Drive manufacturing competitiveness with energy awareness

Regardless of the industry, most operations teams have one thing in common: They’re being asked to cut expenses from already trimmed operating budgets. With the easier-to-cut line items long gone, teams need to start exploring new improvement opportunities, such as energy management.

By Phil Kaufman June 17, 2015

Under constant pressure to squeeze more out of their operating budgets, manufacturers often turn to operations teams to drive innovative improvements that lead to cost savings. Energy management is one area of significant opportunity by using automation technology to extract and interpret data so manufacturers can improve productivity, efficiency, and reliability.

Operations teams constantly investigate ways to increase throughput, improve quality, reduce waste, and optimize labor.

Despite the success of these efforts, management continues to increase its expectations, demanding further reduction in operating expenses while maintaining-or even increasing-production. In the past, achieving these goals has been accomplished through a combination of equipment upgrades, Lean Six Sigma processes, and other incremental improvements. As more expense-reduction opportunities are taken already, considering the role of energy within operation can be a fresh approach. 

The power of information

Energy reductions from the facilities team may have included multiple utility rebates to retrofit shop lighting and motors, HVAC systems, upgraded air compressors and chillers, and added variable speed drives. The team also may have worked with a local utility to generate payment for demand response.

Putting manufacturing productivity at risk to figure out how to save energy isn’t a favorable option, and no one is going to agree that turning dials to save nickels is worthwhile. What about leveraging the operations team’s experience to analyze energy as production data?

New, nondisruptive methods for accessing energy-consumption data are practical from within existing automation systems, without an advanced degree in power quality. It simply requires engaging manufacturing support teams.

Energy awareness through data

As with other useful analysis, effective-energy awareness must have the right information at the right time in the context of what’s happening during production.

To reveal this level of insight, many manufacturers have implemented a top-down approach, analyzing energy-consumption patterns versus production output to determine how operations impact energy use. Limited product lines make these consumption patterns less complex and easier to detect for procurement, production scheduling, and sustainability objectives. The effectiveness of the analysis generally falls short when more complexity is introduced with more product types, where too many factors exist to accurately assess consumption patterns. The impact of nonproduction-energy consumption complicates the ability to correlate energy usage patterns against production schedules.

A common alternative is a bottom-up approach focused on metering energy at the point of production using an energy-management system. While this approach provides accurate, detailed data, it also can be disruptive to production, complicated to execute, and often can require a significant investment with an unclear return.

The availability of easily accessible data sources from energy-aware devices and systems offers a more effective view of energy as production data. This level of energy information helps enhance the data already being collected with top-down and bottom-up approaches.

Some level of energy metering often exists within newer motor control products to more efficiently control motors, but now it can be extracted from the system and analyzed at minimal cost and without affecting production.

Leveraging this information more easily provides a granular view of energy as production data. Coupling these accessible data sources with more traditional top-down and bottom-up approaches helps isolate energy usage on the factory floor. When energy consumption is understood in the context of manufacturing, energy becomes a manufacturing data point. The team can use energy-consumption data as any other production metric to make adjustments to production and machines.

Gather data: connect, collect

Using an 80-20 rule while focusing on the highest consuming assets can net the greatest results. Collecting energy-consumption data on high-horsepower motors can help the operations team identify the top energy-consuming assets and later prioritize improvements that will have the most significant impact on cost savings.

Energy-aware capabilities enabled by motor-control technologies allow nondisruptive extraction of energy-consumption information with minimal manufacturing impact. The addition of an automation controller as a data concentrator on the plant’s EtherNet/IP network allows operators to gain access to the consumption energy, collect the energy attributes, and store the data in a historian for further analysis, without touching the asset or process. Given the advancements in automation technology, this capability is being embedded into motor control technology for easier access.

Insight-based improvements

For years, energy management revolved around finding easy wins. Auditors walked around, collected data, and struggled to add business value when not fluent with plant production processes.

By viewing energy as an element of production, manufacturers use existing expertise and tools to reduce consumption and move beyond the easy wins. Today, in-house engineering staff can examine energy in the context of production and use commercially available optimization tools to pinpoint specific areas of concern. Converting energy to a cost-per-production unit, which easily can be added to Lean Six Sigma tools like value-stream maps, allows the team to find answers to questions such as:

  • Should work-in-progress (WIP) be used as a battery to store low-cost energy for when rates are high?
  • Should equipment be left on-and thereby consume energy-until the next run because of the time it takes for the equipment to return to the required temperature?
  • Can a series of operations be sequenced to take place at times of lower energy demand and still meet production schedules? 

Learn more about defining energy performance metrics and see figure 2; click into the next page, below.

Define energy-performance metrics

As more expense-reduction opportunities are taken already, considering the role of energy within operation can be a fresh approach.

Manufacturers may have more efficient equipment, but are they running it smarter? When it comes to optimizing energy consumption and identifying cost-saving opportunities, the greatest level of success will be achieved by tracking two metrics: energy consumption per production unit (intensity) and operational energy performance (efficiency). Manufacturers can use these metrics to pinpoint the best return on investment (ROI) and set baselines for two key production metrics:

  • Energy consumption per production unit-Use this metric to identify the energy intensity (its contribution to overall cost) and to set baselines for measuring improvements. By understanding the extent to which energy contributes to the cost of a particular asset, unit, cell, or process, operations can determine if further investigation is needed. Remember the 80-20 rule and start big.

