Despite the back-and-forth nature of the fight against COVID-19, we are seeing both industry leaders and regulators talk more about the return to “normalcy” – but normal does not necessarily mean a return to the way things were done, especially in light of the changes and lessons that this epidemic has forced on the industry.
When shuttered factories and manufacturing units reopened half-way through the pandemic, they had to reset themselves for growth beyond the crisis. In several cases, this meant reassuring and bringing back furloughed employees, working out increased staff safety, managing workforce through mandated distancing and remote work practices, building in more resilient processes, and leveraging Artificial Intelligence (AI) and the Internet of Things for productivity.
To resume production, there must be stringent protocols for navigating workers safely through the disruption, such as ensuring overall sanitization of premises, regular deep cleaning of tools and machinery, rearrangements for distancing and rostering per government regulations, among others.
The manufacturing sector is still inchoate when it comes to widespread adoption and application of AI and IoT enablers. However, to be able to get back to a semblance of normalcy, there has been a visible shift in focus: from the pandemic crisis to a viably safe response. What drives this change is primarily worker safety. To resume production, there must be stringent protocols for navigating workers safely through the disruption, such as ensuring overall sanitization of premises, regular deep cleaning of tools and machinery, rearrangements for distancing and rostering per government regulations, among others. Besides the conventions of using personal protection equipment, sterilized gloves, protective shields and masks, predictive technology can be leveraged to safeguard health and wellbeing at the manufacturing units.
Planning A Resilient Return To The Workplace
The ubiquity of technology in all aspects of life only makes it but obvious that IoT technology finds its way into manufacturing as well. With longer periods of heightened uncertainty expected from the coronavirus outbreak, it is imperative that factories and warehouses speed up their digital adoption to get a productive workforce back in place.
IoT has several applications in manufacturing plants. Not only can it facilitate efficient production flow but also auto-detect and monitor manufacturing cycles to manage inventories. The on-site data collected can be used to separate the mission-critical workforce from the staff that can work remotely. With this information, rostering can change from being date-based to data-based, and only those employees who really need to be present for crucial operations can be deployed to the production floor. Data from IoT collection devices can provide a macro view of the entire manufacturing unit, enabling predictive maintenance. One step ahead of preventive maintenance, prediction helps deploy condition-based maintenance of the production equipment, minimizing time taken on fault repair. This ensures that the skeletal staff that is in at work during the pandemic makes complete and optimal use of their workday with minimal production interruptions due to machine failures.
Planned, staggered breaks from work, designated walking lanes and areas of use within the plants, wearables to alert workers when safety distances are breached: these are some of the measures that factories can adopt to create a secure environment for their workforce. However, sustainable transformation cannot be achieved overnight. Even the best-crafted plans evolve with time. A 2020 McKinsey & Company Covid-19: Global Manufacturing and Supply Chain Pulse Survey reported a distinct split in expectations for recovery from the pandemic in the manufacturing sector, with 54% respondents across the APAC region pegging the upturn to be evident not earlier than a year. Let us look at how workspace safety can best be redefined when acclimatizing to the transformations in a post-pandemic scenario.
One of the key safety features of a post-COVID Smart workspace is touchless entry. Touchless entry includes not only access control but also detection of fever, mask, facial features, and mood. This is achieved by combining thermal imaging, computer vision, and fusion analytics on video data using AI techniques. AI models are used to enhance the capabilities of thermal cameras that detect fever and employee mood by tracking historical temperature and mood data and flagging any anomalies at the time of entry.
With wearables distributed to every member of the workforce, health data such as quality and duration of sleep from the previous night, and blood oxygen levels and heart rate throughout the workday can be collated for real-time augmented analysis on Edge, to monitor each employee’s health and fitness indices at a centrally located control-tower or command center. Algorithms analyze and detect inconsistencies in the gathered data, and alerts are raised in the backend system for timely and appropriate action by the command center to protect the health of the collective staff on location. These instantaneous alerts can be sent over SMS, using mobile push, or via Slack to cut time. The data thus generated is stored in a big data repository and is used for training facial recognition using neural network models, thermal temperature analogy models, and wearables data analysis models. Edge analytics also enables real-time on-site data analysis to be seamlessly deployed across multiple sites.
Integrating Workspace Safety With Public Healthcare For Pandemic Containment
The consolidative solution outlined will allow for contact tracing geo-gated to the confines of the work facility in case of a pandemic outbreak. The integrative system can be unified with public healthcare systems as well, merely with the intention to accelerate external contact tracing and not for data sharing. ‘Accelerate’ is the watchword here, as this system is a robust combination of connectivity, advanced analytics, and intelligent IoT technologies. What would have taken a great deal of effort to achieve manually, ensuing in a loss of valuable time and human resources, incurring high costs, and defeating an already squeezed economic sector, can be accomplished with highly sophisticated early warning and detection systems.
Workforce Safety Beyond Pandemics
Every major crisis in the world has seen the implementation of short-term measures that have gone on to influence changes in the economy for decades thereafter. COVID-19 crushed the manufacturing sector in a way that no one imagined, simultaneously impacting demand, supply, and workforce availability. Digital factories can be the solution to help a limping sector back on its feet through the pandemic and beyond it.
The American Society for Quality (ASQ) found that manufacturing companies that have digitized their processes have seen astounding results in the recent past: 82% increased efficiency, 49% experienced fewer product defects, and 45% increased customer satisfaction. Linking manufacturing devices and collecting data has helped manufacturers to reduce overhead, conserve resources, increase profits, and optimize operational efficiencies. But any technology that constantly tracks the workforce, its moods, and behavioral patterns, as well as health and fitness levels daily would raise concerns over the use of personal and highly sensitive data. However, if the technologies are used within the confines of the workspace for the sole purpose of employee safety and wellbeing, with data accessed only by designated health officers at the command center, and alerts and statistics deleted within stipulated periods of time, workers’ privacy can be safeguarded along with their health.
Designing A Unified Front
After discussing the various potential technologies deployed here, we must also recognize that with an integrated approach, we can minimize the challenges of implementing new technologies, while amplifying the benefits of having greater access of new data and better control over processes. Typically this is achieved by working with a technology partner that can not only integrate multiple technologies through a unified platform, but also collaborate with manufacturers to identify the potential areas for synergy and help design custom solutions that take advantage of these.
For example, by combining IoT data used for predictive maintenance and movement control and analyzing it via an AI solution, it is possible to generate processes that can increase productivity.
The short term view on these new protocols and processes may be primarily focused on protecting workers during this pandemic, but taking a long terms perspective, these technologies can bring about benefits that will last far beyond COVID-19.
By combining IoT data used for predictive maintenance and movement control and analyzing it via an AI solution, it is possible to generate processes that can increase productivity.
Article by Vinod Bijlani, Practice Leader (Artificial Intelligence, Data Science & Internet of Things) at Hewlett Packard Enterprise (HPE).
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