Digital technologies, methods and tools towards sustainable manufacturing: Does Industry 4.0 support to reach environmental targets?

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Procedia CIRP 00 (2017) 000–000

www.elsevier.com/locate/procedia

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

28th CIRP Design Conference, May 2018, Nantes, France

A new methodology to analyze the functional and physical architecture of

existing products for an assembly oriented product family identification

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]

Abstract

In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

Keywords: Assembly; Design method; Family identification

1. Introduction

Due to the fast development in the domain of communication and an ongoing trend of digitization and digitalization, manufacturing enterprises are facing important challenges in today’s market environments: a continuing tendency towards reduction of product development times and shortened product lifecycles. In addition, there is an increasing demand of customization, being at the same time in a global competition with competitors all over the world. This trend, which is inducing the development from macro to micro markets, results in diminished lot sizes due to augmenting product varieties (high-volume to low-volume production) [1]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing production system, it is important to have a precise knowledge

of the product range and characteristics manufactured and/or assembled in this system. In this context, the main challenge in modelling and analysis is now not only to cope with single products, a limited product range or existing product families, but also to be able to analyze and to compare products to define new product families. It can be observed that classical existing product families are regrouped in function of clients or features. However, assembly oriented product families are hardly to find.

On the product family level, products differ mainly in two main characteristics: (i) the number of components and (ii) the type of components (e.g. mechanical, electrical, electronical).

Classical methodologies considering mainly single products or solitary, already existing product families analyze the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this

Procedia CIRP 98 (2021) 1–6

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering. 10.1016/j.procir.2021.02.001

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering.

ScienceDirect

Procedia CIRP 00 (2019) 000–000

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of the 28th CIRP Conference on Life Cycle Engineering.

28th CIRP Conference on Life Cycle Engineering

Digital technologies, methods and tools towards sustainable manufacturing:

does Industry 4.0 support to reach environmental targets?

Sebastian Thiede

a

a_{Chair of Manufacturing Systems, Department of Design, Production & Management, Faculty of Engineering Technology, University of Twente, De Horst 2,}

7522LW Enschede, The Netherlands

*Corresponding author. Tel.: +31534892907. E-mail address: [email protected]

Abstract

Sustainability and digitalization are both major change drivers for manufacturing companies. There are also strong interactions in between, e.g., through digital technologies, methods and tools that aim to improve the environmental performance of manufacturing related activities. Thereby also additional efforts for establishing information and communication technologies (ICT) need to be taken into account. Against this background, a reference framework is introduced in this paper based on the necessary factory system understanding and the identification of relevant fields of action. Furthermore, methodological support is provided in order to foster the development of digital solutions that actually lead to environmentally related improvements.

Keywords: sustainable manufacturing; industry 4.0; information and communication technology

1. Introduction

Environmental sustainability is an important change driver for industry nowadays which is fostered by rising social awareness and pressure, economic consequences and stronger regulations. Climate change is one of the major environmental challenges and is strongly driven by human caused greenhouse gas emissions (GHG). While also playing a decisive role for economic and social welfare, industry has strong relevance in this context. More than transportation, agriculture and buildings, industry is the sector with the highest contribution and responsible for almost a third of global GHG [1]. This can be split up into 21% of direct emissions (largely through burning of fossil fuels, e.g., for process heating, and also process emissions) but also – through its electricity and steam demand covered by external power plants – for further 11% of indirect emissions [1].

Besides sustainability, ongoing digitalization is another major change driver. Typically associated with terms like

Industry 4.0, Smart Manufacturing or industrial internet/IoT, information and communication technologies (ICT) are on the rise in factories and impact production in manifold ways [2]. This involves innovative technologies (e.g. for sensoring, communication, data processing, visualisation) but also a variety of digital methods and tools that shall improve planning and operation of manufacturing. Besides targeting customized production and lowering throughput times, also energy and resource efficiency is one of the addressed objectives [2][3].

However, this is not an automatic and easy objective to reach while digitalization also leads to higher environmental impacts and is therefore “part of the solution, but also part of the problem” [4]. Thus, the question remains whether and, if yes, where and how digital technologies, methods and tools can actually contribute best to environmental sustainability in manufacturing. Given that, the paper intentionally focuses on this environmental perspective (in particular on energy demand and related GHG) and is structured into four main parts: first, the most relevant fields of action towards sustainability in

Available online at www.sciencedirect.com

ScienceDirect

Procedia CIRP 00 (2019) 000–000

28th CIRP Conference on Life Cycle Engineering

Digital technologies, methods and tools towards sustainable manufacturing:

does Industry 4.0 support to reach environmental targets?

