IIoT Contributes to Improving Industrial Processes
Integration of data, condition levels and maintenance needs can provide all tiers of the organization an ability to see the whole operation.
With mounting globalization, diminishing information asymmetry, disruptive business models, accelerating innovation and relentless competition, industrial organizations are having to adapt at a pace unrecognizable to previous generations. Additionally, customer expectations are increasing across the supply chain — for customization, personalization and immediacy to tracking, tracing and cost competitiveness.
These changing demands and pace of change acceleration are unfortunately converging at a time when the workforce is most vulnerable to changes. A significant percentage of experienced workers are aging out, and a similar percentage of new hires will be a part of the millennials, a generation that approaches the workplace with different ideas about what it should look like.
To date, Industry 3.0 technologies — the first generation of industrial computer and automation — have been used in plants to automate much of the work at a system level (or possibly machine/line level). Full plant automation, however, requires a higher layer of shared data to be leveraged at the plant or enterprise level. It is at the machine, plant or enterprise levels of data aggregation and analysis that insights for the next levels of significant efficiency gains are most quickly realized — efficiencies that can be worth millions of dollars every year.
To best take advantage of Industry 4.0 technologies — an Industrial Internet of Things (IIoT) autonomously exchanging information, triggering actions and controlling devices on the network independently — a platform is needed. This platform can support current and legacy operations as they are while codifying the knowledge from experienced workers. It will move data as quickly as possible into the digital realm, where the information can be used for its highest and best purposes.
Leveraging the IIoT Platform for Plant Optimization
Many veterans who have lived through the evolutionary steps of industrial equipment and processes can list the pros and cons of each step from Industry 1.0 through to the state-of-the-art 3.0 automation offerings.
In the early days, craftsmen learned their trade and developed an intuition through long hours and their own physical senses to determine that equipment was running correctly. This evolved into equipment that had some electrical controls or hydraulics and other benefits that reduced much of the manual effort involved. Further evolution delivered smart technologies to industrial machinery. Smart machines were able to make adjustments within certain boundaries or limits defined through programming.
Through these stages of evolution, the information that has been made available from sensors embedded in the equipment has been largely underutilized. In some cases, tombs of data have been collected, stored and archived with the hope — some say promise — of doing something useful with it.
The primary challenge with 3.0 technologies is that using the data is localized to the equipment or machine where it is generated. Thus, the data has a far more limited value than if it were aggregated and shared across the whole machine, plant or enterprise.
Adopting an approach — a platform — that allows for the aggregation of data from existing archives, enterprise systems and all sensors in the plants creates an environment in which there is a common source of data and data transparency. This creates the ability to make adjustments at both the local and global levels of the enterprise.
For the plant, this means that all of the important operational data is visible. Areas of waste can be visualized and identified in simplified displays built for any role of users through the facility. A machine (or line) equipped with multiple 3.0 automation systems can be visualized and analyzed holistically to find optimization points that cannot be achieved if data access is limited to the equipment or subsystem level.
While there is a benefit in advanced process monitoring by itself, as well as maintenance and condition monitoring by itself, more powerful results are realized when expertise in these two capabilities are leveraged together on the same IIoT platform.
Utilizing the IIoT Platform for Maintenance Improvement
As each area of process optimization is identified — and driven to new levels of production, raw inputs savings and energy efficiency — the baselines and expectations grow for capacity and availability. With these growing expectations, there also comes an acute awareness that a cultural commitment to an aggressive and longer-term sustainable maintenance approach is overdue. The idea is to get out of “firefighting” mode and into a more proactive mode driven by business objectives. End-to-end solutions exist that tie maintenance information into a data set exist for organizations that have begun to feel this looming in their organizations.
The goal is to make sure that each role or function in the maintenance stewardship of the operation has the needed information to make the best decisions, at the right time, wherever they happen to be located. This data is especially important to mobile workers in the field.
