# Building a collaborative manufacturing system's network resilience through an adaptability potential analysis

Selma Ferhat, Raphaël Oger, Eric Ballot, Matthieu Lauras

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Selma Ferhat, Raphaël Oger, Eric Ballot, Matthieu Lauras. Building a collaborative manufacturing system's network resilience through an adaptability potential analysis. European Journal of Innovation Management, In press, 10.1108/EJIM-12-2023-1144. hal-04834992

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# Building a collaborative manufacturing system's network resilience through an adaptability potential analysis

Selma Ferhat¹,², Raphael Oger², Eric Ballot¹, Matthieu Lauras²

¹ Centre de Gestion Scientifique, École des Mines Paris, France
{selma.ferhat,eric.ballot}@mines-paristech.fr

² Centre de Génie Industriel, IMT Mines Albi, France
{selma.ferhat,matthieu.lauras,raphael.oger}@mines-albi.fr

**Abstract**

**Purpose** - This research investigates how adaptability potential analysis in collaborative manufacturing networks can be used to enhance resilience when addressing innovative production challenges that require change initiatives.

**Methodology** - A literature review on collaborative adaptive systems showed a lack of solution to adapt production to an unpredicted product within a network. A framework is proposed for evaluating the adaptability of collaborative networks and providing inter-system adaptation recommendations. We demonstrate the applicability of this approach through an illustrative experimentation involving a cosmetic company seeking to produce a new product, hydroalcoholic gels in the context of collaborative networks.

**Findings** - The experimentation demonstrates that the adaptability analysis based on ontology can help different manufacturing systems make decisions based on their state and limits of capabilities. Also, our adaptation recommendations may help understand economic impacts of collaboration for different scenarios before launching.

**Limitations** - The research scope does not extend to the consideration of quantity and operational aspects. Additionally, the reconfigurability aspects within each manufacturing system, such as the reordering of layout sequences, has not been addressed yet.

**Implications** - The results allow organizations to compare resilience states from an individual and collaborative perspective, enabling them to make informed decisions about new production opportunities and effectively navigate the changing manufacturing landscape.

**Originality** - This research combines capability-based analysis and a collaborative network perspective to streamline decision-making for systems facing new production demands. It provides new insights into effectual decision-making, empowering organizations to skilfully manage unexpected challenges and identify suitable partners accordingly.

**Keywords** - Collaborative networks, Resilience, Manufacturing systems, Adaptability, Ontology, Innovative production.

**Article Type** - Research Paper.
# 1 Introduction

In the rapidly evolving landscape of industrial production, systems are continually exposed to a myriad of challenges, including hazards, variability, and disruptions. The history has provided sufficient evidence that a failure attributed to one manufacturing company may massively impact the worldwide industry e.g., the widely publicized earthquake in Taiwan in 2018 and prior years affected semi-conductor factories worldwide (Kusiak, 2019). This event starkly illustrated the global economy's dependency on semiconductor chips produced in Taiwan, as the disruptions had cascading effects on supply chains worldwide. Such incidents demonstrate the fragile interconnectedness of global manufacturing networks and emphasize the need for robust risk management strategies in manufacturing operations. Traditional risk approaches are not effective when a company is confronted with unexpected disruptions since they rely on past experimental data and are narrow-focused, which usually cause more vulnerability when dealing with unforeseen events (Ramezani and Camarinha-Matos, 2019a). This is why disruptions and unplanned shocks might become a natural momentum of organizational development (Mithani et al., 2021). Some firms will not survive for these unforeseen events, others will need to adapt to the new landscape (Corvello et al., 2023). Therefore, enterprises should develop the capacities that help them to adapt to the changing environment, aftershocks, or threats, return to normal activities, and to use the lessons learned as a driving force for a unique and competitive advantage (Bartuseviciene et al., 2024). To excel in this challenging landscape, production enterprises must enhance their “organizational capability” to identify potential threats and strategically adapt and reconfigure their manufacturing systems. This approach, known as “Manufacturing Resilience,” goes beyond mere survival, emphasizing the need to adapt and prosper amidst ongoing challenges (Romero et al., 2021). Resilience in this context is intertwined with concepts such as survivability, adaptability, robustness, and sustainability, which form the foundational capabilities of a resilient manufacturing system (Ramezani and Camarinha-Matos, 2020).

Furthermore, to significantly bolster the resilience of manufacturing enterprises against disruptions, whether stemming from internal sources or external factors, a paradigm shifts towards “production innovation” is imperative. This approach is defined as a dynamic process that aligns enabling technologies and methodologies with emerging manufacturing applications and challenges, thereby generating substantial value for the enterprise (Romero et al., 2021). Innovation is a widely recognized concept since (Schumpeter, 2013) introduced the notion of “creative disruption” meaning that the creation of something innovative simultaneously destroys old industries while ushering in new industries and approaches (Ammirato et al., 2019). Moreover, innovation in production transcends the traditional scope of simply developing new products or services. It now encompasses a proactive strategy to identify and seize emergent opportunities, often in real-time, facilitating new adaptive production methods that respond swiftly to changing market demands and technological advancements (Ramezani and Camarinha-Matos, 2020).

