Update on Forecasting vs. Demand Planning

Often, the terms, “forecasting” and “demand planning”, are used interchangeably. 

The fact that one concept is a subset of the other obscures the resulting confusion. 

Forecasting is the process of mathematically predicting a future event.

As a component of demand planning, forecasting is necessary, but not sufficient.

Demand planning is that process by which a business anticipates market requirements.  

This certainly involves both quantitative and qualitative forecasting.  But, demand planning requires holistic process that includes the following steps:

1.      Profiling SKU’s with respect to volume and variability in order to determine the appropriate treatment:

For example, high volume, low variability SKU’s will be easy to mathematically forecast and may be suited for lean replenishment techniques.  Low volume, low variability items maybe best     suited for simple re-order point.  High volume, high variability will be difficult to forecast and may require a sophisticated approach to safety stock planning.  Low volume, low variability SKU’s may require a thoughtful postponement approach, resulting in an assemble-to-order process.  This analysis is complemented nicely by a Demand Plan Sanity Check, which should be an on-going part of your forecasting process.

2.       Validating of qualitative forecasts from among functional groups such as sales, marketing, and finance
3.       Estimation of the magnitude of previously unmet demand
4.       Predicting underlying causal factors where necessary and appropriate through predictive analytics
5.       Development of the quantitative forecast including the determination of the following:

  • Level of aggregation
  • Correct lag
  • Appropriate forecasting model(s)
  • Best settings for forecasting model parameters
  • Deducting relevant consumption of forecast

6.      Rationalization of qualitative and quantitative forecasts and development of a consensus expectation
7.      Planning for the commercialization of new products
8.      Calculating the impact of special promotions
9.      Coordinating of demand shaping requirements with promotional activity
10.    Determination of the range and the confidence level of the expected demand
11.    Collaborating with customers on future requirements
12.    Monitoring the actual sales and adjusting the demand plan for promotions and new product introductions
13.    Identification of sources of forecast inaccuracies (e.g. sales or customer forecast bias, a change in the data that requires a different forecasting model or a different setting on an existing forecast model, a promotion or new product introduction that greatly exceeded or failed to meet expectations).

The proficiency with which an organization can anticipate market requirements has a direct and significant impact on revenue, margin and working capital, and potentially market share.  However, as an organization invests in demand planning, the gains tend to be significant in the beginning of the effort but diminishing returns are reached much more quickly than in many other process improvement efforts.

This irony should not disguise the fact that significant ongoing effort is required simply to maintain a high level of performance in demand planning, once it is achieved.

It may make sense to periodically undertake an exercise to (see #1 above) in order to determine if the results are reasonable, whether or not the inputs are properly being collected and integrated, and the potential for additional added value through improved analysis, additional collaboration, or other means.

I’ll leave you once again with a thought for the weekend – this time from Ralph Waldo Emerson, “You cannot do a kindness too soon, for you never know how soon it will be too late.”

Thanks for stopping by and have a wonderful weekend!

A Demand Plan Sanity Check: Five Best Practices

Sch 1There is a process that is fast becoming a necessary and key component of both demand planning and sales and operations planning.  I have heard it described as “forecastability”and “demand curve analysis”, among other terms, but, here, I will call it a “Demand Plan Sanity Check” or DPSC for short.  I am seeing this across industries, but particularly in consumer products.  The concept is simple – how does one identify the critical few forecasts that require the skill and experience of demand planners, so that planner brainpower is expended on making a difference and not hunting for a place to make a difference.

At a minimum, a DPSC must consider the following components:

  1. Consideration of every level and combination of the product and geographical hierarchies
  2. A very high quality quantitative forecast
  3. A statistically developed range of “sanity” out through time
  4. Metrics for measuring “sanity”
  5. Tabular and graphical displays that are interactive, intuitive, always available, and current.

If you are going to attempt to establish a DPSC, then you need to incorporate the following five best practices:

1.  Eliminate duplication.  When designing a DPSC process (and supporting tools), it is instructive to consider the principles of Occam’s razor as a guide:

– The principle of plurality – Plurality should not be used without necessity

– The principle of parsimony – It is pointless to do with more what can be done with less

These two principles of Occam’s razor are useful because the goal is simply to flag unreasonable forecasts that do not pass a statistical “sanity check”, so that planners can focus their energy on asking critical questions only about those cases.

2. Minimize human time and effort by maximizing the power of cloud computing.  Leverage the fast, ubiquitous computing power of the cloud to deliver results that are self-explanatory and always available everywhere, providing an immediately understood context that identifies invalid forecasts and minimizes the need for planners to sort through and compare massive amounts of data manually and individually.

3. Eliminate inconsistent judgments By following #1 and #2 above, you avoid inconsistent judgments that vary from planner to planner, from product family to product family, or from region to region.  A DPSC tool should present the minimum essential data that will flag forecasts with questionable validity for planners so that they can leverage their skill, experience and intelligence on these exceptions rather than trying to apply their individual assessments to many different sets of data in order to identify the exceptions.

