In production planning and control, assessing the performance of a manufacturing system is a multi-dimensional problem, in which neglected dimensions may lead to hidden ineciencies and missed opportunities for gaining a competitive advantage. In this paper, a data formalisation method is proposed to model a manufacturing system by simultaneously considering value creation and technical, economic, and environmental performance. The proposed method combines the techno-economic assessment of lean manufacturing and sustainable manufacturing with the data- driven approach, typical of Industry 4.0, to overcome the limitations of the lean approaches in addressing complex systems. The method is based on the integration between Multi-layer Stream Mapping and a combination of Enterprise Input-Output and Material Flow Analysis, and it also considers non-value-added activities such as transport and inventories. Pen and papers and digital approaches can simultaneously exploit the method as a shared architecture for data formalisation and integration. The implementation of the method is shown through a numerical example based on a recycled plastic pipeline manufacturing system. KEYWORDS Lean manufacturing; Sustainable production; Industry 4.0; Value stream mapping; Digitalisation; Operational performance; Environmental performance; Material ow cost accounting 1. Introduction The technological revolution of Industry 4.0 (I4.0) and the necessity of a transition towards more sustainable development are impacting the methods and the tools to optimise and control the performance of manufacturing systems (Bendul and Blunck 2019). Data plays such a pivotal role in the I4.0 paradigm (Chiarello et al. 2018) that Klingenberg, Borges, and Antunes Jr (2019) proposed a classi cation of manufacturing technologies, methods, and tools based on their role in the data life cycle: (1)Data Generation and Capture. Technologies that generate and save data at any system level: people, products, machines, and processes. (2)Data Transmission. Technologies involved in data transmission to store and recover data. (3)Data Conditioning, Storage and Processing. Technologies and methodolo- gies of data protection and storage, data recovery and data conformation check, and data transformation to create knowledge. (4)Data Application. Methods, tools, and technologies, exploiting collected data, to control the value creation process. Usually, the methods and tools for production planning and control integrate the characteristics of two or more of these four groups. They specify the required data and the procedures to collect, manipulate, and exploit them to obtain Key Performance Indicators (KPIs). Furthermore, the available methods and tools focus on a subset of the dimensions (i.e., no method considers at the same time technical, economic and environmental dimensions). To consider all the dimensions together, a combined use of tools, methods and KPIs is required (Ferretti et al. 2017). However, this creates a possible redundancy in data collection, conditioning, storage and processing. Also, aggregating results of di erent methods may lead to partial and incomplete system representation rather than a well-rounded overview, since the same phenomenon may be represented di erently from di erent methods. The inclusion of the economic and environmental dimensions beside the technical eciency is essential to support the ecient use of resources through the implementa- tion of the 6Rs strategies of the circular economy, namely, reduction, reuse, recycling, recovery, redesign, re-manufacture (Govindan and Hasanagic 2018). Implementing 6Rs strategies introduces loops and customised manufacturing routes into the value cre- ation process that becomes more complex (Agyapong-Kodua et al. 2012). In general, value chain models present three main limitations (Daaboul et al. 2014): the value considers only the nancial dimension, like the turnover of the activity costs; the representation of the activities follows a speci c and sequential order; interactions between activities and their e ects on created value are neglected. On the one hand, the extensive data availability allows to control such complex sys- tems, even in a more sustainable manner. On the other hand, it increases the complex- ity of production planning and control approaches (Zheng et al. 2021) that must deal with several intertwined dimensions simultaneously. Neglecting some of the dimensions may lead to hidden costs and missed opportunities by a ecting the assessment of the considered ones (de Oliveira Neto and Lucato 2016). This paper proposes a data-driven formalisation method belonging to group (3) of the aforementioned Klingenberg, Borges, and Antunes Jr (2019)'s classi cation. The proposed method, the Multi-layer Enterprise Input-Output (MEIO) formalisation method, combines Enterprise Input-Output (Albino, Izzo, and K uhtz 2002) and Mate- rial Flow Analysis (Rotter et al. 2004) with the Multi-layer Stream Mapping (Holgado et al. 2018) to support the simultaneous assessment of techno-economic-environmental performance and value creation. The method provides a shared architecture of data conditioning, storage, and processing, concurrently exploitable by both digital mod- els (such as simulation and decision-making models) and pen and paper approaches (such as value analysis). Based on a ctitious but realistic system, a numerical exam- ple shows the implementation and highlights the potential advantages of the proposed formalisation method. The remainder of the paper discusses in section 2 some of the most di used meth- ods to assess value creation and techno-economic-environmental performance of man- ufacturing systems. Section 3 introduces the MEIO method. Section 4 presents the numerical example and shows the results, while section 5 discusses the insights and concludes this paper. 2 2. Literature review In the past, the monitoring of value creation and technical eciency was an adequate proxy for economic and environmental performance. The de nition of value as 'what buyers are willing to pay '(Porter and Kramer 1985) also has the environmental ef- fect of minimising waste and use of non-essential resources (Romano et al. 2010). Nowadays, the technical performance of a manufacturing system tightly intertwines techno-economic-environmental performance and value creation, and the methods fo- cus speci cally on a subset of them at a time. Table 1 collects some of the most studied methods and their characteristics to analyse value creation and technical, economic, and environmental performance. The literature is rich with customised versions of the general methods included in Ta- ble 1, which are often case-dependent and hardly extendable to general applications. Therefore, Table 1 shows the general version of the proposed methods for each macro class, highlighting the state of the art from a broader perspective, while a narrower overview of customised approaches follows within this section. The rows of Table 1 list the speci c characteristics of each dimension of performance assessment, and the ticks indicate whether a method (in columns) covers them. The rst rows in Application eld and scope provide information about the uses of the methods and whether they can involve