Another pivotal aspect of modern production systems is their growing reliance on partner networks. The current trend indicates a shift towards collaborative networks, which are considered as “glue that holds supply chain organizations together in a crisis”, are important to build a resilient supply chain (Scholten and Schilder, 2015). For instance, during the COVID-19 pandemic, the HOPE ventilator project was launched by the government to address the urgent external demand for ventilators. Companies like GM, Medtronic, and Philips, along with other partners, collaborated within a network to pool their capabilities and rapidly scale up production. This collaboration enabled them to effectively meet the unexpected demand, demonstrating the power of external collaborative networks in managing global crises. On the
other hand, in a more internally driven scenario, the Ford Mustang Mach-E project focused on the internal demand to develop a 100% electric SUV. Ford collaborated with partners like Webasto and LG Chem, leveraging their specialized expertise and resources to optimize the development and production processes, ensuring that the project goals were met efficiently. These examples illustrate the effectiveness of collaborative networks in addressing both external and internal demands, highlighting the importance of fully exploiting their adaptation potential.

As a resilience capability (Jüttner and Maklan, 2011), collaboration contributes to facilitate building resilience through collective awareness, knowledge co-production and collaborative adaptive management (Ramezani and Camarinha-Matos, 2019a). Recent decades have witnessed the evolution of collaborative networks in various forms, which can be primarily categorized into two distinct classes. The first class encompasses long-term strategic networks, often exemplified by 'Virtual Organizations Breeding Environments' (VBEs). These networks are characterized by their longevity and strategic focus, providing a stable foundation for collaborative endeavours. The second class is composed of 'goal-oriented networks', typically represented by entities such as 'Virtual Organizations' or 'Virtual Enterprises' (VO/VE). These networks are more dynamic and are formed with specific objectives or projects in mind, distinguishing them from their long-term strategic counterpart (Ramezani and Camarinha-Matos, 2019b).

Despite the various strategies aimed at making manufacturing systems more resilient, there's still a gap in approaches that effectively combine two important capabilities: adaptability and collaboration, particularly within goal-oriented collaborative network configurations intended to capitalize on new production prospects. This conundrum raises a fundamental industrial inquiry: "Is it advantageous for a targeted enterprise to harness a new production opportunity within a collaborative network regarding its portfolio of capabilities? If so, how can we automatically assess the adaptability status and provide recommendations?"

This article addresses the complex challenges of enhancing resilience in production systems by exploring innovative tools and methodologies. It emphasizes strengthening resilience in collaborative manufacturing networks through a framework designed to assess adaptability in response to new production demands. Our research approach is structured into three interrelated phases. First, we construct an ontology that is instantiated into a knowledge graph, which captures production requirements and system capabilities. Next, we employ this knowledge graph in a matchmaking process to identify manufacturing candidates that align with production requirements across various levels. Finally, the adaptation selection is conducted through an optimization model, which aims to find the optimal co-manufacturing strategy while minimizing transportation and production costs. The framework is applied through an experiment involving a beverage manufacturing company that adapts its production line to manufacture hydroalcoholic gel, a biocidal product typically produced by the cosmetics and pharmaceutical industries. During the COVID-19 pandemic, regulatory restrictions allowed only products related to COVID-19, such as hydroalcoholic gels, to be manufactured. This led the beverage industry, which already had bottling capabilities, to explore whether it could pivot its production to meet this new demand. The experiment illustrates how the company not only leveraged its existing infrastructure but also sought new partners to supplement its capabilities when needed, ensuring it could effectively seize this opportunity. This case study demonstrates the framework's broader application, showing that the need for adaptability and collaboration is essential across various industries during crises. We chose to use a representative use case to rigorously test the framework in a controlled environment, allowing us to focus on its core functionalities without the biases of real-world cases. Although real-world validation is important and something we advocate for, this illustrative experiment serves to demonstrate the
framework's potential and identify any limitations before applying it more broadly across various sectors.

## 2 Theoretical background

### 2.1 Evolution of manufacturing system paradigms

Due to different internal or external drivers, production systems had to highlight the focus on their reactivity and improve their responsiveness through change, maintaining or increasing a high level of performance. Change drivers can affect strategic manufacturing levels but also operational ones, both require a high degree of adaptation and different kind of responsiveness. Several manufacturing systems paradigms have emerged as a result of these changes. Initially, production systems were predominantly characterized by dedicated manufacturing lines (DMLs), where each system was tailored exclusively for producing a single product (Setchi and Lagos, 2004). This approach, while efficient for mass production, lacked the flexibility needed to adapt to market changes and evolving product designs. (Gola and Świć, 2012) highlight the limitations of such dedicated systems in their work. The advent of automation technology in the 1980s marked a significant shift. The increasing complexity of products and market pressures on manufacturing costs led to the emergence of distributed manufacturing models, such as Flexible Manufacturing Systems (FMSs) and Reconfigurable Manufacturing Systems (RMSs) (ElMaraghy, 2005). Further advancements in technology and the rise of artificial intelligence spurred the digitalization of manufacturing, as discussed by (Jones et al., 2020). Industry 4.0 revolutionized the manufacturing landscape, giving birth to new paradigms such as Cyber-Physical Systems, Cloud Manufacturing, and Knowledge-Driven Manufacturing (Ganiyusufoglu, 2019).