4. Reflect statistical realities.  Any calculations of upper and lower bounds of “sanity” should reflect the fact that uncertainty grows with each extension of a forecast into a future time period.  For example, the upper and lower limits of “sanity” for one period into the future should usually be narrower than the limits for two or three periods into the future.  These, in turn, should be narrower than the limits calculated for more distant future periods.  Respecting statistical realities also means reflecting seasonality and cyclical demand in addition to month-to-month variations.  It also means capturing the actual variability in demand and forecast error so that you do not force assumptions of normality onto the sanity check range(s).  Among other things, this will allow you to predict the likelihood of over and under-shipment.

5. Illustrate business performance, not just forecasting performance with “sanity” ranges.  The calculation of upper and lower “sanity” intervals should be applied, not only from time-period to time period, but also cumulatively across periods such as months in the fiscal year.

If you are engaged in demand planning or sales and operations planning, I’d like to know your thoughts on performing a Demand Plan Sanity Check.

Thanks again for stopping by Supply Chain Action.  As we leave the work week and recharge for the next, I leave you with the words of John Ruskin, “When skill and love work together, expect a masterpiece.”

Have a wonderful weekend!

Thoughts from IBF Conference

I just left the IBF’s Leadership Business Planning & Forecasting Forum and the Supply Chain Planning & Forecasting:  Best Practices Conference in Orlando, Florida.  I’ll share a few of the thoughts that struck me as helpful here in the hopes that they will help you.

From a panel discussion on organizational design at the Forum, I compiled this key point (adding in my own twist):   S&OP is all about integrated decision-making, understanding inter-related tradeoffs, driving toward bottom-line metrics with cause/effect accountability.

Rick Davis from Kellogg pointed out that  “Integrated planning is less about function than about process.

Rick also emphasized managing the inputs, particularly since data and technology are moving at the “speed of mind”.  Decision-makers need to ask themselves, “Will competitors leverage information better than I will?”

A few keys to success in S&OP include the following (see Ten Sins of S&OP for what NOT to do):

1)      Scenario analysis

2)      Leadership buy-in

3)      Quality feeder processes (my point of view)

4)      Remembering that financial targets and demand plans are different

Rafal Porzucek defined supply chain agility this way:  “The speed to react with predictable costs and service delivery.”  I thought that was pretty good.

The consumer products executives felt that the effort to leverage social media for forecasting was in the data collection phase.  In a couple of years, it may be useful for generating more accurate forecasts.

Mark Kremblewski and Rafal Porzucek from P&G made a compelling case for enabling innovation through standardization – and it made great sense.

Mark also shared a profound understanding of how the key numbers of business objective, forecast and actual shipments relate to each other.

I hope some of these points stimulate your thinking as they did mine.

There were other speakers who shared some great insights.  The absence of mention here is not meant to diminish their contribution.

This week, in the theme of anticipating the future, I leave you with the words of the English novelist and playwright, John Galsworthy, who won the 1932 Nobel Prize in Literature, “If you do not think about the future, you cannot have one.

Have a wonderful weekend!

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My Thoughts from the IBF Leadership Conference in Las Vegas

Although it is not yet Friday, I want to take this opportunity to share my thoughts on what I heard at the Institute of Business Forecasting and Planning’s Leadership Business Planning and Forecasting Forum.  I was privileged to spend a couple of days with a rather distinguished group of practitioners, software vendors, academics, and consultants exploring three major areas of interest to most supply chain managers and planners – best practices in Leveraging Integrated Demand Signals, Sales and Operations Planning and Demand Planning.  I managed to leave my notes in my hotel room, but here are a few of my thoughts in no particular order that you may find immediately useful (some completely original, some borrowed, some modified from something I heard):

  • Make use of syndicated data as a leading indicator.  More and more of this is available.  Determine what is available and match it to your business needs, leveraging econometric models.
  • Collaboration is still partly a function of bargaining position.
  • Before you collaborate, make sure you have done your analytical homework so that you understand the total opportunity and how much you need to capture and how much you can afford to give away.
  • A “forecastability” or “reasonability” analysis allows demand planners to be more efficient by highlighting areas where they can engage their education, training, and experience rather than sifting through data is becoming a best practice. 
  • Two key performance indicators that might not have been in your textbook probably ought to be part of your demand planning process:

              √ Forecast Value Added (mean absolute percentage error for new forecast approach  – mean absolute percentage error for old (or possibly naive) forecast approach)

              √ Cost of Inaccuracy (margin and lost goodwill * units underforecasted less safety stock) + (cost of holding inventory * units overforecasted) all summed over the relevant time period