Intensity = kW/unit of production

  • Operational energy performance—This metric helps assess the amount of energy consumed at the time of manufacturing against the total energy used for a period of time. This helps determine how efficient energy is being consumed at a given point in manufacturing. Only energy consumed during production should be considered as operational energy and applied to the numerator. Changeovers, quality holds, machine jams, and lunch breaks are considered nonproduction energy and only will be applied to the total energy per period.

Efficiency = productive energy/total energy

Consider two common scenarios and how these metrics can help the team focus its efforts:

Figure 1 demonstrates inefficiencies throughout the day. Large efficiency swings are the next generation of cost-saving opportunities and generally expose behavioral changes. Employee training, re-sequencing of an operation, or changes to production schedules may result in energy savings. Lunch breaks, morning start-up, and end of shift have the greatest impact on the lines’ efficiency and can point to equipment improvements to place them into a lower energy state during periods of nonproduction. Changeovers typically are considered productive energy, and improvements of changeovers will reduce intensity but may not increase efficiency.

In figure 1 at 9:00 the intensity goes up, but the efficiency remains relatively flat.

Figure 2 shows a higher efficient usage of energy in production. These areas tend to be related to the type of equipment used rather than when it is used. Cost-saving activities generally are found in the area of equipment upgrades (drives, higher efficiency motors, insulation, or higher efficiency heating/cooling systems), maintenance, or the addition of automation.

As with the first figure, low-power states during nonproductive periods, such as lunch, quality holds, or machine jams always will reduce energy costs.

Changes in period intervals bring other important factors into play. Longer periods-such as per shift, per day, or longer-can expose the weather’s impact on energy consumption, what shifts are most efficient, and how the fluctuating cost of energy impacts operating costs. It may even lead to innovations, such as using WIP as a battery to store lower cost energy or operational efficiency based on preventive maintenance schedules. 

Enhance behavior or automation?

When implementing changes to reduce energy consumption, conventional wisdom advocates giving operators responsibility for making changes that can improve performance metrics so long as the adjustments don’t impact throughput or quality. If operators can make minor adjustments to net small savings, empower them with data/dashboards. If the changes they make will affect throughput or quality, the best practice is to automate. High-risk processes or instances when traditional manufacturing metrics can degrade over time are good examples of scenarios when automation is prudent.

Automation offers several benefits over traditional operator-controlled processes because it maximizes reliability and minimizes production risk. When operators are tasked with making adjustments, human error and behavior often mean that processes degrade over time and become less effective. This happens for a variety of reasons: Staff may willingly choose to ignore procedures, information may be lost during job transitions, or people simply forget the rationale behind certain process steps. Automation helps minimize variability and allows producers to make adjustments that will be automatically maintained over time.

Three examples show how behavioral changes or automation may be applied to reduce energy consumption:

  1. An operation where energy consumption fluctuates from batch to batch, and its efficiency varies from 30% to 60%. In this case, improvements should be focused on operator training, work instructions, or material variation. Activities could be as simple as exposing key energy-related performance metrics to the operators on a machine’s display.
  2. A manufacturer would leave equipment running during overnight shifts to increase production throughput when staff arrived the next morning. A value-stream analysis was conducted to evaluate energy costs for production and nonproduction activities. The analysis helped determine that it was more economical to shut machines down and forego additional unit production as labor-overtime charges were less costly than energy costs to leave machines running nonstop. The automation system was upgraded to implement a sleep mode for equipment rather than completely shutting down, ensuring availability of equipment and reduced risk to productivity and profitability.
  3. An operator on second shift always had the greatest throughput on his machine. After exposing energy-consumption data on three of the highest consuming devices, it was discovered that the operator was running the process at higher than normal temperatures to produce more product. The higher manufacturing costs in energy intensity and maintenance exposed a negative impact to operating cost. Automating the process provided consistency and optimized energy usage.

Move energy to the BOM

After manufacturing energy-consumption data is stored and analyzed, trends emerge. The operations team gains insight into how energy has been used in a specific product cycle or batch. Capturing this level of knowledge provides an immediate benefit and also promotes future improvement because operations no longer has to guesstimate energy-consumption levels for similar production runs in the future.

Empirically, tying energy-consumption requirements to the production bill of materials (BOM) helps the operations team make proactive production decisions and better manage energy investments in a way that will generate a greater return. Benefits include predicting usage against production schedules, using schedules for energy-demand planning, negotiating better rates with utilities, and participating in the correct demand-response programs.

Knowing that certain batches require more energy allows operations staff to schedule those batches outside peak demand times. Additionally, data, such as unit-level energy consumption, can become valuable input into a company’s sustainability score cards and other reporting mechanisms.

Energy in operations, a new approach

As operations teams continue to seek new opportunities to reduce expenses, they must think more holistically. Energy management presents one area of significant opportunity. Fortunately, the data necessary to take advantage of this opportunity is already available within existing automation systems. By tapping into commonly used automation technology to extract this data and by using common productivity tools to interpret it, manufacturers can improve productivity, efficiency, and reliability.

– Phil Kaufman, energy technology manager, Rockwell Automation; edited by Eric R. Eissler, editor-in-chief, Oil & Gas Engineering,

Key concepts

  • Knowing when energy consumption is high or low helps operators plan for events during the day to make better use of these times.
  • Automation offers several benefits over traditional operator-controlled processes because it maximizes reliability and minimizes production risk.

Consider this

Manufacturers may have more efficient equipment, but are they running them smarter?

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