Design for lean 5.0: Integrating lean requirements and parameters into production systems in an industry 5.0 context

Abstract

To meet evolving customer expectations, companies need to take into consideration most challenging requirements. To help designers meet these increased needs, various methodologies, known as “Design for X (DfX)”, have been created. Nowadays, companies rely on a conventional view of Lean application during the use phase to integrate new requirements: let the system produce, detect wastes, then apply Lean tools to remedy them. However, this solution confront several limitations and constraints such as the initial design of the existing system, time and cost of implementing new Lean tools and resistance to change, etc. This implies a change of mindset by proactively integrate Lean requirements from the design phase. This paper aims to support designers in improving the overall performance of production systems by designing Leanless (Minimal Lean application required) sustainable, adaptable systems with minimal waste and human-centered considerations. For this purpose, we have analyzed Lean principles and tools in an Industry 5.0 context in the aim to extract a set of Lean requirements and parameters in line with Industry 5.0 principles. In addition, we have conducted a literature review of 34 DfX methodologies and classified them into six categories: Production, maintenance, quality, sustainability, human-centricity and Resilience. For each category, we have identified the Lean requirements and parameters that meet its objectives. To assess Lean requirements and select the appropriate Lean tools to be integrated from the design phase, we recommend the use of Multi-Criteria Decision Making (MCDM) methods. In addition, this study proposes to take advantage of inventive design to resolve potential contradictions between Lean requirement parameters and technical parameters. This study can serve as a guide for designers, assisting them in considering various requirements that can enhance the performance of the system being designed.

Keywords

production system design lean requirements lean parameters industry 5.0 design for X inventive design MCDM

Introduction

Manufacturing companies are faced with increasingly demanding customer requirements and an intensely competitive environment. For this reason, they are used to adopt Lean principles and tools to eliminate waste in production processes. The conventional view is that many companies realize the importance of implementing Lean tools when they notice defects and waste at the manufacturing phase. However, waiting for these problems to occur before taking action is a reactive approach that can lead to additional costs and significant disruption. A more effective strategy is to integrate these tools from the design phase of production systems, thus anticipating and preventing problems before they arise. To design a system with the desired performance from the beginning, it is essential to change perspective and move from a reactive or curative to a proactive and preventive approach.

As companies embraced Industry 4.0 (I4.0) and explored the link between Lean and I4.0, the Fifth Industrial Revolution emerged. Industry 5.0 (I5.0) builds on I4.0 to design future systems that meet industrial and technological objectives while ensuring socio-economic and environmental sustainability. I5.0 includes three core principles: Human-centricity, sustainability, and resilience (European Commission et al., 2021).

I4.0 focuses on smart production driven by technologies like cloud computing, Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), with the primary motivation being mass production. Its energy supply includes electricity and fossil-based fuels, emphasizing technological advancements for process improvement and innovation within business administration. In contrast, I5.0 shifts towards a human-centric approach and sustainability, evolving into two main concepts: human-robot symbiosis and a bioeconomy focus. Renewable energy becomes more prominent, while technologies pivot towards human-robot collaboration and sustainable production methods. The involved areas expand to include smart environments and waste management, reflecting I5.0’s broader emphasis on ecological and societal well-being alongside economic progress (Thomaz and Bispo, 2022). During I4.0, the focus was on automating processes, often positioning humans in competition with machines and leading to the displacement of people in many areas. However, with I5.0, the aim is to strike a balance where human-machine collaboration yields optimal results, fostering cost savings through efficient processes, environmentally sustainable solutions, and the creative customization that customers increasingly demand (Espina-Romero et al., 2023).

I4.0 has limitations in promoting industrial sustainability and ensuring workers’ well-being, as it prioritizes enhancing production efficiency and flexibility through digitalization and technology. I4.0 turned out to be more technology oriented than human being oriented, neglecting the human factor in productive systems (Alves et al., 2023). The integration of Lean principles offers a valuable solution to overcoming the limitations of I4.0, serving as a bridge toward the objectives of I5.0, which emphasizes human-centricity, resilience, and sustainability alongside technological innovation. Lean concepts align well with the core principles of I5.0, supporting the development of more sustainable, adaptable, and people-focused systems. Various Lean concepts highlight this synergy, such as Sustainable Lean (Maqbool et al., 2019), which promotes eco-friendly practices within Lean frameworks; Lean Green (Bhattacharya et al., 2019), which focuses on reducing waste and environmental impact; Human-Centered.

Lean (Hines, 2022), which emphasizes the importance of human involvement and empowerment in Lean processes; Lean Safety (Hafey, 2017), which integrates safety into Lean practices to ensure both efficiency and well-being; and Lean Resilience (Ivanov, 2022), which focuses on creating robust systems that can adapt to changes and disruptions. Together, these concepts among others highlight how Lean principles can support the goals of I5.0, fostering a more sustainable, resilient, and human-focused approach to technological progress.

With the growth of sustainability challenges, companies adopt Design for X (DfX) as a concurrent approach, which considers several issues through different factors Xs, to fulfil customers’ requirements. DfX can be considered as one of the most effective approaches to implement and to address different challenges such as time-to-market, product cost, product quality and customer satisfaction (Benabdellah et al., 2020). The DfX approach is used to improve the product design as well as the design process from a specific perspective X. The “X” represents the different stages in the product life cycle, the desired criteria or domains (maintenance, safety, cost, etc.). Existing DfX methodologies often concentrate on optimizing a single criterion, limiting their ability to tackle the complex and multifaceted challenges faced by modern industries. While DfX can enhance specific aspects of a product or process, it often neglects other critical factors. As industries shift towards I5.0, which emphasizes balancing technological advancement with human-centric considerations, sustainability, and resilience, there is a clear need for a new framework that integrates Lean principles with these I5.0 priorities. This framework would combine Lean’s efficiency with I5.0’s core values.

Integrating Lean requirements in an I5.0 context, which targeting several criteria and domains during the design phases could be a an appropriate solution to enhance the global performance of the system. This early, proactive integration prevents defects and waste from occurring in the utilisation/exploitation phase, reduces the risk of performance decreases and improves the efficiency of the entire production cycle from the specifications phase, thus minimizing Lean interventions in future systems and ensure that the system is aligned with the emerging principles of I5.0. Considering these requirements early in the design phase can guide the integration of various technologies from the beginning, ensuring that Lean and I5.0 factors are integrated and maintained throughout subsequent design phases. This proactive approach mitigates the risks of technological advancements overlooking I5.0’s critical factors, fostering a more harmonious balance between automation on one side and human integration and environmental responsibility on the other.

This paper proposes a methodology for designers named Design for Lean 5.0, to integrate Lean requirements and parameters within an I5.0 context. These requirements are extracted from the literature and satisfy multiple criteria identified from the DfX methodology.

This paper is organized as follows: First, we conduct a literature review on the most cited DfX methodologies and on the integration of Lean and I5.0 concepts from the design phase. Next, we present our new methodology for integrating Lean requirements from the design phase, in line with I5.0 orientations. Finally, we conclude the paper by summarizing the importance of the proposed methodology and suggesting future research opportunities.

