similar pattern is observed in North America, and less so in Europe.

Clearly it takes time for inventories to be built across the entire supply chain, but it is encouraging to see the level of inventories being built in the semiconductor industry. Notice that inventory levels far exceed the levels in early 2008.  In addition, the semiconductor industry is only one segment of the overall electronics industry. But, as I noted earlier, the semiconductor industry is a leading indicator because it is far up the supply chain.

What is your experience? Are you beginning to see an easing in the electronic component supply shortages?