Bull-whipping or driving the Demand?

September 27th, 2014 by Dr. Chockalingam

Bullwhip effect is a phenomenon of demand being overestimated or underestimated as it passes through various stages of supply chain resulting in exaggerated supply fluctuations. The increased variability in demand at successive stages of supply chain trigger increases in inventory carried in different stocking points in the supply chain.

Myth: The Forecast is the primary reason of the bull-whip

Reality: The absence of a demand plan that captures more down-stream customer intelligence is the real culprit.

So contrary to the oft-expressed cliche on the Forecast being the problem, in reality when demand shocks occur, players in the supply chain react to the shock in different ways causing the bull-whip.

What can be done to mitigate this?

We can create more demand visibility or demand clarity by improving the flow of information from one point in the supply chain to another – supplier to manufacturer, manufacturer to distributor, distributor to final customer is how the goods and services flow.  Demand information should flow the opposite way.  How can we facilitate this?

You can learn more about collaboration on our pages devoted to collaborative planning and Forecasting.

  1. A supply planner using just the demand history to create a simple average forecast will NOT do much to help reduce the bullwhip.
  2. A Forecaster just creating a Statistical forecast based on past history can do slightly better.
  3. A Brand Manager creating a Forecast based on YTD trends at the dollar level can actually do more damage to the SKU level supply chain plans, especially if married with the supply planner in (1) above.

Until you have a good demand planning process that incorporates Sales and Marketing (however odd it may seem) and your customer with constantly flowing information, you will continue to bull-ship the demand.  Demand visibility increases with more down-stream customer info on the future.  Demand-driven is a good thing but you need to ensure that it is the customer’s demand info that actually drives you – not just random order flow.

You can join us in Chicago on October 15-17 along with some premier Fortune 500 companies to learn more about the art and science of demand planning.

To view the detailed agenda please visit http://demandplanning.net/demandplanning_tutorialMA.htm.

We will discuss many myths and realities including the buzz-kill on the word “Demand-driven” and what it really means.

Lately I hear a lot of discussion on Demand-Driven without a clear explanation on what this means.

What does this mean to you? Are you using Customer, Sales or Marketing information in any way shape or form to drive your supply chain planning?  You may want to review our web page for the Account Based Forecasting model which holistically integrates customer demand information into the product level demand plan.

Please share your experiences on our Demandplanning.Net Linked in group.

If you are planning to attend our Chicago 2-Day demand Planning workshop, you can get all the information you need in the below URLs:

Demand Planning and Sales Forecasting Workshop – Chicago Oct 15-16, 2014
info | brochure | register | venue | testimonials | photos

Modeling & Metrics in SAP APO DP Hands-On Workshop – Chicago Oct 17, 2014
info | brochure | register | venue | testimonials | photos

Useful training on Demand Planning – Chicago October 2014

September 23rd, 2014 by Dr. Chockalingam

We just completed our two day workshop on Sales and Operations Planning in Boston – Some planners new to the field and some others who are already working on the process and want to take it to the next level – IBP or the Integrated Business Planning.

Our next stop is Chicago for our popular Demand Planning Workshop on way to the India seminar to happen in Mumbai in early November. The workshop will be held at the Four-Point Sheraton at Chicago Airport on October 15-16, 2014

Just to quote some comments from our attendees in May of this year:

“Excellent material given to us during the workshop. This information and new concepts will help us reorganize how our models are being applied to group of items to improve forecast accuracy and reduce forecast errors.”
- Demand Planner at Cupfsa

“This course was well organized and targeted the basic to most advanced users. Speakers are very knowledgeable to answer questions about real business situations.”
Regional Supply Chain Manager at Future Electronics

“Best one day workshop of APO DP concepts and tool knowledge I’ve attended. A very good hands-on experience of stat forecast tool in APO DP, which covered all the large concepts. DPLLC team took time to explain concepts besides the tool.”
- Process Lead – Demand & Supply at Mead Johnson Nutrition

In this specialized two-day course, we will explain the modeling methodology and process behind accurate demand forecasts. 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.

