Saturday, June 27, 2009

Demand Planning - Essence of Forecasting - Part III

In this session, we will look what is CPFR (Collaborative Planning, Forecasting and Replenishment), Consensus forecast and right forecast method during recession period.

Collaborative Planning, Forecasting and Replenishment (CPFR)

CPFR is a collaborative business practice that enables partners to have visibility into one another’s demand, order forecast and promotional data to anticipate and satisfy future customer demand. This is done through systematic process of information and knowledge sharing. IT plays pivot role while sharing the information among partners.

In the past supply chains were burdened by Forecasting, Planning and Inventory systems that were disintegrated from each other, lacked accurate and timely demand information and blurred visibility beyond immediate trading partners. CPFR simplifies and connects overall channel demand planning by providing a single, real-time plan of forecast and supply. The objective of CPFR is to enable channel retailers, distributors, transporters and manufacturers with the capability to synchronize total supply with total demand from one of the channel to the other.

How does CPFR Work ?

Step 1: Initiative begins with collaborative partnership between two or more members of supply chain (retailer, distributors, transporters, manufacturer) with the intent of creating the technical and operating management architectures necessary to address the existing gaps impeding the synchronization of critical supply chain information.

Step 2 : CPFR partners in the upstream (manufacturer) agree to share critical demand information detailing what products are going to be marketed, how they are going to be promoted and merchandized, pricing and when sales cycles are to begin.

Step 3 : Each partners in the downstream (retailer, distributors, transporters) agrees to implement techniques that provide for the real-time sharing of channel inventory levels, point of sales (POS) transaction and internal supply chain constraints. In addition, each trading partner is responsible for ensuing continuous forecast and inventory accuracy as well as database update.
When these requirements have been fulfilled, the illumination of unnecessary inventory buffers and hidden bottlenecks in the distribution network flow should be revealed and initiatives put in place to eliminate them.

Consensus Forecast

There are so many stakeholders like Sales & Marketing, Operation, Purchase, Logistics and Finance in the demand planning decision today. They all need to be involved and agree in a forum to finalize the forecast number. They all need to contribute and may do so at different levels. The level may be product family versus SKU. For example Operation is interested to look forecast data at SKU level whereas Finance and Marketing at product family level. Generally the consensus forecast discussions happen at product family level.

- Operation to confirm production flexibility and capability (capacity planning) to meet the sales projection as per agreed timelines
- Purchase to ensure availability of raw materials and other components in time, to ensure smooth operation
- Logistics to ensure timely availability of infrastructure like warehouse, transportation
- Finance to ensure that sales projected volume are converted to value and find the gap or shortfall against the targeted revenue. This enable Sales & Marketing to plug the gap through appropriate field and Marketing activities.

The S&OP team develops the consensus forecast using information from the strategic plan, the current system forecast, production capacity planning and current inventory levels.

The steps involved in the consensus forecast (bird view).
- The revenue projection from strategic plan (expected turnover) are converted to item unit demand using standard cost information.
- These estimates of item unit demand are modified using historical demand information as well as the latest sales information, marketing activities and other parameters .
- Members of the S&OP team then agree on the consensus forecast according to their capacity. For example if Marketing team plan to run promotional activity during a period can be turned down by purchase due to non availability of materials.

The owner of the demand plan needs to see all inputs in one view. Scenario analysis is key to this process. ‘What if we increase this forecast by 15% next quarter?’ ‘ What is the risk if this customer demand doesn’t materialize?’ Iterate, analyze, decide. This has to be done fast with accuracy.

Let us understand the consensus forecast concept through example.

Generator manufacturing company strategically plan to achieve Rs. 100 Crores sales from generators (House hold and industry models) for the year 2009. This value is converted to item units based on standard cost. Let us assume the quantity to be sold for the year 2009 is 40 Lac units at Product family level. This 40 Lac units further broken into house hold and industry models (say 25 Lac and 15 Lac) and period (monthly, quarterly) wise. At product family level the generators are expected to grow at 8% over last year.

However the actual quantity sold in a period differ from both strategic plan and the forecasted number. House hold model may grow at higher rate (say 12 %) and industrial models may grow at slower rate (say 5%). As per company practice the finance department work out the excess or shortfall of revenue on monthly or quarterly basis against the projected turnover and share the information. In case of shortfall the Sales & Marketing department devise a plan to improve the sales of slow moving product (Industrial generator) through marketing and sales strategy. Despite the marketing plan, the sales are not picked up for industrial model generator. For short term the marketing and sales department focus to improve the sales of house hold generators to meet the annual turnover and this lead to deviate from the projected sales volume. In the final analysis, some product groups may have a lower consensus forecast (industrial generators say 11 Lac instead of 15 Lac) while others have higher forecast (house hold generators say 29 Lac instead of 25 Lac). This change in sales plan should be ratified by production and purchase department as they need to change the production and procurement plans accordingly. Hence any change in sales volume should have the concurrence from Finance, Production and Purchase department. This leads to emergence of consensus forecast.

As discussed in earlier session, the “best fit” forecast was done by package using different statistical models at SKU level and hence there will be less variation against the actual resulting better inventory management. In case of the consensus forecast, various departmental head participate in a forum and decide the volume keeping in mind the company expected growth at product family level backed up by Sales and Marketing plan. This volume is percolate down to SKU level through top down approach by using SKU contribution analysis. Hence there will be variation against the actual, resulting considerable inventory holding.

What is the right forecast approach in the current recession time ?

This topic was discussed in the Infosys blog and it is worth reading. The link is given below.


  1. Good Artilces on Forecasting and worth reading. Especially time series concept, CPFR, Consensus Forecast are difficult to understand. Appreciate your effort in simplyfing the subject to make readers to understand the concept.

    I recommend SCM aspriants to read this articles.


  2. Hi,

    Very nice articles ...... explained nicely with detailed information.....

    however i would like to know more about Multiplicative decomposition forecasting models
    and on what grounds u select seasonal indices.

  3. Thanks Falguni for your comments.

    Initially I would like to touch upon basics on SCM in my blog, to make budding SCM aspirants to get familiar with the subject. Subsequently I would like to cover, advanced and technical topics like advanced forecasting model, distribution network model etc.

    However if you could send me your mail id, I can get back to you on multiplicative decomposition forecasting models.

  4. Thanks Krish..

    The subject is conveyed in a lucid manner and It is really an eye opener. Continue contributing...

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    1. Hi,

      Thanks for good suggestion. In future I will add images or video clips according to the topics published.