Workplace distancing simulation and management: Facilities, manufacturing, offices

To lower risk to COVID-19 exposure, use digital twin design and simulation software, wireless tags and receivers and asset tracking software help manage employee risks and enable workplace productivity. See nine questions manufacturers and facility owners need to ask about COVID-19.

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Integrating wireless, software, and hardware technologies can help with workplace distancing, offering the “next normal” of productivity in the COVID-19 pandemic, as explained remotely on June 4, from Siemens Digital Industries (DI), Plano, Tex., and other Siemens locations.

While the presentation aimed at manufacturers, the same technologies can be applied to distribution centers, warehouses, and commercial and government facilities to lower COVID-19 risks related to employee safety, technology investments, restarting or expanding production, and additional operating costs. (See nine COVID-19 questions to ask, below.)

Workplace distancing modeling, simulation, workspace validation

For manufacturers restarting, maintaining, or expanding operations during the ongoing COVID-19 pandemic, employee safety includes establishing production environments and workflows that address physical distancing requirements. Combining wireless hardware and tracking and simulation software, companies can:

• Quickly and efficiently model how employees interact with each other, the production line and plant design

• Build an end-to-end digital twin to simulate worker safety during the COVID-19 pandemic

• Iterate on and optimize workspace layouts

• Validate safety and efficiency measures to help future-proof production lines.

During and after the COVID-19 pandemic, the hardware and software can be applied to other uses to enhance more efficient workflows.

Bluetooth tags helps distance workers

Using Bluetooth-enabled wireless tags, companies can continuously measure distances among workers, provide real time visual feedback to employees regarding spacing from others and create a log of all movements and interactions over time to facilitates safe distancing during the COVID-19 pandemic.

Combining the wireless tags with digital twin simulation software of the actual manufacturing environment permits companies to model and simulate how employees interact with the equipment and each other, enabling them to iterate and optimize safety and productivity in the short term, and validate a redesign of the operation before more costly physical changes are made to lower COVID-19 pandemic risk.

“We are helping our customers create a safe work environment, which is extremely important as they look to produce efficiently and reliably under unprecedented circumstances,” said Tony Hemmelgarn, president and CEO of Siemens Digital Industries Software, commenting on workplace adjustments during the COVID-19 pandemic. “The combination of real time distancing management and digital simulations will help companies maintain safe work environments today and make educated decisions about ongoing and long-term optimization.”

Siemens’ Simatic Real Time Locating Systems (RTLS) transponders are embedded in badges as COVID-19 personal protective equipment by all employees so transponders can track and record workforce movement in the Next Normal Manufacturing workplace distancing solution. Badges will display a warning, alerting them to risk. Data can be analyzed to identify “hot spots” where risk scenarios occur frequently. Courtesy: Siemens Digital Industries

Wireless badges as PPE to alarm safe distances

The real-time locating system transponders are embedded in badges worn as COVID-19 personal protective equipment (PPE) by all employees. RTLS receivers placed throughout the operation can then continuously track and record workforce movement. When two employees are in a risk scenario (such as less than six feet apart), their badges will display a warning, alerting them to the situation. The data collected over time can be analyzed to identify “hot spots” where COVID-19 risk scenarios occur frequently. Such situations become actionable via the digital twin, which is provided by process simulation software and plant simulation software. Using the collected data, new manufacturing layouts or workflows can be simulated to provide the desired outcomes, which can then be implemented in the physical operation.

COVID-19 track and track in the workplace

Manufacturers can add traceability with on-premise or cloud-based software to help enable rapid, contact analysis in case of COVID-19 illness. All movement and contact with the affected employee can be visualized, enabling rapid notification of those who came into close contact and selective (rather than site-wide) deep cleaning of exposed physical environments.

“Siemens is providing a powerful, rapidly deployable solution that helps manufacturers take control of their operations and achieve better safety, productivity and cost outcomes today and in the post-COVID era,” said Raj Batra, president of Digital Industries for Siemens USA. An implementation “can begin delivering results for most manufacturers in one to two weeks.”