If you are a new demand planner looking to enhance your knowledge of business forecasting, you cannot afford to miss this opportunity!

You can review the info and register at


Here is an outline of the two day workshop:

Detailed Outline of the Workshop:

Day 1

8:30AM to 5:00PM

Day 2

8:30AM to 4:30PM

  • 8:00 AM Registration & Breakfast
  • 8:30 AM Welcome
  • 9:00 AM Demand Planning Overview
    • Why Forecast and Plan?
    • The Service – Cost – Balance Model
    • Introduction to Demand Planning- What, Why and How?
    • Distinction between Business Forecasting and Demand Planning
    • Beyond Statistical Forecasting
    • Key Components of a Demand Plan
    • Key Terminology – Forecast Horizon, Buckets & Periodicity
    • Forecast Pass
    • Constrained vs. Unconstrained forecasts
    • Demand Management
  • 10:30 AM Coffee Break
  • 10:45 AM Data Cleaning and Adjustments for Demand Forecasting
    • What do we know about data?
    • The Forecast Problem and Data collection
    • Definition of Demand
      - Shipment Vs. Orders
      - Gross Demand Vs. Net Demand
    • Data cleaning challenges
    • Adjusting for historical shifts in demand
    • Data filtering
    • Outliers
    • Process to Identify Outliers
    • What is a tolerance band
    • Methodology for outlier correction
  • 12:30 PM Lunch Break
  • 1:30 PM Statistical Modeling
    • Forecast Model
    • Key components of demand
    • Additive Vs. Multiplicative Seasonality in Models
    • Modeling by decomposition
    • Introduction to Forecast Modeling
    • Qualities of a good statistical forecast
    • Balancing between Model Fit Vs. Model Robustness
    • Uni-Variate Time Series vs. Multi-Variate methods
    • Moving Average
    • First Order Exponential Smoothing or Constant Models
    • Holt Models to accommodate trend
    • Holt Winters Model
    • Exponential Trend and Dampening
  • 2:30 PM Coffee Break & Networking Exercise
  • 4:00 PM Product Life Cycle & Long-term Planning
    • Cyclicality
    • Product Lifecycle and trend
    • Launch Forecasting
    • Volume effect on line extension
  • 4:30 PM Assignment of Forecasting exercises to participants
  • 8:30 AM Data Analysis and SKU Segmentation
    • Simple Model of Demand
    • Demand Volatility
    • What is the approach to Data Analysis?
    • Impact of Data Volatility on Forecasting
    • Measuring Volatility
    • Impact of multiple Extreme Observations on Volatility
    • SKU Segmentation for forecast modeling
    • Volume Volatility a 2×2 grid
    • Modeling by exception
  • 9:45 AM Forecast Errors and Model Diagnostic
    • Definition of Demand Forecast Errors
    • Forecast Accuracy
    • Forecast Bias vs. Forecast Error
    • Error and Volatility Reduction
    • Errors across SKUs vs. Errors across time
    • Model Diagnostics – Measuring Errors over time
      - MAD
      - MAPE vs. MPE
      - WAPE
      - Root Mean Squared Error
      - General Illustration MPE, MAPE and RMSE
  • 11:00 AM Morning Break
  • 11:15 AM Event Modeling
    • Event Modeling
    • Baseline vs. Incremental
    • Event Models Identify Volume Spikes
    • Illustration of Event Models
  • 12:30 PM Lunch Break
  • 1:30 PM Discussion and answers assigned exercises
  • 2:30 PM Planning for Intermittent Demand
    • What is Intermittent Demand?
    • Illustration of Intermittent Demand Data
    • What causes intermittency?
    • Strategies to handle intermittent demand
      • Business Perspective
      • Stat Models
    • Statistical Models for Intermittent Demand
      • Application of Croston’s Model
      • Discrete Distribution
  • 3:30 PM Measuring forecast performance
    • Forecast Performance Metric
    • Forecast errors and actionablity
    • Sources of Forecast Error
    • Definition of Demand Planning Metrics – WAPE & Bias
    • Types of Bias
    • SKU Mix Error
    • Error Analysis for Continuous Improvement
    • Forecast Accuracy Reporting

Register Now!

