Available online at ScienceDirect. Procedia CIRP 61 (2017 ) The 24th CIRP Conference on Life Cycle Engineering

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1 Available online at ScienceDirect Procedia CIRP 61 (2017 ) The 24th CIRP Conference on Life Cycle Engineering A software tool for the analysis and management of resource consumptions and environmental impacts of manufacturing plants Claudio Favi a *, Michele Germani a, Marco Mandolini a, Marco Marconi a a Department of Industrial Engineering and Mathematical Sciences, Università Politacnica delle Marche, via Brecce Bianche 12, Ancona, Italy * Corresponding author. Tel.: ; Fax: address: c.favi@univpm.it Abstract The paper presents a lifecycle approach and the related software tool for the analysis and management of resource consumptions and environmental impacts of manufacturing plants. The approach, based on the industrial metabolism model, takes into account all the production and assembly aspects. The tool is able to assess the optimum working conditions for the minimization of resource consumptions (e.g. electricity) or environmental emissions (e.g. CO2). It provides a tangible support to guide decision-making strategies to move manufacturing towards sustainability. A manufacturing plant has been analysed for the model validation and the management of production scenarios, optimizing environmental and energy loads The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 2017 The Authors. Published by Elsevier B.V. ( Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering. Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering Keywords: Sustainable production; Resource consumption optimization; Environmnetal impacts optimization; Plant lifecycle management; Software tool. 1. Introduction Sustainable Development (SD) is becoming a global goal, determining competitiveness of many companies and manufacturing firms. National and international climate action policies are boosting the SD and the establishment of targets for the reduction of greenhouse emissions and carbon pollution (e.g. European Commission, Environmental Protection Agency). Currently, there is an increasing consciousness on environmental problems caused by industrial activities and, therefore, the need to reduce environmental impacts of manufacturing processes and technical facilities. [1]. The measurement of the efficiency of material and energy flow conversions is an essential step towards the quantitative assessment on an economy complying with the principles of SD [2]. In this work, an approach and a related software tool for the assessment and optimization of production schedule and activities is proposed. The tool is based on the concept of industrial metabolism, in which natural resources (utilities, fossils and materials) are consumed within the manufacturing plant. Firstly, the tool shows a picture of the main energy and environmental loads in different areas of a manufacturing plant. Secondly, it provides to plant and production engineers a tangible support for the optimization of production scheduling based on sustainability Key Performance Indicators (KPIs), such as energy consumption, CO 2 emissions, etc. The novelty of the proposed approach and the related tool is the way to plan the manufacturing activities based on the correlation among typical production features (production rate, working shifts, working time, etc.), economical aspects (energy costs, costs of utilities, etc.) and environmental parameters (CO 2 emissions, waste production, etc.). The goal of this work is to develop a representative model to be used in a generic manufacturing plant for the production schedule and management based on the principles of SD. The model has been developed for industrial companies oriented to manufacturing and assembly of mechatronic goods. In particular, a washing machine plant has been analyzed, considering the present production scenario and providing new optimized alternative scenarios on the basis of different working conditions 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 24th CIRP Conference on Life Cycle Engineering doi: /j.procir

2 342 Claudio Favi et al. / Procedia CIRP 61 ( 2017 ) State of the art The implementation of Sustainable Manufacturing (SM) models, means thinking on industrial processes which use natural resources in a responsible manner, promoting the safety and the health of all those involved and integrating the ecological aspects [3]. Industrial Ecology (IE) is considered the first approach implementing the SM principles, with emphasis on sustainability and social sciences, referred to a globally organized closed-cycle economy [4]. Cleaner Production (CP) is more a preventive approach to environmental management, which takes into account impacts over the whole life cycle of products and services [5]. Many firms are now considering the environmental impacts throughout the product s lifecycle and they are just starting the integration of environmental strategies into their own management systems [6]. For example, industry has increasingly adopted CP by reducing the amount of energy and materials used in production processes [7]. Besides, there are still several barriers in the adoption of SM principles in complex systems as manufacturing plants. The first main issue in the implementation of this new sustainable production model is the way to assess the economic and the environmental performance