The Role of Data Analytics in COVID-19 Recovery
This is a thought leadership article by PrimeGlobal member firm Clayton & McKervey which looks at the role that data analytics plays in COVID-19 recovery.
As more and more states begin to reopen their economy, the COVID-19 conversation is shifting from survival to recovery. In early June, Michigan entered phase IV which permits additional businesses to reopen provided they follow state-mandated guidelines. This is welcome news for manufacturers as they are now allowed to open their doors and get back to production. While there are several new COVID-19 guidelines, it does create the opportunity to focus on growth and profitability while maintaining a safe environment.
Looking forward, manufacturing companies need to review their business situation and develop a “comeback” plan to guide them on the path of growth. Although it sounds fairly simple, the process can be challenging because of the various market, supply chain and other business risks involved in planning. While daunting, the good news is important clues impacting recovery may already exist.
Through targeted data analytics, companies can access and analyze data to drive recovery planning. To help clients, prospects, and others, Clayton & McKervey has provided a summary of important details below.
How Does Data Analytics Help?
One important change arising from the COVID-19 pandemic is that organizations around the world have changed how they spend, move, communicate, and travel. Industrial automation and manufacturing companies that leverage data about these shifts can gain important insights about trends and make predictions likely to materialize. Data analytics can lay the groundwork for companies as they work to recover, adjust, and strategize for the new normal.
Multiple Recovery Scenarios
Combating the virus has often been compared to fighting a war against an invisible enemy. However, it is difficult to learn any lessons from previous pandemics because there has not been one in the U.S. for over 100 years. Given the uniqueness of the situation, along with the potential scenarios which could still unfold, it is quite challenging to start the planning process.
It is quickly becoming clear that forward-looking driver-based analysis rather than backward-looking trend analysis is the only type that will provide real insights about the many potential scenarios. In a time like this, that forward-looking analysis must incorporate portions of external data.
Because most manufacturing and industrial automation companies serve multiple industries, it is necessary to analyze the relevant external data:
- Predict future revenue by modeling potential demand scenarios by gathering data about developments in the end markets
- Create multiple recovery scenarios that incorporate various KPI estimates including demand, revenue and the gross value added
- Modeling can help companies design strategies for operation within extreme situations, like sustained periods of decreased supply and demand
Framework for Recovery
As businesses continue to recover, it is essential to have a plan for capturing new opportunities. In fact, McKinsey & Company recently published guidance on steps industry companies may want to consider in the process. There are several steps that can be taken to begin the process and many of them are data-driven.
- Understand Customer Perspectives – Manufacturing and industrial automation companies who seek external data and model various scenarios can then segment customers based on their financial situation, production plans to develop, and other indicators reflecting customer needs. This can lead to the potential for new service or product offerings and alternative financing options.
For example, knowing that a small customer is experiencing significant financial difficulties should trigger an alert they may be reconsidering a purchase of new equipment. The manufacturing company can then:
- Provide customized financing options that might enable them to proceed with the purchase
- Refocus sales teams on solvent customer segments as well as those that may need equipment
- Enhance digital sales or reset prices
- Adjust Sales Processes – Within their networks, companies can sustain and even drive sales by working together, with distributors and sales agents, to find untapped opportunities. Since intuition and experience is less important in an unpredictable market, advanced analytics can identify the most promising opportunities for companies willing to be flexible:
- Adapt sales and marketing strategies in response to market developments
- Make adjustments in response to data in a more rigorous way by establishing preset thresholds for realignment
- Review established criteria for evaluating success
- Drive Supply Chain Transparency – How well do companies understand the supply chain? Often, there are six or more tiers between a manufacturing company and its first supplier. Understanding the supplier’s suppliers and viability is critical. What if one of those in the chain falters? Collect, buy, or bargain for external data that will provide multi-tiered visibility.
If a third-tier supplier is faltering, companies can find themselves without a readily available alternative. This puts outbound deliveries and customer satisfaction at risk. Increase supply chain transparency and reduce risk by:
- Mapping flow of critical parts within your supplier network
- Recalculating replenishment lead times
- Reevaluating current stock levels
Planning for the Future – The Road Forward
When companies begin to seek external data and incorporate forward-looking analytics, they move closer to converting transactional data into actionable insights. They can begin performing exploratory analysis using recovery scenario modeling and enhancing risk transparency. There are many stories of adaptability and innovation among organizations that are reevaluating their operations in the fight against COVID-19 and will likely be more to come.