Sebastian Thiede

a

a_{Chair of Manufacturing Systems, Department of Design, Production & Management, Faculty of Engineering Technology, University of Twente, De Horst 2,}

7522LW Enschede, The Netherlands

*Corresponding author. Tel.: +31534892907. E-mail address: [email protected]

Abstract

Sustainability and digitalization are both major change drivers for manufacturing companies. There are also strong interactions in between, e.g., through digital technologies, methods and tools that aim to improve the environmental performance of manufacturing related activities. Thereby also additional efforts for establishing information and communication technologies (ICT) need to be taken into account. Against this background, a reference framework is introduced in this paper based on the necessary factory system understanding and the identification of relevant fields of action. Furthermore, methodological support is provided in order to foster the development of digital solutions that actually lead to environmentally related improvements.

Keywords: sustainable manufacturing; industry 4.0; information and communication technology

1. Introduction

Environmental sustainability is an important change driver for industry nowadays which is fostered by rising social awareness and pressure, economic consequences and stronger regulations. Climate change is one of the major environmental challenges and is strongly driven by human caused greenhouse gas emissions (GHG). While also playing a decisive role for economic and social welfare, industry has strong relevance in this context. More than transportation, agriculture and buildings, industry is the sector with the highest contribution and responsible for almost a third of global GHG [1]. This can be split up into 21% of direct emissions (largely through burning of fossil fuels, e.g., for process heating, and also process emissions) but also – through its electricity and steam demand covered by external power plants – for further 11% of indirect emissions [1].

Besides sustainability, ongoing digitalization is another major change driver. Typically associated with terms like

Industry 4.0, Smart Manufacturing or industrial internet/IoT, information and communication technologies (ICT) are on the rise in factories and impact production in manifold ways [2]. This involves innovative technologies (e.g. for sensoring, communication, data processing, visualisation) but also a variety of digital methods and tools that shall improve planning and operation of manufacturing. Besides targeting customized production and lowering throughput times, also energy and resource efficiency is one of the addressed objectives [2][3].

However, this is not an automatic and easy objective to reach while digitalization also leads to higher environmental impacts and is therefore “part of the solution, but also part of the problem” [4]. Thus, the question remains whether and, if yes, where and how digital technologies, methods and tools can actually contribute best to environmental sustainability in manufacturing. Given that, the paper intentionally focuses on this environmental perspective (in particular on energy demand and related GHG) and is structured into four main parts: first, the most relevant fields of action towards sustainability in

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manufacturing are identified. Related to that, a reference framework is introduced and potential contributions of digital solutions are structured and shown in a second step. Thirdly, additional ICT induced efforts are taken into account in order to identify the environmental favourability. The paper closes with a brief case study to underline the discussions.

2. Deriving fields of action

2.1. System definition

The industry sector covers different branches which can be broadly differentiated to process industry and discrete manufacturing. Related to that, three main types of manufacturing entities (in a broader sense) can be distinguished which typically build up connected value chains: extractive industry (e.g. mining of iron ore) delivers raw resources to the materials industry (e.g. steel, aluminum, chemical) which converts those into raw materials that can be used in manufacturing and construction in order produce physical goods (e.g. transportation equipment) for customers (Figure 1, [1]). Additionally, used/rejected parts and waste from industry and market is handled through waste industry which brings back remanufacturing products/parts or recycled materials. As shown in Figure 1 (based on [5]), very significant quantities of energy are needed at all stages which lead to respective energy related emissions (plus some direct process emissions e.g., through chemical reactions). Materials industry is clearly leading here. This underlines the important interrelation of material and energy demand (e.g., with embodied energy of material as important indicator) in context of environmental impact. As already pointed out also by other authors, saving material can avoid upstream energy related emissions [6].

Figure 1: Simplified overview of manufacturing value chain and related energy demand (based on [1][5]).

Industrial activities typically take place within dedicated facilities (factories) which are in focus of this work. Factories consist of three subsystems: production equipment, technical building services (for internal energy conversion, providing production conditions like e.g. temperature, humidity) and building shell. Those subsystems are connected through energy, material and information flows [7]. Material flows consist of raw and auxiliary materials, waste streams and (semi)finished products. In terms of energy, different energy carriers (e.g., electricity, gas, compressed air) are externally acquired or internally generated/converted. Information flows allow monitoring and therewith planning and control of operations.

2.2. Energy flows in industry

As indicated, over the whole industrial value chain energy demand is ultimately the major cause for industrial emissions.

According to the system definition given above, process and non-process energy can be distinguished. Process energy is the amount of energy that is directly used in the value adding process, typically in form process heating and cooling, electro-chemical or mechanical energy (e.g., electrical drives) [8]. Non-process energy is needed for supporting industrial activities and often related to the technical building services of factories, e.g., space heating/cooling or lighting play an important role here.