Research background

We present in this section the state of the art of DfX methodologies and the integration of Lean requirements in an I5.0 context from the design phases of the production systems. This section is divided into three parts: In the first part, we present some of the most commonly cited DfX methodologies to identify industrial performance criteria related to Lean and I5.0 concepts. In the second part, we provide a review of the literature on Lean integration from the design phases combined with I4.0 and I5.0 concepts. In the third part, we discuss our analysis.

Design for X methodologies

Traditionally, companies focused on a limited set of criteria when designing their production systems. However, to achieve a high-performance system, it is essential to consider a broader range of criteria that collectively enhance overall performance. Our aim of designing a high-performance system requires us to take into account as many criteria as possible that contribute to improving the performance of the systems we intend to design. We aim to ensure that the systems we design not only meet immediate operational requirements, but also adapt to future challenges, stimulate innovation and maintain a competitive edge in an ever-changing industrial landscape.

Within each “X” domain lies a set of influential variables that deserve analysis. We have evaluated these domains to identify the ones that are most relevant and best aligned with Lean and I5.0 concepts. To identify the most prominent DfX methodologies, we have conducted a literature review. In the following, we presents the selected criteria and summarizes 34 of the most cited DfX methodologies and its description which shows its importance in our aim to design a high-performance system. By grounding our study in the analysis and integration of various DfX methodologies, we are able to create a system that excels in multiple performance dimensions, including operational performance, financial performance, environmental performance and social performance, etc.

Many DfX techniques have the same idea with different names, while others have the same names but have different meaning, approaches, and guidelines. Hence, we have classified the 34 DfX methodologies referenced in an hierarchical representation, grouping them based on their interconnections and shared objectives. This classification is illustrated in Figure 1, where the DfX elements within the same box share common objectives.

The choice of these particular DfX methodologies obeys a distinct logic that aligns with classic domains, which are fundamental objectives of Lean methodologies: Production, quality and maintenance, and the emergent domains of I5.0: Sustainability, human-centricity and resilience. Thus, while Lean principles can be connected to all six categories, their strongest association lies with the first three due to their foundational focus on operational efficiency and cost-effectiveness, which are critical drivers in Lean systems (Rahardjo et al., 2023). I5.0 principles, on the other hand, extend Lean’s scope by emphasizing human-centricity, sustainability, and adaptability, reflecting the evolving priorities of modern industries.

DfX methodologies aligned with lean principles

DfX methodologies can effectively support Lean principles by optimizing key manufacturing domains, with each methodology addressing a specific aspect of Lean. In the following, we highlight several DfX methodologies that contribute to advancing Lean objectives.

Production and cost

Design for Production (DfP) estimates the throughput time of a new product (Herrmann and Chincholkar, 2001). It provides a holistic picture of the entire manufacturing process with the goal of staying current with product development (Maneschi and Melhado, 2010), and evaluates manufacturing system performance (Chincholkar et al., 2003). Design for Cost (DfC) uses management-imposed cost targets as the main constraints for maximizing competitiveness, or as the main objective under the technical and time constraints; Budget analysis, cost estimation, cost planning, cost control (Sheldon et al., 1991). DfC analyses and evaluates a product’s life cycle cost (LCC), then modify the design to reduce the LCC (Chen et al., 2004). DfC considers three important aspects: Cost types, Design-related and Cost analysis (Mörtl and Schmied, 2015). Design for Manufacture (DfM) ensures the simultaneous design of a product and its manufacturing process in order to obtain the best result and, consequently, optimize overall costs (Valentinčič et al., 2007). DfM maximizes the use of manufacturing processes in the design of components (Chowdary et al., 2019). Design for Manufacturability (DfMy) evaluates product design using a performance ratio based on a set of attributes and criteria, it focuses primarily on the geometrical features of the parts in the design (Das and Kanchanapiboon, 2011). It aims for the design process to be efficient; the manufacturing processes to be capable, proactive, and economic (Nguyen Ngoc et al., 2022). Design for Manufacture and Assembly (DfMA) aims at improving the product assembly by reducing the overall number of components, minimizing the number of fixations, standardizing the type of fixations, reducing the part re-orientation during the manual operations, and choosing the most appropriated manufacturing technology among others (Formentini et al., 2022). DfMA promotes the concept of simplifying parts and product design in order to reduce the number of parts, reduce production costs, improve reliability and quality, and increase production capacity (Chowdary et al., 2019).

Quality and process control

Design for Quality (DfQ) is a quality-oriented form of integrated product and process development, but the focus is on functionality rather than manufacturability (Morup, 1992). DfQ aims to design a robust product that improves quality and reliability of product to minimize the effects of potential variation in manufacture of the product and the product’s environment and continuously improve product reliability, performance, and technology to excel the customer expectations (Kuo et al., 2001). DfQ aims to measure and accommodate the customer’s perception about the product’s quality (Nepal et al., 2006). Design for variation (DfV) produce a probability distributions of component or system performance characteristics by explicitly taking into account all sources of uncertainty and variability, including those associated with engineering model uncertainty. This enables the risks associated with meeting requirements to be calculated and managed by making changes to design, materials or processes to directly address sources of uncertainty and variability (Reinman et al., 2012). Design for Six- Sigma (DfSS) is an important concept in DfQ (Benabdellah et al., 2019). It aims delivering new products and services to customers with a high performance that is measured by customers and is critical to quality measures (Jenab et al., 2018). DfSS uses an organized methodology for designing new products and processes using statistical tools to minimize defects and process deviations (Sithole et al., 2021).

Maintenance and serviceability

Design for Maintenance (DfMA) aims to design machines or products to improve ease of maintenance, by adapting them to the specific functions they perform. This ensures that operations can be carried out at reduced cost and in much less time than would otherwise be necessary (Desai and Mital, 2006). DfMA aims to influence maintenance activities during the design phase of new systems. Three categories of design for maintenance exist: maintainability, reliability and supportability (Vaneker and van Diepen, 2016). Design for Maintainability (DfMy) means designing the system in such a way as to find the optimum balance between investment cost and maintenance cost (Tortorella, 2015). DfMy means designing equipment to ensure that it can be repaired quickly and easily (Vaneker and van Diepen, 2016). Design for Reliability (DfR) describes the set of tools that support the design of products and processes to ensure that customer expectations of reliability are fully met throughout the product’s life, with low overall life-cycle costs (Mettas, 2010). DfR is used to affect design for positive improvement in product reliability by using knowledge of failure physics to design out potential problems (Crowe and Feinberg, 2017). Design for Supportability (DfSu) involves the evaluation of all aspects (service, maintenance, repair) of product support at the design stage and create a good link between a technical feature and an additional service for the customer (Goffin, 2000). DfSu aims to taking these aspects (service, maintenance, repair) into account during the design process, as support plays a major role in the post-production stages of a product’s lifecycle, and generates additional revenue (Arnette et al., 2014). Design for Product Service Supportability (DfPSS) aims to offer a solution designed to be easily assembled, manufactured and tested, through concepts of quality, modularity and customization, in order to improve its availability even after the manufacturing phases (Sassanelli et al., 2016). Design for Availability (DfA) helps manufacturers and their customers to efficiently produce and use capital equipment that meets rigorous system availability standards in a cost-effective way (Smets et al., 2012). Design for service (DfSv) is the planning and organization of people, infrastructure, communication and hardware components of the service. It encompasses the consideration of maintainability and reliability as the most critical issues affecting the serviceability of products (Benabdellah et al., 2019). Design for serviceability (DfSy) addresses the capability of performing the service. The term “serviceability” refers to the degree of difficulty for the product to return to normal activity during maintenance interventions (Gobbo Junior and Borsato, 2021).

