Available online at ScienceDirect. Procedia CIRP 29 (2015 ) The 22nd CIRP conference on Life Cycle Engineering

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1 Available online at ScienceDirect Procedia CIRP 29 (2015 ) The 22nd CIRP conference on Life Cycle Engineering Energy Efficiency Benchmarking Methodology for Mass and High-Mix Low-Volume Productions Yee Shee Tan a *, Tobias Bestari Tjandra a, Bin Song a a Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore * Corresponding author. Tel.: ; fax: address: tanys@simtech.a-star.edu.sg Abstract Manufacturing industry is the largest end-users of energy around the world. Continuous improvement of energy efficiency is seen as a key approach to reduce energy consumption, lower greenhouse gas emissions, and achieve sustainable manufacturing. Energy efficiency benchmarking is a technique to identify best practices to serve as possible benchmarks for measurement and management of energy efficiency improvement. However, it is a challenging task to identify the best practices and quantify the energy saving potentials in manufacturing environment, particularly when the operation is high-mix low-volume (HMLV). This paper presents an Energy Efficiency Benchmarking Methodology (E 2 BM), which can be applied for both mass and HMLV production environments. E 2 BM allows the quantification of energy efficiency gaps between manufacturing operations and the corresponding best practices, and hence reveals the potentials for achievable energy savings. Examples are given to illustrate the application of E 2 BM in different manufacturing operations The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the International Scientific Committee of the Conference 22nd CIRP conference on Life Cycle Peer-review Engineering. under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering Keywords: Energy Efficiency; Benchmarking; Mass Production; High Mix Low Volume 1. Introduction Today, efficient usage of energy is becoming a high priority due to the rising concerns about energy cost, energy crisis[1] and climate change[2, 3]. Renewable energy, energy conservation and energy efficiency are deemed effective options for sustainable energy solutions. Among these options, continuous improvement of energy efficiency is seen as a widely applicable approach to reduce energy consumption, lower greenhouse gas (GHG) emissions, and achieve sustainable manufacturing. Many countries have set target and implemented regulations on energy efficiency and conservation as a primary way to reduce GHG emissions. In Singapore, the Government is targeting to reduce its energy intensity by 35% from 2005 levels by To meet the target, an Energy Conservation Act was introduced in 2013, which requires large energy users to deploy energy managers and develop plans to improve their energy efficiency[4]. Most of the large energy users are in the manufacturing industry, which is the largest energy consumer in Singapore and many other countries. However, implementing energy efficiency measures in manufacturing industry is a daunting task due to a number of reasons. Lack of visibility on energy usage, lack of effective ways to set company specific targets, and market and finance barriers are hampering the effective planning and actions on energy efficiency improvement[5, 6]. Energy efficiency benchmarking is regarded as the technique to identify the best practices and achievable energy efficiency improvement target within a company or across an industrial sector. The results set the base for effective management of continuous improvement in energy efficiency. However, it is a challenging task to identify the best practices and quantify the energy saving potentials in a manufacturing environment, particularly when the environment is of high-mix low-volume (HMLV) The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering doi: /j.procir

2 Yee Shee Tan et al. / Procedia CIRP 29 ( 2015 ) In this paper, we present an Energy Efficiency Benchmarking Methodology (E 2 BM), which can be applied for both the mass and HMLV production environment. It allows the quantification of energy efficiency gaps between manufacturing operations and the corresponding best practices, and hence reveals the potentials for achievable energy savings. 2. Review of Existing Benchmarking Methods Benchmarking is a process of searching for best practices that lead to excellent performance. By comparing with the best practices, one can establish the baseline, find the improvement potentials, and identify areas of focus, or hotspots. With such benefits, a variety of methods and studies have been carried out in the area of energy efficiency benchmarking. For example, a European Standard BS EN 16231:2012[7] has been published to provide organizations with a methodology for collecting and analyzing energy data with the purpose of establishing and comparing energy efficiency between or within benchmarks. Other than standards, governments from various countries have made public regulations and supporting tools and program to assist their industry on measuring and benchmarking energy efficiency in products. Included are ENERGY STAR introduced by the U.S. Environmental Protection Agency (EPA) in 1992[8], Benchmarking and Energy Management Schemes in SMEs (BESS) published by Austrian Energy Agency[9], Energy Efficiency Opportunities program introduced by Department of Industry of Australia[10], Industrial Energy Management Training Course promoted by Department of Minerals and Energy of Republic of South Africa[11], Minnesota Technical Assistance Program (MnTAP) conducted by the Minnesota Department of Commerce s Office of Energy Security[12]. Extensive studies have been made to study the energy consumption of various manufacturing