of manufacturing plants. Value Stream Mapping (VSM) in one of the most famous approach in the field of lean and green manufacturing. This is a bottom-up analysis approach, assembling equipment environmental models to describe a system [8][9]. The VSM is a valid approach to describe in detail a process or a set of interconnected processes whit a high degree of detail. Material Flow Analysis (MFA) and Input-Output Approach (IOA) are widespread methodologies for the systematic assessment of flows (materials, goods, substances, etc.) within an arbitrarily complex system defined in space and time [10][11]. These approaches have been customized to take into account environmental issues (energy consumptions, water and land use, pollution, waste production, etc.), becoming an integral part of many environmental impact assessments [12][13]. In particular, the IOA has been recognized as a useful top-down technique and it is used intensively in the context of environmental Life Cycle Assessment (LCA) and economics that relate to sustainable production and consumption [14]. LCA is, currently, the most used approach and tool for the environmental assessment of products, services and human activities. A full life cycle perspective means the examination of environmental impacts related to products, processes, facilities or services from resource extraction through manufacture, till to waste management [15]. However, LCA is a complex science, which requires a high degree of expertise and is resource intensive, especially for manufacturing plants, due to the complexity in the definition of system boundaries, inventory, results interpretation and post-processing. The second main issue in the development of this new sustainable production model is related to the use of KPIs as a tool for a correct management of the whole manufacturing plant, including production schedule and plan. Several metrics have been adopted in this context, such as the Carbon Footprint (CFP), which quantifies the climate change impact of greenhouse gas emissions in a life cycle perspective [16][17]. However, CFP does not represent the overall environmental load in the context of manufacturing and production, keep in mind the human health impacts in the choice of materials for more sustainable production. Therefore, a combination of more representative KPIs needs to be identified and applied. For example, raw material consumption, energy consumption, airborne emissions, liquid and solid waste have been proposed by other researchers [18][19]. The usage of these indices is generally managed by production experts, such as plant or manufacturing engineers. Scheduling of a flexible manufacturing system is currently a task developed with the help of mathematical models and software tools [20][21]. In literature, several tools have been proposed to simulate sustainable manufacturing scenarios based on the optimization of specific parameters. The tool developed by Melnyk et al. includes environmental concerns when converting the master production schedule into the various component schedules. It solves the problem of minimizing environmental impacts when managing industrial waste, by flagging potential component planning and environmentally related problems [22]. Herrmann et al. developed a simulation tool to analyze the effects of implementing lean and green manufacturing strategies based on energy consumption and costs [23]. Cost minimization example has been proposed by Diaz-Elsayed et al. with a tool for manufacturing system optimization [24]. Recently, the problem of sustainable manufacturing scheduling has been considered as a multi-objective problem. Fang et al. proposed a linear programming formulation for optimizing the operating schedule of a manufacturing plant that considers both productivity (i.e. makespan) and energy related criteria (i.e. peak load and carbon footprint) [25]. A required step to build a complete tool for the assessment and management of manufacturing plants is having a holistic approach to implement the sustainable manufacturing model. A clear roadmap for sustainable manufacturing is missing and manufacturing organizations require a specific model and tool to assess the current level of their sustainability and offer a structured transformation plan, towards becoming greener and thus to gain, or maintain, a competitive advantage. In conclusion, the analysis of the literature review pointed out two main limitations in the context of sustainable manufacturing: (i) high level of expertise, resources and time are required for using lifecycle models for the assessment of environmental indices of manufacturing plants and (ii) a lack in a decision-making tools for the correct planning of manufacturing activities based on the defined KPIs. Literature review also highlight that there are not any researches considering environmental or energy related objectives in scheduling a flexible manufacturing system. The present paper aims to answer to these two aspects. Firstly, it proposes a method to collect the data in a simple and structured way providing a tool for an easy management and collection of plant data. Secondly, it provides a sustainability calculation tool for the rapid assessment of KPIs (e.g. CO 2 emissions) considering the current plant situation and giving suggestions for the optimization of production planning based on the changing of production conditions (e.g. production rate).