Is S&OP an Event?

July 14th, 2014 by Dr. Chockalingam

What is the state of S&OP today?

Interacting with supply chain professionals in conferences and consulting assignments across different industry verticals, I see broadly four different classes of companies when it comes to the State of S&OP, may be five:

1. Companies that are Supply heavy – Supply chain function is heavily and actively involved in the S&OP process but lack an active involvement of Sales and Marketing. Management is still trying to figure out how to get Sales and Marketing excited about S&OP.

2. Companies focused on Demand – Demand Planning together with Marketing and Sales run the monthly process but use the process more to signal to the factory.

3. The Starters – Companies that understand and appreciate the value of S&OP. They have been to conferences and training workshops but have not taken the journey yet. They honestly admit that they are not ready to implement an S&OP process yet.

4. The Pretenders – Businesses that continue to run their current monthly process but fashionably renamed the process to S&OP with the appropriate meetings slotted into the calendar.

For a number of years, S&OP as a process was the domain of some one in the supply chain. Supply Chain produced its own numbers, the forecasts, an inventory plan and a Rough Cut plan and invited the Sales teams and the marketers to come in and comment on what they have done.

The good guys in Sales and Marketing graciously agreed to participate and review the colorful charts and nod in agreement. But most did not bother – they either did not have the time or did not understand the importance of this process.

Thanks to all the emphasis and evangelism, C-level management has taken an interest in S&OP and has started pushing the commercial teams to participate. Although the Supply-heavy S&OPs are a lot less these days, they still exist.

In the case of Group 1 – supply heavy S&OPs, I see the following:

1. Brand Managers and Product Managers have joined the Supply Chainers in their S&OP quest but the real challenge is to get Sales teams interested in this and actively participate in this.

2. Invariably in these companies I also see that intelligence from Sales (Sales Plans and Sales forecasts) are not integrated into Demand Planning – either process is not developed or Demand Planners simply ignore Sales Forecasts as biased.

In the case of Group 2 – Demand heavy S&OPs, commercial teams actively participate in the S&OP process. In most cases, the S&OP process is led by either the Director or Manager of Deamnd Planning. Also these processes are tightly integrated into the corporate planning process that feeds Senior Management. Although Supply chainers participate in this process, they look to the S&OP as a source of the latest demand information. Senior Management thinks of the S&OP as a final forecast that they can use to signal to the supply chain.

The biggest benefit of the omni-present S&OP jingle in the supply chain press has been to motivate even small companies to start thinking about S&OP. There are a number of companies trying to investigate if S&OP is for them.

Finally the Pretenders – I recall the reaction from one Division President I talked to recently. “Yes S&OP does exist – But I don’t think people have a clue on what they are doing and how it really benefits them – we just go through a series of meetings that were developed by a Consultant a few years ago…….”

It may be time to take stock of your S&OP Process. How is it doing compared to what it should be doing? There is no point in doing it because every body else claims they are doing it too. Perhaps an S&OP audit may add a lot of value.

Processes change over time as Organizations evolve and the Players change. But it is important to evaluate if the process delivers the the fundamental objective of S&OP – Balancing Demand and Supply.

That leaves the one group I did not talk about – companies that strive to balance demand and supply over the long horizon; companies that have a long-term vision; companies that truly understand that demand and supply will never balance and have an S&OP that is mature enough to recognize the challenges and opportunities in this imbalance!

An inexpensive method to benchmark your S&OP may be to attend our 2-day workshop in September on the subject in Boston, MA.