(Kusiak, 2021) work on universal manufacturing, a concept that expands the decision space beyond a single enterprise, further illustrates this evolution. This approach introduces a 'many-to-many' relationship between production systems and products, implying that multiple production systems can produce various products. A single manufacturing system can produce various products, and a product can be manufactured within a network of systems. This paradigm shift is underscored by two main properties: adaptability and affinity. Adaptability is defined as the ability to respond to changes and uncertainty in the environment (Kjeldgaard et al., 2023). The concept of affinity, outlined by (Kusiak, 2022), encompasses unified representation of processes and products, system connectivity and interoperability, and similarity of digital models. These aspects are crucial for achieving seamless integration and efficient operation within the universal manufacturing framework. The nature of information varies, as each business domain has its unique requirements based on its specific discipline (Palmer et al., 2018). To face these challenges and make effective decisions in manufacturing, it is essential to have access to high-quality information and knowledge at crucial decision-making moments.

### 2.2 Handling interoperability issue within collaborative manufacturing systems

In such distributed and heterogeneous manufacturing systems, knowledge sharing presents a challenge due to each system possessing its unique knowledge structure. This diversity often leads to a lack of understanding of the exchanged knowledge, which in turn adversely affects interoperability and the ability to reuse and share knowledge effectively. (Leitão et al., 2016). Among other problems, these include the lack of compatibility between the information models and the vocabulary used by each one; the lack of formalization in the definition of concepts, preventing the automatic processing of information; multiple definitions of a term and several different terms used to refer to a single concept. Moreover, some terms may be misinterpreted
depending on the knowledge background and domain expertise of each expert analysing the
standard (Fraga *et al.*, 2020).

(Chen, 2006) posits that achieving semantic interoperability initially necessitates a
comprehension of the formal conceptualization underlying the terms used in each relevant
domain. Retrieving and utilizing information from multiple diverse sources puts high demands
on semantic integration solutions (Järvenpää *et al.*, 2019).

Initial approaches to semantic interoperability have been achieved through ontology matching approaches (Palmer *et al.*, 2018). Furthermore, ontologies have been considered the backbone technology in most knowledge-based applications (Adamczyk *et al.*, 2020). One of the definitions of ontologies most frequently referenced is “a formal, explicit specification of a shared conceptualization,” as defined by (Studer *et al.*, 1998).

Several ontologies have been proposed with the objective of facilitating knowledge
management and information exchange for manufacturing process. In the manufacturing
domain, a typical manufacturing system can be characterized according to three notions: (1)
Product; (2) Process; and (3) Resources (Cao *et al.*, 2019). Different frameworks have been
developed to encompass the entirety of manufacturing knowledge, specifically tailored to these
three cited notions. For instance, PRONTO (PRoduct ONTOlogy) is specialized in the semantic
integration of product information systems. It leverages the generative Bill of Materials (BOM)
and product family concepts to manage multiple variants effectively(Vegetti *et al.*, 2011).
However, PRONTO does not align with existing standards for modeling product structure
processes and features(Panetto *et al.*, 2012). In contrast, ONTO-PDM (ONTOlogy Product
Data Management), as identified by (Panetto *et al.*, 2012), is more narrowly focused on product
data management, solving interoperability issues between various enterprise applications. It
specifically harmonizes product-related knowledge and standards, thus concentrating solely on
the product aspect. Turning to the process dimension, the Process Specification Language
(PSL), highlighted by (Schlenoff *et al.*, 2000), offers a fundamental representation of the
concepts necessary for outlining basic manufacturing engineering and business processes.
Moreover, the Manufacturing Service Description Language (MSDL), detailed by (Ameri *et
al.*, 2012; Ameri and Dutta, 2006), serves a dual purpose. Its core provides a static and universal
foundation for service description, while its extension is dynamic, evolving to meet the specific
needs of diverse user communities. MASON (MAnufacturing Semantics ONtology),
introduced by (Lemaignan *et al.*, 2006), stands as an upper ontology built upon key concepts
of entities, operations, and resources. It offers a broad coverage of the manufacturing domain,
encompassing product, process, and resource aspects comprehensive. Finally, the
Manufacturing Core Concepts Ontology (MCCO), defined by (Usman *et al.*, 2011), identifies
a core set of concepts in heavyweight logic, addressing the comprehensive aspects of the
manufacturing domain.

Although these existing ontologies provide varied and specialized methods for knowledge
management in manufacturing, they fall short in effectively tackling resource-product
matching, crucial for bolstering manufacturing system resilience. To address this gap,
(Järvenpää *et al.*, 2019) proposed the Manufacturing Resource Capability Ontology (MaRCO)
model. However, MaRCO's primary concentration on assembly within a single manufacturing
system restricts its wider applicability across the diverse areas within the manufacturing field.

