Demand forecasting: A practical solution in a volatile market

Essence of demand forecasting is aptly captured by the words, “A good forecaster is not smarter than everyone else, he merely has his ignorance better organised”. The one thing that has definitely changed now are intelligent algorithms helping in managing the ignorance or in more technical terms reducing the forecast error. A study by Teraa Technology suggests that new methods of demand forecasting have reduced the forecasting error by 37 percent at SKU level. Demand forecasting tools are widely appreciated and adopted over the past several years across the industries. The companies which have well master this tool are enjoying better returns over the competitors in terms of improved sales, low inventory, and higher profit margin.

Aggressive competition and demanding customer are pushing companies for adding new products on a regular interval. Distinct products available for sale are nearly tripled in last five years. With shrinking product life cycle, increasing numbers of SKU and wide geographic operations, there is high stress on the value chain to ensure on-shelf availability. A report by Efficient Consumer Response group says that stock out in Asia Pacific region is as high as 18 percent. The same survey also highlights that forecasting error and poor ordering are the two major reason of stock out. This by rule of thumb would mean a loss of sales of around 9 percent. A stock out also implies further hidden costs to the value chain such as obsolescence and returning excess inventory to suppliers let alone the margin loss due to 9 percent volume loss.

A recent survey done by Supply Chain Digest shows that 61 percent companies see improving forecast accuracy as a significant area of improvement. Survey also points out that an average 5 percent increase in forecast accuracy increases the profit margin by 2 percent. But there are several challenges in implementing demand forecasting tools. The foremost challenge is to justify the cost involved in infrastructure investment. Off the Shelf Demand Forecasting tools are very expensive and require significant consulting effort and time to implement. Skill availability within the organisation to model business process, market variable, and data together is another challenge. Besides, one has to deal with other challenges like data availability, data accuracy, and data bias. These challenges make demand forecasting a tough problem to solve. It is no wonder that most organisations have continued with their traditional methods of bottom up demand forecasting where a certain percentage is added to actual sales for same period last year as forecasts.

Solutions to the challenge have broadly fallen into two components, either implementation of software with best of breed forecasting algorithms or a consulting effort with excel based tools as one-time investments with the hope that organisation will be able to improvise it further once consultants have left. Either of these approaches has failed. Software implementations without policy level changes have failed to give results. And consulting effort focussed on policy changes with the less sophisticated computing power of excel has users settle for sub-optimal solutions. But the biggest challenge has been in finding the right talent within the business to solve the problem of scientific forecasting.

If one could solve the problem of right talent at affordable price points without worrying about constant up gradation of software tools and methods, one would have found the Holy Grail. Feasible answers would require different thinking models. cloud computing has lowered the cost of computing significantly with world-class data security. Once data security is taken off the discussion table, businesses can then attempt to solve the issue of software, algorithms, and processes to overcome the twin challenge of tools and talent. Most businesses should see this as an opportunity to leverage world-class capability in planning and not see this as another cost reduction outsourcing proposition. Because it is not!

As businesses become more and more complex, using data from external sources will become more and more critical. Businesses will always find it difficult to capture, understand and model the impact of these external factors into the forecasting process. Demand sensing is a global phenomenon and there are many companies like ZARA, P&G, Shell, Unilever, Mondelez, Kellogg, and General Mills etc. which have implemented the demand sensing processes and tools. But using data captured from demand-sensing technologies to better your forecasts is going to even more critical. The world is moving ahead and adopting these cutting edge technologies to resolve their problems. Don’t stay behind, start considering newer ways to use services to solve the demand forecasting challenge.

Alagu Balaraman is a partner in CGN & Associates and the managing director - India Operations & Sujit Sahu is principal at CGN & Associates India Pvt Ltd.