Window-dressing and Supply Chain Score-carding…

July 12th, 2014 by Rohan Asardohkar

When we review Supply Chain Dashboards, there are a variety of colorful metrics. Are they the right metrics – Are they calculated right to show true performance?

Every Senior manager should look at four key metrics:
a. Demand Fulfilment
b. Inventory Level
c. Demand Visibility
d. Supply Adherence

The first measure is the traditional customer service measure or the order fill metric. But there is a lot of confusion in this metric – Should we measure the reality or should we measure to window dress our performance to the customer?

If you don’t measure right, you will be at a loss to know where you are. Although politically palatable, an inflated measure will hide problems and will show up in the financial results.

Here is a survey to assess your measurement methodology. Please review this questionnaire in multiple choice formats and pick the correct answer. If the suggested choices are incorrect, please answer with your calculation in the other bucket.

We will cover the right methodology to measure the supply chain performance in our upcoming S&OP workshop on September 17-18 in Boston, MA. The first 100% perfect response will receive a $500 Gift voucher for attending the S&OP workshop in Boston or Demand Planning workshop in Chicago.

Here goes the survey: https://docs.google.com/forms/d/1aMWyLeQZ7q3PU8NVt6oh2rqokLvAtYZeALxb5s8nHIo/viewform

What can be so difficult about measuring Service Levels?

May 11th, 2014 by Dr. Chockalingam

In some recent visits, I have noticed something interesting about the KPI Reports and the calculations used. It appears that some companies report the Fill Rate or Customer Service measure more as a shipment ratio rather than as a metric to measure the customer’s pain.

I see this calculated as the total quantity shipped over the month compared to the total quantity ordered during the month. In other words this is a ratio of shipments to orders. Does this really measure true service level?

Best-in-class methods suggest that we measure performance on-time and in-full either at the line item level or complete order level.  There is also another school of thought that discusses the Part-fill Rate or the Partial Fill Rate.

The Classic measure is the Line Fill Rate which measures the number of Lines filled complete to the Customers Requested Demand Quantity (CRDQ) and completed on time to the customers Requested Delivery Date (CRDD) over the total number of lines ordered in a period.  The LFR can be calculated over any period whether weekly or monthly.

When you weight the LFR with the number of units shipped or cases, then you have the ULFR or UFR (Unit Fill Rate).  When this is weighted by Price and Units then you have the Dollar Line Fill Rate or DFR.

Now the part Fill Rate measures partial fills.  As the word Part implies, you take credit for partial filled orders.  If you fill an order 80% quantity then you get no credit if you use the traditional LFR.

Let us see the following examples:

1.  You ship 80% of the Order quantity in the first shipment on time with in the CRDD.  The balance is shipped in the second shipment after a week.  Your Part fill rate is 80%.

2.  You ship 100% of the Order quantity always one to two days later than the CRDD.  Your Part fill Rate is 100%.

Some companies use a variation called the Fill Rate.  This measures the ratio of quantity filled to the total quantity ordered.  If you shipped the order completely whether late or on time, whether in multiple shipments or in one shipment, this this will be considered a fill.

You will get less than 100% fill rate only on those items that have an acute shortage during the month.  Otherwise, your fill will be 100%.

In all of the above cases, the LFR is 0%.

Is it difficult to measure and report the LFR?  No. Data and systems are available to to do this correctly.  This entire measurement process can be outsourced to a third party provider that will help you analyze and publish score-cards on the cloud.  Demand Planning Net offers such a service to customers through a secure intranet portal.

However, companies like the Part fill rate since it allows you to portray your supply chain better than what it is.

Does it hurt to use the Part fill rate?  Yes.  It simply does not allow you to diagnose the true performance and hence the true drivers that are hurting the supply chain.  IF you don’t know it you cannot fix it.

From a different angle, it also does not allow you to understand the costs of partial deliveries.  Handling the order multiple times result in a variety of costs – customer service, deployment, accounting, billing as well as transportation.  IT may affect the assortment planning of your customer.