3 Claudio Favi et al. / Procedia CIRP 61 ( 2017 ) The industrial metabolism model The goal of this paragraph is to describe a method for the assessment of sustainability KPIs of manufacturing plants. The method is based on the concept of Industrial metabolism previously defined in [26] and it consists in considering the manufacturing plant as a person during its life. manufacturing plant from the sustainability point of view. The optimization tool and the assessment tool can communicate because most of the data used for the optimization analysis are automatically retrieved by the assessment tool itself. The tool has been developed using the programming language of Excel: Visual Basic for Applications (VBA) The manufacturing plant assessment tool Fig. 1. Industrial metabolism model [26]. In the industrial metabolism, the plant lifecycle is connected to the product to be manufactured. A general rule is to consider the plant lifecycle limited to a specific product technology. Starting from this point, the plant system boundary is temporally defined from the birth of a new technology to the end of the same technology. For flexible manufacturing systems (e.g. automotive, appliances, etc.) the product models are not considered as a system variable or a different technology. In this timeframe, a focus on specific production periods or manufacturing situations can be done. The plant system boundary, instead, is spatially defined and confined within the walls of the plant (facility). In this way, the plant can be considered as a black box with its input/output flows (see Fig. 1). The plant has in input different items, such as materials, fossils and utilities. The output refers to environmental emissions: to water, to air and solid waste. Inputs contain all the natural resources consumed by the different production processes of the factory. Outputs include all the fluxes that exit from the factory boundaries and are not included in final products. This kind of classification has been done based on the literature analysis and following the standard language used by engineers and stakeholders in this context [11][13][27]. It is important to understand that the product itself is not considered as a system output because it is the result of the manufacturing process during the plant lifecycle. Instead, the material scraps are considered as system outputs because they are used during the manufacturing process, but they will not take part of the final product. The detailed classification of fluxes involved in the definition of inputs and outputs is presented in a previous work [26]. 4. The software tool The software tool has been conceived on the basis of the industrial metabolism model. The tool is structured basically in two modules: the assessment tool and the optimization tool. The assessment tool can be used as a stand-alone tool with the aim to provide an overall picture of the current situation of the The assessment tool consists in a guided procedure for the manual input of the parameters involved in the concept of industrial metabolism. The assessment tool goal is to provide an overall picture of the manufacturing plant in a standard working condition from the sustainability point of view. The plant can be analysed as a unique working station or as a set of different working stations (or working processes). In this way, both general and detailed analyses can be performed based on the end-users needs. Anyway, results of different working stations can be easily aggregated by the tool for a global analysis. Inputs and Outputs of each working station are manually filled by the help of this software module which is structured in different sections (windows) to help engineers in data input. The structure of sections is based on the I-O classification. Fig. 2 shows, as example, the window for utilities data input. Data are manged by the software and linked with its repositories for the assessment of KPIs. Fig. 2. Data input window for utilities. The KPIs implemented in the tool are reported below: ReCiPe midpoint Climate change [CO 2 eq./ ReCiPe midpoint Terrestrial acidification [SO 2 eq./ ReCiPe midpoint Freshwater eutrophication [P eq./ ReCiPe midpoint Particulate matter form [PM10 eq./ Comulative Energy Demand (CED) [MJ/ Electric energy consumption [kwh/ Methane consumption [m 3 / Water consumption [lt/ Cost [ / The proposed KPIs have been chosen to give a comprehensive picture of the manufacturing plant including all aspects of sustainability, such as environmental, economic and processes performances. Other KPIs can be also included in the analysis such as waste share (metals, plastics, papers, etc.). EcoInvent 3.2 database has been used to link primary data with LCA environmental indicators and thus for the assessment of ReCiPe and CED indicators. Post-processing

4 344 Claudio Favi et al. / Procedia CIRP 61 ( 2017 ) analysis provides results for each parameter (KPI) identified for sustainable manufacturing model. The results are displayed both in numerical and graphical way (see for example the colour map reported in Fig. 3). Fig. 3. Colour map representation for a generic manufacturing plant The manufacturing plant optimization tool The manufacturing plant optimization tool provides a tangible support for the correct management of production from the perspective of sustainability. The tool uses the data and the results coming from the assessment tool as well as other data for the analysis of different manufacturing scenarios. The tool provides as output, an optimized solution for each defined KPI. Moreover, it is possible to solve the optimization problem taking into account the combination of different parameters with a multi-criteria approach. According to the multi-criteria approach, weights and scores for each attribute (KPI) need to be characterized