Based on previous definitions and data from the US industry, Figure 2 gives in the upper table an overview of the absolute energy demand (in Btu) in industry which is split up according to different sectors and also energy usage patterns. This underlines the overarching priority fields of action with strong leverage on total energy demand. The lower table shows the relative composition of the energy demand within the different sectors. With that, energy demand patterns and priorities for individual sectors are put in focus.

Figure 2: Absolute (in Btu) and relative energy demand patterns in US manufacturing (own illustration based on [8]).

Some important points can be derived from these overviews that are certainly transferable to other economies as well: • Process heating demands by far the most energy in the

manufacturing industry and particular in process industry. • Facility HVAC (heating, ventilation, air conditioning)

follows as second most important field of action.

• While some general tendencies can be found, also differences among the sectors but especially among process industry (strong dominance of process heating) and discrete manufacturing (priority on HVAC, lighting, processing) become clear. Case specific analyses are strongly recommended in order to really identify fields of action with highest leverage.

While this overview allows the derivation of priorities for further actions, it also shows less important leverages on energy

demand. This is of course subject of company specific circumstances, e.g., onsite transportation is certainly one of the least relevant areas from overall perspective but might still be more relevant for the individual case. Careful interpretation is necessary since this energy data provides an aggregated perspective and leaves out the types of energy carriers (such as fuel, steam, electricity) involved. Since e.g., electricity causes much more upstream losses for the generation, it is typically more expensive and might cause higher environmental impacts. To put this into perspective, Figure 3 brings together the absolute primary energy of the sectors with its ratio to the actually applied energy which then reflects energy losses during generation and usage. The diagram underlines that process industry demands more energy (as shown before) but relatively less electricity - therewith more efficient energy usage compared to discrete manufacturing can be observed. Still, the overall low efficiency calls for careful analysis and improvements. In particular for electricity also measures that support substitution strategies are very relevant in order to foster the use of electricity based on renewable sources.

Figure 3: Primary energy demand of sectors and relation to actually applied energy in manufacturing sectors [8].

Besides this aggregated perspective on energy demand also time related aspects need to be considered. The energy demand of factory objects and factory as a whole is not constant but dynamic depending on e.g. operational state. In this context, energy base load is a relevant aspect – e.g., in factories even without value creating production around 50-60% of the energy is used due to active TBS processes and idle energy demand of production [9]. The dynamics of energy demand also play a role when it comes to ensure reliable energy supply as well as improving the economic and environmental impact (e.g., peak avoidance, alignment with renewable energy supply) [10].

2.3. Fields of action

Based on the previous sections, three interrelated fields of action towards more environmentally sustainable manufacturing can be identified (also in line with e.g. [1]): • Improving the energy efficiency as ratio of product output

and the energy input, e.g., through selection of energy efficient technologies, proper dimensioning or better process/factory control, e.g., for avoiding idle losses.

• Improving material efficiency reduces upstream energy demand and related environmental impact in the materials industry, e.g., through avoiding waste, increased quality rate (less rejects) and better utilization as well as recycling. • Measures that support the substitution (or effectiveness) strategy for energy sources (towards switching to renewable resources) but also materials (which cause less environmental impact in the upstream value chain). From energy perspective, improving energy flexibility of equipment and factories is an important aspect. Material substitution is strongly related to the product design – while this is not the main focus of this paper, measures that support to deal with related implications in manufacturing are important. Even more, the substitution of auxiliary materials in processes (e.g. coolants) is relevant.

3. Structuring digital technologies, methods and tools

As mentioned before, “Industry 4.0” is a strong change driver in manufacturing. Cyber physical (production) systems are a core element and based on continuous acquisition of data and intelligent models that are embedded in decision support or automatic control systems [11]. Literature distinguishes between four different Industry 4.0 maturity levels for characterizing such digital solutions (understood here as a combination of ICT technologies and related methods/tools). Those levels range from creating visibility (visualizing data), over transparency (analyzing root causes) up to prediction (considering future scenarios) and ultimately automated adaption functionalities [12]. Higher maturity levels are associated with increasing benefits but also more complexity and efforts. Bringing together those functionalities with the derived fields of action and factory levels results in the reference framework shown in Figure 4. Within the boxes also the use scenario (planning/operation) is indicated. This framework gives an overview of all combinations and allows a systematic structuring and assessment of digital solutions.

Figure 4: Reference framework for digital solutions towards sustainable manufacturing.