DfX methodologies aligned with industry 5.0

Although DfX methodologies are often considered traditional, many DfX approaches align with and support the emerging principles of I5.0. These methodologies, originally developed to optimize specific aspects of design and manufacturing, are now proving their adaptability in addressing modern challenges. Below, we present these methodologies, which have been grouped into three main categories that align with the principles of I5.0: Sustainability and End-of-Life, Human Factors and Safety, and Resilience and Adaptability.

Sustainability and end-of-life

Design for sustainability (DfS) aims to apply parts of life cycle thinking to products to make them more sustainable (social, economic, and environmental) (Clark et al., 2009). DfS approaches are categorized in four different innovation levels (Ceschin and Gaziulusoy, 2016):

- Product design innovation level: Green design and eco-design, Emotionally durable design, Design for sustainable behaviour, Nature-inspired design, Design for the Base of the Pyramid (BoP)

- Product-service system innovation level: reduce the environmental impact of products and production processes.

- Spatio-social innovation level: Design for social innovation; Systemic design.

- Socio-technical system innovation level: Aiding social change without considering technological change as a predeterminant of that change.

Design for Environment (DfE) includes any design action aimed at improving a product’s environmental performance (Hauschild et al., 2004). The fundamental principle of DfE is the integration of environmental considerations right from the start of the product and process design phase (Jackson et al., 2016). DfE covers a wide range of product development activities, including selecting appropriate materials, examining the product’s use phase to reduce its impact on the environment, designing for energy efficiency, minimizing industrial residues during manufacture, designing for end-of-life, improving packaging and reducing the use of substances that have an impact on the environment (Rose, 2000).

Design for life cycle (DfLC) encompasses all aspects of a product’s lifecycle, from initial design through normal use to final disposal (Newcomb et al., 1996). Design for End of Life (DfEoL) aims to integrate the EoL strategies in the early stages of the design process and generate ideas for both system innovations, as well as technical incremental innovations and redesigns. The 3 most dominant EoL treatments were Reuse, Recondition and Recycle (Peeters and Dewulf, 2012). Design for reuse (DfRu) is the ability to define new architectures that are impermeable to new technologies and are malleable to new requirements (Cohen, 1998). Design for Recovery (DfRc) aims to develop products that are both environmentally compatible and commercially viable, in order to play a part in preventing environmental problems before they arise (Navin-Chandra, 1994). Design for Recycling (DfR) aims to facilitate product recycling and maximize output, and pay attention to product disassembly during the recycling process (Hultgren, 2012). Design for Remanufacturing (DfRem) is how a designer must actively consider each stage of remanufacturing, or each problem, and how the design will affect them (Hatcher et al., 2011). DfRem is a combination of design processes enabling an item to be remanufactured. It identify and prevent inefficiencies in remanufacture. DfRem can improve remanufacturing efficiency by: Reducing disassembly and reassembly times, and therefore inspection and evaluation times and costs, specifying materials and shapes suitable for repetitive remanufacturing, and integrating core return mechanisms into the product or component (Charter and Gray, 2007).

Design for Assembly/Disassembly (DfAD) is important because of the considerations involved in repairing, maintaining and recycling a product. It also includes the restoration of parts from end-of-life or rejected products to reduce pollution (Battaïa et al., 2018).

Human factors and safety

Design for humans (DfH) In I5.0 context, must go beyond physical human Factors to take into account the psychosocial effects of technology use and the interactions between humans and technology (Grosse et al., 2023). Design for the Human Factor in I4.0 (DfHFinI4.0) allows the human factor to be placed at the core of I4.0 and the consideration of the relationships between the human and technological factors (equipment and information system) (de Miranda et al., 2020). Design for Safety (DfS) integrate safety knowledge into the design process, it aims to discover the presence or absence of danger and the level of importance of hazardous conditions (Sadeghi et al., 2015). DfS aims to distinguish between the components, design parameters, and functional requirements of an existing system, as well as to define the hazards associated with each (Sadeghi and Tricot 2013). In the DfS method, the system must be both robust and reliable in order to achieve the safety objectives, and this must be taken into account right from the start of the design process (Sadeghi et al., 2013). Design for Human Safety (DfHS) is linked to human–machine interaction and accident prevention in work situations. It aims to study the variability of its main components: human beings, machines and their environment, as well as the variability of possible interactions between these components (Sadeghi et al., 2016). Design for Ergonomics (DfE) was initially aimed at ensuring and communicating high levels of safety and usability for products and services. It then evolved to focus on the overall user experience, emphasizing the quality and impact of interaction between users and the product, environment or service, whether physical or virtual. Ensuring that products are safe, easy to use, pleasant and immediately understandable has become a key factor in market success in recent years (Francesca, 2020).

Resilience and adaptability

Design-for-Resilience (DfRs) requires rapid adjustment of production resources through task reallocation and rebalancing (Gu et al., 2015). DfRs reduces complexity of a system by exploiting commonality among its components. Delayed product differentiation reinforces the design-for-resilience strategy through the repurposing products to address modified needs while retaining the majority of their original configuration, and swiftly responding to demand growth within a short timeframe by utilizing an inventory of the main product configuration (Kusiak, 2020). Design for Modularity (DfMo) aims to design loosely coupled interfaces that allow modules to be varied within the product to facilitate component exchange and sharing (Benabdellah et al., 2019). DfMo aims to produce a variety of products by combining modular components during the product design phase. Modular design therefore means producing different products by combining standard components and sharing the same assembly operations for part of their structure (Kuo et al., 2001). Design for changeability (DfCh) aims to design systems and products in such a way that future engineering changes can be easily and quickly implemented, or even avoided. Changeability can be achieved through the principles of simplicity, independence, and modularity (Iakymenko et al., 2022). Design for Adaptability (DfAd) aims to design the product as a dynamic adaptable system, to able to adapt to change, modify or reconfigure to meet changing fashion needs, or to be upgraded, for physical or economic reasons or to utilize new technology (Kasarda et al., 2007).

Lean requirements integration in the context of industry 5.0 from the design phase

Traditional Lean applications in design have primarily focused on optimizing the design process itself. Notable examples include Lean design (Dombrowski et al., 2014), Lean product development (León and Farris, 2011), and Lean product-service systems (PSS) (Sassanelli et al., 2019). These approaches aim to improve efficiency, reduce waste, and streamline the development process. However, limited research has explored the integration of Lean principles to enhance the overall performance of the systems being designed, with a focus on creating systems that minimize the need for Lean intervention during the operational phase.

