Michael Anderson

John Becker

Monday, 8:05am: your boss calls you into his office and says, “I just got back from the Supply Chain Forecasting & Planning Conference.  We need to start a S&OP process!”  He hands you a 9 inch thick textbook and barks “get it going!!.”

So you dutifully read the textbook, gather your team, collate the data, and have your first S&OP meeting.  It is a resounding and utter . . . failure.  “This data isn’t correct, the sales are off by $1.35!, that’s 0.002%”  “What do I care about OEM capacity, I have a trade show next week!”  “I don’t have time for another meeting, I have work to do!”  Sound familiar?  It does to us, as we lived this nightmare twice.

However, we lived to tell this tale, and currently have a solid but basic S&OP process in place at Fender Musical Instrument Corporation.  Furthermore, we are on the path to implement a full-fledged S&OP process across our organization including subsidiaries and over-seas offices.

In Phoenix at the IBF Conference on Feb 22-23, we will offer some insights into what to do when the best laid plans fail; how we learned from our mistakes, changed tactics, and ultimately got on the right track.  We will discuss how we won over a management team who had lost faith in our data, a sales organization who were indifferent to our efforts, and a marketing team who didn’t realize that S&OP could help them in their effort to create the “Spirit of Rock n’ Roll.”  Please bring questions and your own war stories of successes and failures from the S&OP front.

Of course, we welcome hearing your S&OP challenges here too!

John Becker
VP of Global Supply Chain Planning
Fender Musical Instruments Corporation

Mike Anderson
Manager of Global Demand Forecasting
Fender Musical Instruments Corporation

See JOHN BECKER & MICHAEL ANDERSON from FENDER Speak in Phoenix at IBF’S:

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February 22-23, 2010
Phoenix, Arizona USA

Dr. Mohammad M. Ali

If the answer to this question appears to be obvious, please read on!  There’s been lots of discussion about how forecasting is done, but much less about what is forecasted. Now, this is an important issue for all forecasters, but none more so than Supply Chain forecasters. Let’s suppose you’re using a particular forecasting method and there’s no scope to change it in the short-term.  What can you do to improve forecast accuracy?

As a manufacturer or wholesaler, if you are blindly following your customer’s orders to plan your inventory then you are a victim of the Bullwhip Effect. This is according to the ground breaking research by Hau Lee and his colleagues at Stanford University. The Bullwhip Effect is a demand amplification phenomenon which shows that the more far you are from the actual consumer, the more inventory and/or stock-outs you will experience.

So how do you tackle this? Well, simply by using approaches for being more consumer centric rather than customer centric. The first approach we’ll be considering is Demand Management. For example, by changing order frequencies or batching requirements, demand can be made less volatile and easier to forecast. Even if you make no changes to your forecasting software, accuracy will improve naturally.  Strategies can also be introduced to avoid ‘game-playing’ by customers, who deliberately over-order when they know that stocks are scarce.  Many of these strategies are applied common-sense, but may yield larger benefits than you would expect.

The second approach is less obvious, has been researched more recently and has even greater potential inventory savings.  It is known as Forecast Information Sharing (FIS). This is where the supply chain members either share forecast or produces a single collaborative forecast with their retailers thus giving more attention to the consumer demand rather than the customer demand.

Many companies such as Wal Mart, Procter and Gamble, Hewlett-Packard, Kimberley-Clarke that embarked on supply chain collaboration strategies have reported huge cost savings. Our simulation results on testing the benefits of FIS on sales data of two organisations, a European Grocery Supermarket and a USA computer hardware manufacturer, show similar results.

It is important for organisations to investigate savings in their own supply chain by such simulations. This realisation of the cost savings could provide a more realistic picture for organisations in terms of the expected benefits. This will also help them make decisions on the investments in information technology required for such collaborations. These gains can be estimated by simulation methods discussed in my talk.

The mantra everywhere is the same; huge savings in inventory and stock-out costs are experienced by collaborative initiatives. So when the actions required for being more consumer centric is so obvious what is keeping organisations back. Issues such as confidentiality and trust are huge stumbling blocks that keep companies away from being consumer centric. Experts suggest that although trust builds up after years of partnership, forced compliance by contracts is not an innocuous assumption. Other various reasons for slow embracement of collaboration activities include costs for information systems implementation, misalignment of incentives, forecast volatility, forecast inflation and lack of forecasting expertise. Again there are various suggestions by supply chain experts to surmount these issues.