This narrow focus highlights a critical gap in existing ontological frameworks, underscoring
the pressing need for a more comprehensive, capability-based core ontology. Such an ontology
would need to encompass a broader range of manufacturing systems, extending its reach beyond
a single system to incorporate an entire network. This approach is instrumental in leveraging
knowledge to enhance the resilience of manufacturing systems.
## 2.3 Innovation for manufacturing resilience

### 2.3.1 Strategies to handle disruptions

In the dynamic landscape of modern industries, the ability to cope with disruptions is crucial for maintaining operational continuity and achieving long-term sustainability. (Ramezani and Camarinha-Matos, 2020) presents a comprehensive framework, categorized into six meta-levels, which provides insightful strategies for organizations to manage and adapt to disruptions effectively:

**Discovering Strategies:** emphasize the importance of understanding the environment to identify potential sources of disruptions. Strategies in this category, such as 'mapping', involve a proactive approach to anticipate changes and prepare the system accordingly. This level is about vigilance and foresight in detecting early signs of potential disruptions.

**Avoiding Strategies:** employed to prevent or minimize the impact of threatening situations. Techniques like 'buffering' are used to create safeguards and defences against known risks, aiming to avert disruptions before they materialize.

**Do Nothing Strategies:** represent a risk acceptance approach where the organization decides to 'wait-and-see'. This strategy may be adopted when the cost of intervention outweighs the potential benefits, or when disruptions are deemed as low-impact or highly uncertain.

**Reducing Strategies:** focus on mitigating vulnerability and the negative consequences of disruptions. This involves sharing information and resources to decrease the system's exposure and susceptibility to disruptive events.

**Managing Strategies:** geared towards handling the complexity of systems. This includes 'contingency planning', where organizations develop detailed plans to manage and respond to disruptions effectively, ensuring system stability and continuity.

**Learning and Adapting Strategies:** solving problems creatively and evolving in response to disruptions. This involves 'trial-and-error' methods and other innovative approaches to adapt operations and processes, enabling continuous improvement and evolution of the system in the face of changing demands and challenges.

This six-tiered framework offers a holistic view of disruption coping strategies, ranging from proactive detection and prevention to reactive management and adaptation. By employing these strategies, organizations can enhance their resilience, turning potential crises into opportunities for growth and development. However, when these strategies are juxtaposed with the broader landscape of existing literature on disruption management, several research gaps and thematic divergences become evident (Pal and Altay, 2023). The operations management literature, as described by (Ambulkar et al., 2015), predominantly focuses on the 'what' of disruption management. This is mirrored in strategies like 'Discovering', 'Avoiding', and 'Reducing' within the six-tier framework, which outline specific actions and tactics for handling disruptions. These strategies are prescriptive in nature, offering clear directives on practical measures that can be employed. On the other hand, organizational theories, as highlighted by (Craighead et al., 2020), delve into the 'why' behind disruption management strategies. This perspective aligns with the 'Do Nothing' approach, where the emphasis is on understanding the rationale behind choosing a certain course of action, especially in scenarios where inaction is selected over proactive measures. However, as (Handfield and Melnyk, 1998) note, there is a notable gap in research focusing on the 'how' of managing disruptions. This gap is particularly pronounced when considering the 'Managing' and 'Learning and Adapting' strategies from the six-tiered framework. While these strategies do touch on the processes and methods involved in managing
and adapting to disruptions, they do not extensively cover the decision-making process and the intricate mechanisms that guide the selection and implementation of these strategies.

In conclusion, while the six meta-level strategies provide a comprehensive guide on managing disruptions, there remains a significant opportunity in the academic field to explore the decision-making processes ('how') underlying these strategies. This exploration is crucial for a holistic understanding of disruption management, especially considering evolving global challenges and the dynamic nature of modern industries.

### 2.3.2 Resilience in manufacturing

Resilience is often understood as a system's capacity to adapt to changes and return from an unfavourable state to a favourable one, as outlined by (Alexopoulos et al., 2022). Alternatively, as (Thomas et al., 2015) suggest, resilience can also be conceptualized as the ability to manage disturbances with the goal of averting a transition to an undesirable state. In the literature discourse, it is useful to categorize resilience into three distinct levels. The first, passive resilience, is defined by the inherent capacity of a system to absorb shocks through built-in redundancies (Sterbenz et al., 2014). This level of resilience becomes apparent in manufacturing environments where overlapping capabilities exist, enabling the system to handle disruptions to a certain extent. However, when faced with disruptions of a more significant magnitude, passive resilience may prove inadequate. To address more substantial challenges, the focus shifts to adaptive resilience (Deubelli and Mechler, 2021). This second level involves the enhancement of individual capacities within production systems, enabling them to adapt to evolving conditions more effectively. It signifies a proactive stance towards change, where systems are not just prepared for disruptions but are also capable of adjusting to them dynamically. The third level, transformative resilience, demands a more profound change (Asadzadeh et al., 2022). At this stage, organizations are required to fundamentally rethink and restructure their capabilities and operational frameworks.

### 2.3.3 Adaptability as a Foundation for Resilience

Adaptability (or the ability for adaptation) has a similar meaning as or is closely related to adaptive capacity (Smit and Wandel, 2006), and therefore seems to be compatible with the concept of vulnerability (Weißhuhn et al., 2018). While there are differences in identifying the formative elements of resilience, it is generally acknowledged that these elements combine to develop the adaptive capability of an organization (Scholten et al., 2019). Adaptability, as defined by (Ramezani and Camarinha-Matos, 2020), refers to the capability of manufacturing systems to modify operations in response to disruptions, such as re-routing, substituting requirements, leveraging disruption advantages, alternative technology development, and learning from experience. Research by (Lee, 2004) in "The Triple-A Supply Chain" highlights how adaptable manufacturing systems are more capable of handling disruptions and uncertainties and therefore are more resilient.