  • Consider engaging finance in the demand planning process.
  • Know the difference between your financial or sales objective and the demand plan.
  • Many companies struggle with the harmonizing of qualitative and quantitative forecasting.  A generally helpful concept here is that qualitative input tends to be best from a top-down  perspective and allocated down;  quantitative forecasting tends to be at a lower, if not the lowest level and rolled up.
  • Forecast both shipments and end-customer consumption and the difference.  In  the consumer goods industry, this is essentially “trade inventory”.
  • Microsoft Excel is still the predominant planning software.  It is how people and organizations innovate quickly.  However, building models in Excel, itself, is problematic in terms of scale, maintenance and process standardization.  A useful improvement would be getting IT or a consultant to create your model in Visual Basic, leveraging Excel as the user interface. 
  • Enterprise software is useful, but customers and users need to demand more from their software vendors.
  • Fit both your model and your metrics to the nature of the business and the data.

While we are on the topic, allow me to also point you to my blog post of a few days ago (September 9) where I outline some of the differences between forecasting per se and a robust demand planning process.

Careful, Comprehensive Inventory Management (Part 1)

Manufacturers and distributors usually spend most of their cash on inventory.  In fact, many service organizations like utilities and health care delivery organizations spend lots of money on materials.  But in the case of manufacturers and distributors, just look at the cost of goods sold as a proportion of sales, compared to any other item.  Given that reality, the better part of wisdom mandates a careful and comprehensive approach to managing inventory.

As a memory aid, I use A56σ to represent such a careful, comprehensive, and corporate approach to inventory management.  Each component of A56σ is essential for achieving sustainable, continuous improvement in inventory efficiency.  There are five concepts which I will alliterate with the letter “A” and the tools of six sigma.  Here is the first “A”.

Anticipate – anticipate market requirements

The more you are able to accurately anticipate the demand by your end customer in the marketplace, the more you will be able to move, make, buy and store the inventory that will sell quickly.  This may seem like a self-evident axiom, but this is not easy and the benefits of incrementally better anticipation go directly into additional revenue as well as more efficient inventory and use of cash. Large bodies of knowledge have been built around this subject from rigorous quantitative models for forecasting to methodologies for collaborative forecasting, both within an organization and across organizations.  The point of diminishing returns can be reached fairly quickly, but if you are not there, it’s worth making more of an effort.

Forecasting vs. Demand Planning

Often, the terms, “forecasting” and “demand planning”, are used interchangeably. 

The fact that one concept is a subset of the other obscures the resulting confusion. 

Forecasting is the process of mathematically predicting a future event.

As a component of demand planning, forecasting is necessary, but not sufficient.

Demand planning is that process by which a business anticipates market requirements.  

This certainly involves both quantitative and qualitative forecasting.  But, demand planning requires holistic process that includes the following steps:

1.      Profiling SKU’s with respect to volume and variability in order to determine the appropriate treatment:

For example, high volume, low variability SKU’s will be easy to mathematically forecast and may be suited for lean replenishment techniques.  Low volume, low variability items maybe best     suited for simple re-order point.  High volume, high variability will be difficult to forecast and may require a sophisticated approach to safety stock planning.  Low volume, low variability SKU’s may require a thoughtful postponement approach, resulting in an assemble-to-order process.  This analysis is complemented nicely by a Demand Plan Sanity Check, which should be an on-going part of your forecasting process.

2.       Validating of qualitative forecasts from among functional groups such as sales, marketing, and finance
3.       Estimation of the magnitude of previously unmet demand
4.       Predicting underlying causal factors where necessary and appropriate through predictive analytics
5.       Development of the quantitative forecast including the determination of the following:

  • Level of aggregation
  • Correct lag
  • Appropriate forecasting model(s)
  • Best settings for forecasting model parameters
  • Deducting relevant consumption of forecast

6.      Rationalization of qualitative and quantitative forecasts and development of a consensus expectation
7.      Planning for the commercialization of new products
8.      Calculating the impact of special promotions
9.      Coordinating of demand shaping requirements with promotional activity
10.    Determination of the range and the confidence level of the expected demand
11.    Collaborating with customers on future requirements
12.    Monitoring the actual sales and adjusting the demand plan for promotions and new product introductions
13.    Identification of sources of forecast inaccuracies (e.g. sales or customer forecast bias, a change in the data that requires a different forecasting model or a different setting on an existing forecast model, a promotion or new product introduction that greatly exceeded or failed to meet expectations).

The proficiency with which an organization can anticipate market requirements has a direct and significant impact on revenue, margin and working capital, and potentially market share.  However, as an organization invests in demand planning, the gains tend to be significant in the beginning of the effort but diminishing returns are reached much more quickly than in many other process improvement efforts.

This irony should not disguise the fact that significant ongoing effort is required simply to maintain a high level of performance in demand planning, once it is achieved.

It may make sense to periodically undertake an exercise to (see #1 above) in order to determine if the results are reasonable, whether or not the inputs are properly being collected and integrated, and the potential for additional added value through improved analysis, additional collaboration, or other means.

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