Keywords

1 Motivation for Research

As a part of the digitalization of production systems, the use of Industry 4.0 technologies (for example digital twins, cyber-physical systems and cyber-physical production systems) offers a wide range of options for optimizing manufacturing processes [1,2,3]. In addition to improving product and process quality, predictive maintenance or self-controlled production systems, one of these options is to increase the (resource) efficiency [4, 5]. In general, the use of digitalization technologies has the potential to raise efficiency up to 50% and save costs and resources in production [6, 7]. There are several approaches that focus on increasing resource efficiency by using digitalization technology [8,9,10]. Mabkhot et al. 2021 analyzed that digitalization and industry 4.0 technology can contribute to achieving the sustainability development goals of the UN (i.a. industry, innovation and infrastructure; climate action; responsible consumption and production) [11]. Most of these methods and studies represent the development, the use or the implementation in the manufacturing process and only evaluate the potential savings [12]. However, the used technologies, in turn, generate environmental impacts during their production, operating and recycling phases. A comprehensive evaluation of this in relation to the savings is not considered but needed in case of the current raw material situation and climate change.

2 State of Research

There exist various approaches and methods that focus on resource efficiency and digitalization. A significant part of them handles the advantages that digitalization brings with it to increase resource efficiency, relates to the use of specific technologies and describes the possible saving potential [13,14,15,16,17]. A holistic assessment about a comparison of the saving possibilities with the resources to be used only takes place in exceptional cases [18]. Thiede 2018 and Schehbeck et al. 2017 describe methods to holistically assess environmental impact of digitalization technologies [19, 20]. Thiede 2018 focuses on cyber-physical production systems (CPPS) and describes that their use leads to increasing environmental pollution. Therefore, a holistic assessment of the environmental impact is necessary. This environmental pollution is offset by possible positive effects. To this problem, Thiede compares manufacturing systems before and after the use of CPPS, calculates an environmental break-even point and forecasts the different environmental impacts of the two systems. Thiede made assumptions for the calculation and did not carry out a holistic evaluation (cradle-to-cradle). He recommends expanding the methodology accordingly [19]. Based on the VDI 4800, Schebeck et al. 2017 conducts various case studies about resource efficiency through digitalization. To measure sustainability, comprehensive eco-balance studies are necessary, which provide a comprehensive picture of the environmental impact. If possible, both the positive and the negative environmental impacts are compared and quantified in the case studies. A consistent and holistic accounting does not take place [20].

There is a need to develop methods for holistic balancing, which consider all life cycle phases of the relevant technology. In addition, approaches are needed that are based on existent key figures and ensure applicability in the industrial environment, so that statements can be made about the ecological sense of an investment. Addressed scaling problems (expansion of digitalization technology) and the simplified representation of linear saving curves should be considered.

3 Methodological Approach

A sustainable production is characterized as a system that improves positive and reduces negative environmental parts [21]. For a holistic evaluation of investment decisions in digitalization technology, the achievable benefit and the related negative effort, must be considered. The benefit will be generated in the manufacturing systems in which the digitalization technology is used. For example, Benchmarking and KPI-based monitoring can reduce energy consumption, increase product quality (reduce reject rate) or reduce CO2-emissions [10]. The effort arises from the production, transport, operation and recycling of the digitalization components (Fig. 1).

Fig. 1.
figure 1

Effort and benefit during the life cycle

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The theoretical assumption is based on the simplified approach of the linear effort and benefit curves. In practice, different curves are to be expected over the life cycle. For the benefit system a declining and a progressive curve can be possible. A declining curve progression occurs in systems with a previously low (or no) degree of digitalization. In these systems, there is a large increase in benefit right at the start, which comes up to a saturation over the operating time (quick wins). For systems that follow a learning effect, a progressive benefit curve is to be expected. At the beginning there are small savings, which increase during the learning effect. The curve progression influences the ecological amortization period. Compared to the linear savings, a declining curve has a positive effect on the payback period and a progressive curve has a negative effect. A 3-stage scheme is proposed to evaluate the ecological advantages of a digitalization investment.

Step 1: Defining the Framework Conditions

To determine the environmental impacts of the technology, the environmental impact categories and the specific system boundaries for the effort and benefit system must be defined in step one. Only those categories that are influenced by both systems are suitable as influence categories. On the one hand, savings must be possible during the use of the technology in the benefit system and on the other hand an assessment by the effort system must be possible, too. For the following explanations, the global warming potential, the cumulative energy consumption and the cumulative consumption of raw materials are considered. Other suitable categories for consideration must be verified separately but can be useful. In addition to the influence categories, a specific system boundary must be defined for each of the two systems in accordance with the requirements of DIN EN ISO 14040/44 [22]. For the benefit system, an isolated consideration of the specific manufacturing process is recommended. A comprehensive consideration of the effort-system, with all relevant environmental impacts along the product life cycle from production to recycling (cradle to cradle) is essential for the holistic assessment.

Step 2: Calculation of the Environmental Impact

The environmental impacts are calculated separately for the categories selected in stage one. To calculate the cumulative values, the maximum lifetime of the digitalization technology is assumed to be 10 years (requirement: remaining lifetime of the production machine to be digitalized > 10 years). The impact categories can be calculated with the life cycle assessment method in accordance with DIN EN ISO 14040/44 [22]. To calculate the effort of the digitalization technology, the fixed effort from production and from recycling (positive or negative) and the variable effort during the operating time (depending on the operating hours of the technology) must be considered as shown in Fig. 1. The benefit depends on possible savings accumulated over the defined total operating time. Different scenarios can be created for different saving/benefit levels. The savings are calculated from the difference between the consumption or generation of environmental impacts before the digitalization and the change after the digitalization.