For deeper understanding of root causes, prediction as well as adaption functionalities appropriate digital models are necessary. Those could be based on a variety of modelling of paradigms, ranging from rather black box (data based/top down) to white box approaches (detailed physical modeling/bottom up). Both perspectives have benefits and

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manufacturing are identified. Related to that, a reference framework is introduced and potential contributions of digital solutions are structured and shown in a second step. Thirdly, additional ICT induced efforts are taken into account in order to identify the environmental favourability. The paper closes with a brief case study to underline the discussions.

2. Deriving fields of action

2.1. System definition

The industry sector covers different branches which can be broadly differentiated to process industry and discrete manufacturing. Related to that, three main types of manufacturing entities (in a broader sense) can be distinguished which typically build up connected value chains: extractive industry (e.g. mining of iron ore) delivers raw resources to the materials industry (e.g. steel, aluminum, chemical) which converts those into raw materials that can be used in manufacturing and construction in order produce physical goods (e.g. transportation equipment) for customers (Figure 1, [1]). Additionally, used/rejected parts and waste from industry and market is handled through waste industry which brings back remanufacturing products/parts or recycled materials. As shown in Figure 1 (based on [5]), very significant quantities of energy are needed at all stages which lead to respective energy related emissions (plus some direct process emissions e.g., through chemical reactions). Materials industry is clearly leading here. This underlines the important interrelation of material and energy demand (e.g., with embodied energy of material as important indicator) in context of environmental impact. As already pointed out also by other authors, saving material can avoid upstream energy related emissions [6].

Figure 1: Simplified overview of manufacturing value chain and related energy demand (based on [1][5]).

Industrial activities typically take place within dedicated facilities (factories) which are in focus of this work. Factories consist of three subsystems: production equipment, technical building services (for internal energy conversion, providing production conditions like e.g. temperature, humidity) and building shell. Those subsystems are connected through energy, material and information flows [7]. Material flows consist of raw and auxiliary materials, waste streams and (semi)finished products. In terms of energy, different energy carriers (e.g., electricity, gas, compressed air) are externally acquired or internally generated/converted. Information flows allow monitoring and therewith planning and control of operations.

2.2. Energy flows in industry

As indicated, over the whole industrial value chain energy demand is ultimately the major cause for industrial emissions.

According to the system definition given above, process and non-process energy can be distinguished. Process energy is the amount of energy that is directly used in the value adding process, typically in form process heating and cooling, electro-chemical or mechanical energy (e.g., electrical drives) [8]. Non-process energy is needed for supporting industrial activities and often related to the technical building services of factories, e.g., space heating/cooling or lighting play an important role here.

Based on previous definitions and data from the US industry, Figure 2 gives in the upper table an overview of the absolute energy demand (in Btu) in industry which is split up according to different sectors and also energy usage patterns. This underlines the overarching priority fields of action with strong leverage on total energy demand. The lower table shows the relative composition of the energy demand within the different sectors. With that, energy demand patterns and priorities for individual sectors are put in focus.

Figure 2: Absolute (in Btu) and relative energy demand patterns in US manufacturing (own illustration based on [8]).

Some important points can be derived from these overviews that are certainly transferable to other economies as well: • Process heating demands by far the most energy in the

manufacturing industry and particular in process industry. • Facility HVAC (heating, ventilation, air conditioning)

follows as second most important field of action.

• While some general tendencies can be found, also differences among the sectors but especially among process industry (strong dominance of process heating) and discrete manufacturing (priority on HVAC, lighting, processing) become clear. Case specific analyses are strongly recommended in order to really identify fields of action with highest leverage.

While this overview allows the derivation of priorities for further actions, it also shows less important leverages on energy

demand. This is of course subject of company specific circumstances, e.g., onsite transportation is certainly one of the least relevant areas from overall perspective but might still be more relevant for the individual case. Careful interpretation is necessary since this energy data provides an aggregated perspective and leaves out the types of energy carriers (such as fuel, steam, electricity) involved. Since e.g., electricity causes much more upstream losses for the generation, it is typically more expensive and might cause higher environmental impacts. To put this into perspective, Figure 3 brings together the absolute primary energy of the sectors with its ratio to the actually applied energy which then reflects energy losses during generation and usage. The diagram underlines that process industry demands more energy (as shown before) but relatively less electricity - therewith more efficient energy usage compared to discrete manufacturing can be observed. Still, the overall low efficiency calls for careful analysis and improvements. In particular for electricity also measures that support substitution strategies are very relevant in order to foster the use of electricity based on renewable sources.

Figure 3: Primary energy demand of sectors and relation to actually applied energy in manufacturing sectors [8].

Besides this aggregated perspective on energy demand also time related aspects need to be considered. The energy demand of factory objects and factory as a whole is not constant but dynamic depending on e.g. operational state. In this context, energy base load is a relevant aspect – e.g., in factories even without value creating production around 50-60% of the energy is used due to active TBS processes and idle energy demand of production [9]. The dynamics of energy demand also play a role when it comes to ensure reliable energy supply as well as improving the economic and environmental impact (e.g., peak avoidance, alignment with renewable energy supply) [10].

