How can systems recover and learn from the experience?
Alexander Stolz from Fraunhofer EMI is optimistic: “The financial crisis of 2008, which is perhaps most comparable to the current economic situation, showed that Germany was able to recover very quickly. What benefited us most back then was our flexible and innovative SME sector, our broad manufacturing and research base, and therefore our strong and diverse economy.” In addition to having solid economic foundations, one other thing is vital for a successful recovery phase: here, too, strategic planning is essential. “We should also be thinking about a gradual return to normality during all the phases before recovery – even if there’s still a lot of uncertainty about how things are going to develop,” Stolz explains. “By establishing simple indicators that enable a continuous monitoring of the impact of the measures so far implemented, we can continue testing the effectiveness of our decisions as conditions change. A daily log will tell you where you’re making progress and where there are weaknesses. And it means you can react quickly.”
For companies in the recovery phase after lockdown, it is vital to strike a proper balance between two tasks: on the one hand, the need to protect employees and customers; on the other, the need to ramp up production and thereby recoup some of the losses sustained. In the Virtual CoLAB, an online platform launched in conjunction with Fraunhofer Austria, the Fraunhofer Institute for Manufacturing Engineering and Automation IPA is now helping companies restart production and make their operations more resilient. The expert services on offer include assistance with drawing up a systematic package of measures and benchmarking with other companies in order to derive best practice. “It’s often the simple things that help most at the beginning: taking people’s temperature, managing bus transport, rearranging workspace and analyzing risk,” explains project manager Maximilian Dörr. “Those are the first steps. Building on that, we can then draw up a strategy to ensure greater adaptability once the crisis is over.”
A further challenge for companies is to repair disrupted supply chains and get them running smoothly once again. During lockdown, production stoppages around the world led to shortages in materials. At the same time, the continued delivery of some goods during factory shutdowns caused a backlog of items awaiting further processing. The task now is to resynchronize the supply chain. In a project entitled “Fast ramp-up,” the Fraunhofer Institute for Material Flow and Logistics IML in Dortmund is using simulation to help companies with planning and management. “Our Order-to- Delivery-NETwork Simulator – or ODT NET, for short – is a tool suite for simulating and evaluating supply-chain and ramp-up scenarios, so that companies can get production restarted as soon as possible,” explains project manager Marco Motta. Last but by no means least, it is also important to learn from the measures so far adopted and then to adapt this learning before a new preparation phase ushers in the next cycle. This means collating and fi ling in structured form all the data and observations that have been used to evaluate and manage the crisis. This is no easy task, since such information has accumulated across all phases of the cycle and is therefore extremely heterogeneous and seldom gathered on a unifi ed or centralized basis. Here, AI methods such as machine learning can be of assistance.
Around the world, a number of studies to assess the response to the pandemic are now underway. In CoronaNet, for example, 150 researchers across 18 time zones have investigated and categorized thousands of measures adopted by over 200 countries in an attempt to contain the virus. With the help of machine-learning methods, they analyzed over 200,000 news articles and extracted 16 different types of measure. They also compiled an index to compare when and to what degree different countries introduced specific measures. At the end of April, Germany had a mid-table ranking compared to other countries.
A number of Fraunhofer Institutes have also started research projects in an attempt to learn more from the events of recent months. At Fraunhofer EMI, for example, Stolz is using correlation models to determine the impact of various measures on, for example, the operation of critical infrastructure in selected regions. These models are based on a wide range of data sources: available statistics, information from companies and operators, and also press releases. This diverse data is then processed and structured in such a way that it can be correlated. A project recently launched at the Fraunhofer Institute for Systems and Innovation Research ISI aims to understand and shape the dynamics of system transformation on both a conceptual and analytical level. Here, researchers are developing concepts to help policymakers and companies develop strategies that infl uence system change.
It is still too soon for a fi nal evaluation. Yet the experience of what may be the greatest natural experiment of our time will undoubtedly help us emerge all the stronger from this diffi cult period – as will a new-won confi dence in the analytical tools at our disposal, the discovery of new potential for collaboration and greater fl exibility, and a heightened awareness of our own capabilities. At the same time, this will also give us added momentum to tackle essential processes of transformation – provided we remain committed to rethinking our ways. “In the fi eld of resilience research, we’re used to things happening in cycles,” says Florian Roth. “The danger is that once measures start to work and the crisis passes, people just carry on as before. But this crisis, I’m sure, is going to remain anchored in our collective consciousness for a long time. And people will realize that it’s better to take proper precautions beforehand rather than having to pick up the pieces afterwards.”