Keywords

1 Introduction

Worldwide, the aviation industry accounts for around 2% of all human-induced carbon dioxide (CO2) production [1]. The international community predicts that global aviation emissions will grow by an additional 300–700% by 2050 if no effective measures are taken [2].

The UK’s aerospace sector is a world leader in developing new technologies and vital to the UK economy. It provides over 120,000 highly skilled jobs and has an annual turnover of £35 billion, the majority of which comes from exports to the rest of the world [3]. Recently, UK aviation has committed to achieving net zero emissions by 2050 [4]. In order for the aerospace sector to maintain its spot and achieve the sustainability goals, it needs the use of the latest equipment and processes while minimising negative environmental impacts and conserving energy and natural resources. Therefore, the aerospace industry needs to identify, quantify, assess and manage the flow of environmental waste/emissions and maximise resource efficiency (i.e. energy, people, time).

However, ongoing debates claims that the definition of sustainability and its assessment is vague leading to significative uncertainties in quantifying the return on sustainability investments [5].

2 Mapping Sustainability within Aerospace Industry

2.1 Literature Review on Sustainability in Aerospace Industry

Sustainability is the ability to maintain or support a process continuously over time. In industry, sustainability seeks to prevent the depletion of natural or physical resources in order to maintain a healthy environment while preserving a financially and socially viable business.

As a large and growing industry currently dependent on fossil fuels, to meet sustainability targets, the entire Aerospace industry needs to collaborate and coordinate—not just airframe Original Equipment Manufacturers (OEMs), engine manufacturers, and suppliers, but also energy companies, airports, regulators, and other stakeholders [6].

Focusing on manufacturing, there are four types of practices leading to more sustainable development: product modification, product improvement, process modification, and process improvement [7]. Modification implies an alteration in the product or process, while an improvement keeps them the same, but more efficient. The main problem is that assessing a particular activity’s contribution to sustainability is difficult for several reasons [8]:

  • the concept of sustainability is vast in scope and consequences of some decisions demonstrate over a long time.

  • The level of complexity can be very high because of the vast scope to deal with and multiple interactions to consider among economic, environmental and social elements.

  • Relations among variables are in most cases dynamic.

  • Systems in question often do not exist yet and it may be necessary to investigate the impact of various scenarios or plans on sustainability before actual implementation.

  • Different levels of granularity may have to be handled at the same time.

In recent years, a triple bottom line (TBL) approach has become more prominent [9]. TBL posits that instead on one bottom line (profit), there should be three: profit, people (social responsibility) and planet. The three key principles of TBL should be measured, but they do not, of themselves, provide a measurement system. Therefore, many organisations are developing specific evaluation tools and best practices to help them achieve a suitable balance across Social, Environmental and Economic principles [10]. Currently, aerospace leaders try to touch every aspect of these principles in their operations [11, 12].

To plan and measure sustainability and cope with uncertainties, virtual simulations (e.g. discrete event simulation) are widely adopted. The use of DES plays a pivotal role in designing sustainability related strategies, since it allows the stakeholders to “experiment” what-if scenarios prior to “implementation” [13].

However, a major source of confusion is that the list of requirements used to evaluate alternative scenarios differs from organisation to organisation, leading to omissions, inconsistencies and inaccuracies [5].

2.2 An Assessment on Sustainability Aspirations and Strategies of a Selected Subset of AMRC Aerospace Partners

A survey was circulated across the University of Sheffield Advanced Manufacturing Research Centre (AMRC) aerospace industry partners, including Boeing, Airbus, Rolls-Royce, Spirit AeroSystems, GKN, BAE Systems (and many more) that are currently involved in the grand challenge of sustainable development, to help focus and drive the direction of this project and gauge interest in this activity. The following data was collected:

  • opinions on the most important drivers for sustainability in manufacturing processes.

  • Ideas on the steps that should be undertaken to improve sustainability in aerospace.

  • The technological improvements that responders would like to see to increase sustainability.

  • The challenges industry faces in applying sustainability improvement measures.

