Revolutionary new technologies are finding their way up and down today’s production lines, eliminating the disconnect in the manufacturing value chain. This new era of manufacturing provides the opportunity for companies to focus on a different kind of ROI – the Return on Information. By Thomas Luck, Rick Homman, and Sandy Abraham
U.S. manufacturing is facing unprecedented challenges. Manufacturing businesses must navigate stringent rules, multi-layered trade agreements, and compliance requirements change every time goods cross a new border. Then, political and economic uncertainty challenge planning and margins. However, along with these challenges come opportunities. Among them is increasing demand for precision, speed, and agility of processes, and the need to increase throughput and productivity for on-time delivery to the customer.
The future belongs to those companies that can continuously improve and innovate. Revolutionary technology and systems are finding their way up and down the production line, eliminating the disconnect in the manufacturing value chain. This trend requires the modern manufacturing business to undergo a fundamental transformation. Companies that are slow to change will be left behind in a global and competitive manufacturing environment.
When companies talk about the connected manufacturing business, they tend to think of it in traditional terms – cables and connectors, I/Os, PLCs, and Manufacturing Execution Systems (MES). With the rise of the Industrial Internet of Things (IIoT), the emphasis is on bringing ideas, processes, and people together, as well as those machines. It means linking all the business processes in a fully functionally connected manufacturing organization.
The ultimate goal is to create a truly smart factory to leverage existing and latent data to provide managers with the tools they need to reduce downtime, improve human resource management, cut waste in materials, reduce energy, and improve safety. Every one of those metrics can deliver cost savings right to the bottom line with no change in suppliers, capital expenditures, or personnel. It is the lowest hanging fruit (Fig 1.).
Companies must put aside the old ways in which they gather information and distribute data. The new model requires that they not only collect and analyze the information from machines, supply chain, and the enterprise, but also share that knowledge down to the factory floor. This new era of manufacturing provides companies with the opportunity to focus on a different kind of ROI – the Return on Information.
Connecting the Data
Data is everywhere in manufacturing today. The fourth industrial revolution has introduced tools to obtain powerful insights from that data into the manufacturing operation to help streamline business practices and deliver dramatically measurable savings across the enterprise.
Therefore, a consolidated view of the production process is needed in the modern manufacturing organization. This can be achieved by introducing a standardized business process for better control and visibility.
The early promise of Manufacturing Execution Systems was to streamline data management, deliver knowledge throughout the enterprise, and provide a window into the true operational state of any plant. In many cases, however, MES might better stand for Monolithic Enclosed Silos. Instead of seamlessly meshing plant information, a monolithic MES created walls where given portions of data—sensors, maintenance information, planning, logistics, and supply chain, to name a few—were kept apart from each other.
The places where inefficiencies occur most often is where one part of the organization doesn’t have access to all the information needed to make an informed decision. Individually, each silo of data is important. Still, the power of data is exponentially multiplied when that data can be combined and analyzed to provide a single image of a plant’s total operation, from the shop floor to the top floor.
““The challenge that remains is to transform data into actionable information, a step that many manufacturers still miss.”
Throwing more sensors at a problem might not be as effective as analyzing the information already captured to get a true idea of the current state. A company may have plenty of data, but without consolidating the siloed data, it may never obtain a real understanding of whether that data is too much, not enough, or just poorly organized. For example:
- Sales creates an order with a 24-hour turnaround time, normally within the scope of established operational procedures. Sales is unaware, however, that the maintenance schedule calls for the line manufacturing the product to be down for planned maintenance for four hours that day. The order is delivered 12 hours late.
- A new product design requires new parts to be incorporated into the manufacturing process. Unfortunately, the supplier isn’t notified, and the launch of the product is delayed while the supplier ramps up parts production. This delays sales, marketing, and production planning.
- At the peak of the manufacturing season, the sensors indicate a number of bearings are running hot on one of four production lines. There is an opportunity to bring the line down smoothly to address the issue, but what is the issue? Without historical data, it’s impossible to tell when the bearings were last lubricated. It’s also not clear without more accurate trending data from all four lines whether this is specific to the one line, or whether all four lines might be susceptible to the same issue.
