July 7-8, 2010 Norwood, MA at the Four Points by Sheraton Hotel.

Workshop is conducted on Wednesday and Thursday,  so you can spend the Spring weekend in gorgeous Boston, MA or see the yachts on the Charles River!

Whether you are new to demand forecasting, or a seasoned pro looking to enhance your knowledge, you cannot afford to miss this opportunity.  We will answer questions such as:

1. Do you trust the power of Statistical models to form a clean baseline forecast?
2. When does a forecast actually become a demand plan?
3. What do you use to create the demand history – shipments or Forecasts?
4. What is the right software to plan your demand?
5. Do you model and forecast every item every month?

GET SKILLS YOU CAN USE AT WORK

You will learn to…

• Set up a Demand Planning Process for your business
• Clean your data and adjust for data anomalies
• Use Statistical modeling to create baseline forecasts
• Use exponential smoothing and linear regression models
• Leverage the Regression capabilities in Excel
• Incorporate promotional events into your forecast modeling
• Use Forecast Error as a diagnostic to improve model quality
• Reconcile the top-down category forecast and the Bottom-up SKU level Demand Plan.

We will explain the modeling methodology and process behind accurate demand forecasts and how to effectively use promotional information to arrive at a consensus forecast. The focus will be on demand modeling using statistical techniques, the methodology to perform model diagnostics, forecast accuracy measurement and the process to incorporate market intelligence.

LEARN FROM INDUSTRY EXPERTS

Each training day will also include an industry-specific presentation from a senior supply chain manager:

NETWORK WITH YOUR PEERS

You will have ample opportunity to meet, interact, and learn from other demand planning professionals with team challenges and networking exercises.

ADD TO YOUR CREDENTIALS

Upon completion of the tutorial, you will be awarded a certificate of completion from Demand Planning LLC, attesting to your newly-acquired skills in Demand Planning.

Price is $1,045.  Please call the Demand Planning LLC office for a special room package subject to availability.

REGISTER NOW ON DEMAND PLANNING.NET!

Event URL: http://www.demandplanning.net/demandplanning_tutorialNJ.htm
Registration URL: http://www.demandplanning.net/seminar_anregistrations.php
Event brochure: http://www.demandplanning.net/documents/dpTutorial_NJ_w.pdf

Between SAS and APO DP, the difference is huge. You have an array of statistical models to choose from in SAS while APO DP, has just a few limited set of models. Even among the models available in APO, the optimization and convergence of model selection to result in the parameters are some what inefficient and does not compare to even inexpensive off-the-shelf products like Forecast Pro or Autobox.

For univariate forecasting, you can use Exponential smoothing models, intervention models, Box-Jenkins models, distributed lag models, vector autoregression models etc. SAS allows you to model using a variety of techniques and allows you to customize them, although you require deep knowledge in statistics and programming. It is not a plug and play tool.

APO DP offers the basic exponential smoothing models and linear regression model that use a deterministic time index as the independent variable. The exponential smoothing models work for most purposes but they are not the most efficient – for example the iterations of the smoothing parameters namely alpha beta and gamma are in increments of .5 not anything in between.

And some configurations I have seen and versions I have seen can be quite sticky and may just resort to one model with all parameters set to .3 or some user-defined selection. This is also quite surprising because it assumes the user already knows what the parameter is – from where? By doing the modeling in an external statistical tool and sticking them back in?! But this is not APO’s fault – mostly the configuration team that did not know how to leverage the power of the tool.

SAS also can be used for generating transfer function models when you need to do true causal models. The only option available in APO is to use the MLR models using a multi-variate profile and then inserting your events in a linear model. This may actually produce incorrect results if there is auto-correlation in your data, which is the most likely probability since most sales data will be serially auto-correlated.

So in summary, SAP has some usable statistical models but one has to be careful in choosing what to use where. You need a demand planning expert to set the profile settings and the model selection process and train your users on what to do and what not to do.

