Green Configuration combines product configuration technologies with environmental impact calculations and enables customers to balance cost drivers and environmental impact drivers (such as CO2 footprint) for their preferred product variants. We analyse requirements for configurable products that go beyond the state of the art of classical Life Cycle Assessment (LCA), and we list corresponding challenges for configurators, such as missing environmental impact data, total costs over the product life cycle, confidence in data accuracy, performance of the calculation, multi-objective optimisation, comparability of the results, and eficient explanations. To address those challenges, we discuss three architecture variants which go beyond sequentially calling separate tools for configuration and LCA: loosely coupled (where the configurator communicates via parameters with the LCA tool), tightly coupled (where the configurator also manages the basic environmental data and lets the LCA tool calculate the impact values for assemblies), and integrated (where the LCA calculation is implemented as part of the configurator). We find that all architectures rely on complete and reliable input data (which might be synthesised ofline by data-driven AI methods) and have diferent advantages and disadvantages concerning eforts for tool vendors, product modellers, and customers.
With the European Green Deal [
To persist, companies need to document the Product
Carbon Footprint (PCF) or even Product Environmental
Footprint (PEF) of all their products transparently and reliably,
according to valid or forthcoming regulations like the Digital
Product Passport (DPP) [
Product configuration [
The term “Green Configuration” was established a few years ago1 for this enhancement of configuration tools with environmental impact calculations. This gives the user comprehensive information about the specific efects of their decisions. Small changes in configuration can have a significant impact on the ecological footprint. Multi-objective optimisation strategies make it possible to optimise the product configuration according to desired dimensions (financial and sustainable) depending on specific requirements. Furthermore, provisions must be made so that the final product remains in accordance with the increasingly complex legal framework. This afects not only sales configurators (where customers shall see the expected environmental impact and corresponding costs at the point-of-sale, i.e., before they order a product) but is also vital for engineering configurators (which need to prove that the finally manufactured and deployed product keeps the promises of the sales phase to avoid penalties or non-compliance costs).
Wiezorek and Christensen [
In the next section, we will analyse the state of the art of
environmental data and impact calculation. In section 3, we
discuss which challenges arise when this is to be applied
to configurable products. In section 4, we present the main
architectures for green configuration and describe how they
deal with those challenges. Finally, we conclude what this
can mean for configurator vendors.
1The term “Green Configuration” has been used more by CPQ solution
providers than in academia, e.g., by encoway [
Life Cycle Assessment according to ISO 14040 [
The LCAs are based on the product’s Bill of Materials (BOM) and Bill of Processes (BOP) along its life cycle. Most LCAs are done after the final product design when the materials and processes are identified. LCA can also be applied earlier in the design process of configurators to improve design decisions before finalisation.
In customer communication, EPDs are often used to show the results of the LCA. However, the EPDs are based on a specific, fully specified product or – less individually and less precisely – on a representative product, an average (fictive) product, or the worst-case product of a homogenous product family. Thus, they cannot help customers decide on product details or with customer-specific optimisation. In the best case, they can give a rough orientation based on existing LCAs for product representatives or typicals.
As EPDs are used for customer communication,
Product Category Rules (PCR) and Product Specific Rules (PSR)
are defined to provide comparable results [
Besides the insuficient consideration of the customerspecific usage scenario, the broad range of existing background data sets makes it hard to figure out the productspecific environmental performance, as this is influenced by applied LCA data sets as well. The LCA data sets often provide a market average or a representative example process and do not reflect a specific supplier’s product and production-specific environmental impacts. There is still a gap in using primary data along the supplier chain.
Recent initiatives7 target the PCF accounting and
management to improve the primary PCF data share in product
accounting and to provide trusted and reliable data along
the supply chain. However, even for PCF, several standards
and guidelines are in place [
As PCRs and PSRs try to harmonise the environmental impact assessment within one product category, large-scale systems such as rolling stock, production lines, or process technology are composed of products or assemblies with multiple PCRs and PSRs to be applied, which are not necessarily comparable. Inline environmental assessments are required independently of PCRs and PSRs, especially in large-scale system configuration or turnkey projects. Focusing on customer-specific usage conditions will provide tailored results. However, small changes in the conditions may significantly impact the product’s LCA results.