Step 3: Evaluation of the Ecological Sense

The determined efforts and benefits can be constituted by key figures and enable an evaluation or comparison of the investments in digitalization technology. Two key figures are combined for the presented methodology. An essential feature of the selected key figures is their suitability for decision support and control to provide decision-makers with the essential information in a targeted and understandable manner [23,24,25,26,27]. Using the key figure resource efficiency, the benefit level can be calculated. This marks the ecological limit point, from when digitalization technology is ecologically beneficial [28].

$$ Ressource \;\;efficiency = \frac{benefit}{{effort}} $$
(1)

Within this perspective, three scenarios are possible (Fig. 2).

Fig. 2.
figure 2

Resource Efficiency Scenarios

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Depending on the scenario, a pre-selection can be made as to whether an investment makes sense. A further specification or a ranking between different investment alternatives is provided by the key figure ecological repayment period [29].

$$ Ecologiacl\;repayment\;period = \frac{{damage\;caused{ }\;by\;{ }the\;investment}}{{annual\;pollutant{ }\;savings\;during\;use}} $$
(2)

Once the key figures have been formed, a statement can be made on the ecological sense and a recommendation given regarding the investment in digitalization technology. After the investment has been made, a target-performance comparison of the calculated and the actual values is recommended. In case of discrepancies can be counteracted accordingly or the database can be improved for further decisions.

4 Case Study on a Woodworking Process

The Technical University of Rosenheim operates the production tomorrow laboratory (proto_lab). This consists of industrial furniture production and includes three wood-processing machines (panel saw, edge banding machine and CNC processing center) [30]. This wood-technical production process (benefit system) was subsequently equipped with digitalization technology (effort system) and (costs), energy and resources can be saved. The upgrade includes a hardware measuring system (per machine), data-transmission, -storage and -evaluation. The created cyber-physical system records data every 250 ms (energy, compressed air and exhaust air, machine data and data from the Manufacturing-Execution-System) and evaluates it (Fig. 3).

Fig. 3.
figure 3

Holistic environmental assessment of the Case study

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To calculate, saving scenarios were generated (by using the cyber-physical system) for greenhouse gas emissions and electrical energy (including compressed air and exhaust air, normalized to electrical energy) in steps of 5% - 25%. Therefore, it was assumed that the production (including all upstream chains) of the components takes place in China. The system requires 10.5 W of electrical energy per hour to operate. Operation only takes place together with the production process in a two-shift working period and includes 36,800 operating hours (including production time, set-up time, downtime, disruption time). This results in a total consumption of 38.5 kWh during the usage phase (maintenance-free system). Recycling takes place within a radius of 100 km from the production plant. A full disassembly analysis was realized to analyze the system. During the use of the cyber-physical system 280 kg of CO2-equivalent are generated. In contrast to this, there are 29,000 kg of CO2-equivalent that are generated during the entire service life of the production. The resource efficiency is positive for all saving scenarios (assuming a linear progression). With the current data, no curve slope can be calculated for the progressive and for declining curves. In the manufacturing process under consideration, the savings are achieved by reducing compressed air consumption and optimizing the manufacturing program for drilling and milling. In addition, further savings can be achieved by monitoring the saw blade (reduction of rejects). To evaluate this, the cumulative consumption of raw materials should be considered. For the 5% saving, the assumption of the linear progression is correct. This could be confirmed by test runs. Savings over 10% require new digitalization-technology, such as artificial intelligence applications (planned). These lead to the expectation of a progressive curve, which entails a changed ecological amortization period as well as changing effort and benefit curves. If only the GWP is considered, the installed digitalization-technology (under the assumptions considered) is advantageous.

5 Conclusion and Further Work

The presented methodology allows to evaluate digitalization investments according to their ecological usefulness. This is urgently needed due to the current raw material situation and climate change. Using a 3-stage approach, environmental aspects can be included in decisions. In addition to strategic aspects which make digitalization of production necessary, the method offers key figures for ecological evaluation. The evaluation on a research demonstrator confirms the practicability of the method. The methodology should be critically examined and further developed from the following aspects.

Due to the currently possible consideration of the GWP, a holistic assessment takes place, but decisions regarding the usefulness can only be made one-dimensionally. An expansion of the consideration to include several impact categories is planned. In addition, a decision matrix is required that allows statements to be made about investment-decisions and -comparisons, including economic criteria.

For industrial applications, the consideration of the effort system must be expanded to include software-related influences. Otherwise, a comprehensive consideration and decision is not possible. This allows questions regarding IT capacities (internal vs. external, central vs. decentralized, server vs. cloud). First activities that show the environmental influences of IT-technology already exist [31,32,33].

The presented methodology was tested and evaluated in the research environment. A planned further development and evaluation should take place in the industrial environment (series production). Solutions for the allocation of non-productive machine times should be developed. In addition, for practicality it is necessary to collect data that can be used to create standardized effort models. This would significantly reduce the calculations effort.

Furthermore, the savings and expenses are calculated based on the current situation. Regarding changing framework conditions in the future, there is a need of considerations of dynamic calculation (like discounting in cost accounting) [34].