Crisis-Proof Manufacturing: How to Enable Long-Term Resilience COVID-19: A Litmus Test for Factory Resilience “COVID-19 will likely follow the pattern of a broad crisis, resulting in a rather deep decline and a lengthy recovery.” – Harvard Business Review1 Crises like the COVID-19 pandemic are a litmus test for factory resilience. It exposes the known and unknown vulnerabilities of manufacturing systems and processes. It spurs manufacturers to reevaluate their factory resilience and rethink their crisis management seriously. The impact of COVID-19 on manufacturing processes and supply chain is highly disruptive and rippling. Global process manufacturers had to contend with the following challenges: Larger-scale supply disruptions. Manufacturers heavily dependent on sourcing materials from China, where the outbreak started, faced supply chain shortages.2 Significant demand reduction. The demand for automotive paints and coating, for example, has plummeted due to the pandemic.3 Drastic labor shortage. According to a French trade group ANIA study, food makers suffered a 22% loss in turnover globally due to the health crisis. Personal care and cosmetics manufacturers, on the other hand, had to shut down their firms due to lack of manpower.4 Costly delays. Pharmaceutical manufacturers that conduct clinical trials in China faced study disruptions and slower regulatory approvals.5 Reactive, Uncoordinated According to a study published in IEEE Engineering Management Review in July 2020, faculty members saw that the response of manufacturers to COVID-19 disruptions “has been largely reactive and uncoordinated.”6 To keep their operations running, some manufacturers have taken these temporary measures: Reactive cost-cutting such as overtime reduction, layoff, and discretionary spend cuts.1 Repurposing production to meet new demands goals, which can be costly and full of challenges.7 Partial operations capacity. The majority of surveyed firms across the globe were “operational during the peak of the pandemic,” with 44% at partial capacity.7 These short-term measures can help factories continue operating amid the crisis. But they are just band-aid solutions. As Oliver Wyman puts it, “these measures will only stop the bleeding but not start the healing.” 1https://www.oliverwyman.com/our-expertise/insights/2020/apr/managing-costs-in-times-of-covid-19.html 2https://www.latimes.com/politics/story/2020-03-04/spreading-coronavirus-tears-apart-global-supply-chains 3https://www.pcimag.com/articles/107770-automotive-paints-and-coatings-industry-in-the-battle-against-covid-19 4https://www.globenewswire.com/news-release/2020/04/15/2016351/0/en/Impact-of-COVID-19-on-the-Global-Cosmetic-Industry.html 5https://www.spglobal.com/marketintelligence/en/news-insights/latest-news-headlines/clinical-trials-drug-approvals-slow-in-china-as-covid-19-cas- es-grow-globally-57915442 6https://www.sciencedaily.com/releases/2020/09/200924135327.htm 7https://www.unido.org/news/covid-19-critical-supplies-manufacturing-repurposing-challenge Think long-term, act in real-time To crisis-proof their factories, manufacturers should think long-term. They should also enable their factory systems to execute in real-time. This requires the adoption of a different perspective and approach to building manufacturing resilience. The COVID-19 pandemic underscores why factory resilience should be viewed as more than just the ability to anticipate, react, and recover from a disruption. During a crisis, changes can happen every second, making them even harder to anticipate and mitigate. It is only prudent to anticipate or prepare for a crisis and its possible impact. But using rigid, predetermined models based on historical, outdated, and incomplete data is no longer sufficient. Here’s why: Managing disruptions during a crisis can be more complex because they can change without notice. It can be more difficult to foresee what will happen next and how it will affect factory systems and processes. Crises and their consequences can be highly unpredictable. According to McKinsey, the manifestations of some crises — including meteoroid strikes, systemic cyberattacks, and human-made disasters — “can strike with little to no warning.” Meteoroid strike Supervolcano Extreme Pandemic Solar storm Pandemic Extreme terrorism Global military Systematic conflict cyberattack Financial crisis Major geophysical Acute climate (hurricane) event2 Terrorism Trade dispute Man-made Regulation Local disaster military conflict Common Idiosyncratic Acute climate cyberattack (eg, dirty bomb) (heatwave) event2 Counterfeit Theft None Days Weeks Months or more LESS Ability to anticipate MORE (lead time) ABILITY TO ANTICIPATE DISRUPTIONS Source: McKinsey Likewise, reacting to disruptions using outdated