Big data can drive big energy savings


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Manufacturing pilot programs

Smart energy management: Smart energy management systems offer numerous benefits, starting with the ability to view and share data on energy consumption, and its effects on operations, from a central location. Courtesy: LinkCycleIn a pilot program, a multi-national manufacturer found it could cut energy costs by 20% in a single plant by using a manufacturing virtual audit platform from LinkCycle. The factory in which the pilot took place houses more than 80 injection molding machines manufacturing various plastic products that customers use for food storage. Before the pilot, the plant was consuming approximately $3 million worth electricity each year.  

Plant personnel provided LinkCycle two readily available data sets:

  • 15 minute interval data from the electric utility meter
  • Machine-level production data by shift. 

Without ever stepping into the plant, LinkCycle’s Cloud/Software as a Service (SaaS) business intelligence platform was able to calculate both fixed and variable electricity consumption for each machine by product SKU. Thus, the manufacturer avoided the high costs associated with a physical plant audit or installations of a sub-metering system. The statistical accuracy of the results were found to be greater than 90%, matching available sub-meter data from several injection molding machines. 

By attaining detailed energy visibility for shop floor operations and knowing the electricity profile of each machine, plant management can save electricity through a more systematic approach to industrial energy management. By arming executives with the same data as plant functional managers and operators, operational strategies which include energy conservation measures and improved asset management initiatives can be put in place. Such strategies include:

  • Scheduling production on machines which are more efficient
  • Diagnosing, troubleshooting and repairing less efficient machines
  • Reducing machine idling times through better production scheduling
  • For machines with high energy profiles, moving production to off-peak shifts when electricity prices decline
  • For machines with energy profile volatility, troubleshooting  and scheduling maintenance and/or process control tuning
  • Avoiding peak demand pricing penalties
  • Allocating energy cost per unit to bills of material. 

In the pilot, the plant’s 20% reduction in energy waste translates to lower manufacturing costs. Other potential benefits include improvements in schedule or production attainment and overall equipment efficiency. Deploying LinkCycle’s SaaS solution across its global plants will allow the enterprise to uncover energy saving opportunities at a greater scale faster and at much lower cost.

Other potential applications

Real-world results: A multi-national manufacturer quickly determined it could cut energy costs 20% in a single plant through a pilot program using a manufacturing virtual audit platform from LinkCycle. Courtesy: LinkCycleManufacturers across a spectrum of industries (CPG, food processing, and chemicals) have validated the virtual audit technology for process, batch, discrete and hybrid operations. Some factors to consider when investigating virtual audit technology for manufacturing settings are summarized below: 

The technology is nearly ideal for plants with the following characteristics: 

  • High volume production
  • Continuous, batch, discrete, and hybrid manufacturing
  • Complex facilities with multiple to several hundred production lines
  • Production facility with its own utility interval meter.  

Virtual audits may not work well in plants with these characteristics:

  • Low production volume
  • High degree of customization, not repetitive output (i.e. job shops)
  • Singular production line (i.e. glass production)
  • A lot of work in process (i.e. aircraft manufacturers)
  • No meter for production area (i.e. meter includes large laboratory, pilot plant or R&D space). 

Manufacturing energy intelligence can reveal patterns of energy waste, pinpoint savings opportunities and indicate declining equipment performance. The vast untapped amounts of manufacturing data have in the past been too distributed and disconnected to provide meaningful information which can lead to operational improvements. Innovations in Big Data science and predictive analytics are correlating data sets in new ways to reveal the “where and how much it costs” of energy consumption patterns coming from production. 

An industrial energy management system based on these new and innovative methods will give manufacturers timelier and less expensive energy-related metrics. With this information in hand, manufacturing managers can fine tune their operations in ways that will lower energy costs while improving productivity and boosting the overall corporate bottom line. 

- Chris Davis is vice president of sales and marketing for LinkCycle Inc.,, a technology supplier that helps manufacturers leverage previously unknown data correlations in factories to improve operational decision making and plant efficiencies. He can be reached at Edited for the CFE Media Industrial Energy Management section in February as a Digital Edition Exclusive. Send comments to

For additional information on how Big Data can improve industrial energy management, go to or

Key Concepts

  • Manufacturers find it difficult to accurately measure plant floor energy use, primarily because they lack adequate tools  accomplish the task.
  • The answer to controlling consumption—and thus the cost—of energy on the plant floor lies in the Big Data revolution.
  • Advanced analytic technologies make it easier for plant managers to identify and diagnose operational problems.

Consider this:

Isn’t it time to at least investigate a new form of technology with the potential for fine-tuning your plant operations in ways that will lower energy costs while improving productivity and boosting the overall corporate bottom line?

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