Join me at IBF’s Supply Chain Forecasting & Planning Conference in London, 1-2 February 2010 to further discuss the above.  During the presentation, we will also share the simulation results of testing the benefits of Forecast Information Sharing and will also explore the factors that affect these benefits.

Your comments and feedback are welcome here!

Dr. Mohammad M. Ali
Department Manager
Buckinghamshire New University
(United Kingdom)

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1-2 February 2010
London, United Kingdom

Mike Gilliland

Below details Questions & Answers from IBF’s Webinar “What Management Must Know About Forecasting.”  If you missed it, no worries.  You can view it complimentary by clicking HERE.

1. If a product is not forecastable, what’s the most appropriate step to move the product to become forecastable?

Answer: The most effective way to improve forecast accuracy is to “make the demand forecastable” and a great way to do that is to lower the volatility of demand.  Most of what we do with our organizational policies and practices is to add volatility.  We encourage our customers to buy in spikes, and we encourage our sales people to sell that way.  This is completely contrary to quality management practices, which are all about removing volatility and making everything more stable and predictable.

Review sales and financial practices that are encouraging volatility, and either re-engineer or eliminate them and replace with practices that encourage everything to operate more smoothly.  (Examples of practices that encourage volatility are pricing and promotional activities, and the quarter end “hockey stick” to meet short term revenue goals.)  You should question whether these sorts of practice make sense by contributing to the long term profitability of your business.  If not, pursue ways to reduce volatility and encourage smooth, stable growth.  This will allow you to forecast more accurately and will reduce overall costs, which you can then pass along to your customers.

2. All of this is relative to the base line forecast, correct? What if your items are heavily promotional driven?

Answer: The accuracy of a naïve forecasting model serves as the baseline against which the performance of alternative forecasting methods should be compared.  Thus if the naïve model (say, a moving average) achieves MAPE of 40%, then I want to know how well my statistical model is forecasting, and how well my overall process is forecasting, and compare them to the baseline of 40% MAPE that the naïve model delivered.  This is what I’m talking about as a “baseline.”

This should not be confused with what is commonly called the “baseline” forecast when you try to distinguish baseline demand from promoted demand.  How do you know what demand was baseline and what was due to the promotion?  How do you distinguish the two?  I don’t believe that you can distinguish the baseline demand from promoted demand in a clean or easy or certain manner, so I would suggest not bothering trying to do so.  What matters is “how much total demand is there going to be.”  It isn’t necessary for me to care how much of it is “baseline” and how much of it is due to “promotion” – and I can never know for sure anyway?  Don’t assume you are making your forecasts  more accurate by trying to distinguish the two – you may just be making things more complex.

3. What is FVA?  A tool?  Expert judgment?  Or what?

Answer: Forecast Value Added is a metric, defined as the change in a forecasting performance metric (such as MAPE, forecast accuracy, or bias), that can be attributed to a particular step or participant in the forecasting process.  When a process step or participant makes the forecast more accurate or less biased, they are “adding value.”  FVA is negative when the step or participant is just making the forecast worse.  FVA analysis is the method of reviewing the performance of your process and identifying those non-value adding (or negative-value adding) activities that should be eliminated.  For more information on FVA analysis, see the webinar “Forecast Value Added Analysis: Step-by-Step” or the accompanying white paper.  You are also encouraged to attend the IBF Supply Chain Forecasting conference in Phoenix, February 22-23, 2010, to learn how to do FVA and hear case studies about several organizations (such as Intel) that are using this method.

4. What are the methods used commonly to measure Forecast Accuracy?  (Is MAPE the most common?) And what is a good process to determine forecast accuracy?

Answer: Mean Absolute Percent Error (MAPE) or its variations like Weighted MAPE or Symmetric MAPE seem to be the most popular metrics of forecasting performance.  MAPE has many well known limitations (such as being undefined when the denominator (the Actual demand) is zero), and is not suitable for use with data with a lot of zeroes (intermittent demand).  Also note that with MAPE you can have absolute errors greater than 100%, so you cannot simply define forecast accuracy as 100% – MAPE.

For management reporting I use a “Forecast Accuracy” (FA) metric, defined as:

1 – { Σ | Forecast – Actual |  /  Σ Maximum (Forecast, Actual) }

Note: FA is defined as 100% when both Forecast and Actual are zero.

By using Maximum of Forecast or Actual in the denominator, FA is always scaled between 0 and 100%, so it is very easy for management to understand.  That is why I favor it, even though some professional forecasters are very critical of this metric.

