The timeline for companies to react to the coronavirus has shrunk dramatically. Leaders know that making good, fast decisions is challenging under the best of circumstances, but with a crisis of uncertainty such as the COVID-19 pandemic, they face a paralysing volume of important decisions.

According to EyeOn, there are six key enablers that leadership and organisations need to master and embrace to manage disruptions and make appropriate decisions quickly during the pandemic.

 

A first step is to build a cross functional team that is often referred to as a focus team. There are several steps leaders can take to get these people involved:

  1. Clarify the decisions to be made.
  2. Identify who should have a voice, including relevant stakeholders and experts, and those who will implement decisions.
  3. Create a forum for rapid debate to take place.

The focus team should ask questions like: What is most important right now? What might be missing? How might things unfold from here, and what could we influence now that could pay off later?

Collecting relevant data is the logical second enabler for decision making. In these unprecedented times, proper decision-making also requires data beyond what is currently available within the organisation. External data that are often used, are – amongst other – epidemiological information to understand how infection in the population will change over time, corona measures per country, government guidelines, data per market & industry, GDP and other economical factors etc..

Storing and structuring these internal & external data for value creation is the third enabler for decision making.
Sourcing more information requires a structured data environment or data lake to reduce data latency and feed applications for advanced analytics and innovative techniques that provide answers for taking the right decisions.

This crisis makes it clear that supply chain visibility is of prime importance. Decision making starts with insights on data and parameters. The data will provide your organisation fast queries, reports and dashboard visuals that cope with all internal and external data. If proper applications are in place, innovative machine learning techniques can enable predictive analysis.

Examples of how this is brought into practice is – amongst others – related to the use additional data sources can predict patterns and improve your ability to forecast. This data could include historical sales, order book, stock levels, commodity prices, sell-through information and macro-economic indicators.

Predictive analysis can provide valuable insights to understand how the pandemic will impact population over time. Using the latest information per country to forecast scenarios for COVID development, models can be fed with this data.

During the Corona pandemic traditional forecast techniques will add less value to steer operations. Employing new forecast techniques will increase accuracy and insights. For short-term forecasting, impact analysis and Bayesian enrichment can be deployed. The “bathtub” curve can be utilized for mid-term planning and combined with the “rubber duck” curve to produce a reliable forecast in these corona times. More information on these techniques – and others – can be found elsewhere on this website.

Quality decision making is supported and enabled by E2E scenario optimisation. Following elements for scenario planning amid the Corona crisis should be considered:

  • Identify the product groups sensitive to corona crisis.
  • Build multiple demand scenario’s – small, medium, strong.
  • Build multiple production strategies: conservative, neutral and aggressive scenario (as % of capacity) and try to get insights on supplier commitments (first tier / second tier) and where the source is located
  • Build scenarios on the duration of the Corona measures and constraints per market/ country / region.
  • Reviewing service levels & policies and be aware that operational service levels as you knew them are outdated.
  • Adjust safety stocks for impacted products. Even if demand level drops, the demand variation change can require additional safety stock.

 

Scenario Optimization: What does it take?

 

1. Facilitate decision making by facts on different options.

 

2. Assess impact of uncertainty on the results in € / $

 

3. Fast calculation of scenario’s enabling to run many options

 

4. Continuous review and adaptation of scenarios

 

5. Engage commitment of higher management

 

6. Initiate and stimulate uncertainty reduction initiatives