Industry 4.0 is reshaping production efficiency

by Eleanor Spensley

Industry 4.0 is reshaping production efficiency

The Internet of Things, or IoT, is a network of intelligent systems and computers that are connected to the Internet in order to share and collect data. A ‘thing’ can be any physical object or device that is capable of transmitting data. In this blog, we discuss how industry 4.0 is being used in manufacturing plants to reshape production efficiency.

The Industrial IoT (IIoT) works in the same way as commercial IoT, it just monitors different data such as sensors from a machine. These sensors can monitor anything from temperature to level flow and connect to a variety of higher-level software platforms.

“In IIoT technology, sensors are attached to physical assets,” explains Robert Schmid, Deloitte Digital IoT chief technologist. “Those sensors gather data, store it wirelessly, and use analytics and machine learning to take some kind of action.”[1]

You’re probably aware of IoT without even knowing it. Common devices that incorporate IoT functionality include smart watches that monitor your fitness level and thermostats that automatically adjust the temperature in your home based on your daily activity.

IIoT has enabled manufacturers to experience a 28.5% average revenue increase according to TATA Consultancy services[2]. This production efficiency increase has been possible through the power of data and analytics in the right hands at the right time.

Manufacturing and IIoT

Manufacturing equipment can also be fitted with sensors which will collect data from the machines and report this data back in real-time and in an easily accessible format. With IIoT, you will get faster and better-informed decision making by unlocking critical data about equipment performance and putting the facts into the right hands at the exact moment of relevance.

Previously, when a system failed it could lead to costly unplanned downtime and routine inspections resulting in a change of spare parts. Now, using IIoT, sensors are continually reporting data back to the right people and will notify them before critical failure. And with reports suggesting that 40% of organizations are not using any form of predictive maintenance at all[3], unplanned outages could be cut by up to 50% with IIoT devices[4]. Maintenance is becoming less reactive, and more proactive, with both real time data as well as historical being made available.

Tracking and Reporting

By selecting the problems you want to solve, manufacturers can track and report on data they need the most and immediate action can be taken with the use of reports such as graphs being sent to a dashboard which can be accessed on a PC, laptop, mobile devices and tablets, and even wearable smart devices such as watches.

Often in manufacturing, if the correct action is not taken at the right time it can be detrimental to production and result in a disproportionate loss of profits over  planned maintenance. With 82% of asset failures occurring randomly[5], remote monitoring can indicate how and when the failure occurred and enable operational parties to learn from the event and to predict future events more accurately. This, in turn, allows for faster reactions to problems and reduces potential issues by gaining critical visibility of the data you already have.

To gain visibility over several different manufacturing sites across the globe, a global manufacturer is implementing one such IIoT system to increase production efficiency, demonstrated in Case A.

Case A:

A global manufacturer of healthcare products has multiple sites across several continents. To gain visibility over the performance of each production line, the manufacturer is upgrading to a remote monitoring system known as SAM, from conveying and bulk bag handling specialists, Spiroflow, so that they can track data from each plant. Using a custom dashboard, the key individuals will be able to remotely monitor the data from anywhere in the world and compare each manufacturing process performance against the other. This will allow for global streamlining of their manufacturing costs as bottlenecks can be pinpointed and overall efficiencies improved.

Predictive Maintenance

Another major improvement can be made in the reduction of downtime with planned, predicted maintenance. All manufacturers are aware that at the very core of making high profits is an accurate, high quality and reliable production. If a machine stops working in the middle of a shift and there are no critical spare parts on site, this can result in costly delays.

A move towards Industry 4.0 gives the ability to collect and analyze the data from your machines and develop a program for predictive maintenance, as well as pinpointing potential bottlenecks in your process all moving towards increasing production efficiency. Previously, when a system failed it could result in costly unplanned downtime and routine inspections resulting in a change of spare parts. By using SAM, sensors are continually reporting data back to the right people and will notify them before critical failure. This in turn allows the system to continually learn and adapt to your specific process environment. Manufacturers working towards zero unplanned downtime can benefit from intelligent insights that will improve efficiency and reduce costs.

Just in Time Maintenance

Often in manufacturing, if the correct action is not taken at the right time it can be detrimental to production and result in a disproportionate loss of profits over a planned maintenance. With 82% of asset failures occurring randomly[6], Spiroflow’s SAM in this case, will indicate how and when the failure occurred and will learn from this event, enabling it to predict future events more accurately.

Furthermore, with knowing the optimal time to replace wearable parts, production can move towards Just-in-time (JIT) maintenance allowing for the correct amount of spares stock to be held, ensuring no overstock, but having the correct spare for predictive maintenance. This in turn allows for faster reactions to problems and reduces potential issues by gaining critical visibility of the data you already have, such as in Case B, which occurred with a manufacturer of chemical products.

Case B:

A leading chemical manufacturer suffered a breakdown on a Saturday evening during the night shift. The bearing of a motor had failed. The maintenance manager of the plant estimated the cost of the breakdown was around £10,000 an hour and with the failure occurring on a weekend night shift, no spare part could be sourced until Monday. This had serious financial implications for the business, which usually operates 24/7.

