How self-service analytics enables remote working
Self-service analytics support efficient shift handovers, reduced time loss
The year 2020 is ever fated to remain a point of reference for all of us, a point after which the world was different.
For the manufacturing industry, the timeline for pervasive digitalization and use self-service industrial analytics to work remotely will look remarkably different pre-, during-, and post-COVID-19.
Before this global disruption took hold, process manufacturing companies were in various stages of their digitalization journeys. Many of these companies had yet to implement advanced industrial analytics tooling. Additionally, many companies lagged when it came to digitalization, being slow to evolve and adapt to new technologies. Regardless, working remotely was not part of any factory’s vision for the future.
When the pandemic erupted, processing plants were faced with several new scenarios for their operations: shut down, pivot or scale up. Working remotely became a priority to maintain employee safety and productivity and keep factories running when possible. Now, as we glimpse a post-COVID-19 era, companies are seeing the urgency to complete the factory digitalization.
They also understand the benefits of adopting advanced industrial analytics to increase plant efficiency and support remote working — achieved as part of efforts to sustain operations and prepare for the future and its myriad changes.
The virus has greatly changed industry. The lockdown and safety measures implemented during the pandemic disrupted production and supply chains. To avoid any supply chain disruptions in the future, many companies are bringing their factories back home. They want to become financially and operationally ready to establish production in their domestic markets. It’s predicted that factories will be relocated to domestic markets, or as the new term indicates, they will be “reshoring.”
Digitalization will be key to success in production reshoring, including being data-driven and using advanced analytics. These tools allow factories relocated back to domestic markets to implement flexible work practices such as smart social distancing and remote working to ensure employee safety and productivity. Digitalized factories that use advanced analytics can run with a minimum of on-site personnel monitoring production through dashboards and production cockpits to solve production issues quickly, maintain assets and predict maintenance needs. The result is an increased adaptability to the COVID economy.
Pre-pandemic, before remote working
The Industrial Internet of Things (IIoT), Industry 4.0, Smart Factories and everything associated with those buzzwords already existed pre-pandemic. Many manufacturing companies were on their way to realizing digitalization. Those front runners understood the benefits of adopting new technology: increased operational performance, agility and flexibility. They were early to implement IIoT devices to improve efficiency and make data more accessible via manufacturing software tools, such as advanced data analytics, that allowed engineers to analyze and interpret their plants’ vast amounts of processing data, without the help of data scientists.
However, many other organizations were only thinking about the path towards digitalization and perhaps struggling with the best way to begin.
Self-service analytics streamline work, cut costs, and improve productivity. That said, the approach pre-COVID-19 work did not often involve remote working. In fact, such an approach was not considered viable. One reason, companies wanted data on-premises for security and accessibility reasons.
Companies believed personnel needed to be on plant floors to monitor assets and processes, exchange process information face-to-face, solve production issues in real-time working next to each other and implement shift hand-overs in the traditional manner. Working remotely wasn’t an urgent need. COVID-19 changed all that.
A viral disruption
The word that best describes how manufacturing plants adjusted to the virus is “scrambling.” Companies scrambled to stay afloat and secure the safety of their employees. Never had so many plants around the world shut down for health safety reasons or had to drastically rearrange work procedures to continue operations.
The pandemic brought about three main industrial plant statuses: shut downs, pivoted productions, and continued but scaled-up operations. To safeguard the health and well-being of workers and reduce virus spread, plants that manufactured non-essential products had to shut down for a specified time period.
However, some plants pivoted their productions to make essential goods, for example supplies needed for virus safety, testing and medical purposes. The third group, continued but scaled-up operations, were the factories that ramped up production due to increased demand. The market had shifted, and plants needed to produce more with less. New IIoT techniques, including advanced industrial analytics, became instrumental in helping plants maneuver through the difficult times.