2.3. Fields of action

Based on the previous sections, three interrelated fields of action towards more environmentally sustainable manufacturing can be identified (also in line with e.g. [1]): • Improving the energy efficiency as ratio of product output

and the energy input, e.g., through selection of energy efficient technologies, proper dimensioning or better process/factory control, e.g., for avoiding idle losses.

• Improving material efficiency reduces upstream energy demand and related environmental impact in the materials industry, e.g., through avoiding waste, increased quality rate (less rejects) and better utilization as well as recycling. • Measures that support the substitution (or effectiveness) strategy for energy sources (towards switching to renewable resources) but also materials (which cause less environmental impact in the upstream value chain). From energy perspective, improving energy flexibility of equipment and factories is an important aspect. Material substitution is strongly related to the product design – while this is not the main focus of this paper, measures that support to deal with related implications in manufacturing are important. Even more, the substitution of auxiliary materials in processes (e.g. coolants) is relevant.

3. Structuring digital technologies, methods and tools

As mentioned before, “Industry 4.0” is a strong change driver in manufacturing. Cyber physical (production) systems are a core element and based on continuous acquisition of data and intelligent models that are embedded in decision support or automatic control systems [11]. Literature distinguishes between four different Industry 4.0 maturity levels for characterizing such digital solutions (understood here as a combination of ICT technologies and related methods/tools). Those levels range from creating visibility (visualizing data), over transparency (analyzing root causes) up to prediction (considering future scenarios) and ultimately automated adaption functionalities [12]. Higher maturity levels are associated with increasing benefits but also more complexity and efforts. Bringing together those functionalities with the derived fields of action and factory levels results in the reference framework shown in Figure 4. Within the boxes also the use scenario (planning/operation) is indicated. This framework gives an overview of all combinations and allows a systematic structuring and assessment of digital solutions.

Figure 4: Reference framework for digital solutions towards sustainable manufacturing.

For deeper understanding of root causes, prediction as well as adaption functionalities appropriate digital models are necessary. Those could be based on a variety of modelling of paradigms, ranging from rather black box (data based/top down) to white box approaches (detailed physical modeling/bottom up). Both perspectives have benefits and

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drawbacks – a case specific selection of the most appropriate approach is necessary depending on requirements like accuracy, computational speed, available data or use scenario. As one excerpt of the framework, Figure 5 gives a selective overview of digital solutions in the three main fields of action. Further explanations are given in the next sections.

Figure 5: Selected digital solutions in context of sustainable manufacturing. 3.1. Energy Efficiency

During the planning of production, TBS or factories as a whole, digital methods and tools can play an important role towards more energy efficient solutions. The selection of the most efficient technologies and the correct dimensions according to the designated working tasks can be seen as major leverage for improvement. Studies underline improvement potentials in the range of 20-25% through applying commercially available best practice technologies [1][13][14], further potential 20% are estimated through innovations in the future [1]. This applies to both direct production equipment but also strongly to the technical building services as well as the connection in between. For planning, e.g., simulation can be used on different levels or as combination in multiscale factory simulation approaches [15]. Also data based methods are relevant, e.g. machine learning can be used to systematically identify best practices among production machines or production areas within a company. Other approaches support to analyze the most influencing factors on energy demand [16] [17]. With that, decision support can be given for the strategic (re)planning of factories or dedicated production areas.

More relevant use cases can be found when it comes to support in operation. Based on up to real-time data streams and intelligent models, the parameters of running processes (e.g., temperature setting, machine speeds) can be ideally adapted according to the current production situation and the requirements of the product. Studies mention saving potentials over all industry sectors of 5-15% in the area of process individual but specifically for more integrated control approaches [14]. Another promising example is the reduction of energy base load of production machines and factories [9]. Digital solutions can support here through analyzing the current and short-term future state of production as well as through

deriving and potentially even executing shutdown strategies. For those measures technical building services (TBS) are a feasible and promising application domain, e.g., cooling towers [18]. TBS related “non-process” energy can be a very relevant leverage for improvement and typically strongly contribute to the base load. Due to their technical setting (e.g., often over dimensioned, flexible control possible) and the just indirect coupling to the actual value adding process chain, TBS typically also allow some more degree of freedom for changes.