Integrating Lean requirements and principles from the design phase has the potential to emerge as a highly effective strategy for improving the overall performance of a system and optimizing the interrelationships between its various components. The work of Slim et al. (2018, 2021a, 2021b) can be seen as a first exploration of Lean requirements in the early phases of design. Their approach to incorporating Lean requirements is based on the integration of Lean criteria and functionalities, with a particular focus on how this integration could support the development of I4.0. In the same context, (Gdoura et al., 2024a) highlight the importance of integrating multiple Lean tool functionalities to improve production systems performance by identifying the corresponding parameters that can be considered early in the design phase.

The shift from traditional Lean to Lean 4.0 represents a significant change in how Lean principles are integrated with advanced technologies. Despite the importance of this integration, few studies have explored it from the production system design phase. Dahmani et al. (2021) propose a framework that integrates Lean design, eco-design and I4.0 by incorporating the circular economy (CE) paradigm into product design to design eco-efficient products. Schumacher et al. (2023) present a systematic literature review of the rapidly evolving field of Lean Production Systems 4.0, with a particular focus on their design by industrial engineers. The authors describe the essential requirements and guidelines for the design of future Lean Manufacturing 4.0 systems. Gdoura et al. (2024b) proposes a new framework for designers to integrate Lean 4.0 tools early in the design process to enhance system performance. This approach combines Lean thinking tools with I4.0 technologies and provides empirical evidence through a questionnaire survey, demonstrating the positive impact of Lean 4.0 integration on five key design dimensions: Production system (PS) efficiency, PS reactivity, PS durability, PS quality, and PS intelligence.

The integration of Lean and I5.0 principles is receiving increasing attention, mainly in existing systems. Some of the authors has tried to combine the both concepts of Lean and I5.0. (Mladineo et al., 2021) analyse the importance of human elements in the successful implementation of Lean principles in SMEs in an I5.0 context. Fonda and Meneghetti (2022) introduce the Human-Centric SMED methodology, a framework that integrates I4.0 tools, Lean Management, and Ergonomics. Souza et al. (2022) conduct a systematic literature review of the concepts of the Lean approach, I4.0, and I5.0, providing insights into their interconnections. Moraes et al. (2023) analyse the relationship between Lean and I4.0, further exploring the opportunities for integration with the new concept of I5.0 by the evaluation of the potential integration between Lean and I5.0. Eriksson et al. (2024) explore and explain how Lean production practices and I4.0 technologies may coexist to enhance manufacturing operations in the era of I5.0 based on a longitudinal case study. They identify the challenges that require manufacturing organizations to have the capacity to look beyond Lean and I4.0 philosophies to meet the demand for a human-centered perspective on socially sustainable manufacturing in the era of I5.0.

Few work have tried to integrate the both concepts of Lean and I5.0 in the design of systems. Rahardjo and Wang (2022) present a novel sustainable innovation framework that combines inductive and integrative approaches. Grounded in Lean Six Sigma (LSS) tools and I5.0 technologies, this framework is designed to achieve process excellence. The authors introduce six LSS 5.0 tools and provide detailed implementation guidelines using the DMAIC methodology. In the same context of the sustainable innovation framework, Rahardjo et al. (2023) develop three Lean 5.0 tools, the RIDEM (Requirements, Initiation, Design, Execution, and Monitoring) approach and LSS 5.0 implementation steps. They conduct as well a case study to demonstrate the practical application and outcomes of LSS 5.0 tools.

Discussion

The requirements specification phase is the foundation of any successful system design. This is where we confront the problem to be solved and navigate between the different objectives set by stakeholders and designers. We must first select the problem categories and corresponding requirements that will guide our solution.

Integrating Lean requirements into the design work must starting from the first phase of requirement specification of engineering design process. This early integration has the potential to targeting the following design process to consider the appropriate Lean tool functionalities to be able to improve the overall performance of a system and optimize the interrelationships among its diverse components. Many of the methods discussed in the literature lack sufficient details to address all Lean performance criteria, parameters and essential requirements, specifically, those who can satisfy I5.0 principles. Additionally, these methods often emphasize one phase of a system’s life cycle more than others. In our literature review, we have started by collecting 34 of the most considered criteria in the literature extracted from DfX methodologies. The objective of this first part is to identify the maximum of integrable criteria of different domains, which guide us to extract the corresponding Lean requirements.

Then, a literature review was carried out to identify gaps in the integration of Lean requirements during the design phase. The analysis revealed a significant lack of comprehensive lists of Lean requirements and parameters that could serve as guidelines for the industry. A confusion is found the work of Slim et al. (2021a) related to the difference of definitions between requirements and functionalities. Due to the vague definition of the Lean requirements and the absence of explicit works outlining direct Lean requirements, we have conducted a literature review to extract these requirements that address the key domains derived from the DfX methodologies based on various Lean principles, practices, and tools.

However, it is essential to clarify the difference between requirements and functionalities. Requirements should be seen more as high-level specification. They represent the essential capabilities a system must possess to solve a specific problem or meet a need. They are the “what” - the objectives that the system must achieve. Requirements are problem-oriented and they are usually expressed as explicit statements of what the system must do (criteria, objectives, constraints, performance measures, etc.). They form the basis of the design and provide guidelines on what the system must achieve, but not necessarily how it will achieve it, and form the basis of the functionality definition. Functionalities represent the specific actions or features that a system performs to meet established requirements, the purpose it serves, based on the requirements. In essence, functionalities translate the high-level objectives (from the requirements) into actionable, system-level purposes. They translate requirements into elements that can be used within the production system.

To effectively design a Leanless system, it is essential to prioritize the identification of requirements adapted to specific problems, rather than the definition of general Lean functionalities. By first identifying the problems and challenges addressed by the requirements, a problem-centered approach is established. This directs the design process towards achieving concrete improvements in identified areas of performance and by consequence the areas of non-performance. In addition, focusing on requirements ensures that the system directly addresses the root causes of problems, and reduces the risk of integrating functionalities that may not be relevant to the core problems, and prevents the deployment of generic Lean tools that may not effectively address the specific challenges encountered.

In the literature, occurrences of the expression “Lean requirements”, although rare, are generally accompanied by vague and general statements that lack clarity and are not easily applicable to the design process. However, in response to the current gap of the vague understanding of Lean requirements, we propose a structured guiding methodology for identifying and integrating these Lean requirements into the initial stages of engineering design process. Identifying the correspondent Lean requirements parameters aims to measure, control, and optimize critical aspects of the industrial process. It aims to track performance against targets and objectives, making it easier to identify deviations or areas needing improvement.