5. What are your perspectives on how do you differentiate volatile demand from uncertain demand?  In my opnion, uncertainty is related to an event and volatility is related to demand fluctuations. Is that right?

Answer: Volatility is expressed by the Coefficient of Variation (CV), which is the standard deviation divided by the mean.  For example, look at the last 52 weeks of sales, and compute the CV of that pattern.  In general, the more volatile (i.e. erratic and variable) the demand, the more difficult it is to forecast accurately.  Recall the Accuracy vs. Volatility scatterplot in the webinar.

Sometimes we can forecast volatile demand quite accurately, where there is structure to the volatile pattern.  You might see this for highly seasonal items, where you can always count on a big spike in demand at a certain time.  (E.g. bunny costumes and egg painting kits before Easter.)  Note: I’m not claiming we can forecast bunny costume or egg painting kits accurately, just using them as an illustration of volatility due to seasonality.

Volatility is measured looking back at what really happened.  If we expect high volatility to continue, we would probably have less confidence or certainty in our future forecasts.  If volatility is very low, we can probably feel more secure (and certain) of our forecasts.

6. Is there any ratio to determine the horizon for the forecast to be measured?  Any industry correlation to lead times?

Answer: Forecasting performance should be reported relative to the supply lead times.  Thus, if it takes 3 months to make changes in your supply, you should measure the accuracy of your forecasts made 3 months in advance. Once inside this lead time, it is ok to continue to make adjustments to the forecast, and many companies even report their forecast accuracy based on a forecast immediately prior to the period being forecast.  (Some companies even allow adjustments to the forecast within the time period (e.g. week or month) being forecast – and then report that as their forecast accuracy.)  However, it is the forecast made at the lead time that really tells you how well (or how poorly) you understand your business.  Don’t congratulate yourself on good forecasts made within the month being forecast!
Regarding forecasting horizon – how far into the future you should forecast – this will vary based on your business needs.  A power company forecasts years (even decades) ahead to know if it will need to make capital investments in new power plants.  For most companies, forecasting 12-18 months ahead is sufficient.  And the forecasting process should always be “rolling,” so that you always maintain that horizon of forecasts ahead of you.

Routinely doing 5-year ahead forecasts if you don’t really need them seems like a silly exercise.  If management insists on forecasting farther ahead than you really need, don’t waste much time doing it.  It is very unlikely you can forecast very accurately that far ahead.  It is much better to keep your organization nimble and able to adapt to however your market changes over time, rather than fool yourself into thinking you can accurately predict that far into the future.

7. How can you do calculate “appropriateness for forecasting” when your time series is too short for out-of-sample testing?

Answer: When there is enough data, out-of-sample testing is a great way to help evaluate and select forecasting models.  Good software, such as SAS Forecast Server, allows you to define and utilize a holdout sample in your model selection process.  Poorly designed software will select a model based solely on “best fit” to recent history, and as was illustrated in the webinar, the best fitting model may be a very poor choice for generating forecasts.

When there is not enough history to use a holdout sample, the appropriateness of a model is based on the judgment, experience, and domain expertise of the forecaster.  In the webinar example, Model 4 fit the history perfectly, but the forecast exploded to huge values which probably weren’t realistic (unless you had domain knowledge that demand would be significantly increasing, you were rolling out to new regions, etc.).  Without any other information, using the mean (Model 1) or a simple trendline (Model 2) seemed to be “most appropriate.”

8. Statistical modeling can be difficult in planning service parts demand. Can you give further input for planning service demand volatility.

Answer: Demand for service parts if often intermittent, with lots of periods of zero demand.  Intermittent demand is difficult to forecast accurately.  Although there are various methods to help you forecast and manage inventory in these situations (see Croston’s method and its variations), you should not have high expectations for accuracy.  It may be easier (and just about as effective) to simply forecast the mean demand each period.

Sometimes there is sufficiently high demand for the service parts that you could use standard time series methods to forecast.  It may be helpful to incorporate known sales of the items requiring the parts, so you can base your forecasts on failure rates.  Thus, if you know 100,000 units of a product were sold, and that 10% require servicing every year, this could help you decide that about 10,000 of the service parts will be needed each year.

One other approach, more applicable to high value machinery (e.g. jet engines, ships, factory production lines), is knowledge of the routine maintenance schedule.  If you sell 1000 jet engines and the maintenance schedule says a part is replaced every 6 months, then you can use this to forecast demand for that part.