Most frequent answers highlighted that:

  • “Cost”, “waste” and “energy consumption” are the major sustainability drivers and should be tackled to have more sustainable processes.

  • “Materials”, “processes” and “energy” are the major focus areas and should be investigated, this includes exploration of novel materials (ideally light weight) and ways to process with them.

The survey analysis emphasised that companies struggle with quantifying the return on their sustainability investments and necessitate a standardised framework methodology to aid decision making and quantify improvements against sustainability and profitability.

It appeared clear that the major questions to be addressed by our research were how to harmonise the requirements used to compare the sustainability scenarios and how the existing methodology (simulations) could be efficiently used towards the creation of such a comparison framework.

3 Comparison Methodology: DES and Trade Study

3.1 Introduction

The AMRC’s Manufacturing Intelligence team developed a framework trade study for comparing alternative scenarios, with particular focus on aerospace manufacturing processes, encompassing all the requirements that should be evaluated to assess sustainability.

Measurements were integrated to a Discrete Event Simulation (DES) as a case study to test scenarios. This DES model in conjunction with the Trade Study tool is the framework created to be used in order to make informed decisions based on real data to target more sustainable solutions (e.g. cost, energy, carbon footprint and waste reduction) in the TBL sphere. The developed framework in this study is aiming to help ensuring that Aerospace sector remain profitable whilst also fulfilling environment and societal obligations.

3.2 Trade Study Framework

A trade study (or trade-off study) is a formal tool that supports decision making selection of the best or most balanced solution among the proposed alternatives.

Over the course of recent years reviewing best-practice from a range of projects, the AMRC’s Manufacturing Intelligence team have developed a state-of-the-art trade study process. This enables key stakeholders at the very beginning of a project phase, to have a comprehensive, bias-free understanding of each of the alternative options based on key drivers, and then down-selected the optimum solution for a particular objective. This trade study process has been successfully applied in a number of projects with AMRC’s partners and customers.

Figure 1 shows the main steps to reach decision. Some step may be iterative (e.g. comparing scenarios).

Fig. 1.
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Decision making steps

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3.3 Requirements Definition

In order to compare alternatives with particular focus on sustainability, the trade study framework has been populated (Table 1) with requirements defined in accordance with AMRC partners and industry needs, formulated on the base of the data collected during the surveying activity and literature review. Each requirement is composed by a top level (Key Driver) broken down in Key Performance Indicators (KPIs). The key drivers are the main focus areas to be considered.

Table 1. Requirements.
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3.4 Populating the Trade Study Framework

KPIs receive an importance score from 1 (low importance) to 7 (critical importance). This is used to correct the performance score assigned to an alternative adjusting the results depending on KPIs’ importance. In order to drive the comparison in term of sustainability, the higher is the sustainability implication, the higher is the importance, therefore, for example, KPIs like “energy consumption” or “recyclability” have “7” as importance score. Importance scores may be different depending on project/customer requirements, an investigation with stakeholders is encouraged to properly set the scores at the beginning of any application.

Alternative scenarios receive performance scores against each KPI. These scores may have different nature, quantitative, qualitative, or opinion-based, depending on the availability, maturity of the baseline and scenarios. The easiest way to score is using a scale from 1 (really poor performance) to 10 (outstanding performance). The different scenarios are modelled with the aid of DES to quantify differences.

For real-world industry applications, it is advisable to use quantifiable and measurable data wherever possible (e.g. energy consumption in KWh, energy costs in £), these are automatically converted through a scaling system in values from 1 to 10 to aid comparison. However, the direct application of values on scales from 1 to 10 is always possible for qualitative or opinion-based data. Additional Key Drivers and KPIs could be added in case is necessary for specific applications. Furthermore, the framework allows to switch off requirements if data are unavailable or not applicable.

3.5 Comparing Scenarios and Results

DES is commonly used to test, monitor and predict the behaviour of a manufacturing production process giving the user the possibility to change the system (e.g. workflow) to see results before applying a modification to a real factory environment. With this logic, once a baseline mirroring, for example, a production facility is built, it can be used to optimise a series of factors (e.g. throughput).