### 2.3.4 Production Innovation as a Catalyst for Resilient Systems

Building “manufacturing resilience” requires developing resilience enhancement features in a manufacturing system that improve this adaptive capacity. (Romero et al., 2021) argues that “production innovation”, as a dynamic capability of a firm, enhances manufacturing enterprise’s ability to build “resilience” towards disruption. Production innovation – can be defined as “a process that drives matching of enabling technologies and methods with new manufacturing applications and challenges to create value for the manufacturing enterprise (Larsson, 2017; Romero et al., 2017, 2021). These capabilities are crucial in adapting to new
manufacturing trends, customer demands, and technological advancements, thereby fostering a
resilient manufacturing environment.

Integrating production innovation capability with adaptive capacity is a vital approach to address the "how" in enhancing manufacturing resilience. This strategy involves applying innovative methods to not only mitigate disruption impacts but also to augment the system's adaptability to new challenges. Current research predominantly focuses on reconfigurability and technological advancements separately. However, the fusion of these two capabilities – adaptability and innovation – is less explored and warrants more attention.

## 2.4 Building resilience through collaborative networks

Collaboration is considered as a resilient strategy since it is part of the reducing strategies that handle disruptions (Ramezani and Camarinha-Matos, 2020). There is a growing body of literature recognising the vital role of enterprise collaborations in manufacturing supply chains where autonomous organisations combine capabilities and pool manufacturing capacities (Cisneros-Cabrera et al., 2021). The interorganizational relationships are initiated, nurtured, and maintained through members' contribution to a shared project with time, expertise, contacts and information. Collective benefit is attained through the pooling of expertise, access and resources, while at the same time respecting diverse views, mandates and institutional priorities (Mandell et al., 2017).

Different authors (Berkes, 2017; Ramezani and Camarinha-Matos, 2020; Russo and Ciancarini, 2017) underscore the critical role of collaboration in fostering resilience. This includes:

- Collective Awareness and Intelligence: This entails a mutual understanding within groups, essential for orchestrating activities. It's particularly vital in business ecosystems to adapt to evolving environments and to make choices that are environmentally sustainable.

- Knowledge Co-production: This process engages a variety of stakeholders from different sectors to merge their knowledge, tackling challenges and fostering new ideas. It places a strong emphasis on joint research and the collective generation of concepts.

- Collaborative Adaptive Management: This approach is centred on adaptive governance. It integrates insights and experiences from a broad range of contributors, aimed at effectively navigating situations with high levels of uncertainty, utilizing a flexible and learning-oriented approach to decision-making.

Considering the wide range of collaborative network forms across various application domains, which often employ distinct terminologies, it becomes essential to establish a clear taxonomy of the different organizational structures (Appio et al., 2017). This need for classification and definition has been emphasized by (Camarinha-Matos et al., 2009, 2019) in their research. The taxonomy distinguishes between "collaborative networked organization (CNO)" and "ad-hoc collaboration". A CNO is typically business-oriented with well-defined structures, roles, and governance rules, whereas ad-hoc collaborations are spontaneous and less structured, often emerging in response to crises. Within the spectrum of CNOs, two principal categories stand out: goal-oriented networks and long-term strategic networks, each with unique characteristics and objectives. Long-term strategic networks are designed for sustained collaboration with a strategic, long-term focus, aiming to build lasting relationships and cumulative advantages over time. In contrast, goal-oriented networks are intensely focused on achieving specific, often short-term goals. This category further bifurcates into continuous production-driven networks, known for their stability and longevity, and grasping opportunity-driven networks, which are dynamic entities formed to capitalize on specific business opportunities within a limited timeframe. These opportunity-driven networks, comprising independent entities or individuals
pooling skills and resources, are exemplified by virtual organizations (VOs), virtual enterprises (VEs), and virtual teams (VTs).

This paper places a specific emphasis on grasping opportunity-driven networks within the goal-oriented class of CNOs. These networks are particularly significant in today's rapidly changing business environment, where the ability to respond quickly to new opportunities are crucial to stay competitive and thrive in this uncertain market. Collaborative Networks can significantly contribute for resilience. The potential benefits of collaboration are not limited to providing a survival capability, but can also facilitate the identification and exploitation of new opportunities in such contexts (Camarinha-Matos, 2014).

## 3 Method

While the diverse body of works in the extant literature provide valuable insights, there is a research gap in theories for explaining the decision process of managing disruptions, particularly those leading to novel production requirements beyond the current expertise of the manufacturing system. To address the identified gaps, we developed a conceptual framework based on three interdependent hypotheses that emerge from the theoretical background discussed in the previous section. The framework is structured to address three hypotheses aimed at understanding adaptability within individual enterprises and across a network, and to identify the required knowledge for adaptability (see figure 1).