9. Do you have examples available of cost of inaccuracy metrics?

Answer: I do not have access to the Cost of Inaccuracy metric used at Yokohama Tire Canada by Jonathon Karelse.  However, Jonathan will be speaking at the IBF’s Demand Planning & Forecasting: Best Practices Conference in San Francisco (April 28-30), so you could follow up with him there.

IBF members have access to a cost of inaccuracy spreadsheet available on their website.  Also, analyst firm AMR has published research (which you could access if you are an AMR subscriber) on the costs of forecast inaccuracy.
Any such cost calculators are based on a number of assumptions which you provide, so be cautious in your use of them and in your interpretation of the results.  Personally, I’m very skeptical of claims such as “Reducing forecast error 1% will reduce your inventory costs x%.” If nobody in your organization trusts your forecasts now, reducing the error by 1% is not going to make anybody more trusting of the forecast, and they won’t change any behavior, so you won’t reduce inventory.  It may take more substantial improvement to reap the cost benefits.

10. Does anyone work in the call or contact center environment for an inbound customer service center?

Answer: These principles can be applied to forecasting demand for services, such as in forecasting needs for call center staffing.  The major difference is the time bucket that is of interest.  Call centers often forecast in 15 or 30 minutes increments (rather than in weeks or months for a manufacturer), to make sure they are sufficiently staffed during peak call periods, and not overstaffed during the low call times.

Michael Gilliland
Product Marketing Manager, SAS
IBF Board of Advisor

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February 22-23, 2010
Phoenix, Arizona USA

Michael Gilliland

Emily Rodriguez

Are there some things you wish your organization’s management knew about forecasting?  Those of us who have served time in the forecasting profession know that “serving time” is an apt description of the job.  Being a business forecaster is sort of like being in county lock-up – without the benefit of free meals, charming bunkmates, and periodic delousing.  Forecasting is difficult – we never seem to forecast accurately enough to please management.  And forecasting is thankless – even when we come up with good models that forecast reasonably well, someone above us is likely to change the numbers to whatever they darn well please.  Those forecasters that aren’t already on mood altering substances probably should be.

What the forecaster really needs, are the tools to educate management, and forecast as accurately and efficiently as can reasonably be expected given the nature of your demand.

There are four main reasons why forecasts are wrong:

• Unsound or misused software
• Unskilled or inexperienced forecasters
• Politicized forecasting process
• Unforecastable demand

The best accuracy you can achieve is limited by the forecastability of your demand patterns.  So accuracy expectations have to take that into consideration.  The naïve forecasting model is the proper baseline for accuracy objectives, and industry benchmarks should never be used to set accuracy targets.

New product forecasting is an area of particular angst.  Managers realize that these forecasts are usually way off, yet they forge ahead with supply and revenue plans in full confidence.  We suggest that assessing uncertainty and risk is more useful than forecasting alone.  When management has a good understanding of the likely range of new product demand outcomes, the organization can better align resources to all the possibilities.

We also support Forecast Value Added (FVA) analysis – a method now used by many major organizations to identify forecasting process waste and to achieve better forecasts.  FVA evaluates every step and participant in the forecasting process, identifying those that are not adding value by making the forecast better.  Many process activities are found to be making the forecast worse – and these activities need to be fixed or eliminated.

Intel extensively uses FVA analysis.  Over the last three years, Intel has taken the basic idea of FVA and applied it to a broader range of forecasting and supply chain process issues.  Intel has gone through paradigm shifts in thinking, and how to address the change management issues.

On Monday February 22, at the IBF’s Supply Chain Forecasting Conference in Phoenix, perhaps we (Emily Rodriguez, Program Manager at Intel) and Michael Gilliland at SAS) can help improve your forecasting performance. We are delivering a morning workshop entitled “A Primer for Management: Fundamentals of Business Forecasting and Conducting Forecast Value Added (FVA) Analysis.”  The theme for our presentation is “what management must know about forecasting.”  We will be providing step-by-step instructions for gathering, analyzing, and reporting the data needed for a thorough FVA analysis, along with several brief case studies at organizations, where it has been applied.  Furthermore, Emily will provide an in-depth case study of the use of FVA analysis at Intel.

Whether FVA is a new concept to you, or you are an experienced practitioner of the approach, we look forward to having you join us in Phoenix.  Meantime, stay abreast of the latest innovations and defamations in forecasting, in The Business Forecasting Deal.  See you in February at the IBF Supply Chain Forecasting Conference.