For additional information on APO DP modeling, please see

http://demandplanning.net/sapAPO_workshop.htm

Our recommendation is to exclude the Obsolete Skus from measurement and in computing the aggregate MAPE as a performance measure for the planner or for the Sales Manager responsible.  Clearing obsolete stock is a supply management activity not a demand forecasting activity.

Demand Plans are externally focused and are a representation of what the market wants.  The obsolescence of stock may be a result of inferior demand forecasting in the past but has nothing to do with the demand for such stock in the current period.  In essence, there is no demand for that stock – either it is sold for scrap or just donated.

Making the Forecasted quantity match the actual demand for such obsolete stock will have a downside as well.  This will artificially inflate the forecast performance of the planner on a weighted basis and hence hide poor forecasting on active open stock items.

For example, you sell 25K units of active SKU for which you forecasted only 5K.  Then you have 100K of obsolete stock you sell.   Under your measure the MAPE will result in a 16% error or an 84% accuracy.  In reality the MAPE on your active SKU is 80% or 20% accuracy.  The 100% accuracy on the obsolescence is a manufactured number but wrongly aggregates to influence the divisional MAPE.

There was a post on one of the Linked-in Groups that got my attention today.  How do you reduce demand volatility using better forecasting?

Forecast accuracy and Demand volatility are two different things, though there is a strong relationship between accuracy and volatility in practice.  For the purposes of this discussion, I want to use just common man terminology.

Demand volatility does NOT necessarily mean the demand is unpredictable.  Demand volatility also does not mean the demand is an ugly scatter of points across the map.

For example, the calculated demand volatility for a product could be the result of a highly seasonal demand profile.  Such highly seasonal demand can look very pretty on a graph and could be easily predictable by what I call as an extrapolation by hand-drawing.

In summary, volatility is not necessarily bad – growth causes volatility, so do seasonal and cyclical swings.  There could be other reasons – promotions and phase in, phase out, bonus packs, price actions, just plain old management actions to make the quarter.

Corporate management can try to stay away from some sub-optimal business practices and hence cause a reduction in demand volatility.  They can promote so customers may buy through out the year.  But can we temper seasonality and growth?  Do we want to?

Life is essentially seasonal and we have to live with it.  Christmas does not occur every month of the year!  People like to go to the beach on sunny days, and more sunny days occur during the summer.

Forecast accuracy is an expression of how well one can predict the actual demand – volatile or not.  You may be able to create a very accurate forecast from a volatile demand distribution.   This can be done by good modeling and diligent forecasting.

How ever there is another distribution that the forecaster needs to consider.  The distribution of the actual outcomes around the forecast — if the forecaster can say his forecast has at most 5% upside and a 5% downside, then hats off!  However if the plausible outcomes are widely varying, then more needs to be done to lower the expected error:

-  Model and forecast at the right level and use proportioning techniques

-  introduce more magic in modeling (however you do it, software or otherwise)

-  collect that market intelligence only foretellers can come up with

-  discipline your corporate management to reduce demand volatility (hopefully without losing your job)

-  S&OP process so one hand knows what the other hand is doing and reduce the bias in the forecast coming from functional groups.

The last point is more important.  More seasonal products suffer from more game playing before the season, so do scarce promotional packs.  Similarly, long lead-time products also cause more game playing.  You can expect more  upward forecast bias in these products as well.

In conclusion, improving forecast accuracy involves doing a variety of things and reducing or mitigating volatility is just one of those things.

“”I hate to get technical”. That expression always bothered me, but I am wondering who IS getting technical….who is using Orders to help predict POS? Who is using a “day of the week” dummy variable?

It is my belief that the current simplistic solutions aren’t able to deal with issues like this. Let me explain why….because they can’t solve the technical modeling issues to solve the problem correctly. I defy someone to tell me TWO solutions that can bring in orders to help forecast POS demand and identify and adjust the model based on the lead/lag relationship between orders and POS demand. The same holds true for “day of the week” dummy variable added in automatically when you have daily data. Also “week of the year”….”day of the month(when appropriate)”….fridays before a WEEKEND holiday…do you see my point?….. I could go on all day here…..How about Interventions?….identifying and adjusting the model for a “level shift”….”local time trend”….the challenge has made…..one more thought….the conclusion from the M3 competition that simpler was better did have a lot to do with the “KISS methodology” conclusion which didn’t necessarily help besides the technical difficulty.”