When you increase the uncertainty of your fill rate to your customer, they have to hedge in the form of inventory strategies.  If things become chaotic and if they have an alternative supplier, then it is bad for business.  You may lose the customer.

Sales and Marketing functions do not like anything less than 100% fill for obvious reasons – now the Part fill rate helps calm the tensions between Marketing and Supply Chain as Marketers do not always understand all these calculations.  In the long-run, when they start to realize that product is not always available when they are trying to promote, they will know that this 100% fill rate calculation is some what engineered rather than truly measuring the fill rate performance of the supply chain.

When there is uncertainty with respect to product availability or when there is poor supply visibility, then business strategies will start to change.  Your selling and marketing campaigns are not that strong when you are faced with poor supply visibility.

Demand Visibility is important.  So is Supply Visibility!


Demand Planning and Sales Forecasting – 2 Day workshop May 21 & 22 in Boston, MA

May 1st, 2014 by Dr. Chockalingam

During the Demand Planning and Sales Forecasting workshop in Boston, we will be discussing the demand planning challenges in Oil & Gas, Chemicals, Industrial, Food and Beverage and Consumer Goods companies. We will present demand planning process solutions from our knowledge base of consulting experience.

The two day workshop on Demand Planning and Sales Forecasting will be on May 21 – 22nd and the one-day add-on workshop on Modeling and Metrics in SAP APO will be on Friday May 23rd in Boston, MA.

We also plan to distribute our exclusive survey results on Forecast Accuracy that we compiled from different companies.  All workshop participants will get our white paper on Key Building Blocks for a successful S&OP implementation!

Registration is now currently available at http://demandplanning.net/demandplanning_tutorialMA.htm .  This page also has a detailed outline of the two day workshop.   Since we are close to being fully subscribed to the two day workshop, we will be closing the registration in the next couple of weeks.

If you are using SAP for Demand Planning, we highly recommend that you register for all three days of the workshop. For more information please visit http://demandplanning.net/modeling_metrics_in_apo_2.htm .

We will be arranging for a dinner reception on Wednesday evening May 21 for all attendees for the two day workshop.

Mark Chockalingam

Chief Demand Planner

Demand Planning.Net



Optimizing the Order to cash process – What is your priority?

October 5th, 2013 by Dr. Chockalingam
When companies take a hard look at their business model and the underlying process, what is the key objective that is motivating them?
1. Return on Investment2. Increasing Sales

3. Cost Reduction

Although all of the above are good objectives that lead to healthier businesses, companies are also driven by the latest buzz words – the latest consulting mantra, industry trend, and a flashy new technology from a sexy software company.

In the last year, there is no dearth of these trendy things.

IBP and S&OP, Lean, Demand-driven are all hot. SAP has made it very cool to say Hana!

Big Data, Predictive Analytics and In-memory computing have been dominating themes of many business conferences this Fall.

At times I wonder if smaller companies get driven by these trendy things and focus on the wrong things instead of fixing critical issues that are ailing their supply chain and their business model.

Do you have a good business process that will help you address the following questions:

1. Do you have a streamlined process from taking customer orders to fulfilment and delivery?

2. Do you know where your inventory is? How much of what? What is available to promise?

3. Do you have a decent demand visibility so you can plan ahead?

4. Does your middle management meet monthly or weekly to look at key operational metrics?

Optimizing the basic Order to Cash process will yield the cliched low-hanging fruits and result in a more compacted Cash-to-Cash cycle.

What is your cash-to-cash cycle? Are you a trend setter? Have you measured this lately?

Are you a rule breaker like Apple computer is – Apple has a negative cash-to-cash cycle of approximately 60 days.

Measure where you are and prioritize your building blocks:

1. Metrics and Score-carding

2. ERP and the Order to Cash Process

3. Sales Forecasting and CRM

4. Demand Forecasting and MRP

Then move on to the more esoteric initiatives in your maturity curve!

Consult our Knowledgebase at http://www.demandplanning.net/learn-planning.htm and my blog at http://www.forecastingblog.com/.