Digital twin, provided by Siemens’ Tecnomatix Process Simulate and Plant Simulation software uses collected data from employees so new manufacturing layouts or workflows can be simulated to provide the desired outcomes to be implemented in the physical operation. The on-premise or cloud-based Siemens’ Trusted Traceability Application on MindSphere, a cloud-based, IoT operating system, helps enable rapid, comprehensive contact analysis if COVID-19 is reported, visualizing areas and employees affected for rapid notification and targeted cleaning. Courtesy: Siemens Digital Industries

Nine questions manufacturers and facility owners need to ask about COVID-19

In the presentation, Siemens offered nine questions facility owners and manufacturers should ask about COVID-19 employee safety, technology investments, restarting or expanding production, and additional operating costs.

1. How do we improve employee, union and executive confidence to accelerate plant startup?

2. How do we ensure employees are following new guidelines?

3. How can we monitor employee interactions while respecting their privacy?

4. How can we address COVID-19 recovery AND protect their investment for future use?

5. How can we utilize this new data with digital tools for future safety/production improvements?

6. How can we ensure production line changes are optimal and employees are trained?

7. How can we modify shift change procedures to optimize production?

8. How can we keep cleaning and disinfecting costs down?

9. How can we avoid additional shutdowns and lost production?

How To Improve Production With Autonomous Maintenance

Autonomous Maintenance is one of eight pillars that make up Total Productive Maintenance (TPM). It’s also one of the most important activities that eventually determine the success of any TPM implementation project.

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How Does Autonomous Maintenance Work? 

Autonomous Maintenance is a maintenance strategy that enlists machine operators in the process of maintaining the equipment that they operate. 

Under AM, machine operators carry out basic maintenance activities such as making adjustments, lubrication, cleaning, continuously monitoring their machines, etc. Unlike in other traditional maintenance methods where only dedicated technicians handle these tasks, autonomous maintenance machine operators carry out these activities rather than waiting for a maintenance technician to attend to them.

Direct Benefits of Autonomous Maintenance

• A safer plant floor. Autonomous maintenance follows a structured, step-by-step process that covers everything from restoration and continuous upkeep of each machine to cleaning, clearing, and organizing the general surroundings of the machines. The result is a better-organized surrounding and safer workplace overall.

• Increased efficiency. Since the operators are capable of handling basic but important tasks like cleaning and lubrication, the maintenance technicians have more time to focus on other more critical machine issues that require their special skills. This arrangement reduces the risk of a particular problem that often occurs on busy plant floors; because of the activities involved in attending to so many other repairs or servicing, technicians can be in a hurry and they may not take the time to clean or lubricate every machine thoroughly. Machines that are not cleaned or oiled properly will likely develop more serious problems with time. AM machine operators easily fill in this gap and ensure that each production equipment gets the required attention in a timely manner. 

• Quicker failure detection. Based on the training that the operators will receive during the process of implementing autonomous maintenance, they will be in a better position to notice the symptoms of potential problems in the equipment they manage. They will also know when to intervene or to request for support from the maintenance team before the machine malfunctions.

In all, autonomous maintenance creates a conducive environment for maintenance and production teams to collaborate and achieve better safety, eliminate waste (by avoiding duplicate activities), and boosting workforce productivity. 

The combined efforts of both of these teams also helps to improve overall equipment effectiveness which has a direct impact on production.

Overall Equipment Effectiveness (OEE) and Production Optimization

In summary, OEE is a metric that helps manufacturers to determine the percentage of manufacturing time that is truly productive based on the formula OEE = Availability x Performance x Quality.

The relationship between equipment effectiveness and production levels is clear - the operational efficiency of any manufacturing plant is closely related to how its equipment are managed and run. 

If for any reason equipment are not operating at their peak potential, the results of such poor or subpar performance can manifest in a number of losses that OEE seeks to prevent. They are:

1. Availability loss: unplanned stops, unplanned maintenance, machine failure, etc.

2. Performance loss: slow running, minor adjustments, incorrect setting, alignment problems, etc

3. Quality loss: production defects, scrap, rework, reduced yield, etc.

Improving the condition of manufacturing equipment through autonomous maintenance can significantly minimize the occurrence of the above losses. Remember that TPM as a whole strives for “perfect production” through zero breakdowns, zero small stops/slow running, and zero defects. Since autonomous maintenance aims for the meticulous restoration and consistent upkeep of all machines to prevent deterioration and improve machine health, adopting an autonomous maintenance strategy will put a manufacturing enterprise in the best position to:

• prevent, identify, and attack potential machine issues before they escalate and cause disruptive production problems. 