Emily Rodriguez, Program Manager
Intel Corporation

Michael Gilliland, Product Marketing Manager
SAS Institute

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February 22-23, 2010
Phoenix, Arizona USA

Martin Joseph

I remember the day very clearly when I read Donald J Wheeler’s book “Understanding Variation – The Key to Managing Chaos.” What grabbed my attention was the sterility of the traditional monthly report and, furthermore, the potential for it to be truly misleading.

Let’s consider what is usually provided to managers prior to and, worse, at meetings designed to help them steer the business. The S&OP is one example of such a meeting. Do any of the following describe typical reports in your business? If so, read on!

• Tabular rather than graphic
• Binary comparisons with plan and/or budget
• % difference between current period/month with last period/month
• Units which are not the “common currency” of those attending
• Little or no narrative
• No historical or future context

Few people can absorb real meaning from data presented in tabular format – it is difficult to process and tends to focus the reader on the largest numbers, whether they are positive or negative, important or trivial. Appropriate graphics showing historical trends gives considerably better insight, but even better when overlaid with the forecast and plan.

The inherent bias in the review process created by reviewing tabular data sets is then compounded by making comparisons with plan or with budget. A comparison of sales numbers for, say, September 2009 with the budget created from forecasts made in September 2008, adjusted and phased politically seems to me to be rather pointless! Additionally, using percentage movement rather than absolute change can, as we all know, be very misleading.

Thinking about the S&OP; data is often presented on last month’s sales volumes in SKU’s, inventory in weeks cover, working capital, line capacity…….as a Marketing Manager, I’ve just nodded off and will find a good reason not to attend next month! The challenge is to present the same core data in the units understood by all attendees: $, €, £, or ¥ for Finance and Marketing and volume, SKU’s, weeks cover etc. for Manufacturing folks.

However, the main issue here is “context.” How much more informative would the data be if it was presented in a way which showed historical trend, future trend, historical variation, the forecast, the trend through the forecast numbers, the gap between current trend and plan/budget and the required performance to close any gap? Clear graphical presentation of exceptions is essential to focus the meeting and decision-making process.

Supporting documentation should include a narrative covering commercial intelligence and explanations for historical and forecast changes in trend or variation and, where available for any exceptions. This narrative should also include commentary concerning the tactics used to close any gaps and likelihood of success.

If any of these ideas resonate with you, please attend my session at the IBF’s Supply Chain Forecasting & Planning Conference: Europe in London 1st-2nd February 2010 – see you there!

Martin Joseph
IBF Board of Advisor
Managing Director
Rivershill Consultancy Ltd. (United Kingdom)

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1-2 February 2010
London, United Kingdom

Mark Frost

D. Reilly

The “time value of money” is at stake when you are trying to forecast demand at ATM machines and of course, customer satisfaction. Trying to get the right amount of cash for pay day and holidays requires some pretty complicated models to get this right.  The reality is that these methods and approaches of forecasting daily cash demand are just as necessary when forecasting what is perceived to be “simpler” problems.  The S&OP process has treated the importance of a baseline forecast as just a “stepping stone”.  A big reason why the S&OP process is leaned on so heavily is baseline forecasts are often generated using a simplistic model that doesn’t capture patterns into a model, but rather fits a pre-specified model to the data.  A quality baseline model and forecast can alleviate a lot of the work downstream. Another comment about adjusting forecasts is that if it is for a reoccurring reason it can be added as a causal variable to the model and utilized “in-line” or also “in-model”.

When building a forecasting model, it’s important to recognize how variables like “day of the week”, “week of the year”, “day of the month”,  and holidays can capture the swings in demand and allow you to plan for them.  It’s not just the holidays, but the days before and after the holidays that need special consideration as demand ebbs and flows around these events.

Furthermore, we often hear “I am fed up with fixing forecasts”.  This can be alleviated by taking a more rigorous approach to identifying patterns rather than have some list of 50 models to be forced onto a data-set “hoping for the best” without any care for what patterns are occurring in the data.  The “one size fits all” modeling approach by taking 50 models and forcing them on a data-set is like fitting a square peg in a round hold.  Customized suits are exactly that.  Custom suits are yes custom and they take a little work requiring the expense of a tailor, but you have a proper fitting product at the end of the process. “One size fits all” can result in a hat that just doesn’t fit your head as we have seen!