a single company or a supply chain. The second set of rows tracks whether a speci c method exploits the bene ts of adopting data-driven approaches fostered by I4.0. Then, the dimensions of performance follow. Value creation and technical eciency are the main objectives of lean manage- ment, which aims to make companies technically performing and reactive (Chiarini, Baccarani, and Mascherpa 2018) by reducing eight types of waste (technical dimen- sion in Table 1) and de ning the non-added-value activities (Ohno 1988). In contrast, the time spent in value-added (VA), non-value-added (NVA), and essential-non-value- added (ENVA) activities characterises the value creation dimension. The most famous lean tool to identify waste sources, both at the system design and production control levels, is the Value Stream Mapping (VSM) ( rst column). VSM typically focuses on a factory or a short supply chain by identifying activities contributing to produce the product required by customers (Agyapong-Kodua et al. 2012). Although VSM has been improved, for example, to deal with system sources of waste and risk manage- ment (Ramesh and Kodali 2012; Vernadat et al. 2013), it struggles to include all the used resources, and the outcomes of its application depend on the choice of the ow unit used in the analysis (Shou et al. 2017). Another method based on VSM is the Multi-Layer Stream Mapping (MSM), which extends the value creation assessment of production systems with the evaluation of resource eciency (Holgado et al. 2018). Lean manufacturing methods mainly follow a value chain approach, resulting in inadequate modelling of networks of companies and complex manufacturing systems, such as those involving re-entrant ows, assembly, and disassembly operations (Braglia, Carmignani, and Zammori 2006). The contamination of the I4.0 paradigm with lean manufacturing principles may lead to positive synergies overcoming the current lim- its (Sanders, Elangeswaran, and Wulfsberg 2016), leading the scholars' interest in conceiving combined frameworks helping operational performance (Buer et al. 2020). Potential synergies and incompatibilities are not completely clear yet (Sanders et al. 2017), since lean manufacturing may lead to the adoption of new technologies, and the new paradigm may increase the e ectiveness of some lean principles (Rosin et al. 2020). Moreover, using lean methods with the increasing amount of data and system complexities fosters the risk of using new technologies in obsolete ways, by precluding 3 Table 1. Performance dimensions covered by the proposed methods. Lean approachesNetwork approachesFlow approachesThis paperValue stream mapping Multi-layer stream mapping Enterprise input-output Material ow analysis Material ow cost accounting Multi-layer enterprise input-output Application eld and scopeSystem design X X X X X X Production planning X X X X X X Production monitoring and controlX X X X X X Single company X X X X X X Supply chain X X X X Data-driven characteristicsBene t from real-time updateX X X X X Bene t from automatic updateX X X X X Bene t from big data exploitationX X X X Complex network systems X X X X Technical dimensionDefects X X X X X X Inventory X X X Motion Overprocessing Overproduction X X X Transportation X X X Waiting X X X Waste of human potential Value creation dimensionValue-added activity X X X Essential non-value-added activityX X X Non-value added activity X X X Economic dimensionEconomic interactions among companiesX X Production costs X X X Raw material and energy costsX X X Labour cost X X X Product revenues X X X Technical ineciencies X X X Pro t from 6R approaches X X X Environmental dimensionProduced/consumed resourcesX X X X Wasted resources X X X X Resources embedded in the productX X X X X Resources disposed in the environmentX X X X Bene ts from 6R approaches X X X X Resource eciency X X X X X new paradigms and not achieving good results, especially in sustainable development (Tortorella et al. 2020). In contrast, the Enterprise Input-Output (EIO) method specif- ically focuses on analysing the interactions among processes within a company (Albino, Izzo, and K uhtz 2002), which helps to analyse and represent the exchange of resources within complex systems (e.g., supply chains). The system representation provided by 4 EIO helps to apply other data-driven techniques such as agent-based simulation (Yazan and Fraccascia 2020). The Material Flow Analysis (MFA) is suitable for resource analysis in production planning and control. It statically describes the ows of resources consumed and pro- duced by companies or processes from their introduction into the system to the sale and disposal (Rotter et al. 2004). Material Flow Cost Accounting (MFCA) introduces in MFA the economic value of resources by separately considering four streams: (i) material costs; (ii) energy costs; (iii) system costs; and (iv) waste management costs (Dierkes and Siepelmeyer 2019; ISO 14051:2011). MFCA focuses on resource manage- ment (Rieckhof, Bergmann, and Guenther 2015) by reducing waste, scraps (Lukman et al. 2016), and by improving productivity ( Ozbu gday et al. 2020). It evaluates en- vironmental and waste costs to identify sources of missed revenues, poor resource eciency exploitation, and sources of waste. This analysis provides a deep economic perception of waste costs. The four-dimensional approach MAESTRI Total Ecient Framework combines the MSM with other lifecycle approaches to perform value analysis of process ecoeciency (Baptista et al. 2018) in the design phase. On the contrary, the combination of MSM and MFCA allows for resource eciency improvement in elds closer to production planning and control (Ribeiro et al. 2016). MSM overcomes the limitation of VSM in catching the process aspects linked to resource consumption (environmental dimension in Table 1), and the combined use with MFCA is suitable for production planning and control. However: the lack of a formalism to decompose processes in elementary units limits the integration of lean manufacturing methods with the I4.0 paradigm (Agyapong- Kodua et al. 2012); MSM is value-chain oriented instead of value-network oriented; the integration of MSM and LCA-based approaches may be prohibitive for SMEs because they require knowledge and economic availability, whose lack can result in unhelpful results (Heidrich and Tiwary 2013). 2.1. Contribution This study proposes an integrated formalisation method. Rather than combining dif- ferent methods by aggregating their ndings, the proposed approach models a network of processes and companies. The method develops a shared architecture for data pro- cessing and conditioning to feed other methods and tools. The combined use of the proposed formalisation method with other approaches leads to redundancy reduction in data collection, conditioning and processing while increasing data alignment and reducing the risk of partial information and hidden costs and opportunities. The method integrates a combination of EIO and MFA with the MSM resulting in a new method, the Multi-layer Enterprise Input-Output (MEIO) method, explicitly designed to be data-driven, according to the I4.0 paradigm. This method is helpful for other data-driven approaches and oriented to support the concurrent evaluation of techno-economic-environmental eciency and value creation for production planning and control methods. Therefore, it tackles the limits of lean principles-based methods in comprehensively assessing, quantifying, and monitoring multi-dimensional perfor- mance (Bai, Satir, and Sarkis 2019). Moreover, this study investigates the combined use of MEIO and the MSM and MFCA methods for performing the assessment of value creation process and techno-economic-environmental performance. 