processes in reference to material removal rate[2, 13, 14]. The use of removal rate as the base enables the benchmarking of energy efficiency for all material removal manufacturing processes. The results are useful for the selection of manufacturing processes in process planning as well as for improving the energy efficiency of machine tools. Methods for energy efficiency benchmarking and analysis of manufacturing operations are more complex. This is because the energy efficiency has to be related to the workflow and usage conditions, such as capacity loading levels. Most of the methods in this area are based on statistical data and analysis techniques. Linear Regression Analysis (LRA) is the most common method for establishing baseline, identifying best practices and setting the target for improvement[9-11]. Other than LRA, Data Envelopment Analysis (DEA), one of the clustering methods is also often used for energy efficiency benchmarking, but mainly for research purpose due to its complexity[15-17]. However, current methods only deal with mass production. There is a lack of holistic method that enables the manufacturing industry to benchmark energy efficiency in manufacturing operations that involve diverse processes and produce different products in small batches, i.e. HMLV. 3. Energy Efficiency Benchmarking Methodology (E 2 BM) Energy efficiency benchmarking can be divided into internal and external benchmarking. Internal benchmarking is the comparisons within one company to establish the baseline and best practice. External benchmarking is to compare companies in the same or similar industry sector to establish the best in class performance. Additionally, general benchmarking is the comparisons of practices regardless of the industry field. The E 2 BM was developed for benchmarking mass and HMLV manufacturing operations. The method can be applied at each individual process (e.g. machine), interrelated processes (e.g. production line), and a plant. The aim is to relate energy consumption to materials flows at manufacturing processes and hence visualize the effectiveness and variations of energy usage for developing strategies for continuous improvement in energy efficiency. The method consists of five steps and takes three levels of hierarchy (i.e. plant, production line and machine) into consideration (Fig. 1). It is in compliance with the standards BS EN 16231:2012 and ISO 50001:2011[18]. Each step is explained in the following sections. 5 Improvement Planning 4 Benchmarking and Analysis Energy and Material Flows Modelling Fig. 1. A five steps Energy Efficiency Benchmarking Method (E 2 BM) 3.1. Energy and Material Flows Modelling 1 Production Line Machine Data Collection Metrics Determination Energy efficiency benchmarking should start with the goal and scope definition. When the objectives and system boundary are defined, the processes that are included in the system boundary can be identified. Within the selected system boundary, it is necessary to abstract and model the related energy and material flows at each of the processes. Fig. 2 shows an example of such a model. It is represented using a Sankey diagram which is a specific type of flow diagram, in which the width of the arrows is shown proportionally to the flow quantity[19]. It visualizes energy and material transfers between processes as well as the relationships between energy and material flows. 3 2

3 122 Yee Shee Tan et al. / Procedia CIRP 29 ( 2015 ) With the energy and material flows modelling, it helps to define the parameters and data requirements for the further benchmarking and analysis. certain amount of production or to deliver certain work by a machine. (2) (3) Fig. 2. An example of energy and material flows modelling (Blue: Material flows; Grey: Energy flows) 3.2. Data Collection Once data requirements are defined in energy and material flows modelling, essential energy related information is then collected either by direct measurement using appropriate sensors or by estimation from electric bill, machine specification, production records, etc. The gathered data are being used to populate the modelling associating energy consumption with production data Metrics Determination Denotes the system boundary The determination of correct metrics is crucial in energy efficiency benchmarking. Different energy benchmarking studies use different metrics. For example, Energy Performance Indicator (EPI) has been developed in ENERGY STAR, energy per facility area or employee has been applied by MnTAP, specific energy consumption has been employed in BESS, etc. Expectedly, the determined metrics must be able to support the objective of the benchmarking, to allow the identification of the best practices within a company or in an industry sector, and to reveal sufficient details for identification of hotspots and improvement measures. As the aim of E 2 BM is for continuous improvement in energy efficiency, three metrics are proposed as references. These are 1) energy intensity, 2) specific energy consumption and 3) energy efficiency with reference the production activities and economic performance. Energy intensity is defined as energy consumption per unit dollar (Equation 1). It is a measurement of the energy efficiency of a plant s economic output. (1) Specific energy consumption is the energy consumption per production volume of a production line (Equation 2) or functional unit of a machine (Equation 3). It is a metric for understanding how effectively energy is used to generate Energy efficiency is relative to the product that is processed by the production system. When the products are different as in the HMLV environment, the product based energy efficiency cannot be compared. To overcome the limitation, the theoretical