This manufacturer had a SCADA (Supervisory Control and Data Acquisition) monitoring system in place to review a critical failure, this meant they had to wait for a failure to occur and then the timely task of analyzing and interpreting the data could then begin before allowing for a Root cause analysis (RCA) to then be performed. By moving towards Industry 4.0 and remote monitoring they would in this case have gained ongoing visibility over the motor and with real-time data they may be have been able to prevent the breakdown before it occurred saving the company in the range of £240,000 in this instance.

Accuracy

Manufacturers know that their profitability is reliant on having an accurate, high quality and reliable production output. And as, according to Verizon, 60% of early-movers are improving the reliability or performance of products and services with IIoT[7], being able to pinpoint the issues that can cause a  sub-standard product is essential, especially if the problem is caused by faulty equipment. Improving product quality is always at the forefront for many manufacturers as it leads to potential benefits like increased customer satisfaction, higher sales, increase in selling price, reduced manufacturing costs and waste reduction. Whereas a poor-quality product has the power to damage your brand with costly product recalls and a loss in consumer trust that could lead to serious financial consequences.

Pinpointing the issues that can cause an inferior product is essential, especially if the problem can be spotted early and rectified. If your equipment is not accurately calibrated, not properly maintained or not set up correctly this can lead to issues with the product that could be avoided. IIoT connected devices, such as SAM, will use the sensors to track and analyze the information and report back to key employees in real-time, who could stop production immediately or make relevant changes while the line remains live to resolve issues. Upgrading process equipment inline with the Industrial Internet of Things has the ability to eliminate simple mistakes with quality control, to increase production efficiency and avoid the loss of crucial profits.

Upgrades

Improving energy efficiency can be a further benefit from an IIoT upgrade. The manufacturing sector is one of the largest energy consumers, estimated at more than 31% of global energy consumption[8] and the energy consumption is not strongly related to the production rate; conversely, the amount of consumed energy is mostly related to the time spent in specific operative states.[9]

A study undertaken in 2009 has found that the potential energy savings from the reduction of waiting time, or in the start-up mode of machines is estimated around 10-25%.[10] Before the optimization of the machines can occur, manufacturers must first gain awareness of their current energy consumption. Most plants have a non-stop production schedule where tracking energy consumption would be too time consuming to do on a day to day basis. Furthermore, unless the information is being delivered in real-time there is very little manufacturers can do to improve the efficiency of the machines. With IIoT the issues facing manufacturers are vastly improved because data on energy efficiency is delivered to a dashboard instantly so actions can be taken in real-time. This can result in plant managers driving energy saving behavior in staff and directors developing a clear energy-saving strategy for the shop floor.

Conclusion

In conclusion, if equipment is underperforming, via IIoT, implementation data can be fed back from machines directly to the right person at the right time, whether this is onsite staff or managers with remote access. Real-time alerts can be set up so key individuals are notified when a machine is not operating to a pre-set production efficiency level and action taken. Data can be used as a benchmark against other pieces of equipment to determine which are performing better, while also allowing for proactive underperformance issue solving. Finally, when implementing a new infrastructure, companies will also want to see a return on investment – with implementing IIoT into the energy-saving strategy, this could see the quickest return on investment.

[1] https://www.wired.com/wiredinsider/2018/07/industrial-iot-how-connected-things-are-changing-manufacturing/

[2]  [1] TATA: The Internet of Things: TCS Global Trend Study 2015- A Manufacturing Industry Perspective http://info.tcs.com/rs/120-PTN-68/images/The%20Internet%20of%20Things%20TCS%20Global%20Trend%20Study%202015%20%20A%20Manufacturing%20Industry%20Perspective.pdf

[3] Enterprise Asset Management and Field Service Management, ARC Advisory Group, 04/17/2015. http://www.arcweb.com/market-studies/pages/enterprise-asset-management.aspx

[4] Fortune http://fortune.com/2015/07/22/mckinsey-internet-of-things/

[5] ARC view, Optimize Asset Performance with Industrial IoT and Analytics, August 2015 http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=

WH&infotype=SA&htmlfid=WWL12350USEN&attachment=WWL12350USEN.PDF

[6] ARC view, Optimize Asset Performance with Industrial IoT and Analytics, August 2015 http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=

WH&infotype=SA&htmlfid=WWL12350USEN&attachment=WWL12350USEN.PDF

[7] Verizon: State of the Market with IIoT https://enterprise.verizon.com/resources/reports/state-of-the-market-internet-of-things/

[8] EIA: Annual Energy Review, (2010).

[9] Gutowski, T., Dahmus, J., Thiriez, A.: Electrical Energy Requirements for Manufacturing Processes. 13th CIRP International Conference on Life Cycle Engineering (2006)

[10] Park, C., Kwon, K., Kim, W., Min, B., Park, S., Sung, I., Yoon, Y.S., Lee, K., Lee, J., Seok, J.: Energy Consumption Reduction Technology in Manufacturing – A Selective Review of Policies , Standards , and Research. Precision Engineering and Manufacturing. 10, 151–173 (2009).