5 Finally, the MEIO method can support the system assessment under the circular economy paradigm since it monitors all resources involved in the entire production network, which is crucial for the circular economy paradigm (Bai et al. 2020). It al- lows to model the aforementioned 6Rs strategies by considering both value-added and non-value-added activities. The modelling of transport and inventory activities allows to identify their contribution in nished products depreciation, resource con- sumption, perishability of products and value creation. It contributes to lling the gap between the nancial-operational and environmental levels identi ed by Abisourour et al. (2020); in fact, it improves both the visibility and the assessment of global environmental impacts of operational performance. 3. The Multi-layer Enterprise Input Output method The Multi-layer Enterprise Input-Output (MEIO) method gathers the necessary in- formation to simultaneously assess value creation and techno-economic-environmental performance of manufacturing systems. It models the manufacturing system by per- forming data conditioning, storage, processing, and formalisation through two entities: resources and activities . Resources. The term resource refers to raw materials, energy, products, by- products, and waste. They are identi ed by following the two principles of MFA (Pauliuk and Heeren 2020), namely, identify the unit of analysis, and ensure ma- terial and energy balances. The rst one determines how deep the resource analysis is. For example, it is possible to monitor the water ows (bottles in product industries) or hydrogen and oxygen molecules. The second principle requires tracking all resource ows through all production, stocking, and transport activities until they exit from the system. It aims to ensure the conservation of material and energy while identifying new produced and absorbed resources. Activities. The term activity identi es any added or non-added value operation of the manufacturing system, such as production, transport, and inventory. Every activity tracks the resource input and output quantities by balancing incoming and outgoing materials and energy (including dissipated energy, wastes, and consumables). The MEIO method uses three tables to represent the interactions between resources and activities, which ensure the required exibility to collect and update data: the Resource-Activity (RA) MEIO table, the Activity-Parameters (AP) MEIO table, and the Resource-Function (RF) MEIO table. The tables can be used to create the RA MEIO graph, which is helpful for optimisation models. The tables and the graph are discussed in the following. 3.1. Resource-Activity MEIO table The RA MEIO table consists of Icolumns, one for each activity, and Jrows, one for each resource. The top part of Table 2 shows an example of the RA MEIO table for a system composed of three activities and eight resources. For each resource, the RA MEIO table indicates the quantity produced and absorbed by each activity in the 'input/output 'format. The middle dash '- 'means that the speci c resource is not involved in the activity. For example, activity P1 in Table 2, which is an integrated line consisting of a plastic cleaner and a shredder, receives in input 500 kg/hr of a plastic mix, 500lt/hr of water, and 160 kWh of power, to obtain 498 kg/hr of shredded, cleaned plastic, 56 kg/hr of humid waste and 446 lt/hr of wastewater exiting from the 6 system. Table 2. Resource-Activity MEIO table (in the top) and the normalised version (in the bottom) for a system with three activities and eight resources. Stars in the normalised version identify the key resources used to normalise the others. Resource-Activity table P1 T1 W1 Plastic mix (kg/hr) 500/- -/- -/- Humid waste (kg/hr) -/56 -/- -/- Shredded, humid mix (kg/hr)-/498 4500/4356 10000/9920 Power (kWh) 160/- 15/- -/- Used power (kWh) -/60 -/10 -/- Water (lt/hr) 500/- -/- -/- Waste water (lt/hr) -/446 -/144 -/80 Dissipated heat (kWh) -/100 -/5 -/- Normalised Resource-Activity table P1 T1 W1 Plastic mix (kg/hr) 8.929/- -/- -/- Humid waste (kg/hr) -/1* -/- -/- Shredded, humid mix (kg/hr)-/8.896 300/290.4 125/124 Power (kWh) 2.857/- 1*/- -/- Used power (kWh) -/1.071 -/0667 -/- Water (lt/hr) 8.929/- -/- -/- Waste water (lt/hr) -/7.964 -/9.6 -/1* Dissipated heat (kWh) -/1.786 -/0.333 -/- The 'input/output 'format allows to represent perishability and damages during transport and inventory activities. For example, in Table 2, the activities T1 and W1 present a loss of nished products since during inventory and transport activities, the wet product dries, thus losing water. In manufacturing systems, more than one machine or operator (either identical or di erent) can perform the same activity, a ecting resource consumption and produc- tion. In the case of an activity with di erent machines/operators, the MEIO method represents it with additional columns, considering the multiple con gurations. Con- versely, the activities with identical parallel machines consider the capacity as the aggregated capacity of all the machines/operators. The MEIO method supports both pen and paper approaches and techniques based on digital models. When feeding digital models, to avoid problems related to numerical precision, the involved quantities should have as few digits as possible, thus resource quantities have to be normalized. The bottom of Table 2 shows the normalised version of the RA MEIO table on the top; the stars in the input and output quantities indicate the key resource used to normalise the single activity (e.g., humid waste is the key resource used to normalise activity P1). 3.2. Activity-Parameters MEIO table The AP MEIO table collects all Zavailable technical and economic information, in- dicated in rows, for each of the Iactivities indicated in columns. The information set included in the AP MEIO table represents the current system state, which allows the modelisation of the real system. Table 3 shows the AP MEIO table for the previous example. The rst rows of the table indicate the primary activity information such as ID, activity description, maximum capacity, and the number of machines, followed by technical information. Furthermore, the AP MEIO table allows to customise the information provided by in- cluding additional deterministic and stochastic parameters to extend the set of meth- ods and approaches compatible with the shared infrastructure. For example, in P1, 7 Table 3. Activity-Parameter MEIO table for a three-activity system involving: activity description, distance matrix and, technical, economic, and eciency parameters. Activity-Parameters table P1 T1 W1 Activity IDCleaner and shredderConveyor beltPlastic bin Number of machines, tools, units1 1 4 Number of operators 3 - - Maximum capacities and process times (kg/hour)500 4500 2500 Defective units and impurities (% on total production)0.1 - - Time to fail (hour)Exp(3) - - Time to repair (hour)Exp(0.05) - - Working hours per day (hour/day)24 24 24 Labour cost ( ¿/man*h) 10 - - Speed (km/hr) - 0.9 - P1 distance (km) from - 0 0.05 T1 distance (km) from 0 - 0 W1 distance (km) from 0.05 0 - P2 distance (km) from - - 0 OEE parameter V (%) 0.984 - - OEE parameter P (%) 1.000 - - OEE parameter Q (%) 0.889 - - OEE (%) 0.874 - - thetime to failure and the time to repair follow an exponential distribution with av- erage equal to 3 and 0.05 hours, respectively. Furthermore, the AP MEIO table also contains customised KPIs such as the OEE parameters, which will be introduced in the numerical example of Section 4. The AP MEIO table includes the distance matrix that speci es the connections between the activities, the transport activities, and the connection speed. The middle dash '- 'indicates the absence of connections; a distance equal to 0 indicates the existence of a connection between activities; a distance larger than 0 provides the double information of the distance length and the current lack of transport activities to travel such distance. 3.3. Resource-Function MEIO table The RF MEIO table identi es resource consumption and production when the pro- cessing rate changes. The RF MEIO table collects the mathematical functions that connect the production and consumption of all the resources (in rows) of each activ- ity (in columns) following the 'input;output 'format of the RA MEIO table. In each activity, the RF MEIO table identi es the activity key resource as the independent variable (X), and the production and consumption of the other resources as the depen- dent variable (Y). Table 4 shows the RF MEIO table for the three-activity example. In the table, referring to activity P1, the output of Humid waste is the independent variable, and all the other functions depend on it. For example, the quantity of Power has a constant term (0.5 kWh), and a variable term proportional to 2.857 times the Humid waste output (Y= 2.857X + 0.5). The numerical coecients are the same as the normalised RA MEIO table. How- ever, the RF MEIO table allows for modelling the activity resource production and consumption by introducing further terms to increase accuracy. For example, in P1, 8 Table 4. The Resource-Function MEIO table for the three-activity process. Resources P1 T1 W1 Plastic mix (kg/h)Y=8.929X;- -;- -;- Humid waste (kg/h)-;X* -;- -;- Shredded, humid mix (kg/h)-;Y=8.896XY=300(X-0.1); Y=290.4(X-0.1)Y=125X; Y=124X Power (kWh) Y=2.857X+0.5;- X*;- -;- Used power (kWh)-;Y=1.071X -;Y=0.667(X-0.1) -;- Water (lt/h) g Y=8.929X;- -;- -;- Waste water (lt/h)-;Y=7.964X -;Y=9.6(X-0.1) -;X* Wasted heat (kWh)-;Y=1.786X+0.5 -;Y=0.333X -;- there is a xed consumption of power, 0.5 kWh, independent from the production ofShredded, humid mix (independent variable X) that produces 0.5 kWh of thermal energy dissipated in the environment. The RF MEIO table complexity depends on the modelling assumptions; in fact, the formalisation method can introduce complex functions and distributions to model di erent activity behaviours (e.g. productivity during the warm-up period, the average rate, and the overload working condition). 3.4. The Resource-Activity MEIO graph From the MEIO tables, it is possible to create the RA MEIO graph, in which the nodes and the arcs represent the activities and resource ows, respectively. The graph includes two arc types modelling the potential and the existing network. The RA MEIO table provides the information to create the potential network: for each activity, there is a set of outgoing arcs for each produced resource that connects the activity with all the other activities having that resource as input. The weight of the arcs of the potential network is the distance between nodes (de ned in the distance matrix of the AP MEIO table) to support digital models in assessing the cost of adding the connection. The distance matrix in the AP MEIO table also de nes the existing network, and the weight of its arcs is the quantity absorbed (incoming arcs) and produced (outgoing arcs) by the nodes. Figure 1 shows the Resource-Activity MEIO graph of the example reported in Table 2. The RA MEIO graph includes seven ctitious nodes (dashed circles T0, T2, T3, T4, T5, T6, T7) since each arc must have a source and a sink. These nodes are identi ed as transport activities since they deliver initial resources (T0, T2, and T3) and collect system waste (T4), by-products (T5 and T6), and products (T7). The MEIO RA graph involves two sub-graphs: the potential and the current graphs, identi ed by dashed (d) and solid (s) arcs, respectively. For example, d(W1,T1) indicates the dashed arc from W1 to T1 and s(P1,T1) the solid arc from P1 to T1. The potential graph involves only the main product ow (plastic mix). It performs six connections, namely, d(P1,T1), d(P1,W1), d(T1,W1) and d(W1,T1), and two loops d(T1,T1) and d(W1,W1). Arcs d(W1,T1) and d(T1,W1) are both included because, from the RA MEIO table, the Shredded, humid mix is both input and output for T1 and W1. For the same reason, arcs d(T1, T1) and d(W1, W1) are included in the graph. The ctitious nodes have only outgoing or incoming arcs, whose weight is assigned 9 according to the independent variable of the activity they are connected to (e.g., water from T2 and power from T3 refers to the independent variable of activity P1, that is, XP1). Conversely, the arcs between real nodes (solid ones) have the weight set accord- ing to the independent variable of the source node (e.g., the weight of arc s(P1,T1) depends on the independent variable XP1). When the entire system is balanced, the incoming and outgoing arcs of the nodes respect the material and energy balances. Figure 1. Resource-activity graph for the example of the three-activity system. The RA MEIO graph highlights that MEIO can model the 6Rs strategies to improve resource eciency. For example, arcs d(T1,T1) and d(W1,W1) can model the reuse strategy in which a scrap of a process can be reused as input of the process itself (because it has a similar quality of the primary input). The 6Rs strategies from repair to recovery ideally follow the same circular arc of reuse. However, rather than closing the loop into the same activity, they go back to precedent activities; here disassembling, recycling, repairing, and recovering activities transform the output resource into a raw material ready to re-enter the manufacturing system. 4. Numerical example This section discusses a numerical example to show the implementation of the MEIO method on a recycled plastic pipelines manufacturing system by developing the MEIO tables and drawing the RA MEIO graph. Section 5 will use the same example to apply the MFCA and MSM approaches to assess the techno-economic-environmental performance of the addressed system through the use of the MEIO tables here devised. 