3.2. Substitution of energy sources and materials

The substitution to renewable energy sources is a powerful strategy that can significantly reduce the energy related GHG emissions. Over the last years proactive strategies have become more relevant for industry while companies produce own renewable based energy and/or become an active part of the energy market. Besides potentially providing energy from own (renewable) sources to the market, also the concept energy flexibility is of increasing interest. Within that, companies dynamically change their energy demand behavior depending on e.g., the market price and/or the availability of (either onsite or grid) renewable energy [19]. That can be beneficial from both environmental but also cost side. Again, in planning simulation can support to derive feasible setups of the company energy grid with all involved technical entities (e.g., also storage capacity). For the operation phase, digital support is needed for forecasting and aligning energy supply and demand (e.g., through control of machines), ideally without harming the production output [10]. From material perspective, modelling approaches could be used in planning and operation to predict the impact of substitution e.g. for product related but also auxiliary materials (e.g. for cuttings fluids [20]).

3.3. Material efficiency

As for material efficiency, improvement potentials can be found in avoiding waste materials or quality rejects as well as fostering recycling concepts. There is also a strong connection to other manufacturing fields of action like quality management. In the planning phase, digital methods can support to derive material efficient designs, e.g., for casting processes where losses through necessary gating systems or sprues can be minimized [21]. Advanced nesting methods can improve material efficiencies, e.g., in metal sheet cutting or additive manufacturing. Also in operation, digital methods can help e.g. to derive root causes of material inefficiencies and low quality rates. Furthermore, soft sensor approaches (virtual metering point) can be established which predict the product properties based on current process parameters and state variables. Without too much additional effort for physical measurements, this allows a dense monitoring of quality, avoids further processing of low-quality parts or even enables the adaption of downstream production processes [22].

4. Balancing benefits and efforts

As indicated, there are manifold digital solutions that can potentially support to significantly improve the environmental

impact of manufacturing. However, that comes to a price since additional efforts for ICT are occurring. In general, studies underline the strongly increasing energy demand connected to ICT applications. In 2030, ICT is expected to make up approx. 21% (in relation to approx. 11% in 2020) of the global electricity demand, with an uncertainty in a range of 8% (best case scenario) to 51% (worst case scenario) [23]. These astonishing numbers are caused by strongly increasing demand for network access, ICT devices and data centers but also the related effort for producing those components. Similar developments can be expected within the manufacturing domain. In order to avoid rebound effects, potential environmental improvements of innovative solutions need to be balanced with additional ICT related efforts. For digital solutions in manufacturing this involves e.g., sensors, servers and computers for data gathering and processing, communication infrastructure as well as visualization or control devices (Figure 6 and [11]). For considering ICT induced benefits and efforts, studies are scarce and partly ambivalent in their results but altogether net savings at least from longer term perspective seem to be possible [24] [25] [26]. In the end, a case specific feasibility analysis is necessary in order to identify the potential environmental breakeven (Figure 6) over time.

Figure 6: Environmental aspects of digital solutions (adapted from [11] with data from [27] [28] [29]).

Some guiding questions can help to identify meaningful application cases:

• _{Relevance and improvement potential: related to the}

analysis in section 2, case specific pre-analysis should be done. Is the intended application relevant enough, does it offer enough technical improvement potential?

• _{Continuity and dynamics of potential improvements:}

often major and easiest improvements can be made based on first analysis (maybe even just with temporary measurements). While ICT continuously causes environmental impact during operation, relevant ICT enabled saving potentials should be given, e.g., is there an added value of continuous data gathering and control?

• _{Influenceability: just providing transparency, e.g., on}

energy demand, is not sufficient - in the end dedicated actions are necessary to seize the potential. Does the

envisioned solution includes appropriate (automated?) mechanisms that actually allow to influence the system?

• ICT configuration and dimensioning: different design

options are available to realize ICT based solutions. As example, data processing could take place locally (edge device), in the company network or via cloud services [30]. What is the most efficient and effective setup for the given case? In this context also the potential multiple use of ICT components for several applications is an important factor. This paper intentionally sets an emphasis on energy demand and connected GHG. There are of course more environmental impacts (e.g. resource depletion, electronic waste). But even more, economic criteria play a decisive role and are often barriers towards implementation. With incorporating both investment and operating costs, digital solutions have to pay off over their life cycle. However, similar thinking and guiding principles can support here in order to derive feasible solutions.

5. Case study

Finally, a brief case study (taken from [11]) shall illustrate and underline the necessary holistic thinking for introducing digital solutions towards more environmentally sustainable manufacturing. A small production system consisting of several machine tools is considered here. A continuous energy monitoring system shall be installed which enables visibility of energy demand and should trigger actions towards energy efficiency. The monitoring systems consists of sensors, computer/server for data processing and visualization devices (tablet). Feasibility diagrams (Figure 7) support to bring together the environmental impact of the manufacturing system (here based on energy demand) and necessary improvements through digital solutions. Isopleths depict breakeven points based on the given ICT setup with its induced environmental impact. In this case, savings of 10-18% are necessary in order to reach an environmental breakeven within 3 years. Even without considering the environmental backpack of ICT components, an improvement of over 7% needs to be realized each year to overcompensate ICT operating energy demand. This can be quite challenging for an established manufacturing system whereas the energy monitoring also does not provide automated control functionalities. This is certainly a specific analysis of a rather small case. However, it underlines the necessity of consideration and the methodology is also directly transferable to other cases. As example, a look in the diagram reveals that even for using the same ICT setup for an extended scope (twice as large) 5-10% of improvements are necessary.

