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!

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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!

Resilience Versus Agility

Just a short thought as we move into this weekend . . .

Simple definitions of resiliency and agility as they relate to your value network might be as follows:

Resiliency:  The quality of your decisions and plans when their value is not significantly degraded by variability in demand and/or changes in your competitive and economic environment.

Agility:  The ability to adjust your plans and execution for maximum value by responding to the marketplace based on variability in demand and/or changes in your competitive and economic environment.

You can take an analytical approach that will make your plans and decisions resilient and also give you insights into what you need to do in order to be agile.

You need to know the appropriate analytical techniques and how to use them for these ends.

A capable and usable analytical platform can mean the difference between knowing what you should do and actually getting it done.

For example, scenario-based analysis is invaluable for understanding agility, while range-based optimization is crucial for resiliency.

Do you know how to apply these techniques?

Do you have the tools to do it continuously?

Can you create user and manager ready applications to support resiliency and agility?

Finally, I leave you with this thought from Curtis Jones:  “Life is our capital and we spend it every day.  The question is, what are we getting in return?”

Thanks for stopping by.  Have a wonderful weekend!

Ten Key Questions for Spare Parts Planning

1)      How does demand behave?  To answer this, you must ask yourself the following:

a) How often do you expect to receive a demand for a given spare part?

b) What is the expected magnitude of a demand transaction when it occurs?

c) Are  failures based on age, or use, or both?

d) How large is the installed base of a given spare part?

(Technical note:  Historical data on the time interval between demands (inter-demand interval) and on the order of magnitude of demand transactions (demand order sizes) can be used to estimate the likelihood of a demand occurring in a given time interval and its transaction size using statistical techniques such as Croston’s method or a compound Poisson distribution[i].  If failure of a part (and the subsequent need for replacement or repair) is time-dependent (as many are), then the combined use of a type of Erlang distribution to estimate the interval and a Poisson distribution to estimate the quantity may be more appropriate.[ii])

2)      Are some spare parts much more important than others?  Some of the key questions here include the following:

a)  How expensive is the item?

b)  What does it cost to store and transport the item?

c)  What are the consequences when the part fails?

d) Do the consequences of a failure compound with time?

Factors like these are used to determine a part’s “criticality”.  For more critical parts, you usually need to have more safety stock.  That buffer stock may need to be geographically distributed near potential sources of demand and/or expedited delivery may be necessary.

(Technical note:  Where the answer to “d.” above is “yes”, then the use of an Erlang distribution may be helpful to estimate the duration of the wait time for the customer.)

3)      Is demand affected by the conditions in which the part is used and/or the level of preventative maintenance that it receives?  In cases where the answer is “yes”, then the algorithms and statistical approaches that are used to calculate demand and inventory requirements may need to be tailored for different situations.

4)      Are the magnitude and sources of demand such that requirements can be modeled as a trend over time with appropriate adjustments for seasonality?  If so, then this simplifies the planning considerably in that the requirements for such a spare part may be able to be modeled in a way that is similar to non-spare parts.

5)      Is the supply network composed of a single stage or multiple echelons?  The calculations for safety stock are different for each structure.

6)      Are the failed parts scrapped (consumed) or repaired and used again?  Where parts to be replaced are repaired and used again, a determination must be made as to whether the failed part is beyond repair and should be scrapped.  When new replacement parts should be purchased must also be determined, and tracking by serial number is required.

7)      Are the purchase, or manufacturing, batch sizes significantly larger than the expected demand quantities in a period?  If so, then this should be taken into account when planning resupply and safety stock.

8)      Is the supply constrained by a budget?  If so, you should take this into consideration when planning supply as well.

9)      Will requirements be reviewed periodically or continuously?  Continuous (or nearly continuous) review systems are quite feasible with modern communications and computing technology, and in many, if not most cases, they can yield better results.  A continuous review system reevaluates supply requirements each time an actual demand is generated.  Where a large number of spare parts must be monitored by a limited number of planners, however, it may be more practical to periodically review requirements (once per inter-demand interval or some multiple/fraction of inter-demand interval) for non-critical items and plan safety stock to account for demand variability over lead-time and the review period as well as variability in lead-time for those non-critical items.  Critical items, particularly expensive ones, should probably be evaluated continuously.  It may be useful to segment spare parts by levels of criticality and treat each group of parts accordingly.

10)   How many time periods of inventory do I currently have on hand for each spare part and for each category of spare parts?

A fundamentally sound approach to spare parts management can be summarized as follows:

  1. Understand your demand patterns
  2. Classify your parts (e.g. by criticality, by demand pattern, and/or cost, etc.)
  3. Apply the appropriate forecasting model and statistics
  4. Employ an efficient algorithm to find inventory targets and purchase quantities that meets the specific needs, constraints, and goals of your business including the structure of your value network, requirements of your customers, and the costs and risks/uncertainties that you face.  Keep the approach as simple as possible within those conditions.

(Note:  Many formal statistical approaches require assumptions that may not hold in your business.  In most cases, a heuristic that searches for a high value solution that conforms to real-world constraints, leveraging statistical theory where appropriate, is the most useful approach. )

5.  Deploy this algorithm through an easy-to-use, fast, visual and interactive tool that functionally meets your specific requirements, but  doesn’t “break the bank”.

As we enter this weekend, I leave you with one more thought — this time, from Socrates:  “The wisest man is he who knows his own ignorance.

Have a wonderful weekend!


[i] Lengu, D., Syntetos, A., Babai, M., “Spare Parts Management:  A Distribution-based Approach”, Salford Business School Working Paper Series, 342/11.

[ii] Saidane, S., Babai, M., Aguir, S., Korbaa, O., “Spare Parts Inventory Systems under an Increasing Failure Rate Demand Interval Distribution”, Proceedings of the 41st International Conference on Computers and Industrial Engineering,  2011.

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!

Protected: Five Best Practices for a Demand Plan Sanity Check

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An S&OP Insignt in 45 Seconds

I decided, in the end, to make a post this Friday, but of a slightly different nature.  Click on the picture to watch 45 seconds of my interview with Supply Chain Brain from September.  This may be something you haven’t thought about before.  

Thanks again for stopping by. 

For those in the U.S., I hope that you and yours are enjoying a really good Thanksgiving time.

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