There is little related work concerning combining LCAs
with a dynamic modelling approach to consider
customerspecific usage or to adapt the background database to future
scenarios (cf. Udriot et al. [
Other research reports about work on guidance to
integrate LCAs in general and EPDs in particular into
configurators and its evaluation in the construction sector [
Manufacturing companies need to document not only the PCF but also the PEF (i.e. more environmentally critical substances than just CO2) of all their products transparently and reliably, according to valid or forthcoming regulations like DPP. This must be based on information from suppliers and knowledge about production processes and operations (i.e., usage and end-of-life phases) and includes the selection of suppliers and processes which minimise the overall environmental impact. In addition, economic key performance indicators (KPIs), such as costs for production, transport, usage, disposal, etc. need to be considered and require multiobjective optimisation with good user guidance (including understandable explanations).
The configuration of such an environmentally conscious system is dificult, especially for complex products, because: • Many suppliers are involved, among them many small and medium-sized enterprises (SMEs), which often cannot provide suficiently good documentation on materials and PEF (e.g., several thousand suppliers for parts of metro trains). • Parts have entirely diferent properties as they come from diferent industries such as electrical, engineering, or building technology and may interpret environmental KPIs diferently. • Diferent countries have a wide variety of regulations and certificates (which may even change over time), so diferent solutions (i.e., combinations of components) are necessary. • The environmental impact (e.g., concrete PEF values) depends on the production technologies and locations of the suppliers and the location of deployment and conditions at customer sites. • Sustainability data for many components is missing or questionable, and improvement is dificult as it is out of the control of the system integrator. • The system configuration is often not yet defined in suficient detail at the ofering time, and therefore, the environmental impacts can only be estimated but not precisely calculated. • Adaptations during contract negotiations or after deployment can afect compliance and/or performance and require eficient re-calculation and updating of documentation.
To handle those requirements, we need algorithms and techniques for: • Calculation of all relevant sustainability metrics at point-of-sale: This is not possible in advance (as currently done) because it depends on user decisions, which can lead to billions of potential variants. It must be fast enough to ensure a good user experience and, therefore, requires high performance. • Reliable aggregation of the values of all sub-parts: This includes highly accurate approximations for missing values specific to the current customer selections. For the usage phase, this cannot be based on sub-parts alone (as is currently done) but on the functionality of the whole product or sub-systems. • Guided optimisation of several objectives: It is not suficient to calculate only one (combined, weighted) optimum (as in current tools). The user must be supported in evaluating the Pareto front eficiently and ifnding the best compromise for conflicting goals. • Concise visualisation of the results: This helps the user to easily understand the impacts of their decisions. It shall explain the system’s confidence in its calculations and where to change a decision to achieve a better result (which goes beyond the capabilities of current systems).
In the remainder of the text, we will focus on the following
concrete challenges of Green Configuration:
1. Missing environmental data from suppliers: Many,
especially smaller companies, do not yet disclose
environmental data for their products (partly
because they do not know them themselves). This not
only concerns the supply chain, i.e., the impact of
the production of those sub-parts, but also their
usage and end-of-life processing. To ensure proper
LCA calculation, missing data must be synthesised
as accurately as possible, i.e., by specific
approximations based on machine learning from similar
suppliers and/or components, simulation of
production and/or operation, using intelligent
extrapolation which takes trends into account (e.g., new
versions of components typically get better).
2. Unclear impact data for the usage phase: The
environmental impact is customer- and even
applicationspecific. It depends on the context, such as operating
hours (e.g., whether an engine runs 8 or 24 hours
a day) and energy mix (e.g., how much fossil, how
much wind power or photovoltaic) [
This section presents architectures with increasing degrees of integration, starting with simply using an existing conifgurator and feeding its results into an existing or newly customised LCA calculator. In subsections, we will discuss how each deals with the challenges from the previous section and summarise the whole section in a table at the end.
A naïve approach to Green Configuration is sequential – based upon the availability of two separate tools: configurator and LCA calculator. For the configurator (i.e., the left lane in Figure 1), a modeller defines the product model (i.e., variety and dependencies) in a knowledge base (KB) by using the integrated development environment (IDE) for the configurator. A customer or salesperson uses the conifgurator user interface (UI) to set values to configuration parameters to fulfil their requirements. Continuously, the solver checks compliance with the KB and sets other parameters accordingly. Only when the configuration is finished, the solver hands over the resulting BOM to the LCA tool
(right lane). A (typically other) modeller now collects all necessary LCA data for the materials for all relevant phases (supply chain, production, usage, end-of-life) and calculates the environmental impact values for the product.