energy required to produce each of the products is used to quantify the energy efficiency in the E 2 BM. This is defined by Equation 4: (4) 3.4. Benchmarking and Analysis A set of techniques for benchmarking and best practice analysis are developed to serve the benchmarking objective, i.e. to benchmark internally or externally at either three different levels of hierarchy, as summarized in Fig. 3. Different methods are implemented in different case scenarios given. Each method is elaborated in the followings sections. Internal benchmarking using LRA LRA is the most common method for establishing baseline and identifying best practices due to its simplicity. Fig. 4 illustrates the application of LRA via flow chart. As it is an approach for modelling the relationship between a scalar dependent variable expressing the energy data and an explanatory variable denoting the energy performance influencing factor, the relationship (i.e. specific energy consumption) is first presented using a scatter diagram. A preliminary cause effect analysis is then established by interpreting data pattern. Thereafter, energy baseline equation is established using LRA, as shown in Eqn. 5, where a is the incremental specific energy consumption and b is the base load that representing no production energy consumption. With the determined baseline, areas of energy saving and wastage are identified by the residual analysis. With the understanding of current situation, the data variability is quantified before the best practice is determined. The outliers are filtered out by estimating the standard error of estimate. Eventually, the energy consumption values (without considering outliers) that represent the best energy performance for the given production output is re-plotted. It is presented in the best practice equation which presenting the line that all the energy values are on or above it. By comparing between the baseline and best practice, the energy loss which is also considered as the energy saving opportunity is identified.

4 Yee Shee Tan et al. / Procedia CIRP 29 ( 2015 ) Production Line Machine Energy Data Energy Data Energy Data LRA Profit LRA Production Volume LRA Equipment Functional unit Internal Input (e.g Energy) Input (Energy) Input (Energy) DEA Output (e.g. Profit) DEA Output (e.g. Production Volume) DEA Output (e.g. Equipment Functional Unit) EI (kwh/$) EE (%) Best in class External A B C D E F G H I J Best in class A B C D E F G H I J Energy System Measurement of the energy efficiency of a plant s economy Identification of best in class among plants Measurement of the energy efficiency of a system performance by using theoretical energy analysis Identification of best in class among similar systems Data in LRA are modelled in a linear relationship and constant returns to scale are assumed. Thus, it is inadequate if multiple inputs and outputs related to different resources, activities and environmental factors are considered. Performance influencing factor Energy data Fig. 3. A set of techniques for benchmarking and best practice analysis Input P 5 P BCC model 4 P 8 P 6 P 7 P 3 P 2 N Present relationship Interpret data pattern P 1 CCR model M Output Fig. 5. Illustrative of CCR and BCC models of DEA Set energy baseline Identify areas of energy saving and wastage Start (1) Quantify and understand data variability Consider only a single energy related variable? No Deal with constant return to scale technology? Yes Apply DEA CCR model Determine best practice Yes No (2) Estimate energy loss Deal with constant return to scale technology? No Apply DEA BCC model Energy saving opportunities Fig. 4. Flow chart of LRA application Internal benchmarking using DEA Other than LRA, an input-oriented DEA where inputs (e.g. energy data) are minimized and the outputs (e.g. performance influencing factors) are kept at current levels is applied to overcome the limitation of LRA. It is a non-parametric method for evaluating the relative efficiency of Decision Making Units (DMUs) on the basis of multiple inputs and outputs is taken into consideration. Two basic DEA models are employed in E 2 BM, i.e. Charles, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) models[20]. Fig. 5 illustrates the example for single input and output. As presented, CCR model assumes constant returns to scale (the line MN) while BCC model assumes variable returns to scale (the envelope P 1, P 2, P 3 and P 4 ). As a result from minimization, those DMUs that lie on the line or envelope are identified and considered as the best practice frontiers. Eventually, by comparing each DMU with the frontiers, the relative efficiency can be evaluated. However, DEA is applicable only for internal benchmarking as only similar kinds of processes can be compared. Fig. 6 shows the flow chart on selecting an appropriate method for conducting an internal benchmarking in different scenario. Yes Apply LRA model (3) Note: (1) HMLV production with constant return to scale technology (2) Mass or HMLV productions with variable return to scale technology (3) Mass production with constant return to scale technology Fig. 6. Flow chart on selecting an appropriate method for internal benchmarking in different scenario External benchmarking using energy intensity as key metric Energy intensity at nation level is defined as a measurement of the energy efficiency of a nation s economy. It is calculated as units of energy consumption per gross domestic product (GDP). With that, by deriving from the energy intensity at national level, energy intensity at plant level is estimated as units of energy consumption per profit (Equation 1). It is applied as a key metric to benchmark the energy efficiency across plants economic performance. With the determination and evaluation of metric, best in class across the plants (i.e. best 10% points[7]) is identified. External benchmarking using energy efficiency as key metric Determination and evaluation of energy efficiency of energy system is applied at production line and machine levels to address the limitation of LRA and DEA considering only similar kinds of processes. Theoretical energy analysis (TEA) is implemented as theoretical energy is the minimum energy required to generate certain amount of production or End