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Big picture of the research method.</figcaption></figure>
The foundational block, Knowledge Representation (H1), entails assembling and structuring essential concepts and data. It utilizes an ontology model to crystallize business data into a knowledge graph, thus forming the bedrock that informs and facilitates subsequent analytical processes. Progressing to the second block, Adaptability Analysis (H2), we leverage the knowledge graph to identify and examine the adaptability potential within individual enterprises and comparatively across the network. This phase synthesizes queries and algorithmic analyses, culminating in a nuanced understanding of adaptability states, which subsequently informs decision-making processes. The culmination of this framework is embodied in the Adaptation Evaluation and Selection (H3) block. Here, we engage in a comprehensive assessment of the network's overall adaptability, integrating and evaluating interconnected solution sets. The output is a set of targeted recommendations for adaptation, predicated on the interdependencies and insights gleaned from the prior blocks.

In essence, our framework presents a systematic and innovative approach to assess and fortify adaptability in manufacturing collaborative networks. It fills a significant research void by offering a comprehensive decision-making paradigm, specifically tailored for managing novel production demands. This approach is pioneering in its holistic integration of knowledge representation, adaptability analysis, and evaluation processes, thereby empowering manufacturers to make strategic, informed decisions in the face of a new production demands. The following section will explain in detail the proposed model to answer our three hypotheses.

## 4 Proposed model

In this section, we detail our proposed model, which is structured into three distinct sub-sections, each aligned with a specific hypothesis, as explained in the previous section. We begin by elaborating on the construction of our core ontology, focusing primarily on the "knowledge representation" aspect of our framework. This involves a detailed explanation of how various elements are integrated to form a coherent ontology that supports decision-making processes. Then, we explore the "adaptability analysis" sub-section, describing the decision-making algorithm and the queries used to assess system adaptability. This analysis is vital for identifying potential improvements in system responsiveness. Finally, in the "optimal inter-adaptation selection" sub-section, we present an optimization model designed to efficiently integrate new adaptations into the manufacturing setup.

Together, these elements form a comprehensive framework for enhancing adaptability in manufacturing systems, as illustrated in Figure 1. This approach provides both theoretical and practical insights into adaptability implementation.

### 4.1 Collaborative Production Networks' Ontology

To provide a clearer understanding of the construction of our ontological model, we adopted a structured approach to align product requirements with manufacturing resources. As mentioned in section 2.2, several ontologies have been proposed to facilitate knowledge management and information exchange in the manufacturing domain. Typically, a manufacturing system can be characterized by three key notions: Product, Process, and Resources (Cao et al., 2019). These domains are essential for capturing the core elements of manufacturing, as they represent the fundamental aspects that drive production and resource allocation decisions.
Our ontology development process began with capturing the relationships within these critical domains—product, resource, and enterprise—while also incorporating a broader taxonomy of materials and operations. The goal of this ontology is to gather essential information that can be used to leverage collective knowledge across various manufacturing areas without detailing every class exhaustively. The inclusion of these domains allows for a comprehensive understanding of manufacturing processes, making the ontology adaptable and applicable across different contexts.

We based our ontology on the Ontological Engineering Methodology (METHONTOLOGY) (Gómez-Pérez et al., 2010), a well-established framework for transforming informal knowledge into a formal representation. Our process involved three key phases: Specification, Refinement, and Implementation. In the Specification phase, we defined competency questions to guide the ontology's development, ensuring that it reflected the necessary knowledge and relationships within the manufacturing system. During the Refinement phase, we applied Description Logic (DL) to ensure the ontology's logical consistency and completeness. Finally, in the Implementation phase, we used the Protégé ontology editor to create and populate the ontology with data, utilizing the "Cellfie" Plugin. This plugin, supported by the "Mapping Master" Java library, provided a graphical interface for converting spreadsheet content into an OWL ontology within Protégé.

The tools we relied on include Protégé for ontology creation and editing, the Cellfie Plugin for data population, the Mapping Master Library for rule transformation, and Neo4j for managing and visualizing the knowledge graph. By following this methodical approach, we aimed to construct an ontological model that can effectively harness the knowledge embedded within the manufacturing system.

### 4.1.1 Taxonomy

Our ontology's taxonomy provides a standardized framework for categorizing "Operation Type" and "Material Type" classes, ensuring consistency in how operations and materials are defined and utilized across different companies and clients. This uniform approach is essential for integrating diverse information into a cohesive system (see table 1 and table 2).

The taxonomy is structured hierarchically, with main classes that are further subdivided into more specific sub-classes. For example, within the "Material Type" category, the main class of plastics includes sub-classes such as PE, PET, and PP, each representing a specific type of plastic. This detailed classification allows for precise identification and management of both materials and operations, which is critical for research and practical applications in manufacturing.

To develop this taxonomy, we drew from established material taxonomies, including those by (Groover, 2020; Marschallek and Jacobsen, 2020), as well as operation type taxonomies from sources like (Erasmus et al., 2020; Förster and Förster, 2018; Groover, 2020; McCabe et al., 2005; Roustan et al., 1999; Sorensen et al., 2018; Theodore et al., 2017). We combined these existing systems to create a flexible and extensible taxonomy that can be expanded based on specific needs and desired levels of detail. Our approach involved defining key attributes for each class and organizing them into a hierarchical structure.

