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Demand planning in the COVID-19 pandemic: how to forecast for sharp peaks and troughs

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 | Sep 2020

With COVID-19 constantly impacting customer needs and behaviours in new ways, how can CPG companies and retailers confidently forecast demand?

 

COVID-19 has fundamentally changed how people live and behave – including how they shop. It’s had a major impact on how, where, and when consumers around the world buy, and brought with it a whole new set of customer expectations for brands and retailers to meet.

So far, we’ve seen a number of notable changes in consumer shopping behaviour, including:

Each of these trends represents a significant shift in demand that many CPG companies and retailers – and their supply chains – simply weren’t prepared for. But together, they paint a more complex picture; one of a rapidly shifting retail landscape that’s set to stay volatile for the foreseeable future, forcing companies to re-evaluate how they forecast demand.

 

Why traditional forecasting methods can’t keep up

If this was a single short-term event, CPG companies and retailers would be able to recover and return to business as usual within a few weeks. Unfortunately however, events and trends between March and September have shown us that market volatility is here to stay – for the time being at least.

Perhaps the most challenging aspect of this crisis for CPG companies and retailers is that the impacts aren’t completely consistent between businesses, products and geographies. Some products and thriving, others are seeing sharp falls in demand. And for many companies, demand for some of their lines as dropped in one geography, while sharply rising in another.

One thing however is clear – static, singular demand forecasts based purely on historical trends and data are no longer fit for purpose. Instead, companies are starting to step back and observe and analyse what’s happening today in as much detail as possible.

However, several challenges still stand in their way.

  • Capability

In highly stable categories, companies can get complacent. They often have basic forecasting solutions – frequently based in Microsoft Excel – and can’t enhance them quickly when a situation changes.

  • Data

Forecasting has always relied on historical data. But nothing like COVID-19 has occurred for over 100 years – which means previous data isn’t representative, and organisations will have to look for new sources.

  • Structural

Traditional methods no longer provide accurate forecasts – but they’re so tightly integrated with sales, operations and budgeting processes that it can be difficult to uncouple them and introduce new systems.

  • Scalability

One method that many companies are using to better forecast post-COVID demand is manually adjusting existing forecasts in current inputs from sales teams. This is a good start, but it’s a manual process, and isn’t easily scalable.

  • Assumptions

All forecasts are built on established assumptions that can generally be relied on. However, the shifts triggered by the COVID pandemic have invalidated many of them, forcing organisations to ask fundamental questions of their forecasting methods and structures.

There are a lot of new factors for CPG companies and retailers to consider and account for – and for many, this will seem like an overwhelming challenge. However, like all challenges faced in customer-driven industries, if companies can successfully adapt and overcome them, there are huge potential competitive advantages to be gained.

If those companies can find new ways of identifying insights and using them to create more reliable demand forecasts, they’ll be well positioned to make the right products available in the right places at the right time – while their competitors are stuck operating on best guesses.

Collaborating to navigate the future of demand planning

There are major questions that need answers: how can we predict short-term demand amid market volatility? Has consumer behaviour fundamentally shifted, or will it reset to pre-COVID levels in the future? What’s the long-term impact on sources, manufacturers, shipping routes and distribution networks?

For decades, retailers and suppliers have shared limited information to help each other answer supply and demand questions like those. The demand-related questions many are faced with today however are a lot more complex than those asked routinely in the past. To answer them confidently, all impacted parties need to take their collaboration to the next level.

Business units need to share complete data with one another. Suppliers and sellers need to operate with complete transparency to support one another’s operations. And organisations must open up their data stores to more players across the supply chain to create a complete picture of demand today.

 

Preparing to ADAPT

 

Equipped with complete, collaboratively gathered demand data, CPG companies and retailers can start to ADAPT their approach to demand forecasting:

  • [A]lternative data signals

With data shared across the supply chain, organisations can compare and analyse alternative data sets in new ways. For example, point of sale and shipment data can be brought together to better anticipate order size and product mix requirements. Data about local COVID restrictions, macroeconomic indicators, consumer confidence and sentiment, and shopper behaviour can all help build a complete picture of the situation.

  • [D]rivers of demand

To sustainably forecast demand today, organisations need to challenge underlying assumptions and key drivers. Reliable data and insights can be used to develop driver-based models and monitor and simulate levers, supplementing unidimensional historical data that companies relied on previously.

  • [A]I and advanced analytics capabilities

With larger, more up-to-date and deeper data sets available to them, retailers and suppliers can start utilising cutting-edge techniques as an alternative to static forecasting models developed in excel. Machine Learning for example can help attain higher accuracy at more granular levels, taking into account local effects on predictions.

  • [P]rocess

Cross-functional collaboration is the foundation of strong forecasting. Cross-functional teams must work together to make business planning processes more agile and sprint-based, ensuring consistent forecasting across the organisation, while overcoming many of the structural challenges that have persisted for years.

  • [T]echnology

Technology can help teams respond quickly to changing market and supply conditions. By empowering people with self-service scenario modelling capabilities and decision-making tools, every function can respond faster to change, and adjust forecasts accordingly.

It’s an approach that The Smart Cube has already seen prove successful. With our demand forecasting solution, we have helped a Global CPG client achieve 92%-96% forecast accuracy for every product, channel, and region combination even during the crisis period.

Opportunity in the midst of a crisis

Retailers and suppliers should recognise COVID-19 for the challenge it is, but not lose sight of the opportunity it presents. Now is the time to change the way organisations forecast demand. In the short term, this will help to focus dedicated effort and energy to plan and to ensure consumers have access to the brands and products they need during the crisis. In the long term, businesses stand to gain significant market share through a forward-looking view of the external landscape, intelligent planning and agile decision-making.

At The Smart Cube, we’ve been working on advanced, data-driven demand planning techniques and technologies for years – and now it’s coming into its own. If you’re interested in how your organisation can adapt and sustain your business during the crisis, without losing sight of the future, we can help.

Get in touch today to talk to one of our experts about the future of forecasting, and how you can get started right now.

Co-authored by: Nitin Aggarwal and Nisha Purswani
  • Nitin Aggarwal

    Nitin is an analytics executive and a seasoned business leader with 15+ years of experience across industries and functions. Based out of our Chicago office, Nitin leads the Retail, CPG & Consumer Markets practice in the US. Prior to this role, Nitin developed and scaled the data analytics practice, and managed operations across the globe. He also drove the practice strategy in terms of new capabilities, solutions, and technologies from India.

    Nitin studied electrical engineering at Punjab Engineering College, Chandigarh, MBA at University of Notre Dame, and got his Executive education in Analytics from SDA Bocconi. An avid sports person, he loves playing tennis and badminton, and is a committed follower of American football.

  • Nisha Purswani

    Nisha is an advanced analytics and consulting professional with over 12 years of experience in retail, CPG and pharmaceuticals. In her current role, Nisha is responsible for managing large analytics accounts, designing and developing data science and analytics solutions for retail and consumer goods. She is an expert in marketing strategy, CRM, measuring promotion and campaign effectiveness, test and learn, forecasting, time series analysis, and driver analysis. 

    When Nisha isn’t helping clients solve business problems, she can be found reading books, or in the kitchen trying out new recipes. She also enjoys travelling, meeting people of different cultures, and exploring new places.