• improve OEE and get it closer to world-class levels (85% and above) 

These are the endpoints and objectives of an AM program; limit production losses due to preventable deterioration of equipment.

In conclusion

Autonomous maintenance is not just about getting production teams to take on maintenance activities. Operator knowledge and skills also need to improve. For a plant that wants the highest production levels, training operators to just switch on their machines and run them at full capacity leaves too much room for disruptions every time the machine(s) requires the slightest attention. Why not try autonomous maintenance instead and expose them to some basic problem-solving skills that will improve availability, performance, and quality?

When implemented and structured correctly, AM is a powerful and safe tool for continuous improvement and for achieving the state of perfect production that TPM advocates.

System designed to improve database performance for health care, IoT

Purdue University scientists developed a system called SOPHIA designed to help users reconfigure databases for diverse applications ranging from metagenomics to high-performance computing (HPC) to IoT.

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A team of computer scientists from Purdue University has created a system, called SOPHIA, designed to help users reconfigure databases for optimal performance with time-varying workloads and for diverse applications ranging from metagenomics to high-performance computing (HPC) to the Internet of Things (IoT), where high-throughput, resilient databases are critical.

One of the big challenges for using databases – whether for health care, Internet of Things or other data-intensive applications – is that higher speeds come at a cost of higher operating costs, leading to over-provisioning of data centers for high data availability and database performance.

With higher data volumes, databases may queue workloads, such as reads and writes, and not be able to yield stable and predictable performance, which may be a deal-breaker for critical autonomous systems in smart cities or in the military.

“You have to look before you leap when it comes to databases,” said Somali Chaterji, a Purdue assistant professor of agricultural and biological engineering, who directs the Innovatory for Cells and Neural Machines [ICAN] and led the paper. “You don’t want to be a systems administrator who constantly changes the database’s configuration parameters, naïvely, with a parameter space of more than 50 performance-sensitive and often interdependent parameters, because there is a performance cost to the reconfiguration step. That is where SOPHIA’s cost-benefit analyzer comes into play, as it performs reconfiguration of noSQL databases only when the benefit outweighs the cost of the reconfiguration.”

Purdue’s SOPHIA system has three components: a workload predictor, a cost-benefit analyzer and a decentralized reconfiguration protocol that is aware of the data availability requirements of the organization.

“Our three components work together to understand the workload for a database and then performs a cost-benefit analysis to achieve optimized performance in the face of dynamic workloads that are changing frequently,” said Saurabh Bagchi, a Purdue professor of electrical and computer engineering and computer science (by courtesy). “The final component then takes all of that information to determine the best times to reconfigure the database parameters to achieve maximum success.”

The Purdue team benchmarked the technology using Cassandra and Redis, two well-known noSQL databases, a major class of databases that is widely used to support application areas such as social networks and streaming audio-video content.

“Redis is a special class of noSQL databases in that it is an in-memory key-value data structure store, albeit with hard disk persistence for durability,” Chaterji said. “So, with Redis, SOPHIA can serve as a way to bring back the deprecated virtual memory feature of Redis, which will allow for data volumes bigger than the machine’s RAM.”

The lead developer on the project is Ashraf Mahgoub, a Ph.D. student in computer science. This summer he will go back for an internship with Microsoft Research, and when he returns this fall, he will continue to work on more optimization techniques for cloud-hosted databases.

The Purdue team’s testing showed that SOPHIA achieved significant benefit over both default and static-optimized database configurations. This benefit stays even when there is significant uncertainty in predicting the exact job characteristics.

The work also showed that Cassandra could be used in preference to the recent popular drop-in ScyllaDB, an auto-tuning database, with higher throughput across the entire range of workload types, as long as a dynamic tuner, such as SOPHIA, is overlaid on top of Cassandra.

SOPHIA was tested with MG-RAST, a metagenomics platform for microbiome data; high-performance computing workloads; and IoT workloads for digital agriculture and self-driving cars.