But the highlight for me was the Influencer Panel I hosted as the last event of the conference.  We had great participation from:

• Andy Coldrick of Ling-Coldrick
• Bob Ferrari of The Ferrari Research and Consulting Group
• Russell Goodman of SupplyChainBrain.com
• PJ Jakovljevic of Technology Evaluation Centers (TEC)

While all the panelists added greatly to the discussion, two highlights for me were comments made by Andy and PJ.

Andy said that we have moved from thinking we need a single number forecast (and therefore single number plan) to understanding that we need a single perspective and a range of plans that cover range of possible business conditions under which we will operate over the next period. My take on Andy’s point is that “what-if” analysis is an absolutely core capability at every level of planning, be that strategic , tactical, or operational.  Being able to understand what levers are available to you and the impact that pulling these levers will have on financial and operational metrics is crucial to developing flexibility and agility in your supply chain and broader operations functions.

PJ used the dramatic failure of the Boston Red Sox in August to illustrate that planning is not enough.  As PJ told it, the Red Sox had done a tremendous amount of planning over the past few years which is what resulted in their great season up to August.  But what management failed to do is monitor the health of the star performers and only realized that some players had put on as much as 15 lbs.  Even worse their mechanisms for responding were not in place meaning that they had no way of getting relief pitchers or other key players at such short notice. PJ’s anecdote captured my view that planning is not enough very well.  It also ties in very well with Andy’s observation about a range of plans.

How Andy’s and PJ’s comments link together is that you need to monitor how your operations, particular customer demand, are matching up with what you anticipated (your operational plan), and respond very quickly when the two do not match.  Having pre-evaluated a range of possibilities means that you are able to respond with confidence, even though reality will never quite match any of the scenarios you had pre-analyzed

Planning is not enough, but we all have to do it. Not planning would be very stupid. But not building the capabilities to detect when reality does not match the plan very quickly and then respond profitably to reality is equally short-sighted. Plan-Monitor-Respond.

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.

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 benefits of increased forecast accuracy include:

• Increased customer satisfaction due to reduced stock outs
• Reduced write-off and obsolescence costs due to unsold inventory
• Lower inventory carrying costs, as less buffer stock is required to cover missed forecasts

Significant benefits can be realized in a company’s top and bottom line results if a more accurate forecast generation process can be realized. One of the cardinal rules of forecasting is that a forecast becomes more inaccurate as it goes further out in time. Another is that a forecast becomes more inaccurate as the granularity is increased (ie. It is easier to get an accurate forecast at the business unit revenue level than it is to forecast the number of units of each SKU that make up the sales for that business unit).

This second rule can be better addressed by focusing not an individual SKUs sales history or possible future trends, but by looking at the ‘group’ this SKU(s) fall into and analyzing its history and trends. The attributes shared by these SKUs constitute their forecast group. The advantage of grouping forecast SKUs are many:

• A lot of market research is available at a higher level in the marketplace (at the channel product category level versus an SKU level). Market share analysis must be done at this level
• Aggregating SKUs reduces the month to month demand fluctuations seen at the SKU level, allowing for better data analysis for trend forecasting (reduces the noise)
• Revenue forecasting generally occurs at the product group level or higher. Since revenue projections are the most common driver of forecast models, a better alignment between forecasts at the SKU level and revenue forecasts can be achieved if there is a clearly understood link between the two
• A better alignment of revenue and SKU forecasts allows the business to more clearly drive production (also known as the sales versus production forecast). A poor alignment between the two can lead to missed sales numbers, increased inventory carrying costs, or stock outs.

The breakdown of the revenue forecast into a realistic SKU level forecast can be complex, and can vary greatly among different business units and markets. While these different processes can lead to a more accurate forecast based on local market conditions, it makes it very difficult to get a true picture of the alignment between the global revenue forecast and the individual unit forecasts, as the methods to get from one to the other can vary greatly.

This leads us to the recognition for the need of a formal, structured process for generating SKU unit forecasts from revenue forecasts that can generate a better forecast for the business as a whole. In order to allow the process to accommodate the differences between markets and regions, it must be have the functionality to allow for market specific processes, while maintaining a common underlying process that can tie back to the revenue forecast. The best way to achieve this goal is to use region and market independent product attributes, as they can ‘filter out’ the variations that can occur due to different business cultures. This also describes the best product attributes to select, as they should reinforce the underlying commonality of the SKUs being forecast. Market specific processes can then be applied to fine tune the forecast for a specific market or region.

In order to more accurately predict future demand at the SKU level, a tool is required can process large amounts of historical data and knowledge based assumptions quickly. This allows the analysts to perform what if simulations and determine the effects of various assumptions, as well as incorporate any trends that are evident in the marketplace. This leads to another “must have” feature: the ability for the analyst to easily and quickly incorporate trend simulations into the numbers. By using aggregated attributes on multiple levels and presenting the numbers in a percentage of total view, the analyst can quickly modify the splits at each attribute level to reflect known assumptions or model future trends, while keeping the totals within the revenue guidelines (the sum of the percentages must always equal 100). As well, by modifying percentages versus actual numbers at the lower attribute level, the overall revenue forecast can be kept consistent throughout the forecast process. The revised percentage splits can then be applied to the revenue numbers to break them down into a SKU level forecast that ties back to the revenue in an understandable way.