Ideally, the information from the various enterprise systems — data historians, drawings, service logs, maintenance schedules, replacement parts, ERP, CRM and the like — would be made available when needed by users. This level of access assists in their roles, allowing these workers to make use of the up-to-date information while ensuring the most efficient and effective repairs, replacements and schedules are consistent with the objectives of the enterprise as a whole.
If this information were available, the organization could — after digitizing much of its paper files and getting information into its enterprise resource planning (ERP) system — move from a mostly reactive mode to a more predictive or prescriptive mode of maintenance.
- Predictive implies that the system would forecast the likely failure of the element of interest and place a maintenance task at a repair point in advance of the predicted failure.
- Prescriptive implies that the system would generate potential cases, ranges and conditions and offer several options for the user to choose from. Some companies may want low probability of failure, so they conservatively replace if risk is 50 percent of failure. Others may want to run equipment until it fails.
Coupled with maintenance excellence is condition monitoring. Condition monitoring serves as the “canary in the coal mine.” It supports both the predictive and prescriptive approaches by providing real-time inputs into the forecasts and estimates related-to-failure models. In real-world cases that use maintenance and conditioning monitoring systems, plant maintenance costs sustainably decrease by as much as 10 to 50 percent after getting data integrated and coordinated.
Exploiting the IIoT Platform
There is obviously a benefit in advanced process monitoring by itself as well as maintenance and condition monitoring by itself. More powerful results are realized, however, when expertise in these two capabilities is leveraged together on the same IIoT platform.
Consider the advanced process monitoring as the mechanism that delivers significant value each year. As it does, it also moves the baseline while the maintenance and condition monitoring is making sure that the equipment is available and aligned with the various levels of objectives throughout the enterprise. Of course, the maintenance and condition monitoring also is delivering its own savings on an ongoing basis by moving the maintenance schedules adaptively. This optimal scheduling is determined by direct control by the users or through advanced analytics that balance the various metrics to ensure the plant operations are optimized within the context of the whole enterprise.
With the data — both historical and real time — on equipment performance and any early condition warnings, forecasts on availability, inventory levels, sales orders, sales forecasts and other market information, a plant manager or corporate executive could elect to accelerate or decelerate production and run some models to verify such a decision in advance.
This integration of data, condition levels and maintenance needs can provide all tiers of the organization an ability to see the whole operation and explore various possibilities. They can leverage global computing power to identify areas for savings and production improvements while assessing the trade-offs to ensure improvements in net cash flow. It is estimated such an IIoT platform could result in significant net cash flow improvement across an organization’s operations.
Industry 4.0 platforms are designed to control motor-driven machines for efficient control of compressors and pumps.
Making the Most of the IIoT Platform in Industrial Organizations
In order to get the most out of any system, tool or technology, the operators and users should possess both the will and the skill. Recognizing the digital divide between the traditional models of production and the Industry 4.0 or IIoT models of production, training for digital dexterity becomes an important topic.
In this multi-year journey, we must move the culture to be digitalization-ready. The vocabulary, constructs and proof points of digitalization become part of the daily thinking in the organization, and the desire to adapt and adopt accelerates.
Part of the training involves employees: assessing their current state of readiness, the gaps that will need to be addressed and assembling the initiatives needed to create a blueprint (or roadmap) to incrementally move the facility to where it wants or needs to be. In this way, workers and users at all levels are contributing to the evolution of the internal systems and realizing benefits for each step that they take toward making the data more accurate and more complete. Leveraging machine learning, artificial intelligence and big data analytics make even more things possible.
In conclusion, imagine a day when a plant is incentivized to reduce its capacity to zero percent for one day. That time that could then be used for preventive maintenance (and a day-off reward for line workers). Imagine another plant is incentivized to ramp its capacity to 80 percent because its maintenance and capacity trends show it can spin up, and the enterprise has determined that it is better for everyone — especially the customer — to shift capacity. It is a bit scary, but it is also a powerful concept. It is no wonder companies are entering into IIoT cautiously but optimistically. The only other option is to be left behind.