Join us at IBF’s Supply Chain Forecasting & Planning Conference in Phoenix to further discuss the above.  Plus, our discussions will also cover “motherhood and apple pie,” what you need to know to make better decisions about what makes a good baseline forecast.

Your comments and feedback are welcome here!

Mark Frost
Director of Business Strategy and Decision Science
Fiserv

David Reilly
Sr. Vice President
Automatic Forecasting Systems

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February 22-23, 2010
Phoenix, Arizona USA

IBF's S&OP Workshop

Last week, I attended IBF’s Executive S&OP workshop with Tom Wallace and companies such as LG, Celegene, Integra Life Sciences, UGG Boots, and more.   Some folks flew in to New Jersey from beautiful Southern California just for the S&OP education.  Bless them for coming to the North East.  If that doesn’t show dedication to the field, I think it certainly comes close.

I am often amazed how demand planning, forecasting, and S&OP always initiate great discussion.  This program was no different.  We’ve been covering S&OP for some time and I truly enjoy when a program provides nuggets of new thought to take back.

Here are some thoughts, questions, and/ or takeaways from the workshop:

1) There is still confusion between what is demand planning software and S&OP software.  Of course, any S&OP software does need input from demand planning & forecasting.  But, is it ok to call a demand planning application a S&OP application?

2) Some companies call the S&OP meeting the compromise meeting.  This certainly resonates well with me when I think of shopping with my wife on a budget .

3) What are the steps that need to be taken during and after a company acquisition?  Tom says, S&OP should be leading the discussions prior to acquisition.

4) How do you change behaviors and thinking when a company has been extremely successful with double digit growth? How can we be proactive during this time to ensure we have a process in place to handle anything which might arise in the future?

5) Is one number planning truly one number or one set of numbers that drives planning?

6) There are many companies that have successfully implemented a S&OP process without a  consultant. This, of course, depends on the knowledge of the person driving the new process.  If completely new, it may be in the best interest of a company to hire an S&OP consultant to save time and resources in order to reach the benefits of the process much faster.

7) Tom always says the S&OP process is balancing Supply with Demand, not the other way around. However, for example, Ugg shoes uses supply to drive demand?!  I will talk more about this in a later post.

If you have any further thoughts on the above, or have an answer to these questions, we would love to hear from you.

Thanks for listening.

Anish Jain
Managing Director
Institute of Business Forecasting & Planning – IBF

Gustavo Guttierez

Sushil Srivastava

Do you have nightmares that your critical suppliers will make short or no notice revisions to your critical component supply orders? How would your company adapt if suppliers decided to switch to buying their components from low-cost, low-quality countries and delivered unreliable or faulty parts for your products? How would your delivery performance fare if one or more components were delayed by a month or even a week? These are just a few of the challenges that we’re facing at TTI Floor Care North America (the makers of Hoover and Dirt Devil), where a single delay from a key supplier could cause huge sales losses. This is a perfect example of how formally collaborating with your suppliers provides a valuable benefit.

Managing and collaborating with a global supplier network is not an easy task.  At TTI, short term and long term forecasts are generated from the supplier level down to the individual SKU level and shared with suppliers utilizing a real-time internet collaboration portal. TTI and its suppliers can monitor and respond to events in the procure-to-pay cycle, view and acknowledge purchase orders, submit change requests, create advance shipment notices, view receipts, inventory levels, invoices, and payments. This closed loop collaboration enables TTI and its suppliers to thrive, even in this challenging economy. This pursuit has improved the forecast-to-delivery-to-pay cycle tremendously.

We would enjoy hearing your stories of successful collaboration with suppliers and also the supply chain challenges you face.   We look forward to sharing insights from the entire TTI Floor North America journey with you at IBF’s Supply Chain Forecasting and Planning Conference in Phoenix, AZ.

Again, your comments, feedback and challenges are encouraged and welcome!

Gustavo Gutierrez
Director of Operations
TTI Floor Care North America

Sushil Srivastava
Associate Director
Fujitsu America Inc

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February 22-23, 2010
Phoenix, Arizona USA

Constance Korol

I have been following Jeff Ashcroft’s informative tweets and interacting with him for some time now even though we never actually met in person prior to last month.  It was at the IBF booth at the APICS annual conference last month when Jeff approached us unassumingly. As soon as I looked up and saw Jeff, I said “I know you! I know you from Twitter” Jeff immediately responded by saying “No one has ever said that to me before”. Welcome to the Web 2.0 world.