What is needed is a way of connecting those data silos together in a network that fully captures and considers all the data that is already available. IIoT technologies, for example, represent an operational revolution that promises to demolish those information silos through the powerful insight of consolidated data. They provide company management with the ability to see, know, and act on-demand and in concert. Such connectivity to the shop floor not only makes the business more transparent and helps minimize costly impact in real-time, it also allows for predictive modeling of data, so that manufacturing leaders can assess decisions before execution on the plant floor. The most significant benefit IIoT has to offer is the power to save on the most critical asset managers have, time.
Manufacturing is undergoing a seismic disruption in data collection and data analytics. Almost gone are the days of the spreadsheet and the factory floor burdened with paper; today’s digital manufacturing operation is transforming into smart factories, where Big Data analytics are available instantaneously and in real-time. The challenge that remains is to transform that data into actionable information, a step that many manufacturers still miss.
However, there are still more reactionary plants in the U.S. today than proactive plants, suggests a recent manufacturing industry survey. Reactionary plants are among those that spend most of their time firefighting problems, which leaves less time to work on advancing and transforming the business.
“Effective data mining and management allows leadership to perform more accurate planning and better business results.”
The leadership of manufacturing businesses are constantly concerned with cash flow, unit costs, utilization rates, and the overall issue of return on investment (ROI). If the plant itself had the same kind of financial-based data transparency, critical alerts such as machine downtime and waste reduction, would translate to impact on profits and performance goals.
So, to remain competitive, manufacturers need a more profound knowledge of how to apply digital technology on the shop floor, which allows them to apply processes and controls to improve production capabilities throughout the entire enterprise. Powerful metrics, such as Overall Equipment Effectiveness (OEE), integrate machine and production data for unbiased insights and real-time accuracy in operational performance. These operational insights are the basis of any future improvement process.
Using the OEE metric, for example, companies can chart the development of performance over time, or depict a specific point in time, to help decide on a course of action. More importantly, the manufacturing team can dive deeper into data points to learn more about the actual cause of a performance increase or decrease and formulate a decisive response.
A manufacturing plant manager may typically look at data indicating information on equipment availability as a primary KPI. This is one of the KPI’s that American manufacturers were found to look at when thinking about using Manufacturing 4.0 technologies, the recent survey suggests. So, once the plant collected its data, it would typically run a report and chart out the performance over time.
Figure 2 indicates a positive upward trend. If a manufacturer has not yet reached the stage of data sophistication to appreciate the importance of a natural variation in a process, they might misinterpret the chart and miss an opportunity for improvement.
Figure 3 depicts the same dataset with the same technology in a very different way, which may suggest analyzing it in a more focused approach. The same upward trend illustrates a gross increase in the level of performance. However, there are now well-defined periods of incapability at the beginning of the measurement period (red diamonds), followed by a statistically valid trend (green diamonds), and finally a performance plateau with a significant increase in performance variation (blue dots). All of this insight was available in the dataset that produced the simple-line-chart, but a higher level of data sophistication produced a more precise and actionable narrative.
Effective data mining and management, therefore, allows leadership to perform more accurate planning and deliver better business results.
The manufacturing industry and the many sectors defining it, and the many sectors defining it, have their own pace adopting technologies that transform their manufacturing businesses into smart factories. It comes as no surprise that data sophistication levels vary. Once a manufacturer has adopted data collection as a methodology to improve performance, the tool in use should always complement the existing IT infrastructure and allow for expansion to satisfy future business requirements. What is needed is a systematic way to bridge data gaps while breaking through traditional technological silos. And in destroying those silos, manufacturers want, and need, a new way to see their data without untying their existing network. The promise of IIoT is in the development of robust and intuitive solutions that can provide a bridge between the existing data and the new analytics, providing plenty of insight to act. How meaningful this action will be is defined by the power of insight. Ultimately, the most significant benefit IIoT has to offer is the power to save on the most critical asset manufacturing leaders have, time.
The result is a closer and faster connection between the shop floor and the top floor of a plant. This helps to generate significant returns on production performance, downstream, upstream, and in the plant’s ability to capture and deliver data at the source.
