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 A forecast which takes a month to prepare is already out of date by the time it is released.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.

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!

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.

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?

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!

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?

Ah, enough of that….There is a great article by David M. Katz in CFO magazine about demand planning leading up to the holiday season. With all the talk of a double-dip recession, CFO’s are asking themselves do they ramp up for growth or hunker down and wait out another drought? 

Ultimately, the question is – how and what do you forecast?  There are risks on both sides if you get it wrong – excess inventory or missed sales opportunities.  As such, at times like these where there is little to no predictability, experts are now saying that the ability to respond and react to demand is more critical than the ability to forecast/plan it.  Can I hear a hallelujah!

As the article points out…

In these volatile times, it’s hard for companies to get a reliable read on what to expect from their customers and how to deal with rapid shifts in demand. Here are four steps that supply-chain experts say are essential to coping with a fast-changing economic landscape:

1.Ditch the long-range forecasts.

2. Avoid gut feeling.

3. Go granular.

4. Launch an S.O.P.  

Any other steps that should be included?

Much has changed over the years since the 1980’s when few knew the term Supply Chain Management (SCM) and even fewer knew what it meant, including me.  The closest one could come to study SCM at university was Operations Research or Industrial Engineering.  Having done this at the graduate level, I can attest that the focus was very much on factory optimization with little emphasis on the inter-connectivity between demand, production, and supply.

I remember going to a pulp and paper manufacturer in Florida in the mid-1980’s because they wanted to optimize the utilization of their digesters, even though they were at over 90% utilization.  It didn’t take long to discover that there was over 9 months of finished goods and over 4 months of raw material on site. I had no idea and never bothered to find out how much finished goods inventory there was in the distribution channel.  Clearly, the company’s supply chain problems were not related to the utilization of their digesters.  However we had been brought by the plant manager to optimize the digesters and he had no responsibility for either raw material (Purchasing) or finished goods (Sales) inventories.  The question we were faced with was who owns the problem?  We could have gone to the Sales VP or to the Purchasing Director, but they each only owned a part of the problem.  Notice also the titles of the people responsible for Sales, Production, and Purchasing.  Eventually we managed to get a meeting with the CFO on the pretext of getting data for production costs.  The CFO was a lot more receptive to our message but really didn’t own the problem either, so he sent us to the CEO.  He thought we were a bunch of engineers, and he was right.  We couldn’t really articulate the issue in a manner in which he would understand.  Somehow, we managed to get enough across to the CEO to ensure that project focus changed to profitability rather than capacity utilization.

And yet, in a recent study by SCM World and Aberdeen titled “The Evolving Role of the Chief Supply Chain Officer”, there still appears to be a mismatch between the expectations of the C-suite and the people actually running the supply chain.  There have been a lot of positive developments since the 1980’s including the establishment of university courses and the knowledge of the function and strategic value of SCM within the C-Suite.  The use of the term “chief supply chain officer” is a testament to this change.  While the gulf between the C-suite and operations has narrowed, the very first chart in the SCM World report indicates a mismatch in objectives and perception.

The good news is that the C-suite is focused on using the supply chain as a competitive weapon; however, it reminds me of Peter Drucker’s definition of the difference between being effective and efficient.

“Efficiency is doing things right; Effectiveness is doing the right things.”

Perhaps this is the correct split in which the C-suite focuses on what should be done, while the rest of the organization focuses on doing this in the most efficient manner. The SCM World report indicates that there is a much closer alignment between the C-suite and the rest of the organization when it comes to the areas of efficiency gains.  However, even here it appears that the C-suite has a greater focus on the more strategic goal of restructuring the supply chain organization (effectiveness), whereas the rest of the organization has a greater focus on reducing inventory (efficiency).

I think there is still the need for people in the supply chain to understand the objectives and perspectives of the C-suite and to align themselves with these, and to communicate in the language of the C-suite.  The arrival of the “chief supply chain officer” in the C-suite is certain to close this gap.

Planning & Execution

Some months ago, Lora Cecere wrote a blog post about the gap between planning and execution, what she calls the supply chain black hole.

Lora states that:

Technologies are evolving to eliminate the supply chain black hole. In this first generation of supply chain applications, we have built a fixed response with very little sensing.

How can we effectively respond when we cannot sense?

Lora goes on to state that

The first generation of supply chain applications got us started down the path, but they must be cast-off to move forward.  ERP is not the backbone of supply chain management for the future.  The new technologies will not come from the ERP consolidators.

We are at a discontinuity between inside-out and outside-in technologies.  The new technologies will be outside-in. They will help us sense before responding.  They will help drive an intelligent response.