In summary, it is desirable to have a close correlation between the revenue forecast and the SKU level forecast. The best way to get there is to use product attributes, historical patterns, and the analyst’s expert knowledge to break the revenue forecast down to an accurate SKU level forecast. A tool that can support an underlying, formalized process with enough flexibility to allow for regional and market differences will deliver the optimum solution.  This tool must be intuitive to use and allow the analyst to quickly generate a valid forecast that supports the consensus revenue projections.

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Seeing jack-o-lanterns, skeletons and witches on front porches and store-front displays reminds us that—as difficult as it may be to believe–the holiday season and end of the year are right around the corner. And while that’s historically good news because holiday-season sales play a considerable role in many companies’ revenues, the sales season this year looks to be challenging.

One issue is that as orders fell during 2009, inventory levels for many companies stayed high. Since then, they have been able to deplete that surplus inventory, which is good. On the other hand, with that safety stock now either gone or very slim, these companies have become much more dependent on their suppliers’ responsiveness and overall performance.

The problem, as has been noted by both Bob Ferrari and Jim Fulcher, is that suppliers have had difficulty meeting their customers’ requirements this year. Texas Instruments, a supplier of several component chips for the DroidX, EVO and iPhone4 smart phones, is struggling to keep pace with customer demand. It is, however, far from being the only company with supply chain challenges. Mobile phone manufacturers themselves are responding to component shortages by switching to other components that may have more availability.

Furthermore, that situation isn’t just limited to the smart phone market. According to an MFG.com survey, 42 percent of small and medium-sized suppliers said they have received queries or work from larger companies that are urgently in need of assistance due to their own supply chain problems.

The second issue companies increasingly struggle with is demand uncertainty. Whether it’s toys, shoes or smart phones, no one really knows what future consumer spending will look like. In recent months consumers have shown a willingness—albeit somewhat guardedly at times—to spend. But with unemployment hovering around 10 percent and persistent fears of possible end-of-year layoffs, consumers may scale back their spending on holiday gifts this year.

The watchword in all of this then is “volatility.” That applies to suppliers’ ability to meet manufacturers’ demands as well as unpredictable consumer demand.

Addressing those market conditions requires companies to walk a tightrope of sorts. No one wants to lose possible sales and fail to meet customer expectations, so faced with uncertain demand and supplier volatility, the temptation is to build inventory levels. That way, there always is sufficient inventory on-hand to meet a potential sudden increase in demand. However, carrying that inventory presents drawbacks as well. Most notably, there is the high cost of carrying that surplus inventory—not to mention the possibility of being stuck with excess and obsolete inventories in case potential demand never materializes.

Use of S&OP helps alleviate these concerns because, as a cross-functional planning process, it helps organizations better identify and respond to demand so they can not only maximize opportunity—but also mitigate risk. That’s possible because S&OP helps users strike a balance between the sales and operations planning disciplines.

Key capabilities play an immense role. The ability to conduct what-if analyses–for instance—for both supply and demand is crucial, as is being able to evaluate analyses against both financial and operational targets. Secondly, event-driven S&OP is increasingly valued because it delivers the ability to actively monitor the current plan and notify the appropriate people when the plan is at risk. That, in turn, allows them to take immediate action to correct the course.

So, how is your S&OP process? Will you receive treats, or tricks, in the coming months?

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?

• 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.

3D TVs were the big story at the CES show in January and  now I’m starting to see them show up at Best Buy.    While I tend to be in the early majority of the technology adoption curve, I don’t think I’ll be buying a 3D TV any time soon.   So, am I representative of the population, or am I just receding to the technological late adopters side of the curve? Is my geekiness declining as my age advances? Or are others as apathetic about this as I am?

On the one hand, there is definitely a “gee whiz” factor that you simply can’t deny.  It’s that coolness that have been drawing people into 3D movies since the 1950’s   Classics like House of Wax and  It Came from Outer Space drew people into the movie theatres  despite having questionable plots and an excess of things flying out of the screen at you.  Recent hits like Up , Avatar and Alice in Wonderland are fanning the flames of interest in 3D.  Now you can have this experience in your home.