<table><caption>**Table 1: Class Material type.**</caption><tr><td>Material type</td></tr></table>
<table><caption>**Table 2: Class Operation type.**</caption><tr><td>Is part of</td><td>Instances</td><td>Material type</td></tr></table>

<table><tr><td>Operation type</td><td></td><td></td></tr><tr><td>Is part of</td><td>Instances</td><td>Operation type</td></tr></table>

### 4.1.2 The Product domain

The product domain is intricately structured to delineate the specifications of the required products. In this domain, the core ontology defines classes like "Required Product" and "Required Operation," classes which represent the different levels of a Bill of Materials (BOM) and the operations needed for the product's production process, respectively. It's crucial to note that when a class designates that an operation requires a product, it implies that there is a direct physical contact between the operation's resource and the product itself. This interaction is a pivotal aspect of the ontology, reflecting the integral relationships that dictate the composition of inputs and their sequential use in operations, as further elucidated in table 3 and table 4.

<table><caption>**Table 3: Required product class.**</caption><tr><td>Required product</td><td></td><td></td></tr><tr><td>Is composed of</td><td>Instances</td><td>Required product</td></tr><tr><td>Has feature</td><td>Instances</td><td>Product type feature</td></tr><tr><td>Is composed of material</td><td>Instances</td><td>Material type</td></tr><tr><td>Corresponds to product type</td><td>Instances</td><td>Product type</td></tr></table>

<table><caption>**Table 4: Required operation class.**</caption><tr><td>Required operation</td><td></td><td></td></tr><tr><td>Requires</td><td>Instances</td><td>Required product</td></tr><tr><td>Is followed by</td><td>Instances</td><td>Required operation</td></tr><tr><td>Corresponds to operation type</td><td>Instances</td><td>Operation type</td></tr></table>

### 4.1.3 Resource Domain

Within the Resource domain, we categorize the diversity of procured resource types acquired by various enterprises. This categorization is multifaceted, identifying resources based on their operational type capabilities — which operations they can perform — and their material incompatibilities, which prevent certain resources from being used with specific product material types. Moreover, this domain elucidates parameter restrictions, setting forth the limitations and allowances for each resource type parameter, including minimum and maximum values, and defining string values where applicable. The resource domain focuses on "Resource Type" and "Resource Type Parameter" classes reflecting the machinery used in production and their parameters (see table 5).

<table><caption>**Table 5: Class resource type.**</caption><tr><td>Resource type</td></tr></table>
<table><tr><td>Can do</td><td>Instances</td><td>Operation type</td></tr><tr><td>Has parameter restriction</td><td>Instances</td><td>Resource type parameter</td></tr><tr><td>Is incompatible with material type</td><td>Instances</td><td>Material type</td></tr></table>

### 4.1.4 Enterprise Domain

The Enterprise domain embodies the procurement dynamics of the system, specifying how each enterprise interacts with product and resource types. The enterprise domain includes the "Enterprise" class, which is differentiated by its procurement capabilities (such as whether it procures product types, resources, or both) and its position within the production network. This allows for modelling the supply chain dynamics and potential collaborations or conflicts between different enterprises (see table 6).

<table><caption>Table 6: Class enterprise.</caption><tr><td>Enterprise</td><td></td><td></td></tr><tr><td>Procures product</td><td>Instances</td><td>Product type</td></tr><tr><td>Procures resource</td><td>Instances</td><td>Resource type</td></tr></table>

This ontology is designed to be flexible yet detailed, enabling nuanced modelling of production processes, resource management, and enterprise interactions. It supports the representation of a manufacturing ecosystem's multi-faceted nature and can be used in a collaborative network for any type of enterprise in a high-level granularity (see figure 2).

<figure><img src="image_3.png" /><figcaption>Figure 2: Core ontology.</figcaption></figure>

## 4.2 Automatic detection of innovation opportunities in production

### 4.2.1 Decision process algorithm

To fully leverage the instanced ontology and its knowledge graph, we initiate a querying process that stratifies the adaptability of the manufacturing system. This adaptability is categorized into three potential outcomes: fully adaptable, partially adaptable, and non-
adaptable, each of which informs the readiness of the manufacturing system to adapt for a
new production demand.

The querying begins with an examination of the knowledge graph against three key dimensions: name, material, and feature compatibility. Through a systematic filtering process, we first apply the name filter, followed by material and feature filters, to identify matched resources and operations at each level. The result of this process indicates the degree to which the manufacturing system meets the requirements: fully (all requirements met), partially (some requirements met), or not at all (no requirements met).

<figure><img src="image_4.png" /></figure>
<caption>**Figure 3:** *Internal adaptability analysis algorithm – operation requirement.*</caption>

Initially, the process assesses the internal adaptability of the manufacturing system—specifically, the system that instigates the adaptability analysis. It identifies the requirements that are met and those that are not within the system. Subsequently, depending on the internal adaptability state determined, the manufacturing system can choose to extend the search to its collaborative network. If the system is fully adaptable, meeting all internal requirements, the process concludes, assuming no need for further external comparison (see figure 3). Should the system opt for an external adaptability analysis—indicating the need to explore beyond internal capacities—the knowledge graph, along with the identified resources and inputs, are re-examined through the algorithm. The crucial difference in this external analysis is the end goal: achieving a fully adaptable state is necessary to progress to the final evaluation phase. Otherwise, the process terminates, acknowledging that the full adaptability criteria have not been satisfied. This structured approach ensures a comprehensive and systematic adaptability assessment, crucial for the optimization of manufacturing systems within and across collaborative networks.