Traditional Lean integration primarily focuses on applying Lean tools to enhance the performance of existing systems. However, an evolution has emerged, emphasizing the consideration of Lean principles during the design phase. With the advent of Lean 4.0, the integration process has evolved by incorporating digital technologies, enabling the digitalization of Lean tools and the use of advanced technologies such as Big Data Analytics, Automated Guided Vehicles (AGVs), virtual simulations, sensors, robots, etc. Various studies have explored the relationship between Lean tools and I4.0 technologies. For example, Mayr et al. (2018) present a matrix illustrating how I4.0 tools can support Lean methods. Valamede and Akkari (2020) propose different combinations of I4.0 technologies and Lean tools, while Valamede and Akkari (2021) present a relationship matrix between Lean manufacturing wastes and digital technologies.

Despite the significance of Lean 4.0, it has several limitations that our work seeks to address. These limitations are particularly evident in its lack of attention to human and social factors within organizations, such as leadership, employee integration, and training for new roles and tasks. Additionally, Lean 4.0 frameworks often overlook critical aspects of sustainability, including social, financial, and environmental concerns. Lean 4.0 tends to focus primarily on technology integration and operational efficiency, with minimal consideration for long-term business sustainability or resilience. Specifically, environmental sustainability is frequently identified as a gap in Lean 4.0 literature, and the social dimension is often neglected, limiting the Lean 4.0’s ability to address the full scope of modern industrial challenges (Moraes el al. 2023).

With the emergence of I5.0, additional constraints have arisen. Technologies must now support human roles within the production system, promoting human-centric design, while also respecting environmental considerations. Therefore, the technologies integrated into the design must be considered with these I5.0 principles from the beginning. Examples include collaborative robots (cobots) and biotechnologies, which work alongside humans to enhance productivity and ensure sustainability.

In our research, we emphasize the importance of articulating Lean requirements that directly target identified issues in the production process and cover multiple performance criteria and domains. Our focus goes beyond simply integrating Lean requirements; it extends to aligning them with the I5.0 principles. This preparatory step is important to ensure that the system is designed from the beginning with both Lean and I5.0 principles in mind. By incorporating sustainability, resilience, and the role of humans as key factors during the requirements specification phase of the production system design, we can ensure that automation and advanced technologies, when integrated in subsequent design stages, are already aligned with these essential considerations. Another key advantage of our proposal is that, when modifying existing systems, employees often struggle to adapt to new tools and practices, preferring traditional work habits. By integrating Lean requirements and I5.0 considerations from the design phase of future systems, employees will be more adaptable, as they will find that both principles have been considered from the beginning.

In the next section, we propose our methodology, which enables to design a Lean 5.0 system. The main purpose of this methodology is to integrate and evaluate Lean requirements in an I5.0 context and identify the appropriate Lean tools to satisfy multiple criteria extracted from DfX methodologies.

Trying to integrate several Lean tools into the design of a production system to satisfy multiple performance criteria can generate contradictions. For this reason, we recommend the use of inventive design to solve contradictions between Lean parameters and technical parameters.

Design for lean 5.0 methodology

The main aim of our methodology is to integrate a set of Lean requirements within an I5.0 context, ensuring coverage of the maximum possible criteria and domains. This methodology concerns the first step of engineering design process, the requirements specifications phase. Figure 2 presents a flowchart that outlines the initial phase of the process for designing production systems, with the goal of integrating various requirements to ensure alignment between Lean principles, I5.0 considerations, and the unique needs of different customers and companies. Thus, we propose the following approach for designers, as illustrated in Figure 3, comprising six steps.

Identify customer and user requirements

When designing a production system, customer requirements and user requirements have distinct objectives. Customer requirements refer to the expectations and needs of the end customer who will receive and use the product or service produced by the system. They focus on the quality, cost, functionality and delivery time of the final product. User requirements, on the other hand, concern the needs of the people who will interact with the production system itself, such as operators, engineers and maintenance personnel. These requirements focus on aspects such as system usability, safety, efficiency and ease of maintenance, to ensure that the production system is not only effective in producing the desired results, but also user-friendly and reliable in its day-to-day operation. It is essential to balance both sets of requirements to create a system that satisfies customers while being practical and efficient for users.

Identify technical requirements and industrial norms

According to Slim et al. (2021a), the main tasks of engineering design are to determine the most effective technical solution to satisfy a set of requirements and constraints encompassing human, material, technological, economic and environmental factors.

Technical requirements define the specific functionalities, capabilities and performance criteria that the system must meet to efficiently produce the desired performance. These requirements encompass aspects such as: Equipment specifications, energy efficiency, workplace safety, workplace organization, etc. Gdoura et al. (2024b) identifies 26 of the most widely recognized performance criteria extracted from literature, aligned with the evolution of production system paradigms. These criteria were categorized into five design target dimensions: production system (PS) efficiency, PS reactivity, PS durability, PS quality, and PS intelligence. These criteria are presented in Table 1.

Table 1.

List of performance criteria (Gdoura et al., 2024b).

PS efficiency	PS reactivity	PS durability	PS quality	PS intelligence
Productivity	Agility	Innovation	Customer satisfaction	Automation
Maintainability	Communication	Re-manufacturability	Quality	Maintainability
Cost	Complexity	Reusability	Usability	Diagnosis
Availability	Flexibility	Sustainability		Human Skills 4.0
Reliability	Speed	Inventiveness		Re-configurability
Safety				Smart tools 4.0
				Supplier and customer integration

It is also essential to recognize the various constraints, including budget, time, resources, cultural and ethical considerations, as well as technological limitations. Equally important is compliance with industry norms which encompass standards, guidelines and best practices specific to an industry or sector, and often relate to quality, environmental regulations and safety requirements.

Identify design objectives derived from DfX methodologies and the sub-criteria for each X

This step involves the initial selection of key design criteria, known as “Xs”, derived from various DfX methodologies. The literature review identified 34 distinct DfX methodologies, each focusing on optimizing different aspects of System/product design. The choice of the preferred DfX methodologies depends on information gathered from customer and user requirements, as well as technical requirements, and varies according to the nature of the product and the target market.

Once the appropriate X has been selected, whether for cost, manufacturability, durability, reliability or any other relevant criterion, it is then decomposed into more specific sub-criteria. These sub-criteria provide detailed guidelines on how the system should meet the broader design objective. For example, if cost is the selected X, sub-criteria may include raw material costs, production costs, maintenance costs and operational costs. Each sub-criterion defines more precisely how the design will optimize profitability, providing measurable objectives that can guide designers.

Identify lean requirements and parameters in an I5.0 context

Design for Lean requires the identification of specific Lean requirements for each of the six identified categorized domain, based on their shared objectives. Since Lean concepts address the majority of criteria and domains within various DfX methodologies, we examine the connection between Lean and each domain/criteria, along with their sub-criteria. For example, in the “Sustainability” category, Lean-related concepts such as Lean Sustainability, Lean Green, Lean Life Cycle Assessment, and Lean Disassembly are relevant. Each “X” was analyzed in relation to its corresponding Lean concept, followed by the extraction of the relevant requirements.