For plants that shut down, process experts had the time to analyze the plant’s performance and efficiency by studying its data. With easy-to-use, plug-and-play industrial analytics software, these process experts could use their historical time-series data to not only analyze processes, detect inefficiencies, and resolve issues, but also to come up with new ways to improve operations. They could view the historical data, contextualize it (add information about the operating conditions at the time the data was captured), add comments, start discussions with other specialists in the organization, and even attach files for further clarification and instructions for issue resolutions.
They could create and share complete overviews of the performances of production processes. And they could do all of this remotely. With the right self-service industrial analytics solution, they could work from home while at the same time collaborating with team members at other locations to brainstorm and discuss ideas to improve plant operations. Thus, the whole team was able to remotely leverage process data to understand how to maximize the manufacturing processes. They were able to maintain work productivity even when the plants were not running.
Other companies switched to manufacture supplies needed for the pandemic. Self-service industrial analytics allowed teams to turn process data into actionable information to monitor and control the new production processes, solve problems quickly, optimize equipment effectiveness and performance, and reduce waste. They could solve issues immediately and leverage plant data to easily search for trends and question process data directly — without help from a data scientist. These analytics tools allowed teams to work remotely and collaborate with team members at different site locations around the world. As a result, plants quickly got up to speed, functioning optimally for the new production plans.
It was the same for the plants that continued operating but needed to ramp up production. Due to market shifts, plants needed to produce more to meet increased demands and address the challenges associated with scaling upward. Again, advanced industrial analytics was key to managing this situation. Process experts could work remotely or at least adhere to work-distancing guidelines allowing for fewer personnel on the plant floor to monitor production. The tool also allowed users to team up to discuss and solve process issues remotely and then share this information with colleagues wherever they were working. Continuing production was streamlined and eased and at the same time employee health security policies were implemented, all enabled by the use of advanced analytics.
Given a sense of urgency, manufacturing companies now know that to survive the post-COVID period — whenever that may be — and to better face any future disruption, they must concentrate on digitalization and establishing smart factories. The issue is no longer whether they will embark on a path towards digitalization, but whether they will address this pressing task in time to survive.
The pandemic has emphasized the need for change and the adoption of technology such as manufacturing software to fully exploit smart factories and the associated big data. Companies need to respond to disruptions with agility and develop resilience to cope with whatever difficulties come their way in the future. They simply must be prepared.
Many people working in the process manufacturing industry now see the logic in digital transformation. Companies that were slow in implementing it had their reasons, one probably being that they thought they had time. The current situation tells them otherwise; they do not have time and must act now. With the future of the virus unknown, companies must plan to continue operating by applying smart social distancing and must continue operating by applying remote working.
Self-service industrial analytics allows for 24/7 process monitoring through dashboards that permit reductions in on-site personnel and establish a system to communicate and collaborate remotely. Process experts can gain important insight into production giving them data-driven information to base their decisions on. They can solve issues quickly and accurately, to better predict and manage maintenance.
With new work distancing protocols, self-service industrial analytics allow for efficient shift handovers, reducing time loss and cumbersome exchanges of notes and information. Moreover, workers have access to all production information, breaking down data silos and increasing efficiency. Ultimately, self-service industrial analytics provide efficiency, continuity and support under work distancing and remote working approaches.
COVID-19 is a game-changer, not just for all people and businesses but for the manufacturing industries as well. Today, we are in an unprecedented global health and economic crisis. It is unclear when the disruption of COVID-19 will end or how the rest of the year will play out. Industrial manufacturing companies that plan have a better chance of survival. This includes incorporating new ways of operating to stay competitive while ensuring employee safety. Companies can accelerate and innovate forward, using this period as a time to learn.
Fundamentally, it is a matter of business sustainability now and for years to come. Companies can achieve this by establishing digital and technological resilience and agility to face any disruption, by implementing the needed work approaches to ensure employee safety, and by establishing the necessary efficiency, productivity and flexibility to compete in any market.
Realizing plant digitalization and adopting self-service industrial analytics are key for companies to weather the COVID storm and to survive future storms. This tool will allow process personnel to analyze and monitor production processes remotely especially with the help of production cockpits. Moreover, self-service industrial analytics will allow for global work collaboration and the leveraging of expertise from all over the world.