Best wishes as you start your Fall Season!



Is there a correlation between Inventories and Forecasting?

September 3rd, 2013 by Dr. Chockalingam

Some say yes………. and some say no!

There are many things questionable about Statistics and Modeling and of course, the famous or infamous, Normal Distribution.  Those who question the value of forecasting invariably point out that it is gravely inaccurate (and unfortunate) to assume that your demand is normally distributed.

Is Normal the tendency to be perfectly normal?  Or should we accept approximate normals and just only look for those that are perfectly Non-Normal?  Either your glass is half-empty or half-full!

So that we can come to meaningful conclusions, I am going to assume life is generally normal.

The classic formula for Safety Stock states thus:

Safety Stock = Service Level Constant * SQRT (Lead Time) * Root Mean Squared Error of (Demand vs. Actual),

assuming you are using a Demand Forecast to produce and stock inventories.  There is a lot of debate as to what to use as the Demand Forecast Error.  Debates range from dismissing the Fitted Forecast Error (in-sample forecast error) to the question of whether we can even establish an Expected Forecast Error.

In any case, using the standard deviation of actual historical demand will over-state the Safety Stock in a majority of cases.

In the current state, you may be carrying inventory to compensate for poor demand visibility besides what you need for demand over lead time.  There is a portion of inventory you are carrying for safety stock.

Any improvements in Forecast quality can be related to improvement in Safety stock levels, if we hold other things constant.

For simplicity sake, let us assume that your committed service level is 98% and your lead time is 2 months.  Then the above formula reduces to the following:

Safety Stock = 2.05  {Service} * Sqrt (2) {Lead time} * Expected Demand Forecast Error

Safety stock = 2.05 * 1.414 * EDFE (expected demand forecast error)

Safety stock = 2.90 * EDFE

In simplistic terms, the above means for every unit reduction in Error (Note Units not %), Safety stock will decrease by 2.9 units other things remaining constant.

If we can assume what our current level of forecast error is and what is our expected forecast error or target improvement is, we can establish a precise benefit in the form of safety stock reduction.  This assumes there is no forecast bias.  If there is a forecast bias (generally there is in organizations new to Demand Planning & S&OP), then there is another sizable reduction possible through the amount of inventory carried over lead time by reducing the bias.

Getting back to the no-bias assumption, what does it mean for a percent point improvement in WMAPE?  What is the percent improvement in safety stock for every point reduction in WMAPE?

This entirely depends on what your current level of forecast quality is.  If your current WMAPE is 50%, then every % point improvement in MAPE will result in 2% reduction in Safety Stock.  Please see the chart that shows the reduction in safety stock for every point reduction in MAPE at different Starting points.



If your current WMAPE is 60%, then every % point improvement will result in 2.5% improvement in safety Stock.

At higher levels of Forecast quality, it is difficult to make further climbs in forecast accuracy.  So any marginal improvement in Forecast quality with a higher starting point will result in more than proportionate benefits in inventory reduction.

There are other routes to go as well:

1.  Lead time Reduction – Reducing the lead time will result in Safety Stock as well but not as much compared to the reduction in Forecast Error.

2.  Variability in Lead time – If our supply is quite variable this has a much larger impact on Safety Stock.  This is not in the classic formula but the extended formula uses variability in lead time.

In practice I have seen this being mis-used and abused with very poor proxies for variability in lead time.  In an uncertain world of demand, it is important have better control over our own supply and schedule adherence.

We will cover the topic of safety stock in a special session in our workshop on September 18-20.  Please learn more details at


How to create models for weekly Forecasting in SAP APO – A Primer

May 5th, 2013 by Dr. Chockalingam

Generally demand planners forecast at the monthly level since forecasts are well behaved and seasonality is easy to identify in months.

