Three Men & The Cloud

The Relevance of Demand-forecasting for CPG Distribution

Imagine three men standing in an open field on a bright, sunny day. Far above them, there’s a small white cloud passing by. One of them happens to look up & notice the cloud. The other two do not & will never have an inkling about the existence of that cloud.

There’s an old quantum physics question on ‘alternate reality’ which goes somewhat like this: If you did not see the moon last night, can you conclude that the moon did not rise last night? The answer is not simple (involving the concept of multiple realities for different viewpoints) which can be experienced when you travel at very high speeds (preferably approaching the speed of light!)

Today’s online sales via large aggregator platforms also happen at very high speed, sometimes  in short  bursts. And there are many factors which can directly affect sales. For example, getting a “Buy Box” for your brand promotes immediate sales. But Buy Box percentage (based on the number of times the Buy Box showed your brand  when customers viewed the listing) is  not known till much later – after the sale is won or lost. But the factors which may affect the chances of increasing Buy Box percentage are known & these values can be maintained within a range to ensure higher Buy Box percentage.

One of the factors is stock delivery consistency, which depends on stock availability. This is in turn is connected to distribution.  Read on.


Evolving Distribution Models


Marketing began with local products which were medicinal in nature and were consumed locally. Enterprising “Doctor” got the idea of selling their concoctions in other markets, across the country. They produced a few hundred bottles of his  product & traveled across the country to market & sell them. (A dance troupe was often a part of the caravan to attract the public!) Soon, local sellers were incentivised to stock the products in each city. (The shop owner would buy at a discount & sell at a higher price.)

When sales volumes grew, a “distributor” was roped in, whose role was defined as  follows: Buy the stock in bulk from the seller at a higher discount and sell to the local shops (retailers).  As business grew, this evolved into a network of multiple distributors, focusing on markets, client segments, etc.  The basic need was to stock goods at the regional distributor level, so that products could be shipped to retail shops quicker. As chains of hyper-markets & malls evolved, these were classified under “Modern Trade”, with separate teams set up to manage their distribution.

The next stage was to deploy technology. The available ICT (information & communication technology) was used to track sales in the market. This served 2 purposes:  real time information was collected regularly to estimate the demand – which was also used as feedback for production planning.  Large companies have made this into a fine art with a tight linkage between their physical distribution & their production planning! The whole idea was to plan production to replenish stocks systematically on a frequent basis.

Next came the Direct-to-customer model and more significantly, the  E-tail-to-customer model.


E2C Distribution Model
In the past decade, large e-tailers have become accepted as a common platform for multiple competing brands, giving rise to the E2C  (E-tailer-to-Consumer) Model. You just may be surprised to learn that the percentage share has crossed well over 50% for many product categories.  The prime difference between E2C and Modern Trade is the speed, volume & real-time peaking of sales consumption. With  large e-tailers, a pack sale could be gained or lost every second from any micro market.

As these giant E-tailers stand to earn their margins from the sale of any brand (yours or the competition), they are largely brand & seller agnostic. The only thing they fear is an angry retail customer because of poor product quality, false promises & delayed delivery. The first 2 are managed reasonably well with ratings & peer reviews. It’s the 3rd (delivery) which needs more controls to manage.

For example, these giant E-tailers ensure that Buy Boxes are given to those brands are which satisfy a set of conditions, the most important of all being the track record of consistent delivery which depends on availability of stocks. (Other factors like quality of customer ratings, pricing, are less real time & can be addressed with some leeway.)

After all, this does seem to be a good way to ensure that their online customers leave the e-tail platform because of delayed deliveries by the sellers.


Demand Forecast-based Distribution 

Now consider this. Every minute, there are thousands of enquiries coming from hundreds of  micro markets across the country. In each case, a seller of a brand will be shown in the Buy Box (prime position) by the e-tailer if there the seller has a record of consistent delivery. This is possible only if the seller maintains adequate stock levels  in the dedicated warehouse (linked to the micro market.) In addition to availability of stocks, there would be other considerations like pricing, review ratings &  feedback, margin-to- e-tailer, etc. The e-tailer protects his own reputation by ensuring consistent sales fulfillment by the seller.

All said & done, a single brand / seller is showcased to a prospective buyer, provided there is ability to deliver the product consistently. This ultimately translates to maintaining adequate stock levels daily basis. The  demand could change hourly.  But an increase in demand may not necessarily continue to translate into sales for long, if the seller is unable to  complete deliveries! 

The buyer, who sees a single brand / seller (which fulfill certain conditions at that time) has limited exposure to the competing brands / sellers.

That means the brands which were not showcased by the e-tailer, could lose an opportunity. Since this is happening in real time, brands could be losing out to the competition – with no knowledge of these fleeting opportunities! Somewhat like the passing cloud which was not seen by the majority!


Consider the factors involved:


  1. Business cost of stocking excess inventory.
  2. Threat of losing sales opportunities due to low inventory.
  3. Lead time for shipping of physical goods to regional warehouses.


Demand forecast analytics could play a vital role in the new Distribution Model – where stocking could be based on accurate demand forecasts. Supported by regular monitoring of warehouse stocks, this could of course, help in better production planning, using distributed production models.

Look forward to a reply with your critique.  


Wish you a prosperous 2023.
Biswajit Das

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