Then, for each Lean requirement, we have identified the corresponding Lean parameters. These parameters serve as quantitative measures that enable the system to be evaluated and optimized. By understanding the specific numerical values associated with Lean requirements, designers can make informed decisions on process layout, equipment selection, labor allocation and inventory levels. This quantitative approach ensures that Lean requirements are explicitly considered and respected. In addition, considering the Lean parameters facilitate the creation of performance metrics, enabling measuring and evaluating performance during system operation and monitoring of the system’s effectiveness in achieving Lean objectives. During the design phase, Lean parameters are used to evaluate different design options and select the one that optimizes performance in relation to the defined objectives. It allows assessment of the impact of all requirements on the global performance of the system to be designed. Table 2 shows the extracted Lean requirements and their corresponding parameters, each Lean requirement is presented by one parameter. This list is not exhaustive, but it serves as a guideline for designers to help them incorporate the identified Lean parameters into their system design.

Table 2.

Lean requirements and corresponding parameters.

Lean requirements	Lean parameters
1-Design for Production
Lean concept: Lean Production System/Lean Manufacturing
-Small lot size	-Average number of units per lot
-Uniform work load	-Operator idle time
-Visual factory	-Number of visual aids implemented
-Cellular layout (Jasti and Kodali, 2015)	-Cycle time within the cell
-Reduce internal setup time	-Number of internal operations
-Minimizing the number of transfer operations	-Number of transfer operations
-Eliminate temporary storage	-Amount of temporary storage
-Reducing variety of parts	-Part variety index
-Eliminating inspection	-Number of inspections
-Synchronizing activities	-Degree of alignment between related activities
-Flexible work assignment (Houshmand and Jamshidnezhad, 2006)	-Number of operators cross-trained for mul-tiple tasks or workstations
-Assure the free flow	-Waiting time
-Seek for a hybrid and flexible layout	-Changeover time
-Reduce traffic of materials and people	-Handling time
-Allow the incorporation of additional modules	-Scalability index
-Able to absorb needs of layout changes (Barbosa et al., 2014)	-Reconfiguration time
-Process simplification	-Number of process tasks
-Identification and elimination of non-value-adding tasks	-Percentage of non-added-value time
-Establish control of the manufacturing process	-Number of control tasks
-Reduce variability	-Number of adjustment tasks
-Production leveling and smoothing	-Workload per operator/per day
-Standardized work (Browning and Heath, 2009)	-Number of standard tasks
Lean concept: Lean Assembly
-Reduce number of components	-Total number of components per product
-Use standard components	-Percentage of standard components
-Use standard manufacturing processes	-Percentage of standard manufacturing processes
-Use materials compatible with the production process	-Percentage of materials compatible with the production process
-Avoid complex geometries	-Complexity index of component geometries
-Avoid secondary processes	-Number of secondary processes required
-Use modular architecture	-Modularity index
-Simplify assembly	-Assembly complexity index
-Minimize number of assembly axes (Possamai and Ceryno, 2008)	-Number of assembly axes
-Availability of components or materials for assembly at the right time	-Number of assembly operators
-Identify the required number of assembly operators	-Time of assemblage operation
-The use of assembly cells instead of assembly lines	-Assembly component handling time
-U-shape assembly lines and material handling systems	-Operator handling time between assemblage stations
-Minimize work-in-progress stock (WIP) (Miqueo et al., 2020)	-WIP Visibility Index
-Use the tools’ labels, materials and instrumentation	-Tool organization degree
-Workplace ergonomics	-Ergonomic risk index
-Arrangement of the machines in the order of the process	-Flow distance or time between sequential operations or workstations
-One piece flow (Kowalski et al., 2020)	-Overall cycle time
2-Design for Quality
Lean concept: Lean six sigma
-Ensure the production of requisite units within the specified quantities and timeframe	-Production Capacity
-Establishment of a robust and dependable manufacturing system	-Percentage of units produced without defects on the first attempt
-Facilitate immediate error detection upon its occurrence (Rahardjo et al., 2023)	-Error detection speed
3-Design for Maintenance
Lean concept: Lean Maintenance
-Identify and evaluate potential failure modes of a system	-Risk Priority Number (RPN)
-Autonomous or independent maintenance	-Percentage of maintenance tasks performed by operators independently/frequency of autonomous maintenance activities
-Collect equipment performance and reliability data for development of equipment histories (Mostafa et al., 2015)	-Percentage of required data collected
-Availability of standard tools and instruments for regular maintenance	-Tool availability rate
-Availability of similar materials and spare parts for future maintenance	-Spare parts inventory accuracy
-Availability of skilled workers	-Percentage of maintenance tasks that can be performed by the available workforce
-Minimize waste in the maintenance budgets	-Maintenance Cost
-Feedback systems	-Feedback response rate
-Recording systems (De Silva et al., 2012)	-Data recording accuracy
-Evaluate the Failure Rate of Component	-Mean Time Between Failures (MTBF)
-Manage the resources necessary to rectify the failure	-Mean time to repair (MTTR)
-Identify potential failure (Kolanjiappan 2015)	-Failure accuracy detection
4-Design for sustainability
Lean concept: Lean Sustainability
-Visualize and understand the flow of material and information in a production line	-Material and information Flow visualization index
-Visualize Value added and Non value added in the production line	-Value-added ratio
-Reduction of resource usage and energy consumption	-Resource and energy use rate
-Use of usable waste generated during production, of the whole product or of its components after the end of its life cycle	-Waste utilization rate
-Collect the end-of-life products, sorting, recovering useable products or components from released products for post-use	-End-of-life product recovery rate
-Conversion of waste into useable material after post-use cycle (Maqbool et al., 2019)	-Post-use waste conversion rate
Lean concept: Lean Green
-Reduce the environmental impacts of companies while reducing their manufacturing costs	-Environmental impact index
-Diagnostic of the wastes in the manufacturing process	-Waste identification accuracy
-Decrease the number of products that must be scrapped	-Scrap rate
-Decrease the amount of energy, raw material and waste that is used or generated to repair defective products that can be passed on	-Consumption rate of repair resources
-Reduce energy consumption, scrap and emis-sions to the environment (S. Silva et al., 2020)	-Emission rates
Lean concept: Lean Re-manufacturing
-Material compatibility with recycling, remanufacturing, or reuse processes	-Material compatibility rate
-Disassembly plan for each returned product	-Disassembly time
-Inspection of parts disassembled into individual components	-Component inspection accuracy
-Repair or replace defective and worn-out parts by new ones and then finally reassembled as a remanufactured product (Vasanthakumar et al., 2016)	-Remanufacturing process rate
Lean concept: Lean Life Cycle Assessment
-Reduce consumption usage such as electricity, materials and labor availability	-Consumption rate
-Identify and quantify energy and materials consumed and wastes released to the environment	-Resource and waste tracking accuracy
-Eliminate the use of high-emission materials (Cheung et al., 2017)	-Green material usage rate
Lean concept: Lean Disassembly
-Decontamination, disassembly of reused or remanufactured parts	-Disassembled part ratio
-Minimize changing working zones and dis-placements (Dayi et al., 2016)	-Number of work zone changes per task
5-Design for Humans
Lean concept: Human-Centered Lean
-Higher competency and problem solving	-Competency and problem-solving index
-Improve the user safety	-User safety index
-Mental and physical wellbeing (Hines 2022)	-Wellbeing index
Lean concept: Human centered Lean automation
-Adding the human element in the automation system	-Human integration in automation rate
-Improve the humans’ capacities to undertake physical work	-Skill index
-Perform repetitive tasks by using human-robot collaboration	-Human-robot collaboration rate
-Use the real-virtual interaction	-Human interaction rate
-Alerts the operator by providing work related information and notifications (Malik and Bilberg, 2019)	-Communication
-Collaboration between human intelligence and machines	-Human-machine collaboration index
-Provide real-time information about the working conditions	-Real-time information availability
-Connect people, data, processes, and things	-Connectivity index
-Integrate communication equipment	-Communication equipment integration rate
-Improve the transparency of material and process movement	-Real-time tracking
-Leave work-intensive operations to the cobots (Rahardjo and Wang, 2022)	-Cobots utilization rate
Lean concept: Lean Safety
-Make safety more apparent and visual	-Safety visibility index
-Enables workers to assess whether or not they are performing their activities correctly, and whether or not they are safe	-Activity correctness and safety
-Clean the entire work area and equipment	-Work area cleanliness score
-Interaction of workers autonomously in their workplace	-Autonomous interaction rate
-Reduce the time wasted in searching materials (Sá et al., 2020)	-Material search time
6-Design for Resilience
Lean concept: Lean Resilience
-Building redundant functions with a flexible use	-Redundant function utilization
-Reconfigurable supply chain	-Supply chain reconfigurability index
-Risk mitigation inventory	-Risk mitigation inventory ratio
-Omnichannel distribution systems	-Omnichannel integration rate
-Multiple sourcing, diversified logistics networks, and flexible production lines (Ivanov 2022)	-Number of logistics possibilities
Lean concept: Lean Modularity
-Standardize the production processes, parts and modules	-Standardization rate
-Independence of components and interfaces	-Component and interface independence rate
-Select an appropriate set of module variants from the product family	-Number of Module variant
-Assembly of customized products based on a configuration chosen by the customer (Jensen et al., 2009)	-Number of customized products
Lean concept: Lean Changeability
-Easily understandable production technologies	-Technology usability index
-Easily operated and simple convertible production means	-Convertibility index
-Simple and easily understandable work steps	-Adjustment time
-Means of production that do not require permanent fixing to the ground	-Mobility of production means
-Each workplace must be readily adaptable to the needs of workers (Klemke and Nyhuis 2009)	-Workplace adaptability