Last week, Jeff was cordial enough to spend some time with me, providing insight on his work experiences and how he sees social media as playing a role within Supply Chain organizations.

CK: Tell us a little about yourself. How long have you been working in Supply Chain & Demand Planning and how did you get started?

JA: When I retire I am going to write a book called “I Fell into Logistics” which is what happened.  Following part time work at The Hudson’s Bay Company distribution centre I graduated during a major recession and needed a full time role as I was getting married that year. From there I moved through to top logistics job over a 15 year career learning by experience all along the way.  After the corporate VP role I was grooming for was eliminated, I left HBC to join Tibbett & Britten Group a major early mover in North American third party logistics.

As 3PL was new to North America at that time, the business grew rapidly from 20,000 sq ft of distribution space to over 20 million in five short years.  Most of these were major retail DC operations with the most notable being the creation of Wal-Mart’s coast to coast Distribution & Transportation operations across Canada in 1994 supporting their successful entry into the country. After leaving TBG I started my own consulting firm Strategic Logistics Partners providing many services in traditional logistics consulting as well as facilitating leading edge end-to-end multi company supply chain initiatives.  I moved these projects into PricewaterhouseCoopers in 2005 as Vice President Logistics/Supply Chain and Retail Advisory Leader for Canada over the following two years.  Strategic Logistics Partners continues to provide a variety of services to all levels of the supply chain and has taken an active role in the development & use of social media in the supply chain profession.

CK: When and how did you first enter the world of Social Media? How has it helped in your career thus far?

JA: Depending on how you define social media, you could say it goes all the way back to 1995 when I launched my first website Logistics World where I “blogged” before the term was even coined. In 1999 I built and launched the About.com Logistics/Supply Chain site with many community features through a Discussion Forum, weekly Newsletter and interactive chats held on a regular basis with key industry folks generating tens of thousands of page views per week.  In 2004 was when I first began using LinkedIn which was again before anyone used the term social media, but it was a very useful “networking” tool and also allowed you to always find a contact name for any company you wished to approach, a very useful tool in sales.

However, within a year of the acquisition of About.com by The New York Times I left the About.com site and created Supply Chain Network www.supplychainnetwork.com which continues today as a successful logistics blogzine site with a weekly SCN Newsbrief newsletter. Additionally in 2008 Green Supply Chain Network www.greenscn.com was launched to allow us to cope with the volume of information being generated in regards to green logistics and supply chain management and discuss the associated issues we now all face. In addition to the blogging, use of both LinkedIn and now extensively Twitter.

On the career side, I have found that LinkedIn allows me to be a better networker than if I was to work without it. For example, over my career I have met , interacted and worked with many people who are constantly moving and by being “out there” on LinkedIn allows those people I’ve either lost touch with or decide they need to reach out to me to find and reconnect with me more rapidly and easily than through conventional means.

CK: What social media networks do you utilize and which do you feel is the most engaging?

JA: As noted above, the two I use most and most frequently are Linked In and Twitter. There are many, many, many social media sites and services out there and my feeling is if you try and use them all, you will drown and potentially not be effective on any of them.

On LinkedIn I now have   2,116 direct connections, 706,600+ 2^nd level, 12,839,100 3^rd level for a total potential introduction reach of 13,547,800 people.  And one of the most important features and functions of LinkedIn is the Groups function where I run 10 Groups and belong to 40 more. Specifically relevant to IBF folks would be the SCN Inventory Management Group http://ow.ly/FkD1 , the Vendor Managed Inventory Group http://ow.ly/FkyI and the overall  SCN Supply Chain Network Group http://ow.ly/FkDv .

On Twitter now across all my supply chain and logistics related accounts I have a total Followed of 16,100 and 12,200 Followers and the growth of these is accelerating as the total number of Twitter users grows and also as more and more logistics & supply chain professionals join Twitter.  Some of the relevant Twitter accounts I utilize that would be of interest are:

@supplychainntwk

@Jeff Ashcroft

@TheSocialCSCO

@TheSocialCPO

@TheSocialCLO

Some of the uses of Twitter are mentioned in the ISM article however one big benefit which every individual should capitalize on is the ability to leverage use, position and activity in social media in order to enhance your role and status within organizations. The early adopters in this case have much to gain over laggards who insist in keeping their heads in the sand when it comes to engaging in the social media world and do this at their own peril.

Now also emerging is Google Wave which basically allows you to track waves of communication threads from across all social networks and have more immediate chat type discussions. Initially it looks like Twitter on steroids, but I am still evaluating if it will get a long term place in my social media toolkit.

