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Death Of The Forecaster
Demand planning tools that are currently used by organizations sit on siloed legacy systems that lack the capability to pull together various leading indicators of demand.
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COVID-19 has been a once in a lifetime black swan event that has upset the best laid plans of just about everyone in the world. This crisis has seen an immediate response from companies worldwide for innovative ways to minimize supply chain disruptions. In addition, this is just a start, which will have a significant impact on supply chains of the future. The pandemic shock has caused significant disruption in demand in various forms- channel shift (shift from brick and mortar to online channels), product shift (shift towards core and essential items) and regional shift (lockdown/ reopening schedules are impacting demand by country/state/city). Supply was impacted in the form of stopped or reduced production and companies are struggling when it comes to quick response and impact analysis.
With the online channels dominating the physical retail stores, it becomes extremely vital for companies to focus on their demand planning process. Demand planning tools that are currently used by organizations sit on siloed legacy systems that lack the capability to pull together various leading indicators of demand. With the shift to online/e-commerce channels, demand drivers such as discounts, promotions, events, conversion rates, social media/consumer sentiments, competitor pricing, demographics, market intelligence and other macroeconomic data etc. need to be incorporated in demand forecasting models to yield high forecast accuracy. Moreover, the current planning frequency is usually limited, monthly planning is not enough, and weekly demand sensing is the need for today.
Companies that are innovative and are clued on to this dynamic trend are adopting new age platforms, which are built on a bedrock of Artificial Intelligence and Machine Learning Technologies. This enables them to maximize their reach in the marketplace and be ready for the rebound. Examples are of a leading Luxury Cosmetics company that has seen tremendous growth even in the middle of a pandemic. This company used to get most of their sales from duty-free shops at the airports and their exclusive outlets or shop-in-shop outlets at the big malls. Now as we all know, both these channels were pretty much shut down for most of this year or not fully functional. However, they witnessed a tremendous growth in their online channels, but not in the same products. It really became a guessing game for them every month to predict what product category or brand of theirs will sell through the different online channels they served. By leveraging a cutting-edge AI and ML technology enabled platform, this company will be able to understand the drivers behind the growing channel shift across e-commerce, modern trade and general trade channels to orchestrate their supply chain appropriately to service the demand across the various channels.
For instance, a leading liquor brand will need to leverage such platforms and technologies to predict uncertain demand exacerbated by the steep hikes in excise duties , also one has to keep in mind the various rules regarding liquor outlet openings in various states; and supply chain planning from end to end – from sourcing to distilling.
Several states (and even countries) have increased their excise duty on liquor by 100 to 200%. This means, forecasting demand is a bit of a crystal ball gazing. Would a whisky drinker of a particular brand for years suddenly on seeing his favorite tipple double its price, would still buy the same brand but less frequently? Alternatively, would he downgrade to a lower priced whisky or would he move to a different category - say rum? Add to this, the lockdown induced complications - are bars and clubs open, are liquor shops open all the time and so on were all the questions that traditional planning tools were not equipped to give answers to the supply chain professionals.
New product introductions which are usually the growth -drivers of most consumer brands in the FMCG industry have also increasingly become a lottery - is a new product really a new product breaking new ground or it is just a “new improved “version? By changing the packaging colors from green to blue, will a new product take off significantly or bomb? Again, these are questions that with the help of latest science and math can be answered to a great extent by applying AI and ML concepts to demystify the art part of it into a more predictable science is also increasingly adopted by the industry leaders.
The bottom line to all this is that demand forecasting as it used to be done all these years is for all practical purposes’ dead. Increasingly, using past historical data as the sole predictor of the future is recognized by most organizations as a limited exercise in forecasting. Using market knowledge models, with demand drivers like weather, temperature, local events, and macroeconomic parameters monsoons, changes in international travel and even social media trends and sentiments are the way to sense and shape demand as adopted by the visionary leaders increasingly.
It can be a leading paint company leveraging insights from their past sales data, also drivers like the overall GDP growth predicted for the year, the number of residential houses and corporate buildings planned across rural and urban clusters, monsoon predictions to get a more comprehensive picture of their demand. For auto companies, it could be drivers such as changes in new car vs. old car sales, fuel prices, driving patterns by each local market impact demand for replacement tires & other auto spare parts, any other government regulations like constant increase in diesel prices and increasing subsidies on electric cars.
All this does not of course mean that machines and algorithms will be taking the decisions; it will still be the smart managers and executives who will drive the decision-making with the aid of such knowledge, analytics and learning powered Integrated Business Platforms. This is naturally, instead of just relying on their accumulated wisdom and experience or being supported by just traditional tools.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.