For each Lean parameter, it is essential to identify the corresponding constraints, which define the operational limits within which the system must operate. They influence the choice of components, processes configuration, capacity planning and layout. Lean constraints are translated into design specifications. For example, a constraint to minimize setup times can be translated into a design requirement for quick-change tools.

The identified Lean requirements encompass technical, process, and behavioral aspects that should be considered from the requirements specification phase. The alignment of these requirements with the technological aspects of I5.0 can be addressed in subsequent design stages.

Automation technologies, such as collaborative robots (cobots), can streamline production while allowing human workers to focus on value-added tasks. Smart sensors and AI can enhance quality control through real-time monitoring, and predictive maintenance enabled by IoT devices helps reduce downtime. Sustainability can be achieved through green technologies and sustainable materials, while resilience is supported by AI-driven systems that adapt to changing demands. Additionally, humancentric technologies like exoskeletons and virtual training tools empower workers, creating a collaborative and efficient work environment. Further investigation into the I5.0 technologies corresponding to each Lean requirement and their integration from the design phase will be the focus of future research.

According to our analysis, the majority of Lean requirements identified fundamentally require the integration of eight key Lean tools: SMED (Single Minute Exchange of Die), 5S, Kanban, TPM (Total Productive Maintenance), Poka-Yoke (error prevention), Just in time (JIT), Heijunka and Value Stream Mapping (VSM).

Evaluate lean tools to be integrated using MCDM

In this study, we use Multi-Criteria Decision-Making (MCDM) due to the need to integrate multiple, often conflicting, and non-commensurable criteria. MCDM is well-suited for situations where several criteria must be considered simultaneously, especially when the alternatives are already known. Unlike other decision-making frameworks, MCDM allows for the comparison and prioritization of these conflicting criteria, which would be difficult to address with other methods. Since we have a predefined set of alternatives, multi-objective optimization methods would not be suitable either, as they require uncertainty about the available alternatives. Therefore, MCDM provides a structured and adaptable approach that can address the evolving and interconnected needs of modern production systems. Its flexibility enables the integration of new criteria and adjustments as technologies and priorities evolve, making it ideal for ensuring alignment with both operational and broader human-centric and environmental goals, ensuring that all relevant criteria are considered.

The Integration of Lean tools into the design phase vary considerably depending on the objectives being pursued, such as the ones we have identified from the DfX methodologies. Each of these objectives has specific requirements and implications for the design process, requiring a focused and strategic alignment of Lean tools. To effectively prioritize these objectives and ensure that the integration of Lean tools aligns with a company’s specific preferences and objectives, the use of MCDM methods could be benefit.

The integration of Lean tools must be carefully adapted to the specific objectives of a design project, as each objective requires a distinct set of technologies and practices. This alignment is crucial because Lean tools are not always one-size-fits-all solutions; their effectiveness depends on their ability to address the unique challenges and goals associated with each objective. For example, the requirements required to improve production efficiency differ significantly from those needed to improve maintenance or guarantee product quality. By gearing the integration the specific requirements of each objective, companies can leverage the most appropriate effective tools, technologies and practices. This precise focus prevents misalignment of resources and efforts, ensuring that every aspect of the Lean integration process is strategically focused on achieving the designated objectives.

The critical step in applying a MCDM methodology is selecting the criteria, and alternatives of Lean Tools that the decision-maker must consider. The selection of the most appropriate Lean tools to integrate to achieve specific objectives is fundamentally guided by the decision-maker’s judgment. This process involves a strategic evaluation of the various Lean Tools (alternatives) in relation to the set objectives (criteria) that the company wishes to achieve. The decision-maker, with his in-depth knowledge of the company’s objectives, operating context and constraints, plays an essential role in this selection process. By drawing on their expertise and insight, they can assess which Lean Tools are most appropriate to meet the selected objective. The criteria for selecting the optimal alternative are diverse in nature, which means that managers have to analyse and choose criteria that contribute to the improvement of the system performance. The criteria on the basis of which the considered alternatives (An) were evaluated are: C1: Productivity, C2: Maintainability, C3: Quality, C4: Sustainability, C5: Human-Centricity and C6: Resilience. For each Criteria, the decision-maker selects the relevant sub-criteria (C1n, C2n, etc.) and the corresponding Lean requirements that respond to their specific objectives. This selection can be guided by the requirements described in Table 2, where each Lean requirement is defined to meet a specific objective or problem. The selected requirements will guide the selection of Lean tool functionalities to be integrated in the next design process steps.

The hierarchical structure of the problem developed by the AHP methodology of our study is shown in Figure 4.