But there are occasions when you may need a very good weekly split of the forecast. This can be achieved through a combination of approaches in APO DP

1. Proportional forecasting
2. Using historical weekly splits as a reference keyfigure or
3. Statistical modeling.

In our up-coming hands-on workshop on May 24, we will be adding a brief session on modeling at the weekly level so good intra-month splits can be achieved for the purposes of Production Planning and Detailed Scheduling (PPDS).  We will evaluate the usefulness of weekly models to achieve good weekly splits of the monthly forecasts.  What is the incremental value add of this process compared to using the APODPDANT proportioning keyfigure to derive the splits.

As we enter the last week of registration, only a few seats are left at rush pricing:

May 24th ‘Modeling & Metrics in SAP APO DP 1-day Workshop’ Boston, MA $995

This is an inter-active workshop facilitating you to work in your own SAP APO implementations under our supervision so you can test models on the fly.

May 22-23 ‘Demand Planning and Forecasting Workshop’ – Boston, MA $1,295

Companies that have been in attendance include Bose, BASF, Novartis, Ghirardelli, Michael Foods, Newell Rubbermaid, Brown-Foreman, Spectrum Brands and many others.

SAP SCM APO users will hugely benefit by attending all three days of the workshop at the package price of $1,995.  Group of three or more can get additional 10% discount with one payment method from the same company.

I look forward to discussing weekly forecasting and modeling with you in a few weeks!

Planning Software – Part 1: How important is Integration?

April 21st, 2013 by Dr. Chockalingam

Continuing on this discussion on the important properties of a Planning Software application, I will discuss integration this week.

What is integration?  Why is it important?  How well should my planning tool integrate with the rest of the system landscape?

The word integration can be misused and misunderstood.  There are two different perspectives when it comes to integration.

  • The first and most common one is from an IT perspective – Does the tool integrate and work well with my overall system landscape.   Integration is generally supposed to allows an easier implementation.  Many components come in pre-built to plug this into the enterprise system, so the re-work necessary to make data talk from one system to the other is minimal.
  • The second one is from a business perspective – will the software allow me to integrate the business operations globally?  Does it allow the corporate management to see the aggregate or whole quickly every month to make decisions?  Does it also allow a global IBP process to drill down quickly into areas of specific issues and challenges and highlight them for potential future risks and opportunities.

For the business definition of integration, a distinction needs to be made between analytical work and reporting.  Most of the global decision making process probably needs a good Business Intelligence (BI) platform that will allow reported numbers to bubble up to global management and allow for what if queries and provide exception based reporting.  I do not believe any one is thinking about a super-integrated analytical platform where the CEO can question the Safety stock setting globally and make a change!!


We will discuss the pros and cons of this criteria in our upcoming workshop with specific reference to SAP APO Demand Planning – Modeling and metrics – May Workshop


Having ruled that out, then we focus on how the system can co-exist with other global ERP and Planning systems.  Do we want a different planning system in each country?  Or should every one be using SAP and speaking German?

I have seen this question being answered by the corporate head quarters.  The main business unit picks a planning platform and then inspire or influence the other local business units to move that platform over a period.

In summary, we should think about the following aspects while considering a planning tool from an integration perspective:

1.  Can the planning tool work with the ERP backbone to get data in and out easily with minimal custom interfaces?  Data can be directly sourced through the ERP tables in some applications without going to the data mart or data ware house.  However in many practical instances, there may be customizations necessary to extract data even with in the same ERP/planning software family.

2.  Does it fit with the overall system landscape in terms of the touch and feel?  This may be a criterion for the simple reason of user adoption.  The interface and familiarity of the user interface will make it more easily saleable and trainable to the user.

3. How does it relate to other planning applications used?  When the demand forecast is passed to the Supply planning system, do both systems interpret the forecast to mean the same thing – units, detail, horizon, expected forecast error etc.

4. If it is a best-of-breed tool that is NOT from the ERP family, how easy or difficult is it to build the integration capabilities to the ERP system?

In summary, integration may be one of the important criteria.  But is this a critical criterion to the exclusion of others in choosing a planning software?