Figure 7: Feasibility diagram (for given example, adapted from [11]).

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drawbacks – a case specific selection of the most appropriate approach is necessary depending on requirements like accuracy, computational speed, available data or use scenario. As one excerpt of the framework, Figure 5 gives a selective overview of digital solutions in the three main fields of action. Further explanations are given in the next sections.

Figure 5: Selected digital solutions in context of sustainable manufacturing. 3.1. Energy Efficiency

During the planning of production, TBS or factories as a whole, digital methods and tools can play an important role towards more energy efficient solutions. The selection of the most efficient technologies and the correct dimensions according to the designated working tasks can be seen as major leverage for improvement. Studies underline improvement potentials in the range of 20-25% through applying commercially available best practice technologies [1][13][14], further potential 20% are estimated through innovations in the future [1]. This applies to both direct production equipment but also strongly to the technical building services as well as the connection in between. For planning, e.g., simulation can be used on different levels or as combination in multiscale factory simulation approaches [15]. Also data based methods are relevant, e.g. machine learning can be used to systematically identify best practices among production machines or production areas within a company. Other approaches support to analyze the most influencing factors on energy demand [16] [17]. With that, decision support can be given for the strategic (re)planning of factories or dedicated production areas.

More relevant use cases can be found when it comes to support in operation. Based on up to real-time data streams and intelligent models, the parameters of running processes (e.g., temperature setting, machine speeds) can be ideally adapted according to the current production situation and the requirements of the product. Studies mention saving potentials over all industry sectors of 5-15% in the area of process individual but specifically for more integrated control approaches [14]. Another promising example is the reduction of energy base load of production machines and factories [9]. Digital solutions can support here through analyzing the current and short-term future state of production as well as through

deriving and potentially even executing shutdown strategies. For those measures technical building services (TBS) are a feasible and promising application domain, e.g., cooling towers [18]. TBS related “non-process” energy can be a very relevant leverage for improvement and typically strongly contribute to the base load. Due to their technical setting (e.g., often over dimensioned, flexible control possible) and the just indirect coupling to the actual value adding process chain, TBS typically also allow some more degree of freedom for changes.

3.2. Substitution of energy sources and materials

The substitution to renewable energy sources is a powerful strategy that can significantly reduce the energy related GHG emissions. Over the last years proactive strategies have become more relevant for industry while companies produce own renewable based energy and/or become an active part of the energy market. Besides potentially providing energy from own (renewable) sources to the market, also the concept energy flexibility is of increasing interest. Within that, companies dynamically change their energy demand behavior depending on e.g., the market price and/or the availability of (either onsite or grid) renewable energy [19]. That can be beneficial from both environmental but also cost side. Again, in planning simulation can support to derive feasible setups of the company energy grid with all involved technical entities (e.g., also storage capacity). For the operation phase, digital support is needed for forecasting and aligning energy supply and demand (e.g., through control of machines), ideally without harming the production output [10]. From material perspective, modelling approaches could be used in planning and operation to predict the impact of substitution e.g. for product related but also auxiliary materials (e.g. for cuttings fluids [20]).

3.3. Material efficiency

As for material efficiency, improvement potentials can be found in avoiding waste materials or quality rejects as well as fostering recycling concepts. There is also a strong connection to other manufacturing fields of action like quality management. In the planning phase, digital methods can support to derive material efficient designs, e.g., for casting processes where losses through necessary gating systems or sprues can be minimized [21]. Advanced nesting methods can improve material efficiencies, e.g., in metal sheet cutting or additive manufacturing. Also in operation, digital methods can help e.g. to derive root causes of material inefficiencies and low quality rates. Furthermore, soft sensor approaches (virtual metering point) can be established which predict the product properties based on current process parameters and state variables. Without too much additional effort for physical measurements, this allows a dense monitoring of quality, avoids further processing of low-quality parts or even enables the adaption of downstream production processes [22].