5 124 Yee Shee Tan et al. / Procedia CIRP 29 ( 2015 ) to deliver certain work by a machine. Thus it serves to indicate the limit of possible energy reduction. Furthermore, it also provides an equivalent basis for comparison as it is independent from product variants while taking variables, e.g. scale of operations, operating conditions, etc. into consideration. As a result, by dividing the theoretical energy required with the actual energy consumption (Equation 2), energy efficiency is obtained and considered as a metric for comparison. Similarly, those best 10% points are determined as best in class in the industry sector. With this approach, as shown in Fig. 7, by using a set of data as a basis of comparison and control, company can understand the baseline in terms of current energy consumption, reveal the achievable energy savings by learning from the best practices within the company or even in the industry sector and lastly, find out the room for improvement by understanding the minimum theoretical energy required. Here, the theoretical energy required serves to indicate the limit of possible energy reduction. Theoretical energy required Savings in industry sector Fig. 7. Achievable energy savings from setting baseline to understanding theoretical energy required 3.5. Improvement Planning With the benchmarking result obtained, improvement planning is then executed by identifying hotspots, proposing, evaluating and prioritizing initiatives, and lastly developing action plans to assign the responsibilities for the energy efficiency improvement. Identification of hotspot is carried out in order to focus the proposal of initiatives that can solve the root cause of the problem. Thereafter, proposed initiatives are evaluated based on cost and environmental benefits analysis and then prioritized based on the trade-off between energy efficiency, cost and environmental benefits. Eventually, an action plan is developed to assign the responsibilities to ensure the effectiveness of improvement initiatives 4. Illustrative Examples Savings within company Savings Baseline Internal benchmarking using LRA Here, an example on an enterprise with three plants that are producing ground granulated blast-furnace slag (GGBS) is described to demonstrate the internal energy efficiency benchmarking exercise. As presented in Fig. 8, different types of energy sources are consumed in three of these plants due to their geographical location. Monthly energy consumption and production data were collected for two and the half years. Internal energy efficiency benchmarking is conducted to identify the best practices and reveal the achievable energy savings. Since it is in a mass production environment (only (a) Power (kwh) Blast Furnace Gas (m 3 ) Coke Gas (m 3 ) A GGBS (ton) Diesel (ton) (b) Power (kwh) Coal Powder (kg) B GGBS (ton) Diesel (ton) (c) Power (kwh) Blast Furnace Gas(m 3 ) C GGBS (ton) Diesel (ton) Fig. 8. Energy sources for a) plant A, b) plant B and c) plant C. single product, i.e. furnace slag is produced) with constant return of scale (the correlation coefficient between energy consumption and production volume is greater than 7[12]), LRA is applied in this study by normalizing the energy values with their calorific values as total energy consumption while denoting production volume as activity data. As a result, plant A achieves the best energy performance with the lowest specific energy consumption, i.e. 118 kwh/ton, followed by plant C and B with specific energy consumption of 154 kwh/ton and 182 kwh/ton. The comparisons among plants A, B and C are illustrated in Fig. 9. By comparing the baseline with the best practices within each plant, 23.8%, 10.9% and 3.5% of energy savings can be achieved in plants A, B and C, respectively. This can bring around a total of S$ 1 million of savings in energy costs in a month and show that even though plant A is performing the best among these three plants, however it still can attained energy savings by comparing with its best practices. Furthermore, by taking best practices in plant A as role model, plants B and C are achieving 49% and 47% of further improvement, respectively. Monthly Energy Consumption (kwh) Monthly GGBS Production (tons) B C A B (Best Practice) C (Best Practice) A (Best Practice) B (Baseline) C (Baseline) A (Baseline) Fig. 9. analysis result of plants A, B and C Internal benchmarking using DEA As shown in Fig. 10, an example on a plant with single input and multiple outputs is applied to illustrate the internal benchmarking exercise using DEA. In this study, six types of plastics (i.e. high density polyethylene (HDPE), low density polyethylene (LDPE), linear low density polyethylene (LLDPE), polypropylene (PP), polyvinyl chloride (PVC) and polystyrene (PS)) are produced while fuel is the energy source that is consumed during the manufacturing. Each monthly production data, i.e. from January to December in a year, was assumed and considered as DMU while specific energy consumption was referred to Thiriez s thesis[21]. As it is a HMLV production with constant return of scale (the correlation coefficient between input and outputs is greater than 7[12]), CCR model in DEA is implemented to identify the best practice frontiers and reveal the achievable energy savings.

