Optimizing Demand Forecasting Process in FMCG/Retail Businesses during the Covid-19 pandemic
The outbreak of Coronavirus has affected millions of people and businesses globally. The fangs of its economic impact are quite visibly closing in on the FMCG and Retail sector. During and beyond this pandemic, consumer goods companies in the US will have a constant task to inspect changes in consumer behavior which will then directly affect their product categories, channels of sale, and price tiers. Most companies in the US are adopting an action-plan backed by advanced AI-based Data Analytics to accurately forecast demand and to ensure that their supply chains are well optimized during and after this crisis.
What has changed?
As is evident from social media and news media images of people hoarding basic household supplies while deprioritizing other purchases, consumer behavior in the US has drastically changed in the last 2 months. On top of that, the stay at home orders imposed by the Government has altered the frequency and volume of purchases off the shelves as consumers prefer online purchases over store visits, even for their daily groceries.
According to market surveys conducted in the US, in the first three weeks of March alone, there was over 70% increase in the sales of household supplies and over 30% increase in the sales of packaged food, dairy, pet-care, alcoholic and child care products. At the same time, electronic goods sales have gone down by 30% and there is a noticeable fall in the sales of stationery, hardware, and beauty products. These bifurcated changes in the volume of sales of consumer goods have had a huge impact on business revenues in an unprecedented manner. Moreover, 30-40% of US consumers are switching to other brands in the same product category based on availability, preferred channels of purchase, price tiers, and product sizes.
Going by what we already know about previous recessions, the economic strain on consumers goes up with rising rates of unemployment and salary reductions. In this specific pandemic situation of the Covid-19 outbreak, the strain has intensified manifolds as the US has witnessed the largest number of unemployment filings in the history of the country. Businesses have a threat of losing the loyalty of these customers if they fail to consider their plight.
Why do companies adopt AI-backed Demand Forecasting methods?
An AI-backed action plan is the need of the hour to weather the impact of these factors on business revenues. Consumer goods companies in the US are using advanced analytics models to accurately segment demand changes in every product category, channel of sales, and price tier.
Broadly, the following categories of demand changes have been seen in the market:
- Demand has increased and will continue to increase even after the pandemic (Hygiene products, Sanitization products, Cooking Oil, Cereals, Pasta, Food supplements)
- Demand has increased but will be back to normal with time (Pet-care products, Toilet Paper, Napkins)
- Demand has shifted to home deliveries (Alcohol, Daily Groceries)
- Demand has decreased but will be back to normal after the pandemic (Beauty products, Packaged Beverages, Fitness equipment)
Regardless of these widely discernible changes, Business System Managers or Supply Chain Managers should assess and project demand on a granular level through the analysis of their product categories, customers’ consumption patterns, locations, SKUs, and channels.
Why the best-performing supply chains rely on AI-backed demand forecasting methods is clear now. It reduces errors by 50% in the supply network. Furthermore, there’s about a 60% decrease in ‘lost opportunities of sales’ as availability of stock is ensured through optimized inventory management. Additionally, AI-backed demand forecasting and machine learning engines run on real-time data with an increased capability to use data based on demography, online feedback, and social media whispers. Supply chain networks that are backed by AI and machine learning algorithms can perform 10x better than businesses that depend on Data Analysts to manually process their data.
However, this Demand Forecasting Action Plan can only yield results if it is closely followed by a dynamic Demand and Inventory Planning process. Companies should be constantly lining up their e-commerce performance, pricing plans, promotional campaigns, warehousing, logistics, and sales plans as per the forecasted demand curve to prevent inventory backlogging of slow-moving SKUs.
What needs to be done next?
Optimization of FMCG and Retail business processes with a focus on an AI-backed Demand Forecasting Action Plan has become extremely crucial in these times. This cannot be delayed due to internal processes and deliberations of a company’s annual planning timeline. Considering the rate at which the market is evolving in this pandemic phase, the action plan should aim to revamp and recalibrate the demand forecasting process of businesses right away to stay afloat and sustain this phase of severe uncertainty.
If you are unsure about kick-starting a Demand Forecasting Action Plan that is tailor-made for your business, please get in touch with our team of Data Scientists by emailing us at email@example.com.