4.1. Empirical context Figure 2 shows the recycled plastic pipeline manufacturing system involving three production activities (P1, P2, and P3), two transport activities (T1 and T2) and two inventory activities (W1 and W2). P1 receives the plastic waste mix ready to be washed and shredded. The company earns 0.45 e/kg to treat the plastic waste mix, and P1 can nominally treat 500 kg/h. 10 Figure 2. Recycled plastic pipeline manufacturing system. P1 requires three operators, and it works on three 8-hour daily shifts. At the end of the line, 10% of the entire production is lost by falling out of the conveyor belt. The clean, humid and shredded plastic mix that falls on the oor is a waste, successively disposed of in the land ll. P1 requires 500 lt/h of water, and it consumes 160 kWh. The conveyor belt (T1) connecting P1 with the stocking area W1 is 0.05 km long, it can move up to 4500 kg with a constant speed of 0.9 km/h by absorbing 1 kWh. The stocking area W1 contains four bins for the shredded plastic mix holding up to 2500 kg each, and they feed the two-machine process P2. P2 is a pelletiser line consisting of two parallel machines transforming the shredded plastic mix into a homogeneous product, the plastic pellet, by adding some chemical additives. Each of the two lines can treat up to 350 kg/h of the shredded plastic mix by proportionally adding up to 20 kg of chemical additives and consuming 0.25 kWh of power per kg. The homogeneous product, the plastic pellet, is packed in bags of 10.3 kg, accumulated to ll the capacity of the truck that delivers the bags of pellet to the nal production process. During the three 8-hour shifts, some interruptions to the ow of shredded plastic mix cause jams (on average one every 3 hours with 15 minutes to solve them); moreover, 8% of the produced pellet has a poor quality because of an ine ective mix with the chemical additives. The truck (T2) has a capacity of 5.5 t/delivery, equivalent to 534 bags of pellet per delivery. It covers a distance of 10 km by consuming 0.066 lt of diesel and producing 33 g equivalent of CO2. The truck connects P2, which is in the plant area devoted to recycling urban waste, to the storage area W2, located in the area of the plant devoted to the production of products in recycled plastic. The stocking area W2 consists of a pellet bin able to store up to 10 tons of plastic pellet. The last activity is the extrusion process (P3) to produce plastic pipelines with 200 and 600 mm diameter, with a length of 10 m and a weight of 29.765 kg, and a length of 2 m and a weight of 5.95 kg, respectively. The extrusion line works on two 8-hour shifts, one for each product, including the setup to change production, which takes 30 minutes, and it can treat 750 kg/h of pellet by consuming 168 kWh and requiring 50 kg of chemical additives. The nominal production time is 0.04 h for a 200 mm diameter pipeline and 0.008 h for a 600 mm diameter pipeline. The defectivity is 0.001 and 0.005 for the 200 mm diameter pipeline and the 600 mm, respectively. The disposal in the land ll of defectives, poor quality pellet, and the discarded shredded plastic mix is a cost for the company since the disposal fee is 25 e/m3. Melted plastic jams the extruder on average every 16.67 h and 83.33 h during the 200 mm and 600 mm diameter pipeline production, respectively, and 0.83 h are on average required to restore the production. Table 5 summarises all the involved resources providing their market prices/purchasing costs. The company earns a commission for each treated kg of plas- tic mix. The disposal cost is the same for all wastes sent to the land ll, that is, the 11 shredded humid waste felt out of the conveyor belt, the poor quality pellet and the defective pipelines. The shredded plastic mix has a market price of 0.6 e/kg, while the plastic pellet reaches 1 e/kg (the bags have 10.3 kg of pellet; thus, they have a market price of 10.3 e). Power, water, chemical additives, and fuel are the other re- sources involved in the system, and the indicated costs refer to their purchasing. The production processes produce heat, which is dissipated in the environment rather than used as a resource having a purchasing cost of 0.5 e/kWh. The environmental cost ofCO2comes from the cost of the CO2equivalent emissions; however, it is scarcely relevant for the proposed example. Table 5. Economic parameters for produced and purchased resources. Resources Price Cost Resources Price Cost Plastic mix ( ¿/kg) 0.45 - Power ( e/kWh) 0.17 - Humid waste ( e/kg)- 1Used power ( e/kWh)- - Shredded, humid mix ( e/kg)0.6 - Water ( e/lt) 0.004 - Plastic pellet ( e/kg)1 -Waste water ( e/lt)- - Under q. pellet ( e/kg)- 0.0262Dissipated heat ( e/kWh)0.5 - Bags of pellet ( e/bags)10.3 -Pipeline d200 ( e/piece)35 - CO2(e/delivery) - 0.00005Pipeline d600 ( e/piece)7 - Chemical additives ( e/kg)1.5 -Defective pipeline d200 ( e/piece)- 0.78 Fuel ( e/delivery) 0.0014 -Defective pipeline d600 ( e/piece)- 0.156 4.2. Application of the MEIO method The MEIO table development consists of 4 phases, described in the following. Phase 1. The rst phase develops the RA MEIO table by: 1) identifying the ac- tivities and resources; 2) applying the two MFA principles. The aim is to identify some potentially neglected resources during the initial data collection and to de ne the unit of measure for all the resources. Table 6 shows the normalised RA MEIO ta- ble, while Table A1 in Appendix A shows the initial not normalised RA MEIO table, which reports the nominal data provided by the machine manufacturers and de ned by agreement for transport services. The emerging inconsistencies from the initial application of the material and en- ergy balances ( rst MFA principle) requires further analysis to identify the neglected resource ows. For example, in P1, the sum of water with the initial plastic mix does not correspond to the output of the process, because it considers only the shredded mix and the waste of the process, and it neglects how the water used in the process could be re-used. Therefore, from further analysis, 446 out of the provided 500 lt/h are disposed of as wastewater; the remaining 54 kg follow both the humid waste and the shredded plastic mix. Also, the entire line consumes 160 kWh, but the e ective use of power is estimated at 60 kWh, while the rest becomes dissipated heat energy. Furthermore, there is a material loss during activities; for example, in T1 and W1, the water mixed with the plastic raw material leads to weighing inputs and outputs, causing a 3.2% weight reduction due to water falling out of the conveyor belt and evaporating. To facilitate the understanding, P2 reports redundant information about the output, 12 Table 6. Normalised Resource-Activity MEIO table for the plastic pipeline manufacturing chain. Resource-Activity table P1 T1 W1 P2 T2 W2 P3 Plastic mix (kg/hr) 8.929/- -/- -/- -/- -/- -/- -/- Humid waste (kg/hr)-/1* -/- -/- -/- -/- -/- -/- Shredded, humid mix (kg/hr)-/8.896 300/290.4 125/124 4.716/- -/- -/- -/- Plastic pellet (kg/hr)-/- -/- -/- -/4.587 -/- -/10.3 15/- Under q. pellet (kg/hr)-/- -/- -/- -/0.399 -/- -/- -/- Bags of pellet (bags)-/- -/- -/- -/0.445 8.08/8.08 1*/- -/- CO2(g/delivery) -/- -/- -/- -/- -/0.5 -/- -/- Chemical additives (kg/hr)-/- -/- -/- 0.27/- -/- -/- 1*/- Fuel (ml/delivery) -/- -/- -/- -/- 1*/- -/- -/- Power (kWh) 2.857/- 1*/- -/- 1.179/- -/- -/- 3.36/- Used power (kWh)-/1.071 -/0.667 -/- -/1* -/- -/- -/2.22 Water (lt/hr) 8.929/- -/- -/- -/- -/- -/- -/- Waste water (lt/hr)-/7.964 -/9.6 -/1* -/- -/- -/- -/- Dissipated