based on the requirements of each manufacturing company. A minimum set of data is required for the development of the proposed analytical model. It includes information about the upper and lower limits of production: the over-load capacity, the minimum capacity and the no production. Other production sets can be inputted in order to have a higher number of possible combinations for the assessment of the optimized solution. The minimum amount of data required for the solution of the optimization problem is reported in Fig. 4. Over-load capacity Data type Unit Working shifts mandatory Working time mandatory [h/ Working days mandatory [day/year] Pieces produced mandatory [pcs/year] Max number of months in overload capacity mandatory [months/year] Electricity consumed mandatory [kwh/year] Methane consumed optional [m3/year] Water consumed optional [lt/year] Oil consumed optional [kg/year] Others optional based on flux type Minimum capacity Data type Unit Working shifts mandatory Working time mandatory [h/ Working days mandatory [day/year] Pieces produced mandatory [pcs/year] Electricity consumed mandatory [kwh/year] Methane consumed optional [m3/year] Water consumed optional [lt/year] Oil consumed optional [kg/year] Others optional based on flux type No production Data type Unit Max number of months without production mandatory [months/year] Electricity consumed production (offices, etc.) mandatory [kwh/month] Methane consumed whithout production (offices, etc.) mandatory [m3/month] Water consumed whithout production (offices, etc.) mandatory [lt/month] Others optional based on flux type Production Target mandatory [pcs/year] Fig. 4. Minimum dataset required for the optimization problem. It is important to note that the optional data type is necessary only if the optimization process includes these items for the assessment of related KPIs or they are relevant for the analysis itself. The optimization is based on the productivity target, which is usually defined as pieces per years [pcs/year] for flexible manufacturing systems. The kernel of the optimization tool is the combinatorial analysis. Feasible manufacturing scenarios are pointed out by the software as a combination of defined manufacturing sets (e.g. over-load capacity, standard capacity, etc.). For the calculation of feasible scenarios, the formula takes into account the general constraint of twelve months of production and, additionally, specific constraints of each scenario (i.e. maximum number of months in overload capacity and maximum number of months without production). For each feasible scenario, the quantity of pieces produced in a year (T f) is calculated. All feasible scenarios are filtered and only the ones closer to the production target are shown to the enduser (T t). In particular, the filtering is based on the eq. 1 Target T Target + 5% (1) t where Target is the production target defined by the end-user and T t is the production target calculated by the tool for feasible production scenarios satisfying the constraint of the eq. 1. The optimized scenario is finally assessed following the minimization of a specific indicator (eq. 2) Optim Scenario min KPI i (2) where KPI i is the selected indicator to minimize. In case the end-user wants to adopt a multi-criteria optimization approach, weights and scores needs to be established on the bases of company requirements. Finally, the tool points out the best configuration (production schedule) able to achieve the productivity target, as a combination of the previous defined manufacturing sets. 5. Case study analysis and optimization An existing washing machines (WM) factory plant has been analyzed using the proposed approach. The presented plant can be considered as an interesting test case because it includes in-house manufacturing processes, assembly lines, testing laboratories, warehouses, ancillary systems, etc. Data collection has been carried out as the first step of the analysis on the basis of the proposed industrial metabolism model. Afterwards, the data has been inputted to the software tool for the analysis. The standard capacity considered for the analysis is a production rate of [pcs/year]. The analysis has been conducted for each area of the factory plant. Results obtained with the assessment tool and with the optimization tool is reported in the following sub-sections KPIs Assessment Production rate of [pcs/year] (standard capacity) has been considered for the analysis performed by the tool

5 Claudio Favi et al. / Procedia CIRP 61 ( 2017 ) assessment module. Results for each KPI have been reported in Table 1 and colour map representations for CED and climate change indicators are reported in Fig. 5. Table 1. KPIs calculated for the washing machine manufacturing plant in standard capacity. Manuf. area (process) Indicators CO 2 SO 2 P PM 10 CED Elect. CH 4 H 2O [CO2/ [SO2/ [P/ [PM10/ [MJ/ [kwh/ [m 3 / [lt/ Cabinet 2,6e4 151,3 7,2 50,4 1,5e6 7,6e3 3,9e3 3,5e3 Drum&GO 6,6e3 31,9 1,6 16,6 1,3e5 7.8e3 0,0 0,0 Assembly 8,9e2 3,5 0,2 1,1 1,6e5 1.4e3 0,0 0,0 Warehouse 1,8e2 0,7 0,0 0,2 3,3e3 2,8e2 0,0 0,0 Testing Lab. 2,4e3 9,7 0,4 3,1 4,5e4 3,8e3 0,0 5.8e5 Packaging 8,9e2 5,5 0,2 1,8 6,0e4 2,1e2 1,3e3 0,0 Finite prod. 1,3e2 0,5 0,0 0,2 2,4e3 2,1e2 0,0 0,0 Compressor 5,3e3 21,0 2,0 6,7 9,8e4 8,4e3 0,0 0,0 Over-load capacity Data type Unit Working shifts 3 Working time 18 [h/ Working days 220 [day/year] Pieces produced [pcs/year] Max number of months in overload capacity 4 [months/year] Electricity consumed [kwh/year] Methane consumed [m3/year] Water consumed [lt/year] Oil consumed 800 [kg/year] Others Minimum capacity Data type Unit Working shifts 1 Working time 8 [h/ Working days 220 [day/year] Pieces produced [pcs/year] Electricity consumed [kwh/year] Methane consumed [m3/year] Water consumed [lt/year] Oil consumed 200 [kg/year] Others