4. Balancing benefits and efforts

As indicated, there are manifold digital solutions that can potentially support to significantly improve the environmental

impact of manufacturing. However, that comes to a price since additional efforts for ICT are occurring. In general, studies underline the strongly increasing energy demand connected to ICT applications. In 2030, ICT is expected to make up approx. 21% (in relation to approx. 11% in 2020) of the global electricity demand, with an uncertainty in a range of 8% (best case scenario) to 51% (worst case scenario) [23]. These astonishing numbers are caused by strongly increasing demand for network access, ICT devices and data centers but also the related effort for producing those components. Similar developments can be expected within the manufacturing domain. In order to avoid rebound effects, potential environmental improvements of innovative solutions need to be balanced with additional ICT related efforts. For digital solutions in manufacturing this involves e.g., sensors, servers and computers for data gathering and processing, communication infrastructure as well as visualization or control devices (Figure 6 and [11]). For considering ICT induced benefits and efforts, studies are scarce and partly ambivalent in their results but altogether net savings at least from longer term perspective seem to be possible [24] [25] [26]. In the end, a case specific feasibility analysis is necessary in order to identify the potential environmental breakeven (Figure 6) over time.

Figure 6: Environmental aspects of digital solutions (adapted from [11] with data from [27] [28] [29]).

Some guiding questions can help to identify meaningful application cases:

• _{Relevance and improvement potential: related to the}

analysis in section 2, case specific pre-analysis should be done. Is the intended application relevant enough, does it offer enough technical improvement potential?

• _{Continuity and dynamics of potential improvements:}

often major and easiest improvements can be made based on first analysis (maybe even just with temporary measurements). While ICT continuously causes environmental impact during operation, relevant ICT enabled saving potentials should be given, e.g., is there an added value of continuous data gathering and control?

• _{Influenceability: just providing transparency, e.g., on}

energy demand, is not sufficient - in the end dedicated actions are necessary to seize the potential. Does the

envisioned solution includes appropriate (automated?) mechanisms that actually allow to influence the system?

• ICT configuration and dimensioning: different design

options are available to realize ICT based solutions. As example, data processing could take place locally (edge device), in the company network or via cloud services [30]. What is the most efficient and effective setup for the given case? In this context also the potential multiple use of ICT components for several applications is an important factor. This paper intentionally sets an emphasis on energy demand and connected GHG. There are of course more environmental impacts (e.g. resource depletion, electronic waste). But even more, economic criteria play a decisive role and are often barriers towards implementation. With incorporating both investment and operating costs, digital solutions have to pay off over their life cycle. However, similar thinking and guiding principles can support here in order to derive feasible solutions.

5. Case study

Finally, a brief case study (taken from [11]) shall illustrate and underline the necessary holistic thinking for introducing digital solutions towards more environmentally sustainable manufacturing. A small production system consisting of several machine tools is considered here. A continuous energy monitoring system shall be installed which enables visibility of energy demand and should trigger actions towards energy efficiency. The monitoring systems consists of sensors, computer/server for data processing and visualization devices (tablet). Feasibility diagrams (Figure 7) support to bring together the environmental impact of the manufacturing system (here based on energy demand) and necessary improvements through digital solutions. Isopleths depict breakeven points based on the given ICT setup with its induced environmental impact. In this case, savings of 10-18% are necessary in order to reach an environmental breakeven within 3 years. Even without considering the environmental backpack of ICT components, an improvement of over 7% needs to be realized each year to overcompensate ICT operating energy demand. This can be quite challenging for an established manufacturing system whereas the energy monitoring also does not provide automated control functionalities. This is certainly a specific analysis of a rather small case. However, it underlines the necessity of consideration and the methodology is also directly transferable to other cases. As example, a look in the diagram reveals that even for using the same ICT setup for an extended scope (twice as large) 5-10% of improvements are necessary.

Figure 7: Feasibility diagram (for given example, adapted from [11]).

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6. Conclusion and Outlook

The paper addresses the question whether and how digital technologies, methods and tools can support the environmental related improvement in manufacturing. Based on the derivation of three main fields of action and the necessary holistic factory understanding, a reference framework was introduced that allows a structured allocation of available digital solutions. Finally, also related ICT induced efforts need to be incorporated to avoid rebound effects and problem shifting. To overcome this, guiding questions and methodological support (e.g. feasibility diagrams) are introduced.

The mentioned digital solutions are obviously just selective examples. Many more approaches are available in research and industrial practice but can be structured and assessed in similar manner as presented. Additionally, the case study is of course also just an example focusing on a specific case with energy efficiency as considered field of action. Whereas the general methodology is fully transferable, more detail work needs to be done for assessing energy flexibility (e.g., due to the dynamics of demand) and material efficiency (e.g., diversity of potential materials) related impacts. Finally, the intentional focus here is on energy and GHG emissions – while the same principle can be applied, next steps need to incorporate further environmental but also economic and social aspects.

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