On the other hand,  there are several issues that could impact the adoption of 3D;

• Glasses – Today’s TV and movie theatre technology requires you to where special glasses to view 3D. A CNET article points out that the 3D glasses are specific to a given set and cost as much as 150 per pair.  This might not be prohibitive if you are just watching yourself, but if you have a family, you need a pair for each person.  And what if you have friends or family over for a visit – do you need a pair for them as well?  Besides…I have enough trouble keeping track of my remote control; who needs another thing to look for?
• Content – Aside from a few movies, and some sporting events, there is not a whole lot of content currently available.  Similar to the implementation of HD, there will be more and more content coming as devices become available and more common.  It’s that traditional chicken and egg problem.  From the consumer’s perspective;  What is the point of spending thousands of dollars for a new TV if there is nothing to watch?  From the content producers perspective; Why should I spend millions making 3D content when there are very few people with 3D sets? 
• Consumer fatigue – Many consumers have just upgraded their home theatre set-ups to allow them to consume high definition content;  Large 1080p televisions, HD Tivos, new set-top boxes from the cable company or satellite provider and of course new upconverting DVD players or Blue-ray decks.   Those who have just recently upgraded are not likely to want to go back and buy another new system.   Those who haven’t yet gone HD will likely not go 3D when the time comes to replace their systems.
• Technical issues – multiple formats, differences in perspective (kids see 3D differently than adults do because their eyes are closer together), all put the market in a high state of flux.

So the question then becomes if you are a TV manufacturer and are offering a 3D TV, how do you forecast future demand?  Do you go bullish and forecast big sales?  Or do you play conservative and risk stockouts and the loss of significant potential revenue?  

The big concern is that the technology is still in a state of flux and if you are holding significant inventory when the next advance hits, you could be stuck with a lot of obsolete inventory.  One example of potential game changing technology is the introduction of 3D televisions that don’t require special glasses.   You don’t want to be sitting on a pile of old stock when that change hits.

Traditional forecast accuracy mitigation issues can’t help in this case. Normally, you could use techniques like postponement strategies to limit inventory costs while improving responsiveness.  With 3D TVs it is entirely likely that technology changes and that the majority of your strategically placed inventory would need to be replaced. 

So what to do?  Personally, I’d probably go conservative, but as I’ve already pointed out, I’m not that excited about 3D. Let’s hear from you…  Are you excited about the upcoming “3D revolution”?  If you were to forecast 3D TV sales, what factors would you consider?   Comment back and let us know.

I do not know what the details are around the supply of this product, whether the forecast was low or manufacturing capacity was just not there, or if it was just a distribution issue, but it got me thinking about what can be done by OEMs to mitigate this type of situation.  The situation being that at some point, the forecasted demand was a lot different than the actual demand.  As I was taught years ago, the first rule of forecast is that they are always wrong.  My conclusion is that since the forecast is always wrong, the solution has to be in quickly responding to the changes or variances between the forecast and the actual demand.

I recommend you read the short white paper “Are Yesterday’s Solutions Conflicting with Today’s Challenges?”, by Charlie Barnhart when he was with Technology Forecasters.  While the paper was written a bit ago, the premise remains very true. In this paper, Charlie describes a response management approach to dealing with change (such as variations between forecasted demand and actual demand) especially in an outsourced manufacturing environment and given shrinking product life cycles and rapidly shifting customer preferences.  Also, some interesting points are raised in the white paper as to why increasing inventory is not a viable solution.  As stated in the paper,

“Response Management provides organizations the ability to rapidly test and score options for responding to change by identifying what’s possible today with today’s resources”.

What are your thoughts?  Do you have any insight into this particular supply issue with the Wii Fit or similar ones?  Do you feel that the response management approach described in the white paper can effectively mitigate forecast errors?

I’m sure no one was predicting the need for these products in 100ml sizes a couple of years ago. And what if the airlines lifted the size requirement on liquids or reduced it to 50ml? What chaos would that cause the demand planners of the world? Walking to the front of the store I noticed some Olympic wear. As you know, Vancouver just finished hosting very successful Olympic and Paralympic games. I could only imagine the heroics and horrors that were experienced to make these games the success they were. Everything from scheduling materials for the new venues to the clothing, flags, food and everything else required for the games. Will the promotions to sell off Olympic paraphernalia make up for the excess inventories now on the shelves and in the warehouses?

In a discussion thread on the supply chain expert community, Joshua Gao asked what your “Vision of the Supply Chain” is?  Well, if we look to the past many things are different from our grandparents’ supply chain. Two of the biggest stand out.  First, customers are more demanding. I mean that in a positive sense, in that customers can quickly research products, understand trends in technologies and purchase what they want with a few clicks of a mouse. The second is that supply has become more fragile. Outsourcing, margin pressures and even catastrophic events can cause supply challenges. So this gets us back to the vision of the future and the question, how accurate does the forecast need to be.

In the past, good enough may have worked because there were fewer demand and supply pressures. But today and in the future, is it better to have an accurate forecast or should the focus be on handling the deviation?

If the focus is to manage the deviation and leverage your supply chain as a competitive advantage, then how much effort should go into developing the forecast if you know it is going to be wrong anyway? This is where it would be helpful to get your feedback since the answer may vary based on industry etc. Does the forecast need to be more accurate given the supply chain challenges of today or do you just need some number to start with since you will have to handle change regardless what the forecast states? How close does the forecast need to be, 40%, 60%, 80%?

Just one final request for feedback: if you were involved in any Olympic related supply chain stories, it would be great to hear them. Maybe your story will make the podium and win gold, silver or bronze!