The research, led by Amin Esmaeili with Osmani Albernas and Lin Li, introduces the Eco-Efficiency Value Stream Mapping framework, or EE-VSM. Its intellectual ancestry lies in lean manufacturing, the discipline born of the Toyota Production System, which taught engineers to visualize every step of production and strip out activities that absorb resources without creating value. Traditional value stream mapping is superb at exposing waiting time, excess inventory and unnecessary motion, but it is famously blind to environmental performance. Over the past decade researchers have patched this gap with variants that add energy flows, carbon emissions, water use and even social indicators to the familiar lean diagrams. The problem, the authors contend, is that these variants rarely allow one factory to compare itself meaningfully against another, which is precisely the benchmarking function that small manufacturers need before committing scarce capital.

To build a benchmarking foundation, the team turned to one of the richest publicly available datasets in industrial efficiency: the database of the Industrial Assessment Centers, a program funded by the United States Department of Energy since 1976 that pairs engineering schools with local manufacturers. Across its history the program has identified more than 287 quadrillion British thermal units of potential energy savings, worth over 2.6 billion dollars and roughly 37 million tons of carbon dioxide equivalent, through more than 22,500 assessments conducted at 37 universities. The researchers filtered this trove down to 11,624 manufacturing assessments performed between 2001 and 2025, then applied percentile-based filters on plant area, employment and energy consumption to isolate a representative population of small and medium-sized facilities. The final dataset comprised 8,707 assessment records.

The scale of the data allowed the team to quantify something rarely visible at this granularity: how much money each type of improvement actually saves relative to a facility’s energy bill. They defined a new metric, the Saving Magnitude Ratio, calculated by dividing a recommendation’s estimated annual cost savings by the plant’s annual energy expenditure. Because both numbers are validated by assessment teams and peer-reviewed at Rutgers University, which developed the reporting standard, the ratio rests on unusually reliable ground. The analysis revealed that 93.4 percent of the 65,651 recommendations in the dataset concerned energy management, with waste minimization accounting for 3.9 percent and productivity enhancement for 2.7 percent. Compressed air improvements, targeting what engineers call the industrial fourth utility, were by far the most frequent recommendation, appearing 14,418 times, followed by electric motor upgrades at 6,474 instances and lighting at 13,591.

The averages conceal a striking hierarchy of opportunity. Productivity enhancement measures carried the highest category average, a Saving Magnitude Ratio of 0.053, meaning such projects typically claw back more than five percent of a plant’s annual energy spending. Defective product minimization reached 0.074, warehouse optimization 0.085, and combined heat and power installations topped the energy category at 0.092. Waste minimization averaged 0.031, with liquid waste recycling at 0.039 and contaminant removal at 0.042. Energy management, though overwhelmingly dominant in volume, averaged only 0.018, because most of its recommendations, such as boiler tuning and insulation, deliver modest but reliable returns. The distributions were strongly right-skewed, with medians consistently below means, a statistical caution the authors flag for anyone using the benchmarks.

The second half of the paper demonstrates how a factory can translate these population-level statistics into a map of its own inefficiencies. The illustrative case is a metal fabrication plant producing chrome-plated components for refrigerated containers through three stages: die-casting of a zinc alloy melted at around 410 degrees Celsius, CNC machining and polishing, and electroplating. The EE-VSM diagram layers energy and material flows beneath the conventional lean timeline, organizing energy into four end-use groups, process heating, motors, compressed air and other uses, chosen because Department of Energy footprint data attribute nearly 90 percent of manufacturing energy consumption to the first three. Water, solvent and solid material flows are mapped alongside them.

Two new dimensionless metrics give the map its analytical teeth. The Value-Added Magnitude Ratio divides the estimated cost of genuinely productive resource use by the facility’s total annual energy cost, while the Muda Magnitude Ratio, named for the Toyota term for waste, captures the non-value-added remainder. Because both share a common denominator, they convert kilowatt-hours, gigajoules, liters and kilograms into a single comparable currency of wasted money. At the demonstration facility, motor systems emerged as the largest sink, with a cumulative Muda Magnitude Ratio of 0.145 driven largely by idle time at the machining and polishing stations, while the melting furnace’s 52 percent efficiency made heating the second-largest contributor at 0.074, and an assumed 15 percent efficiency for compressed air end-uses produced a ratio of 0.065.

The framework’s payoff comes when these internal ratios are read against the ITAC benchmarks. Matching the plant’s motor inefficiency against the database’s 6,474 motor improvement cases, which average a Saving Magnitude Ratio of 0.015, yields a projected annual saving of about 4,560 dollars, equivalent to roughly 49,600 kilowatt-hours. Benchmarking the furnace against 692 comparable cases suggests 5,776 dollars and 542 gigajoules per year, while water minimization strategies drawn from 829 assessments project 7,600 dollars and nearly 2,500 cubic meters of water annually. Compressed air leak repair and pressure reduction, benchmarked against 14,418 cases, translate to 3,344 dollars and more than 285,000 normal cubic meters of air. For a manager deciding between competing capital projects, these are the kinds of concrete, defensible numbers that sustainability proposals have historically lacked.

The authors are candid about the framework’s limits. The efficiency assumptions used to separate value-added from non-value-added consumption, such as the 52 percent furnace efficiency and the 38 percent value-added share of electroplating water, come from published literature rather than site-specific engineering analysis, and practitioners are advised to calibrate them with their own measurements. The benchmarks themselves represent averages across many industries, and the Saving Magnitude Ratio should be read as a relative indicator shaped by local energy prices and operating conditions rather than a universal performance standard. The team recommends that future work extend the framework with life cycle assessment, carbon footprint analysis, real-time sensor data and eventually social sustainability metrics such as absenteeism and physical workload, building on the three-pillar tradition of earlier sustainable mapping methods.

What makes the study resonate beyond its technical contribution is its timing. While headlines celebrate smart factories and industrial artificial intelligence, the majority of small manufacturers still run conventional processes little changed in decades, and those processes harbor exactly the kind of embedded, cost-effective inefficiencies that decarbonization strategies identify as the lowest-hanging fruit. By fusing a half-century of federal assessment data with a visualization tool that shop-floor engineers already understand, the EE-VSM framework offers a plausible route for sustainability to compete on equal footing with any other investment proposal on an executive’s desk. If it achieves even part of its promise, the quiet revolution it enables may unfold not in gleaming flagship plants but in the thousands of modest factories where most of industry’s emissions are actually made.

Subject of Research: An eco-efficiency value stream mapping framework for benchmarking sustainability and operational performance in small and medium-sized manufacturers

Article Title: Developing an Eco-Efficiency Value Stream Mapping (EE-VSM) Framework for Benchmarking Sustainability and Operational Performance

Article References: Esmaeili, A., Albernas, O., & Li, L. (2026). Developing an Eco-Efficiency Value Stream Mapping (EE-VSM) Framework for Benchmarking Sustainability and Operational Performance. Cleaner Engineering and Technology, Article 101336. https://doi.org/10.1016/j.clet.2026.101336

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101336

Keywords: eco-efficiency, value stream mapping, lean manufacturing, sustainability, SMEs, energy efficiency, benchmarking, industrial assessments, waste minimization, compressed air, carbon emissions, manufacturing