CK: How do you feel social media has changed the face of the Demand Planning Field and how do you feel it will effect the field in the future?”

JA: Yes I certainly do feel there will be major impacts and these will relate initially to increased communication and collaboration up and down supply chains and the breaking down of the silos between functional areas and between trading partners, carriers and all stakeholders and actors.

There are a number of other supply chain uses and implications and rather than restate them all here, it is easier to direct you to my Social Media in the Supply Chain article recently published in the ISM’s Inside Supply Management publication.  http://ow.ly/u2mn

If social media evolves as it currently appears, it could also become a great medium for consumer demand sensing, especially for new products which are very difficult to manage.     And for all products there is no doubt that getting an earlier demand signal can be very valuable. Also, there are an untold number of neural network inputs which will now become more readily and cost effectively available through social media channels that can be integrated into making forecasting even more accurate in the future.

CK: Do you have any recommendations for those new to demand planning and or social media?

JA: Get on with it!! Don’t waste another day wondering what the value will be, the value is there, it is real and the sooner you get on these networks and acclimatized to them, the sooner you and your organization will be in a position to reap the benefits. Get the jump on both your co-workers and competitors so you are working from a position of information strength. What this is all about is communication, communication, communication one thing I think most will agree that there is not enough of in the world today which leads to many of the problems we face in supply chains.

CK: Jeff, thanks again for your time in sharing social ideas in the supply chain.

IBF moderates conversations happening on Linkedin, Facebook and Twitter. The global networks are growing every day.  Join the conversation if you have not done so already.  I look forward to seeing you online as well as onsite at an upcoming event.

Luis Carlos Rojas - CPF, IBF Speaker, GOODYEAR & Anish Jain, IBF

I recently returned from Mexico City from our Spanish based Supply Chain Forecasting & Planning Conference: Latin American Experiences, produced together with Corporate Resources Management – Mexico City and IBF – New York USA.

As the IBF continues to deliver more International programs, it is clear that many of the challenges we face in the United States are the same challenges that are being faced in other countries, including companies from Mexico.  Of course, they too are dealing with a declining economy, maintaining proper inventory levels, ensuring product is available for their customers when they want it, and abiding by the rules that large retailers, including Walmart Mexico, dictate to their suppliers.

I have to say, it was a bit embarrassing that I had to keep saying to folks, “No Hablo Espanol” (I don’t speak Spanish) at the conference.  However, my newly made friends at the conference were extremely forgiving, which was “mucho” appreciated.  Therefore, although, the sessions were in Spanish, based on my conversations in English with attendees, here are a few takeaway’s:

• A person from the makers of Tecate Beer mentioned that they are just exploring CPFR relationships with one of their customers, but it continues to be slow to develop. However, it helps to share information and business plans when your retail customers are partially owned by your own company.

• Attendees from Coca-Cola Mexico certainly recognize the value of Sales & Operations Planning (S&OP) and how it can bring significant benefits in terms of profitability, long-term visibility of the business, and competitive success.  However, they can’t seem to find the magic pill to get everyone to buy-in and change their behaviors to support the process…stick to the plan?  Folks, does this not sound familiar?

• Revlon Mexico leverages the demand data from Revlon USA 6 months after launch in the States.  Although, different cultures have different dynamics, it provides Mexico something to go on during consensus forecasting and inventory planning discussions.

• Another, “surprise, surprise” common in the States is many companies that are trying to get their S&OP processes off the ground, continue to struggle with having the right data at the meeting.  Again, no different from many companies in the States.

Anish Jain, IBF & Jesus Campos, CEO, Corporate Resources Management

Furthermore, although the sample of attendees at the conference did not represent Mexico on a broader level, I was impressed with how hungry they are for knowledge to implement or improve their demand planning, forecasting, and supply chain planning processes to stay ahead of the competition. And you also have to thank their bosses/ senior management for recognizing the value of such an event.  As I said in my opening speech (which was translated to Spanish) it is the best companies that continue to invest in their people in terms of professional development, training, conferences, certification, and programs that provide access to learn how others are handling the business in this never seen before, fast changing market place.

Please share your experiences, that will surely encourage others to share theirs in an effort to help us all grow! Your comments and input on the challenges you’re currently facing in demand planning, forecasting, S&OP, and supply chain management are sincerely welcome.

Anish Jain
Managing Director
Institute of Business Forecasting & Planning – IBF
New York USA