No production Data type Unit Max number of months without production 2 [months/year] Electricity consumed production (offices, etc.) [kwh/month] Methane consumed whithout production (offices, etc.) 3000 [m3/month] Water consumed whithout production (offices, etc.) [lt/month] Others Production Target [pcs/year] Fig. 6. Input data for the solution of the optimization problem. It is important to note that over-load capacity and no production scenarios can be kept only for a limited number of months due to external issues not linked with the production itself (worker conditions, health and safety regulations, etc.). This is a limitation inputted in the software tool together with other parameters. Fig. 7 reports feasible production scenarios able to reach the defined production target. Fig. 5. Colour map representation for CED and Climate change indicators. An overall picture of the factory has been pointed out with this assessment. Post processing evaluation highlights that Cabinet, Drum&GO and Assembly are the most critical areas of the plant considering the CED indicator. Cabinet and Compressor area, instead, are the most critical in relation to the climate change indicator Optimization of manufacturing plan A new productivity target of [pcs/year] has been established by the company. This target is lower than the standard capacity previously analyzed ( [pcs/year]) and an optimization analysis is required for the correct management of natural resources and environmental impacts of the plant. For the proposed example only four production sets (standard capacity, over-load capacity, minimum capacity and no-production) have been inputted as possible production scenarios for the solution of the optimization problem (minimum required dataset). The established parameters for each production scenario are reported in Fig. 6. Scenario Production Months Q.ty Electric En. H2O Oil CO2 eq. # sets # [pcs/year] [kwh/year] [lt/year] [kg/year] [CO2/year] Standard E E E E E+06 1 Over-load E E E E E+06 Minimum E E E E E+06 No product TOT E E E E E+06 Standard E E E E E+06 2 Over-load E E E E E+05 Minimum E E E E E+06 No product E E E+04 TOT E E E E E+06 Standard E E E E E+06 3 Over-load Minimum E E E E E+05 No product E E E+04 TOT E E E E E+06 Standard E E E E E+06 4 Over-load E E E E E+06 Minimum E E E E E+06 No product E E E+04 TOT E E E E E+06 Standard E E E E E+06 5 Over-load E E E E E+05 Minimum E E E E E+06 No product TOT E E E E E+06 Standard E E E E E+06 6 Over-load E E E E E+06 Minimum E E E E E+05 No product E E E+04 TOT E E E E E+06 Standard E E E E E+06 7 Over-load Minimum E E E E E+06 No product TOT E E E E E+06 Fig. 7.Example of KPIs assessment of different manufacturing scenarios considering a production target of [pcs/year]. Each KPI is calculated by the tool and displayed to endusers. In Fig. 7, only KPIs involved to the optimization process are reported. The cost item has not been reported in

6 346 Claudio Favi et al. / Procedia CIRP 61 ( 2017 ) the table and accounted in the optimization problem, even if it has been considered and evaluated, separately, in the analysis. The optimization module allows to highlight the best production scenario and the production schedule based on the selected indicator. As reported, the production scenario #7 can be considered the optimum in terms of water consumption, on the other hands, the production scenario #6 is the optimized one for the electric energy and oil consumption, as well as for CO2 emissions. Concerning the cost output, a small gap has been noticed between the two scenario (less than 3%) in favour of scenario #7. This is considered acceptable for the purpose of the analysis. 6. Conclusions and future outlooks The present paper aims to develop a representative model and a decision-making tool for the production management of manufacturing plants based on the principles of sustainable development. Three main steps beyond the current state of the art has been achieved: (i) the definition of a general method for manufacturing plant data classification, (ii) the development of a calculation tool for the rapid assessment of sustainability KPIs (energy consumption, CO 2 emissions, etc.) and (iii) the development of a decision-making tool for the management of the production schedule based on an optimized manufacturing plan (production rate, working shifts, working time, etc.). Results obtained for the specific case study shows how the minimization of environmental impacts (i.e. climate change indicator) is consistent with the minimization of electric energy consumptions due to the fact that the two parameters are in deep relation for this kind of manufacturing plant. For this reason, the analysis of other KPIs is necessary to have a general overview of the plant considering the overall aspects of sustainability. In particular, the cost item, which is one of the most relevant KPI, are usually evaluated separately. The analysis of the obtained results highlights some limitations. The most important one is the possibility to aggregate the result of each KPI in a final end-point, able to consider each aspect of the sustainability, only by the definition of weights and scores for each attribute. A more accurate multi-criteria mathematical model can be implemented to solve this issue, as for example the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. Another limitation is related to the necessity to know several inputs for the optimization analysis. In some cases, these data are not available (i.e. new manufacturing plant or new production technology), thus this kind of analyses can be performed only on the basis of statistical considerations and previsions. References [1] Fijal T. An environmental assessment method for cleaner production technologies. 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