6 Yee Shee Tan et al. / Procedia CIRP 29 ( 2015 ) With the minimization result, the relative efficiencies are evaluated (Table 1). As presented, operations in April, May, July and August are identified as best practice frontiers while others are classified as inefficient processes. By comparing between the baseline and best practices, 5.46% of energy savings can be achieved. Fig. 10. Product variants in plastic manufacturing plant Table 1. Relative efficiency of each month Month Relative Efficiency Month Relative Efficiency January 87.5% July 100.0% February 94.9% August 100.0% March 97.5% September 99.2% April 100.0% October 94.3% May 100.0% November 90.5% June 85.6% December 85.9% External benchmarking using TEA Another example on two metal casting plants, i.e. X and Y, that involve different types of metal, operating conditions, and material and energy flows (Fig. 11) are demonstrated for the external benchmarking exercise. To compare these two plants, TEA is implemented as theoretical energy is an equivalent basis for comparison. 4,500 ton 1,312,537 kwh Fuel (kwh) Fig. 11. Casting parameters in plants X and Y To estimate the theoretical energy required in these metal casting production lines, two processes are taken into consideration, i.e. the sensible and latent heating. Sensible heat is the heat exchanged by a thermodynamic system that changes temperature while the energy required is calculated as the product of the body s mass with its specific heat capacity and the change in temperature. While for latent heat, it is the heat exchanged by a thermodynamic system during a constant temperature process and the energy is calculated as a phase change of a unit of mass with its specific latent heat. As a result, the total theoretical energy required in plants X and Y are 972 and 1,979 MWh, respectively. By dividing with the actual energy input in each plant, the energy efficiency of plants X and Y are 74 and 78%, respectively. This shows that plant Y performs better than plant X in terms of energy usage. 5. Conclusion X Fe Casting Working hour: 480 hr/mth Melting point: 1149 o C 4,200 ton 7,300 ton 2,515,307 kwh HDPE (ton) LLDPE (ton) LDPE (ton) PP (ton) PVC (ton) PS (ton) Y Al Casting Working hour: 600 hr/mth Melting point: 660 o C 6,600 ton The E 2 BM has been developed for manufacturing industry to benchmark energy efficiency in different levels of hierarchy, i.e. plant, production line and machine levels. At each level, benchmarking can be performed internally within a company or externally with other companies in an industrial sector. The methods have been tested in some examples. Based on the studies, the energy efficiency benchmarking has resulted in valuable benefits to the participation plants. Mainly included are the determination of achievable energy reduction targets, establishment of energy efficiency baseline and best practice within the company and last, identification of best in class in the industry sector. Further cases involving more variations in product types and HMLV are being studied and necessary enhancement to the E 2 BM will be made. References [1] Karlsson, M. The MIND method: A decision support for optimization of industrial energy systems Principles and case studies. Applied Energy, (3): p [2] Duflou, J.R., et al. Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Annals - Manufacturing Technology, (2): p [3] Drumm, C., et al. STRUCTese Energy efficiency management for the process industry. Chemical Engineering and Processing: Process Intensification, : p [4] Singapore National Environmental Agency. Energy Efficiency in Singapore. Available from: Booklet/E2S%20Publication.pdf [Retrieved on: 13/11/2014]. 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