Improved downstream connectivity, for example, can help minimizing downtime costs and delays. The ability to take down a piece of machinery at a chosen time, rather than when the machines break down, is not only just an equipment replacement cost. Other factors include idle employees, damaged or ruined products, production scheduling, and safety. All these softer costs are often overlooked when machines go down, yet they contribute to the most significant part of the cost of unplanned downtime. This is especially true if there is a safety incident. In the new world, operations and maintenance teams are all working from the same playbook, relying on plant sensors and controls to provide data and predictive models that instantly alert workers and managers to potential trouble spots.
“The most significant benefit IIoT has to offer is the power
to save on the most critical asset managers have, time.”
Improved upstream connectivity, meanwhile, can deliver improvements in inventory intelligence. No one wants to carry more inventory at either end of their supply chain than is necessary. With a business system that can sense raw material needs and also create the right level of inventory, the needs of the business unit and the production team become homogenized. Waste is replaced with efficiency.
In both directions, the increased modularity and mobility of today’s technologies allows companies to both capture and deliver data more easily at the source. In an effective IIoT system, devices can be connected simply and seamlessly. Outfitting maintenance teams with hand-held devices can also speed the delivery of data from IIoT to the site of a breakdown. Scaling and connecting such a system is relatively easy and most devices can be bought off-the-shelf. The system is only as large as it needs to be.
Return on Information
When companies start deploying such IIoT networks and making better sense of their data, they should also expect their manufacturing organizations to change.
Shop Floor: Legacy machines may still have value, but there is a great deal of aging equipment in a plant. Understanding the state of each piece of equipment, maintaining it on a prescriptive maintenance model, and replacing equipment on time and as needed preserves both uptime and capital. In the new world of equipment, machine monitoring and data collection make inroads by accessing controllers of the machine directly to lead the plant floor to make the right decision at the right time.
Top Floor: As the levels of data maturity rise across the manufacturing sector (Fig. 4.), the evolution of data management helps the C-Suite bring Enterprise Resource Planning (ERP) systems and other business and operations systems into an interconnected and yet cooperative data stack. Now, a work order can be executed at one part of the plant, the purchase order for the replacement parts can be ordered and delivered through procurement, and the C-Suite can see at a glance how money is being spent and how decisions are being made.
IT Infrastructure in Plain Sight: Information technology no longer is “the man behind the curtain.” Operational teams will get knowledge of not just what must be instrumented, but why, and how the data is being used. Predictive analytics and modern manufacturing tools work in harmony to accelerate throughput and maximize uptime. Today, IT teams support smart factories with a robust and secure infrastructure of wired and wireless networks and data science that transforms Big Data into actionable information that the plant floor can turn into productivity and profitability, a necessity to stay ahead of the competition.
Culture Shift: What these first three steps really do is raise awareness of the interconnected roles of each part of the overall enterprise and demolish the silos that have separated these teams (Fig 5.). This not only serves to let each department in a manufacturing organization understand how they all are parts of the whole but also provides a fresh set of eyes. From this comes, new perspectives and new ideas that can be more easily shared, evaluated, and implemented.
Return on Information: In a smart factory, the speed of change is profound, but so is the improvement of processes. Using the power of consolidated data insights managers can evaluate manufacturing costs in real-time. Maintenance projects can be done with a purpose instead of a calendar. The supply chain between two ends of the building or between plants across continents can be streamlined and optimized. And the customer can count on the free flow of products that can be produced and delivered almost as soon as they are ordered. That leads to faster delivery, enhanced turn time, and more product out the door, without adding employees or equipment. In the end, the customer is the final link and the ultimate beneficiary of connected and smart manufacturing.
These are some of the fundamental steps that companies can take to help develop their approach to determining a new data-driven ROI – a Return on Information – across their end-to-end enterprises.
The biggest barrier to achieving that new approach, however, is not the underlying technology itself, but leadership team insight and understanding. And that starts at home. To paraphrase what Glinda told Dorothy in The Wizard of Oz, you’ve always had the power to solve this problem. The solution, Dorothy finally surmised, is right in your own back yard. M