We will not go into more detail because this approach does not really combine the two tools and is impractical due to the typically long duration of the manual LCA assessment.
To achieve faster results for the user, one can automate the
process. Such a loosely coupled approach was taken by, e.g.,
Tacton [
The main challenge for the configurator vendor is to deifne a clean generic mapping between configuration and LCA concepts and continuously maintain this interface to comply with the evolving versions of both the configurator and the LCA tool and API. Modellers need much expertise and additional efort because they must specify the LCA model separately from the configurator model and make sure that both are in sync (i.e., define the core structure and the dynamic parameters, include all relevant materials and components, map those included components to configuration features, i.e., parameters). They may even need to involve a tool specialist, at least for the first setup of the system. The configurator users benefit from the proven LCA processes and the typically up-to-date data in the corresponding databases (e.g., ecoinvent). On the other hand, user experience may still be weak because of possibly long response times in interactive use (due to the overhead of calling an external tool and – especially for the first calls – the comparably long time to calculate the resulting value). Optimisation is challenging as the configurator cannot easily access intermediate values for sub-assemblies, thereby steering optimisation in the right direction. This loosely coupled architecture covers the challenges from section 3 in the following way: 1. Missing environmental data from suppliers: Available LCA data for the sub-parts (from suppliers), for the manufacturing tasks (in the own production process), for various time periods in the operations phase (depending on details of usage and surroundings), and for end-of-life (e.g., recycling eforts) can be reviewed and – if necessary – extended by the modeller in the LCA tool’s UI before the configuration process starts. Additionally, an external tool based on machine learning could help to synthesise data ofline (this needs to be implemented by other experts). 2. Unclear impact data for usage phase: Information about expected usage can be collected as configuration data and handed over as parameters to the LCA calculator to achieve customer-specific values. 3. Complexity of PEF calculation: The LCA calculator can be trusted to comply with the rules for proper calculation (PCR, PSR). 4. Confidence in calculated data: Current LCA tools do not (yet) suficiently inform about (missing) accuracy of values. 5. Multi-objective optimisation: LCA values of subparts and sub-assemblies are not available to the optimiser, which can lead to weak (sub-optimal) performance. 6. Efective explanations and user guidance: The conifgurator UI cannot access the internals of LCA calculation and thus cannot assist the user with explanations and recommendations. 7. Comparability of data: The LCA tool is typically certified. Therefore, the resulting LCA values are comparable to other calculations based on the same standards.
Some LCA tools, e.g., Green Digital Twin™ (GDT) from Siemens, are generic and expect that the LCA data for the LCA calculation is handed over at the call. This can be used for a tightly coupled architecture, where the configurator manages the LCA data and just calls the LCA tool (see Figure 3).
Again, the advantage for the customer is that they are facing just one UI (for configuration and LCA values). But now, the same is true for the modeller (a single UI for configuration and LCA models). This means that the configurator vendor must supply such a modelling UI, which allows the binding of configuration variants to their LCA data (typically extracted from LCA data sets), and a solver which hands the LCA data for the selected variants over to the LCA calculator. The LCA calculator can even be called for parts of the product (not only for the whole product). The tightly coupled approach covers the challenges from section 3 in the following way: 1. Missing environmental data from suppliers: Similarly to the loosely coupled approach, LCA data for the relevant sub-parts can be prepared or synthesised ofline. 2. Unclear impact data for usage phase: The configurator hands those LCA data over to the LCA calculator, corresponding to the customer’s expected usage. 3. Complexity of PEF calculation: The LCA calculator can be trusted to comply with the rules for proper calculation (PCR, PSR). 4. Confidence in calculated data: Current LCA tools do not (yet) suficiently inform about (missing) accuracy
One can go one step further and directly integrate LCA
calculation into the configurator by extending the modelling
environment (IDE) with a component for LCA and
calculating sustainability values directly in the configurator (see
Figure 4). Such an approach was taken by, e.g., CAS Merlin
[