Lora’s statements are supported very strongly by the findings of the SCM World study, with very clear differentiation between the best-in-class SCM companies and those that received an average rating.  In the case of closing the gaps between planning and execution, there is a huge difference of 17%, and 28% to the laggards.  While not as dramatic, there is also a significant difference in the degree to which information from trading partners is included in SCM processes.

How can you sense what is happening in your supply chain quickly and effectively if you do not get demand or supply signals from your trading partners?  Or as Lora states, “how can we effectively respond when we cannot sense?”  Sensing is only half of this statement, responding is the other part.  How can you have an effective  response without closed-loop integration between supply chain planning and execution?  As Nick LaHowchic states in an interview in SCDigest,

Companies need to respond much faster tactically. You can’t wait for a monthly S&OP meeting to make most of those tactical decisions any more.

Summary

These commentators and the results from the SCM World survey clearly indicate the importance and benefits of closing the gaps between planning and execution.  The business drivers behind this need start from customer expectations for reduced order-to-delivery lead times and competitive pressures to bring new products to market in ever shorter cycles.

But the gap is wider than just between supply chain planning and execution.  It starts from the gap between financial plans and supply chain planning.  This is, of course, the primary gap between the C-suite and the supply chain function.  One talks in financial terms and the other talks on operational terms.  One talks effectiveness, the other talks efficiency.

Perhaps the answer is to have a single integrated planning and execution system, from business planning all the way to execution, including S&OP in between.  Only then can we bring these gaps together.  Any change in the business plan will be reflected immediately as changes of goals and objectives for operations.  Equally, changes in planning and execution will give the C-suite visibility in future performance.

What are your thoughts?  Do these gaps exist in your organization?  How effectively do you communicate with the C-suite?

• There is a huge consumer market in the rapidly developing economies (these being principally the BRIC countries) largely untapped by companies in the developed economies.
• To reach the consumers in these markets will require a different type of innovation, exemplified by the Nirma case study, focused on product simplicity (and price) and distribution effectiveness.

In their April 15th, 2010 edition, the Economist ran a special report called “The new master’s of management” (subscription may be required) in which the authors state

“Emerging countries are no longer content to be sources of cheap hands and low-cost brains. Instead they too are becoming hotbeds of innovation, producing breakthroughs in everything from telecoms to carmaking to health care. They are redesigning products to reduce costs not just by 10%, but by up to 90%. They are redesigning entire business processes to do things better and faster than their rivals in the West. Forget about flat—the world of business is turning upside down.”  They go on to say “the rich world is losing its leadership in the sort of breakthrough ideas that transform industries.”

In a supplemental report “The world turned upside down”, the Economist states that

“They (the BRIC countries) are coming up with new products and services that are dramatically cheaper than their Western equivalents: $3,000 cars, $300 computers and $30 mobile phones that provide nationwide service for just 2 cents a minute. They are reinventing systems of production and distribution, and they are experimenting with entirely new business models. All the elements of modern business, from supply-chain management to recruitment and retention, are being rejigged or reinvented in one emerging market or another.”

On the issue of reaching the broad consumer market the Economist goes on to state that

“It is not enough to concentrate on the Gucci and Mercedes crowd; they have to learn how to appeal to the billions of people who live outside Shanghai and Bangalore, from the rising middle classes in second-tier cities to the farmers in isolated villages. That means rethinking everything from products to distribution systems.” (My emphasis.)

And then there is Apple, with record sales into the BRIC countries confusing the issue.  A Wall Street Journal article in September 2009 titled “Apple Rides Recent Growth in Asia to Earn Top Honors” states that “Apple held just a 1.6% share of the personal-computer market in Asia in the second quarter of this year, and a 0.6% sliver of the region’s mobile-phone market, according to technology market-research firm IDC.”  It is Apple’s latest results that are startling.  Shipment of iPhone units grew 474% in Asia Pacific, 183% in Japan, and 133% in Europe. Total revenue from iPhones was $5.45 billion, and China accounted for $1.3 billion, up 200% following the iPhone’s launch at China Unicom.  I am not sure what this means in terms of market share growth, but the unit growth is impressive.

I must say I consider Apple’s results to be the exception rather than the norm.  I think Nokia’s approach is a safer bet for most Western companies that do not have the “trendiness” of Apple, even though Nokia’s stock price has plummeted on the back of Apple’s gains.  Focus on bringing innovation to large populations, not the elites in the BRIC countries.  Work out how to get your products to the “last mile” in countries that do not have the most sophisticated infrastructure.  On the other hand, perhaps Apple’s approach is correct because of the huge increase in disposable income in the BRIC countries.

Whatever your approach, I think it is absolutely necessary for Western companies to place a lot of emphasis on their growth in the BRIC countries.  Many of the large companies are doing this already.  What about the mid-sized companies that employ the bulk of the people in the Western countries?  What are they doing in terms of supply chain innovation to reduce costs?  I’d really like to hear your stories and opinions.