Predictive Intelligence for Consumer Goods During a Crisis
For many businesses, AI is still too much of a novelty. If they don’t understand its value, they will refrain from investing in it. Herein lies the conundrum: an unpredictable phenomenon that benefits from analysis using AI, and a business environment in which AI is perceived as high risk with uncertain rewards.
Meanwhile, the spread of COVID-19 is picking up speed, making time more precious. While AI-based predictive models that align with business needs are prime solutions, they can be cost-prohibitive and time-consuming to implement.
The bottom line is that companies need AI to solve modeling problems of the complexity of COVID-19 disruption but can’t afford the dollars and the time. Companies are attempting innovative go-to-market and business models.
One such company attempts resolve this dilemma by providing free AI-based COVID-19 solutions, which can create value now, and then provide low cost access to an inventory of intelligence capabilities that leverages the collective buying power and value across a broad number of businesses.
In reality, the more solution utilization through low cost engagement by more users — the better for everyone. A “rising tide lifts all boats” — lower cost, better outcomes and firmer ROI.
A warehouse of AI components will expedite the delivery of cost-effective consumer goods and retail solutions by accessing proprietary datasets and modeling approaches. This approach produces low cost, high impact COVID analytic models.
These models reveal how COVID-19 will impact sales and production, and provide vital hyper-local information to help decision makers with:
- Demand Forecasting
- Supply Chain Optimization
- System Overload Prediction
- Staff Allocations
- Store Closing/Opening
- Marketing Allocations
- Digital Media Allocations
Companies need more than partial answers and best guesses when faced with the tragedy of the commons — when individuals rapidly consume resources, resulting in severe shortages. Businesses not only look at the present crisis, but also need help to envision how it will affect their future competitive environment.
Flattening the curve will be a reality but it won’t lessen business disruption. Instead, it extends the length of this disruption. Once any given business is post-peak, accurate data is still critical. A major distributor that is currently down 35% will need to know how much product it should store in its warehouses. Without this granular hyper-local data, it will not be able to forecast.
Coronavirus will eventually ebb, but businesses need to think beyond, “How do I respond now?” They should be thinking about how to respond post-pandemic — and even beyond that to the next pandemic.