heat (kWh)-/1.786 -/0.333 -/- -/0.179 -/- -/- -/1.14 Pipeline d200 (kg/hr)-/- -/- -/- -/- -/- -/- -/7.991 Pipeline d600 (kg/hr)-/- -/- -/- -/- -/- -/- -/7.96 Defective pipeline d200 (kg/hr)-/- -/- -/- -/- -/- -/- -/0.009 Defective pipeline d600 (kg/hr)-/- -/- -/- -/- -/- -/- -/0.041 that is, both the bulk pellet production in terms of kg/h and the number of pellet bags. Furthermore, in T2, the required fuel and produced CO2are assessed over the assigned journey since the truck always follows the same route. Phase 2. The RF MEIO table collects all the functions of the system activities, making them available for digital models that can vary the level of consumption and production of the activity by varying its production rate or transportation speed. Table 7 shows the RF MEIO table for the plastic pipeline production chain. The production and consumption functions have some constant terms, for example, Shredded, humid mix in T1 (0.1) and Power in P1 (0.5), modelling the power absorbed by those devices in monitoring and supporting tasks. Furthermore, some functions can be independent of the actual key resource con- sumption and production, such as, in T2, the fuel consumption and the CO2produc- tion. All the functions related to P2 are multiplied by two, as P2 has two parallel machines. Phase 3. This phase collects activity information and KPIs for economic and envi- ronmental performance for the AP MEIO table. Table 8 presents the AP MEIO table of the plastic pipeline production chain. The rst parameters describe the activity itself, the number of used resources, and the potential labour requirement and maximum capacity. Maximum capacity indicates kg/h for production activities and maximum inventory capacity and truckload for inventory and transport activities. In P3, the multiproduct allocation parameters ( and ) indicate the allocation of production capacity to each produced product. Information about the defectives, failures, setups, and time to restore machine productivity can be speci ed with more than one value, separated by semicolons, if they have di erent values for di erent 13 Table 7. Resource-Function MEIO table for the plastic pipeline manufacturing chain. Resources P1 T1 W1 P2 T2 W2 P3 Plastic mix (kg/h)Y=8.929X;- -;- -;- -;- -;- -;- -;- Humid waste (kg/h)-;X -;- -;- -;- -;- -;- -;- Shredded, humid mix (kg/h)-;Y=8.896XY=300(X-0.1); Y=290.4(X-0.1)Y=125X; Y=124XY=2(4.716X);- -;- -;- -;- Plastic pellet (kg/h)-;- -;- -;- -;Y=2(4.587X) -;- -;Y=10.3X Y=15X;- Under q. pellet (kg/h)-;- -;- -;- -;Y=2(0.399X) -;- -;- -;- Bags of pellet (pcs.)-;- -;- -;- -;Y=2(0.445X) X;Y=X X;- -;- CO2 (g/delivery)-;- -;- -;- -;- -;Y=5 -;- -;- Chemical additives (kg/h)-;- -;- -;- Y=2(0.27X);- -;- -;- X;- Fuel (ml/delivery)-;- -;- -;- -;- Y=0.6;- -;- -;- Power (kWh) Y=2.857X+0.5;- X;- -;- Y=2(1.179X+0.5);- -;- -;- Y=3.36X+0.5;- Used power (kWh)-;Y=1.071X -;Y=0.667(X-0.1) -;- -;2X -;- -;- -;Y=2.22X Water (lt/h) Y=8.929X;- -;- -;- -;- -;- -;- -;- Waste water (lt/h)-;Y=7.964X -;Y=9.6(X-0.1) -;X -;- -;- -;- -;- Wasted heat (kWh)-;Y=1.786X+0.5 -;Y=0.333X -;- -;Y=2(0.179X+0.5) -;- -;- -;Y=1.14X+0.5 Pipeline S (kg/h)-;- -;- -;- -;- -;- -;- -;Y=7.991X Pipeline L (kg/h)-;- -;- -;- -;- -;- -;- -;Y=7.96X Defective pipeline S (kg/h)-;- -;- -;- -;- -;- -;- -;Y=0.009X Defective pipeline L (kg/h)-;- -;- -;- -;- -;- -;- -;Y=0.041X products; otherwise, only one value is reported. The labour unit cost indicates the total cost paid by the company for an hour of work of an operator. In contrast, the operating costs are proportional to the production rate. As the MEIO method will be used coupled with MSM in performance analysis, in Section 5, Table 8 also includes the Overall Equipment Eciency (OEE) to esti- mate the process maximum e ective capacity. Companies and practitioners widely use OEE because of its clarity and ease of use (Muchiri and Pintelon 2008). According to the version proposed by De Ron and Rooda (2006), the OEE (Equation (1)) can be estimated by multiplying the availability of machines V (Equation (2)), the perfor- mance eciency P (Equation (3)), and the percentage of products with good quality Q (Equation (4)). OEE =VPQ (1) V=loadingtime downtime loadingtime(2) P=theoreticalcycletimeXprocessedamount operatingtime(3) Q=processedamount defectamount processedamount(4) 14 Table 8. Activity-Parameters MEIO table for recycled plastic pipeline production chain. Activity-Parameters P1 T1 W1 P2 T2 W2 P3 Activity ID:Cleaner and shredderConveyor beltPlastic binPelletizer TruckPellet binPipeline extruder Number of machines, tools, units1 1 4 2 1 1 1 Number of operators 3 - - - - - - Maximum capacities and process times500 4500 2500 350 5500 10000 750 Parameters for multiproduct allocation- - - - - - : 0.5; : 0.5 Defective units and impurities (% on total production)0.1 - - 0.08 - - 0.001; 0.005 Mean time to fail 3 - - 3 - - 16.66; 83.33 Mean time to repair 0.05 - - 0.25 - - 0.83; 083 Mean Time to setup - - - - - - 7.5 Mean setup time - - - - - - 0.5 Working hours per day 24 24 24 24 16 16 16 Labour cost 10 - - - - - - Operational cost - - - - 80 - - Speed (km/hr) - 0.9 - - 35 - - P1 distance (KM) from - 0 0.05 - - - - T1 distance (KM) from 0 - 0 - - - - I1 distance (KM) from 0.05 0 - 0 - - - P2 distance (KM) from - - 0 - 0 10 - T2 distance (KM) from - - - 0 - 0 - I2 distance (KM) from - - - 10 0 - 0 P3 distance (KM) from - - - - - 0 - OEE parameter V 0.984 - - 0.923 - - 0.971 OEE parameter P 1.000 - - 1.000 - - 0.938 OEE parameter Q 0.889 - - 0.913 - - 0.997 OEE 0.874 - - 0.843 - - 0.908 Phase 4. The last phase involves the RA MEIO graph creation. Figure 3 shows the RA MEIO graph for the numerical example, in which the dashed and solid arcs identify the potential and the existing network, created by exploiting data from RA and AP MEIO tables, respectively. The RA MEIO graph has the twofold goal of enabling the adoption of graph ap- proaches during the network design phase and the adoption of performance monitoring approaches based on indicators. 5. Performance analysis The following performance analysis shows the potential bene ts in redundancy, time and cost reduction achievable by adopting the MEIO formalisation method as a shared architecture. MSM and MFCA methods are fed by the MEIO tables to assess value creation and techno-economic-environmental performance. The performance analy- sis highlights the data alignment brought by the MEIO method, which limits the cases of partial information, con icting results, and the possibility to neglect aspects (which usually happens when aggregating results of several methods to obtain multi- dimensional performance). In the following, the MSM is rst applied to the numerical example, then the economic-environmental assessment is performed through the MFCA. 5.1. Value creation and technical eciency: the multi-layer stream mapping The MSM considers the system constantly working at the e ective maximum rate (i.e., considering also failures and defectives). The AP MEIO table provides the cur- rent network con guration and the OEE values. Figure 4 shows the VSM of the MSM 15 Figure 3. The RA MEIO graph for the recycled plastic pipeline manufacturing system. approaches, in which triangles on grey arrows indicate bu ers, inventories, and trans- port activities, and