I was initially puzzled by the order of the POS versus orders – what is forecasting what?  or even Why?  Is it useful to predict POS using the orders?  Even if there is a significance, would that be a spurious variable just proxying inefficiently for the inventory levels and perhaps promotional activity?

We have always tried to solve client problems that needed the POS data to try to predict the orders.  Now you mention using Orders to predict POS, which seems to be a novel idea.

Orders can predict POS?  If so, how?  If I am the retailer and I can place orders, how will I use my historical order pattern to predict what my consumer is going to buy?

Perhaps just use the old adage “stack ‘em high and see ‘em fly”?!

Just order from the manufacturer and build your shelves and stores with the inventory, so the mere visibility of this inventory creates POS demand?!  If so, this will make the push concept very usable.  Seems very consistent with the supply side argument of the economists on the monetarist side.

It is good to see a refreshing view amidst all the supply chain forecasters subscribing to the pull-based forecasting methodolgy.  And I am one of them too……. I believe that using the POS can better predict Orders, the opposite of what you are proposing.

And this is what we preach and educate to our constituents.  How to better use your POS demand and inventory patterns to better create an order forecast.

You may want to review our web workshop information at

http://www.demandplanning.net/cpg-demand-planning-web-workshop.htm.

If you want to improve forecast accuracy, then you have to ask why that is important.  First of all measure and calculate forecast accuracy, identify the value drivers in the form of inventory and service.  Then connect the dots — connect inventory and service to forecast accuracy.  See if there is a correlation.  This should define your value opportunity.

If you take this approach, then you will understand the importance of developing a balanced metrics process for your entire value chain.  This metrics process should include all metrics necessary to improve the profitability of the business.  These metrics should interest the Chief Executive Officer of the business.

We at Demand Planning LLC, have worked with clients to design a balanced, holistic, and comprehensive  set of metrics for the entire Value Chain.   This should help understand the root-causes of inefficiency – obsolescence, inventory levels and unbalanced cost structures and drags on corporate profitability.  You also need to measure the impact of sub-optimal customer service levels on your topline.

The holistic value chain metrics may incorporate:

• Profitability Metrics – Return on Investment and Return on Sales, Gross Margins and Net Margins
• Capacity Utilization – Asset Turnover
• Demand Metrics – Forecast Error, Forecast Bias etc.
• Inventory Metrics – Days on Hand (DOH), SLOBs, Inventory Turns
• Working Capital Metrics  - Receivables
• Service Measures – First Time Fill Rate or FTFR, Perfect Order, In-Stock levels
• Manufacturing or Execution Adherence
• Logistics, Transportation and Distribution Performance

It is not only important to design and develop these metrics, but also to continuously measure and monitor corporate performance.  This should really be the starting point for any value opportunity.  You may also want to set a target as the goal for the improvement efforts.  Then find the people and partners that can commit to achieve those targets.

• Data availability (especially Point Of Sale or channel data)
• Lack of internal demand communication to enhance accuracy
• Inappropriate processes and tools for promotional forecasting
• Frequent, unexpected events affecting planners’ assumptions and models

In our Demand Forecasting Web workshop to be held on January 20 and 22, 2010, we will illustrate the methodology to address challenges specific to pull-based Demand Planning for CPG companies
1. How you can leverage customer inventory and POS in creating accurate customer demand plans?
2. How are the principles of Account Based Forecasting (ABF) used to create the total supply chain forecast for manufacturing?
3. Why is event modeling important for CPG companies, and how should the impact of promotional events be captured in your forecast models?

This workshop will explain the process to create a demand-driven pull forecast that incorporates the effects of changes in market, consumption patterns, and inventory cycles to model a supply chain forecast.
You can view more info and register at
http://demandplanning.net/cpg-demand-planning-web-workshop.htm

The discounted price of $299 is available until December 31, 2009 and covers both modules. The price includes all materials and calculation templates.