approaches and rigid models can result in less desirable outcomes. Oliver Wyman suggests that instead of implementing reactive measures like cost-cutting, manufacturers should focus on transformation and long-term game planning to crisis-proof their factory. Besides, adapting to real-time changes requires real-time intelligence and real-time execution. Manufacturers need to enable a seamless flow of data and intelligence across the entire value chain. This will allow factories and supply chain systems and processes to persist and absorb any disturbance. Manufacturers also need to supercharge their data-to- execution process with automation and AI so that their smart factory can respond and adapt to changes on its own. Manufacturers need a new approach to building factory resilience that allows their smart factory to sense, respond, and adapt to changes and the impact of these changes in near-real-time and with less human intervention. Building long-term resilience starts with data “In crisis, getting facts quickly and basing your response on them, are key to successful outcomes.” – PwC According to PwC’s Global Crisis Survey 2019, many businesses “emerged stronger” and “experienced revenue growth” after a crisis. Their secret sauce is their ability to quickly gather appropriate information and make timely and deliberate decisions,8 which not all businesses can do. Four in ten surveyed companies cannot obtain information quickly, communicate effectively, and make fast and factual decisions. Ability to gather appropriate information quickly 41% 29% Ability to make timely and deliberate decisions 40% 30% Most vulnerable Most confident Source: PwC Global Crisis Survey of 2084 repondents To enable long-term resilience, manufacturers need to liberate data Manufacturers need to enable data consumers — both humans and smart machines — to harness the right data in real-time and without interruptions. Here are some practical steps they can take: Break data silos by bringing internal and external data together in one intelligent hub. Automate data integration, contextualization, and analysis for a seamless and accelerated data-to-execution process. Use powerful connectors that allow data intake from any tier and any device. But for a more sustainable approach, manufacturers should enable their smart factory systems to crunch data, understand insights, and execute the right action in an automated fashion. 8https://www.pwc.com/ee/et/publications/pub/pwc-global-crisis-survey-2019.pdf Turn smart factories into adaptive plants using Mareana’s Manufacturing Data Hub (MDH) Right-time data curation and insights generation is crucial for enabling long-term factory resilience. But what sets an adaptive factory apart from traditional and “simply smart factories” is its ability to execute the right actions at the right time with minimal to zero human intervention. Mareana’s Manufacturing Data Hub (MDH) uses ML/AI algorithms to create a responsive, adaptive, and connected core and empowers all kinds of data consumers including IIoT machines to speed up execution, collaboration, and innovation. MDH helps create a highly responsive, adaptive, and hyper-connected system that can learn, adapt, and self-optimize to increase yield, throughput, quality, and safety while reducing downtime, cost, and waste. Mareana’s Manufacturing Data Hub enables long-term resilience by allowing smart factories to sense and respond automatically and in real-time: SENSE. MDH’s smart algorithms contextualize and harmonize data, eliminating complex data handling and wrangling tasks. Its schema independent design allows plug-and-play for data from different systems and vendors. Its pre-built connectors allow data intake from L0 to L4 systems including IOT sensors. RESPOND. MDH gives the right people the right data to speed up insight generation, execution, collaboration, and innovation. MDH provides pre-configured dashboards for operational users, Python libraries for sophisticated data scientists, and web services for third-party system interactivity ensuring all data consumers have constant access to data. SENSE RESPOND MDH Historical production data Process & Quality Alerts Deep Learning and Connectors with ALL systems Algorithmic Intelligence (L1 to L4 + IOT) Digital Twin & Knowledge Library Real-time data Foundation For Advanced Modeling with Phyton Libraries (In stream processing & chunking) Factory 4.0 Data contextualization & Feedback capability for Al Models Harmonization Mareana’s MDH allows for data for everyone and every “thing.” It helps optimize manufacturers’ Big Data strategy by democratizing data and bringing it to the edge. It helps human and non-human data consumers to absorb changes and withstand disruptions whenever and wherever. Conclusion: Innovate