Data capture is the key part of the project as this directly influences the accuracy of the simulation model. The AMRC has created a generic model to experiment different scenarios and defined the following data set, which is needed to drive the DES model: process-flows, cycle-times, operators, shift-patterns, buffers/store areas.

In order to test the methodology, a baseline and 3 alternatives have been used. These are speculative scenarios, not linked to a real-world manufacturing process but a simple exemplary representation based on project experience (Fig. 2).

Fig. 2.
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Discrete Event Simulation model

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Lanner Witness DES software was used to simulate the scenarios. The baseline was a simple manufacturing route to produce aerospace metallic components across 4 manufacturing areas: manual lathe, RD (radiographic) inspection, CNC (computer numerical control) machine and CMM (coordinate measuring machine). Improvement scenarios used different batch sizes and improved cycle-times/resource allocation. A change in design, resulting in reduced waste for the product, was considered for Alternatives 2 and 3 resulting in even more appealing solutions as shown in Fig. 3. For instance:

  • Alternative 1 – reduction of cycle-times by 5%

  • Alternative 2 - reduction of cycle-times by 10%, change in component design to target waste reduction by 2%

  • Alternative 3 - reduction of cycle-times by 12%, change in component design to target waste reduction by 6%

Key drivers of “Product”, “Process”, “Local Environment” and “Factory Infrastructure” have been evaluated reflecting the improvements. Scores have been calculated using a scale from 1 to 10 based on the alternatives’ values. The performance score used for the baseline scenario was “5” (considered average). Analysis showed that the alternatives resulted better than the baseline, with Alternative 2 and 3 performing significantly better (Fig. 3). The bar breakdown shows the performance results against specific drivers for each alternative scenario. Sustainability performance is highlighted in Table 2.

It is important to notice that best overall alternative may perform worse against specific drivers as highlighted in both Table 2 and Fig. 4. For instance, Alternative 3 had overall the best cumulative score but Alternative 2 was better for “Local Environment” and slightly better for “Process”. Specifically, reduction of cycle-times by 12% on Alternative 3 resulted in a slight increment in energy consumption (worse score for “Process” in comparison with Alternative 2) and more manual operations (reduced operator well-being and therefore reduced score for “Local Environment”). Before eventual adoption, it is worth to quantify in detail why Alternative 3 is worse in those areas and what can be done to eventually increment the scores.

Although this example is quite simple, in real-world cases the level of complexity can be very high, with multiple interactions, often dynamic, among the KPIs. The framework is thought to capture all these interactions and investigate the impact of various scenarios before application.

Fig. 3.
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Scenario comparison (scores normalised on a scale 0%–100%–100% being the maximum achievable cumulative score)

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Table 2. Scenario comparison: Sustainability performance (improvement on baseline).
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Fig. 4.
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Best alternatives comparison

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4 Conclusions

The following objectives were reached by the research described in this paper:

  • main sustainability drivers, focus areas and current issues in sustainability assessment were identified collecting information from industrial experience and AMRC’s Aerospace partners.

  • Major objectives for the sustainability improvement efforts were highlighted.

  • A framework encompassing trade study analysis and DES modelling was generated, capturing all relevant aspects in form of requirements to compare alternative scenarios, with particular focus on aerospace manufacturing processes.

  • This framework has been tested on speculative scenarios to highlight the methodology capabilities.

The long-term aim is to apply the framework developed to case studies and projects to highlight sustainable solutions, quantify improvements and justify decisions.

This has the objective of addressing one of the major findings from the surveying activity within the aerospace sector: quantifying the return on sustainability investments and the necessity to aid decision making and quantify improvements against sustainability and profitability.

Further engagement with AMRC relevant partners and industry collaboration will be vital for the successful application of the described framework methodology. Dissemination of the work undertaken in this project will be part of the first step to gauge major interest and spark further discussions towards more sustainable solutions.