grey boxes value-added activities (i.e., manufacturing processes), which report the activity name and the four OEE parameters. The number above triangles and boxes indicate the number of machines, tools, and bins involved in the activity. The black broken arrows show the informative ows, while straight red arrows highlight the monitoring. Red dashed arrows follow the material ow. The time unit is minutes, while each activity considers 1 kg of the primary raw material to assess value creation. The white boxes report the total cycle time (CT) and the contribution of VA, ENVA, and NVA activities to the CT. Equations (5)-(8) report the CT determination in formulae, and the numerical calculation for activity P1 as example. The nominal capacity is provided by the AP MEIO table, and the total CT is in Equation (8). CTVA=60 NOMINALCAPACITY P1=1 500= 0:12min (5) CTENVA =60 NOMINALCAPACITY P1PVCTVA= =60 50010:9840:12 = 0:002min(6) CTNVA =60 NOMINALCAPACITY P1QCTVA= =60 5000:8890:12 = 0:015min(7) 16 Figure 4. Value Stream Mapping of plastic pipelines manufacturing chain. CT=CTVA+CTENVA +CTNVA = 0:12 + 0:002 + 0:015 = 0:137min (8) The production management assumes as acceptable ( essential ) the working time spent in failures, setups, transport and inventories, while the defects represent NVA activities. The VA activities are far smaller than ENVA activities, especially the wait- ing time for truck delivery and the large inventory of W2 (98.7% of total CT). The RA MEIO table supports the resource eciency analysis, reported in Table 9, which concludes the MSM approach. The showed percentage represents the used quantity of the resources by distinguishing, within each activity, the input and the output resources. For example, in P1, the Plastic mix is not entirely converted into Shredded, humid mix , since a little of it (i.e., 10% of the mix) falls out of the line during the machining. In contrast, the Shredded, humid mix arrived at the end of the line is entirely assigned to the conveyor belt T1. Thus, only 90% of plastic mix gains value. In P1, only 29% of the Power creates value since, according to the RF MEIO table, the rest becomes dissipated heat. The used power (i.e., that 29% of Power ), is 90% ecient ( Used power is 90%). In fact, the 10% of inecient power use is related to the 10% of plastic mix fallen out of the line. The same holds for Water . MSM highlights that some resources, such as wastewater, dissipated heat, and the under quality plastic pellet remain unexploited. Moreover, it also shows the loss of the raw materials that were added to a resulting defective or wasted product. For example, in P2, part of the chemical additives mixed with plastic pellets results in a defective output successively discarded. Adopting the MEIO formalisation method led the MSM to focus also on the trans- formation of consumables, energy, and raw materials in waste and by-products. In fact, through Phase 1 , many resource ows have been included in the analysis leading to the detailed ndings of the resource eciency analysis. For example, MSM shows the 10% of inecient use of water in P1, but the use of the MEIO method also high- 17 Table 9. Resource eciency evaluated according to Multi-Layer Stream Mapping. The percentage indicates the amount of exploited resource. Resource eciency P1 T1 W1 P2 T2 W2 P3 Plastic mix (kg/hr) 90% - - - - - - Humid waste (kg/hr)0% - - - - - - Shredded, humid mix (kg/hr)100% 97% 99% 92% - - - Plastic pellet (kg/hr)- - - - - 100% 100% Under q. pellet (kg/hr)- - - 0% - - - Bags of pellet (bags)- - - 100% 100% 100% - CO2(g/delivery) - - - - 0% - - Chemical additives (kg/hr)- - - 92% - - 100% Fuel (ml/delivery) - - - - 100% - - Power (kWh) 29% 65% - 55% - - 57% Used power (kWh)90% 97% - 92% - - 100% Water (lt/hr) 90% - - - - - - Waste water (lt/hr)0% 0% 0% - - - - Dissipated heat (kWh)0% 0% - 0% - - 0% Pipeline d200 (kg/hr)- - - - - - 100% Pipeline d600 (kg/hr)- - - - - - 100% Defective pipeline d200 (kg/hr)- - - - - - 0% Defective pipeline d600 (kg/hr)- - - - - - 0% lights that the entire amount of water is used only once, in P1; further analyses should investigate the exploitation opportunities through the 6Rs strategies. MSM neglects the economic aspect of the performance assessment, such as the economic cost of defectives and their impact on the inecient use of materials. Also, the potential value of disposed waste rather than its exploitation through the 6Rs strategies is not quanti ed. In the following section, the application of the MFCA method sheds some light on these points. As MFCA and MSM both use the same data provided by the MEIO tables, the results of the two methods are coherent with each other. 5.2. Economic and environmental eciency: the Material Flow Cost Accounting The MFCA indicates both quantities and economic values helpful to measure environmental-economic performance. This analysis focuses on three streams of re- sources: raw materials, energy, and labour. MFCA monitors the resource ow from their introduction into the system until they exit by observing the activities producing and consuming them. All the resource ows are coupled with their economic value or their costs, such as environmental costs, disposal costs, operating costs. Table 10 reports the ows and the economic values related to the case example. In the table, each activity has two columns to indicate quantities (Q) and economic value (+/-), reporting used and consumed quantities and contribution to the pro t of each activity, respectively. Activities W2 and T2 are not reported due to their limited relevance. According to the general accounting rules, 18 the nished products assume their market value at the end of an activity, becoming an operating cost at the beginning of the next activity. For instance, in Table 10, the initial 1867 kg of plastic leads to the production of 1736 kg of pipelines, which includes the addition of 102 kg (in P2) and 115 kg (in P3) of chemical additive. Table 10. Produced and purchased quantities of each resource and their contribution to the nal pro t. ResourcesP1 T1 W1 P2 P3 Q +/- Q +/- Q +/- Q +/- Q +/- Plastic mix1867.33 840.3 - - - - - - - - Humid waste209.13 -209.13 - - - - - - - - Shredded, humid mix1860.43 1116.26 1800.89 - 1786.48 - 1786.48 -1071.89 - - Plastic pellet- - - - - - 1737.62 1737.62 1736.29 -1736.29 Under q. pellet- - - - - - 151.15 -3.96 - - Bags of pellet- - - - - - 168.57 1736.29 - - CO2 - - - - - - - - - - Chemical additives- - - - - - 102.28 -153.42 115.75 -173.63 Fuel - - - - - - - - - - Power 599.38 -101.9 6.3 -1.07 - - 448.00 -76.16 390.20 -66.33 Used power223.98 -38.08 4.2 -0.71 - - 378.81 -64.4 256.97 -43.69 Water 1867.33 -7.47 - - - - - - - - Waste water1665.52 -6.66 60.49 -0.24 14.41 -0.06 - - - - Wasted heat375.41 -187.7 2.1 -1.05 - - 69.19 -34.6 133.23 -66.61 Pipeline S - - - - - - - - 31.08 1087.66 Pipeline L - - - - - - - - 154.86 1083.99 Defective pipeline S- - - - - - - - 0.04 -0.03 Defective pipeline L- - - - - - - - 0.8 -0.12 Hours 3.8 114 - - - - 2.76 - 2.54 - Di erently from the general accounting rules, MFCA accounts for the costs of wastes and unexploited resources (Zhou et al. 2017). Moreover, it introduces some ctitious operating costs (not charged to the company) to underline the value of unexploited resources. For