Jan 20 – Module 1 11am to 2pm EST / 8am to 11 am PST

Jan 22 – Module 2 11am to 2pm EST / 8am to 11 am PST

If you register for both modules using the discounted price, you can join a special Q&A session at the end of Module 2 on practical problems that you face at your company.

Please review other training offerings for 2010 at http://demandplanning.net.

This fear on the part of the policy makers has created a put option or a gamble of sorts for Wall Street. This TBTF policy has created loads of easy money. The printing press is running non-stop and the Fed is flying helicopters all over the country and sprinkling greenbacks (or may be just on the backyards of the big banks).

V-Shaped Recovery

With essentially zero-cost money, the likes of Goldman Sachs, are now taking bigger risks in the markets and other asset investments. Savers faced with zero yield on their deposits are also pushing to take on more speculative risks and risky investments.

If this TBTF policy is going to cripple us, then why let anything be too big? The Big ones think of it as a right to come hat-in-hand, at the slightest problem that they suspect is systemic. If the Government and the Fed have the obligation to save the Big ones when in trouble, do they also earn the right to NOT let them become too big? Sounds fair enough.

W-Shaped Recovery

Perhaps policy makers of the 21st century need to think about a TBTF premium and collect monies from financial companies that are beyond an acceptable size. Perhaps policy makers can collect this insurance premium or levy on a tiered basis that increases with the size. Maybe they also have a say on how big they can become.

The reluctance of the Fed to mop up the excess liquidity and the lack of any developing financial regulation are puzzling facts.

* Will this lead to Financial crisis 2?
* Is America disadvantaged by the sliding dollar?
* How will it affect your plans and forecasts?

Is the recovery V-shaped or W-Shaped?

If you are c-Level executive, you need to wonder about these issues in your intermediate and long-term planning process.

We want an inflection point for the drop to stabilize and then we expect a bottoming up and a pick up so we are back on positive trend.  However, turning points are difficult to forecast.  There was a lot of talk about the V shaped recovery and its challenges for demand planning.  The drop was sudden and the demand fell off a cliff.  Then the pick up was sudden as well.

Companies had substantial drop in their demand earlier this year but the consequent inventory depletion and the sudden pick up in demand had resulted in businesses scrambling to ramp up production.  The decision to deplete inventories earlier this year had compounded the woes.  Companies through most of this year have been suffering from customer service issues and higher costs on expediting and execution.

Forecasting is indeed difficult when things are not business as usual.  You need extra information or additional insights to call these turns correctly.  However, when that additional driver is outside of your control, then it is scenario planning that you need to count on.

Exponential smoothing will catch up with the new trend with a time lag.  The first few forecasts will be way off.  This depends on the smoothing parameter in the model.

• SMSC
• J&J
• Tools Group
• Bush Brothers
• Merck
• Crabtree and Evelyn
• Avon Products
• Niles Audio

Thanks to the great audience, the workshop was very interactive with people talking about their real world planning experiences and comparing notes on how they dealt with specific situations.  A number of questions were addressed during this interactive forum.  I truly enjoyed the participation and nothing better than conducting a workshop with an engaging audience.  Thank you.

The major focus on the first day was forecasting in the current economy.  There was a lot of discussion on the V-shaped recovery and how to forecast for it.  Although exponential smoothing is an adaptive technique that normally catches up with a lag, it is difficult when the demand suddenly drops and then sharply recovers a few months later.  We all discussed the importance of scenario planning and other techniques that are important like leading indicators.

The guest speakers Mark Temkin and Jay Nearnberg also provided valuable insights to the group.  There was discussion on inventory optimization, S&OP and demand metrics.  There were number of questions on modeling and metrics, particularly the usage of MAPE and the methodology to compute the MAPE.  The weighted Mean Absolute Percent Error seemed to be the most common performance metric used by most organizations, although some had used a variation of it.

Please feel free to post any follow-up questions on this workshop either here or in our Linked-In Group for DemandPlanning Net Training.

Mark Chockalingam

Woburn, MA

November 3, 2009.