The integrated approach has the advantage that it does not need an explicit mapping to an LCA tool during modelling and can use environmental data during reasoning and optimisation to come up with a more preferred solution. On the other hand, it needs considerable efort for the configurator vendor to implement the calculation, care for certification (for LCA calculation according to ISO 14040, for EPD generation according to ISO 14025), and continuously maintain it to keep compliance with standards up to date. Development eforts can be reduced if certification is unnecessary, e.g., because customers need not compare their products with competitors but only with their internal variants. The integrated approach covers the challenges from section 3 in the following way: 1. Missing environmental data from suppliers: Similarly to the coupled approaches, LCA data for the relevant sub-parts can be prepared or synthesised ofline. 2. Unclear impact data for usage phase: The combined solver and calculator can directly access the expected usage information as specified by the customer to compute the LCA values. 3. Complexity of PEF calculation: Simple impact calculations (e.g., the addition of upstream) can be easily integrated into the solver. Covering the same functionality as an LCA tool and achieving certification requires much more efort by the configurator vendor. 4. Confidence in calculated data: The combined solver and calculator can keep track of the accuracy of the
calculated LCA values for assemblies if the accuracy of the input data is known or can be estimated. 5. Multi-objective optimisation: As the optimiser and LCA calculator are fully integrated, intermediate LCA values can eficiently control optimisation. 6. Efective explanations and user guidance: The complete integration of the solver and LCA calculator and full access to all their intermediate data allows for detailed explanations and recommendations. 7. Comparability of data: The extension of the solver with LCA calculation leads to highly individualised LCA values. If the configurator vendor does not achieve certification (e.g., due to high costs and/or eforts), the LCA values may not be comparable to commercial LCA tools.
Summing up, all three approaches have strengths and weaknesses when dealing with the challenges. Challenge 1 (missing data) is not discriminating, and the best way to cover it is by extending and/or improving input data ofline, e.g. with the help of data-driven AI. Therefore, we rate only challenges 2 to 7 in Table 1 and use a three-valued scale – the approach has strengths, is neutral, or has weaknesses – to condense the arguments from the preceding subsections.
The integrated approach ofers more value to the customers, e.g. more optimisation possibilities and better explanations. On the other hand, this requires more efort for the configurator developer because they must implement LCA calculations (not just call existing tools or libraries) and care for the necessary certification to make the calculations transparent and comparable.
The coupled approaches take advantage of re-using ofthe-shelf LCA calculators and can even hand over configuration information as parameters, but neither (especially the loosely coupled architecture) can easily integrate the calculation results into their reasoning (e.g. for optimisation and explanations). The tightly coupled architecture can access values from sub-assemblies to achieve better usability.
A product modeller may prefer the tightly coupled approach and especially the integrated approach because data management can be done with only one tool: the configurator.
Green Configuration, the combination of product configuration technologies with environmental impact calculations, is a vital approach to address sustainability challenges. We Challenge 2 - usage phase 3 - calculation 4 - confidence 5 - optimisation 6 - explanations 7 - comparability o + + + + o o o + + o + + + o have analysed requirements and challenges and discussed several architectures for configurators implementing a green configuration approach.
We have seen that the diferent architectures have different strengths and weaknesses, advantages and disadvantages. All of them are feasible and require diferent eforts from stakeholders, i.e., tool vendors, product modellers, and customers. From the viewpoint of a product owner, the selection of their individually preferred architecture depends on the product‘s complexity, the level of product customisation, the number of ofers per year, the LCA impact of the usage phase, and the need to enhance customer experience and operational eficiency.
There is much room for future research on eficiently merging sustainability management with configuration lifecycle management, e.g., reference architectures, reliable data exchange, individualised impact calculation, multi-objective optimisation, elaborate standards, etc.
As one of the most important, we see the monetary assessment of PEF as a means of providing an estimate of the TCO. Visualising the monetary impact of configuration decisions over the whole lifecycle of the product will create a real incentive for the customer to choose the more sustainable product configuration (e.g., less energy costs during the operation phase). Green Configuration extended with TCO minimisation can lead to a triple-win situation: minimised total cost of ownership for the customer, increased demand for high-quality products for the industry, and less environmental damage.
Green Configuration enables the creation and scale of
application-specific EPDs and DPPs based on more precise
information and assumptions on the concrete product
properties and usage. To make such specifically customised
values comparable between tools, existing standards like
ISO 14040 and the ISO 14020 series [
Disclaimer: Much of the presented work is “thought work”. Currently, we are working on prototypes to conifrm the ideas and results in practice.