example, dissipated heat is accounted for with the price of district heating and wastewater with the market price of water for industrial facilities. General accounting rules neglect the defectives and consider the purchasing costs for the raw materials and resources as operating costs, together with the costs of waste disposal. MFCA unbundles the operating costs referred to the waste production charging it to the wastes, while the costs that e ectively contribute to the production become the new operating costs. Furthermore, MFCA accounts for the unexploited resources with their opportunity costs. Table 10, developed by identifying the resource quantities through the RA MEIO table, is used to create Table 11, which shows the manufacturing system accounting by combining general accounting and MFCA rules. The rst part of Table 11 follows the general accounting rules to determine the net activity pro t, subsequently enriched by the potential pro t coming from the reuse of unexploited resources (wastewater and dissipated heat). The second part of the table presents the total costs of the resources embedded in the wastes, according to the MFCA principles. The MFCA enriches the MSM ndings by highlighting the hidden costs of failures and defectives and their environmental impacts on inecient resource use. The aggregation of MSM and MFCA ndings provides a multi-dimensional perfor- 19 Table 11. General and material ow cost accounting for the manufacturing chain of plastic pipelines. General Accounting and Material Flow Cost AccountingP1 T1 W1 P2 P3 Raw material value Plastic mix 840.3 0 0 0 0 Shredded humid mix 1116.26 0 0 -1071.89 0 Bags of pellet 0 0 0 1736.29 -1736.29 Pipeline S 0 0 0 0 1087.66 Pipeline L 0 0 0 0 1083.99 Other raw material 0 0 0 Chemical additives 0 0 0 -153.42 -173.63 Fuel 0 0 0 0 0 Power -101.9 -1.07 0 -76.16 -66.33 Water -7.47 0 0 0 0 Labour Workhours -113.91 0 0 0 0 Cost of waste disposal Humid waste -209.13 0 0 0 0 Under q. pellet 0 0 0 -3.96 0 Defectives pipeline S 0 0 0 0 -0.03 Defectives pipeline L 0 0 0 0 -0.12 CO2 0 0 0 0 0 Net pro t 1524.15 -1.07 0 430.86 195.25 Potentially reus. resources Waste water 6.66 0.24 0.06 0 0 Dissipated Heat 187.70 1.05 0 34.60 66.61 Net pro t with reus. Resources 1718.52 0.22 0.06 465.46 261.86 Resources trapped in Waste Labor -12.76 0 0 0 0 Raw material -125.48 -36.3 -8.64 -151.15 -6.81 Chemicals 0 0 0 -12.98 -0.58 Power -4.26 -0.02 0 -5.45 -0.15 Water -0.84 0 0 0 0 Total cost of waste -143.34 -36.32 -8.64 -169.58 -7.53 mance assessment of the manufacturing system. The MEIO formalisation method has avoided redundancies in data collection, processing, and conditioning activities and has allowed data alignment. The MEIO method provides several additional insights neglected by the simple use of MSM and MFCA. For instance, it shows the improve- ment opportunities through new layout con gurations highlighted by the RA MEIO graph and the match between activity producers and consumers of the same resource. Also, it shows that dissipated heat may be used to reduce the humidity content of the valuable resources to reduce the misconception of losing valuable materials from an activity to the other. Furthermore, the MEIO method improves the ndings of the other methods; for example, it identi es that the Shredded, humid mix has a consistent amount of water that decreases from an activity to the other, reducing valuable resource quantity, later quanti ed by the MFCA. Moreover, the MEIO method highlights that the power required by the conveyor belt is proportional to the weight of the material that it conveys, and the MFCA quanti es the inecient consumption of power caused by the water weight together with the nished product. 6. Conclusion This paper proposes a new method, the Multi-layer Enterprise Input-Output (MEIO), which integrates the MSM with a combination of MFA and EIO. MEIO allows to develop a shared architecture that can be used to support the simultaneous assessment 20 of techno-economic-environmental performance and value creation of manufacturing systems. The method allows to aggregate the ndings of other methods focused on a subset of dimensions, thus reducing redundancies in data collection, processing and conditioning while improving data formalisation. The alignment of environmental, technical, economic, and value creation informa- tion between operational and strategical levels is crucial to avoid decision making with obsolete or incomplete data. Hence, the method might support decision-makers in production planning and control of complex production systems, especially those focused on 6Rs strategies. Moreover, the MEIO method can integrate data from real manufacturing system and nominal data of new processes, to support the strategi- cal decision making in the system design phase. Speci cally, as it considers techno- economic-environmental performance, it can be used for the monitoring and the ex- tension of industrial symbiotic networks, in which the waste of a company becomes the raw material of another. MEIO can also be an easy to use method to allow companies (currently focused only on technical performance) to investigate their economic and environmental per- formance, showing them how crucial they are. In fact, the relationships between sys- tem performance and all the identi ed resources, including waste and by-products, are as crucial as those involving nished products; thus, there is no more privileged resource path to be analysed. The MEIO method enhances the rapid implementation of tools currently used to investigate technical performance on nished products, such as MSM, by rearranging the focus from the primary nished products to the other critical materials. This study follows a static data-driven approach, however, the MEIO tables can be automatically updated with routines connected to Programmable Logic Controllers, Manufacturing Execution Systems, and ERP modules. Moreover, MEIO can support other methods through the shared architecture, or it can be used alone to develop KPIs to monitor manufacturing system performance. It can also be combined with other digital models (such as in Cyber-Physical Systems and Decision Support Systems based on optimisation and simulation models) to consider value creation and techno- economic-environmental performance simultaneously. However, this great exibility needs an e ort to customise the use combined with each of the di erent approaches. Future research directions start from the limitations of this paper. The RF MEIO table can be improved to consider also multivariate functions; in fact, the production and the absorption of a resource can depend on the production and consumption of more than one other. Modern production systems involve 'plug and play 'machines and robots able to change their roles according to contingent situations. Thus, the methods exploiting a priori knowledge of the system can rapidly become obsolete; further research can be devoted to the development of data-driven strategies to auto- matically identify and update new activities. Moreover, the update process of MEIO tables requires further studies to identify and distinguish the occurrence of failures or exceptional events from the detection of trends that modify the activity parameters.