The impact of the criteria on selecting Lean tools is influenced by the decision maker's subjective judgment. This implies that any given criterion can become critical in the decision-making process, depending on the context and the requirements that have been chosen for each objective, and that the choice of criteria can lead to different alternatives of Lean tools emerging as the most preferred.

Using AHP method, the decision-maker first completes a pairwise comparison matrix to evaluate the relative importance of each criterion. This involves comparing each criterion against every other criterion using a scale (e.g., 1 to 9) to determine their relative importance. The decision-maker then calculates the weights for each criterion based on these comparisons and checks the consistency of the matrix to ensure logical coherence in the comparisons. If the consistency ratio (CR) is within an acceptable threshold (typically below 0.10), the comparisons are deemed consistent; otherwise, adjustments are needed.

Next, the decision-maker constructs a series of pairwise comparison matrices for each criterion to assess the relative importance of each alternative (in this case, each Lean Tool) with respect to that specific criterion. These comparisons are also made using a scale and result in local weights for each alternative under each criterion.

To integrate these local weights with the criterion weights, the decision-maker calculates the global weights for each alternative. This involves multiplying the local weight of each alternative (for each criterion) by the weight of that criterion. The cumulative effect across all criteria gives the overall score for each alternative.

Finally, the decision-maker compares the overall scores of all alternatives. The tools are ranked based on these scores, and the Lean tools that best satisfy the criteria are selected. This final selection ensures that the chosen tools align with the established priorities and deliver optimal outcomes according to the decision-making criteria.

This work can be complemented by a robustness and sensitivity analysis to validate the chosen method and compare it with other MCDM methods.

Analyse of contradictions between lean parameters and technical parameters and search solution (s)

Integrating Lean Tools into the design phase means synchronizing its requirements with technical specifications. It may occur that the corresponding parameters of Lean requirements conflict with technical parameters or do not respect technical constraints. When such conflicts arise, it is essential to pinpoint these discrepancies and resolve them using innovative design methodologies such as the theory of inventive problem solving “TRIZ”', or by adjusting certain constraints, either by removing certain Lean requirements, or by modifying specific constraints.

In case of Lean Requirements are aligned with technical requirements and constraints, the next step is to provide functional analysis and identify the Lean technical functions that combine the technical specifications and Lean considerations. These functions define the desired actions and behaviours of the system to achieve Lean requirements. They influence design decisions in terms of desired system operations, reflected in:

- Specify process flow or system architecture: Lean technical functions are translating into specific steps or activities within the production process. They provide the basis for the design of workstations, handling systems and information flow mechanisms.

- Dictate system capabilities: Lean technical functions define what the system needs to achieve in term of performance. They can influence the selection of sensors, controllers and automation technologies to achieve the desired output. Future research will explore how to implement Lean technical functions to achieve the desired operation.

Conclusion

In this article, we present our work aimed at providing designers with a new methodology for designing a high-performance production system, in particular by identifying a set of Lean requirements and parameters in an I5.0 context.

In comparison with existing literature, which examined the application of Lean and I5.0 in existing systems, this paper presents a comprehensive investigation. Besides the limited existing research that analyses the integration of Lean in the context of I5.0, there is also a notable lack of a guide to support designers in fundamentally building their systems based on Lean requirements.

We observed this significant gap and we proposed a new methodology that aim to extract and integrate multiple Lean requirements that respond to a set of criteria and domains which we have identified from a literature review of 34 DfX methodologies. Compared to previous research that primarily focuses on the traditional integration of Lean or the automation of Lean tools, particularly within the context of Lean 4.0, this study represents one of the initial efforts to align Lean tools’ functionalities with I5.0 principles. By incorporating Lean requirements that support I5.0 considerations from the early design phase, the system is fundamentally designed with sustainability, human-centricity, and resilience in mind. As a result, when technologies are integrated in the subsequent design stages, they align with these critical principles. The diverse set of requirements identified in this study provides opportunities to enhance performance across both traditional areas, such as production, maintenance, and quality, as well as emerging domains related to I5.0.

Integrating Lean and I5.0 concepts from the early design phase can be considered as a crucial step to eliminate unnecessary activities, saves resources, promotes a human-centric approach by minimizing unnecessary tasks for employees and enables the integration of sustainable development practices, ensuring that the system design is aligned with environmental and social responsibility objectives.

Our research has certain limitations that will be addressed in future studies. One limitation of this study is that it focuses primarily on the first phase of design, the requirements specification phase. Thus, in our future research, we will address the subsequent stages of the design process and explore how these requirements translate into practical implementation. Additionally, the framework’s effectiveness heavily depends on the availability of accurate data inputs, which may be challenging to obtain in some industrial contexts. The complexity of integrating both Lean and I5.0 principles could also present challenges in terms of system implementation, requiring careful consideration of how technologies, human factors, and sustainability can be balanced effectively. These areas will need to be explored in future work to refine and fully develop the approach.

In the light of this study, our research can be developed to test the proposed methodology across various industrial sectors, assess its impact on designers’ workloads, and refine the steps needed to create a model that can serve as a practical guide for designers. By combining MCDM with inventive design methods, our approach aims to optimize the integration of Lean requirements and foster the creation of innovative production systems. This aspect of our work will be further developed in future publications. Additionally, future research will focus on evaluating the effects of integrating Lean requirements on a company’s digital transformation, identifying I5.0 technologies that align with these requirements, and exploring the potential of advanced AI tools in Lean 5.0 systems.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Rami Gdoura

Author biographies

Rami Gdoura is currently pursuing a PhD in Industrial Engineering, through a scientific collaboration between the University of Strasbourg, France (ICube-CSIP laboratory) and the University of Sfax, Tunisia (OLID Laboratory). His research primarily focuses on enhancing the performance of production systems. His main areas of interest include lean management, industry 4.0, industry 5.0, and inventive design. His research explores the integration of these concepts during the design phase of production systems, as well as the analysis of various relations between these concepts.

Rémy Houssin is a Professor in Industrial Engineering since 2015, actively contributing as a researcher at ICube Laboratory. He serves as the Head of the Design of Information System and Inventive Processes Team (CSIP) and teaches at the Faculty of Physics and Engineering, University of Strasbourg since 2004. His research focuses on enhancing global artefact performance during the early design stage, particularly through the innovative behavioural design method. This method evaluates product performance, aiming to optimise inventive design processes by integrating Lean principles. He is also dedicated to knowledge management for innovation in inventive design processes.

Diala Dhouib is a Professor in Quantitative Methods at the Higher Institute of Industrial Management (University of Sfax – Tunisia). Currently, she is the Head of the OLID Research Unit. Her teaching and research interests include supply chain and logistics management, mathematical programming, multi-criteria decision aid, operations management and practical approach of optimisation modelling and resolution. She has initiated several research projects with economic agents and public organisations.

Amadou Coulibaly is an Associate Professor at INSA of Strasbourg, serving as the Scientific Manager of the Innovation and Engineering Platform. His expertise lies in several areas encompassing the design, modelling, and engineering of products and systems. He is also experienced in developing technical information systems, advanced databases, and client/server applications.

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