### 4.2.2 Competency questions and query formalisation

By posing informal competency questions in the building process of our ontology, we addressed these two competency questions (CQ), corresponding to the two types of requirements inputs and operations, that has been divided into three sub-competency questions that are associated to the three-filtering level of queries. In the following we will address the competency questions for each requirement (input or operation) and their associated queries.

As detailed in section 4.1; after building the instantiated ontology in Protégé, we transferred it to Neo4j via a Neo4j driver, enabling dynamic representation of the knowledge graph within a graph database. Using the Cypher query language, we performed complex queries to analyse relationships within the graph. This analysis enabled us to assess the ontology's flexibility and adaptability across different scenarios.

#### Operation requirement

To discern the resources capable of executing the required operations for a newly production demand, we formulated the primary CQ1:

**CQ1:** *Which resources and their respective enterprises possess the capability to perform the required operations for the novel product?*
Recognizing the necessity to determine the extent of an operation's match, we have delineated this primary question into three sequential sub-competency questions, each corresponding to a level of query filtering:

- **CQ11: Which resources can conduct operations that align with the requisite operations (e.g., operations with a matching or similar name)?**

We initiate with a set of resources, operation types, and required operations. The query $Q_{11}$ is designed to correlate required operations with those provided by resources based on name compatibility, producing outputs: a set of resources $R_1$, and a set of operations $O_1$ at the name level. The logical formulation of this query is illustrated in Figure 4.

<figure><img src="image_5.png" /><figcaption>**Figure 4:** Filtering query for operation requirement on name level.</figcaption></figure>

- **CQ12: From the resources and operations selected previously, which resources have parameters that align with the product characteristics entailed in the required operations?**

Inputs for this phase include the sets $O_1$ and $R_1$ resulting from $Q_{11}$, a collection of required products, product features, and resource parameter types. The query $Q_{12}$ aims to match the features of the required product with the parameter restrictions of the resources, ensuring compatibility. For instance, integer values must fall within the machine's acceptable range, and string values must match precisely. The outputs are a selected set of resources $R_2$ and operations $O_2$ at the feature level, with the logical expression presented in Figure 5.

<figure><img src="image_6.png" /><figcaption>**Figure 5:** Filtering query for operation requirement on feature level.</figcaption></figure>

- **CQ13: Considering the resources and operations previously identified, which resources are compatible with the product materials required for the respective operations?**

The final query $Q_{13}$ utilizes the outputs $O_2$ and $R_2$ from $Q_{12}$, alongside the product's material specifications. It filters for resources that do not list the required product materials as incompatible, yielding a set of resources $R_3$, and operations $O_3$ at the material level. The logical underpinnings for this query are depicted in Figure 6.

<figure><img src="image_7.png" /><figcaption>**Figure 6:** Filtering query for operation requirement on material level.</figcaption></figure>
## Input requirement

In our ontology-based approach, we investigate the capacity of enterprises to procure the necessary inputs for new production demands. This inquiry is structured around a central competency question (CQ2):

**CQ2: Which enterprises can supply the required inputs for a new production demand?**

To thoroughly assess the match between available inputs and production requirements, we segment this central question into three nuanced sub-competency questions (sub-CQs), each targeting a specific aspect of input compatibility:

- **CQ21: Which enterprises supply products that align with the new product's requirements (e.g., products with a matching or similar name)?**

We begin with a collection of enterprises, product types, and required product specifications. The query $Q_{21}$ is crafted to find enterprises that offer products matching the required product names, yielding two sets: enterprises $E_1$ and products $P_1$. This name-level matching process and its logical basis are illustrated in Figure 7.

<figure><img src="image_8.png" /><figcaption>**Figure 7:** Filtering query for input requirement on name level.</figcaption></figure>

- **CQ22: Among the enterprises identified, which ones supply products with features that correspond to those of the required product?**

This stage involves sets $P_1$ and $E_1$, resulting from $Q_{21}$, and additional data on required product features and types. The query $Q_{22}$ further refines the search, aligning product features from the enterprises with those required, ensuring detailed compatibility. The outcome of this feature-level filtering is two new sets: enterprises $E_2$ and products $P_2$, explained in Figure 8.

<figure><img src="image_9.png" /><figcaption>**Figure 8:** Filtering query for input requirement on feature level.</figcaption></figure>

- **CQ23: From the selected enterprises and products, which enterprises can supply products with material compositions that align with the required product?**

The concluding query $Q_{23}$ takes the results $P_2$ and $E_2$ from $Q_{22}$ and assesses them against the material requirements of the new product. It identifies enterprises capable of providing products
with the same material specifications, resulting in final sets of enterprises $E_3$ and products $P_3$ at the material level. The rationale for this material-level query is presented in Figure 9.

<figure><img src="image_10.png" /><figcaption>Figure 9: Filtering query for input requirement on material level.</figcaption></figure>

Through this structured approach to competency questions and query formulation, we ensure that our ontology can systematically identify and match operations to the appropriate resources and match inputs to the appropriate enterprise, reflecting a deep understanding of the various requirements within the manufacturing process. This methodology enhances the precision and reliability of the ontology in practical applications in order to identify potential enterprise candidates that procures resources and/or inputs.

## 4.3 Automatic assessment and recommendation of innovation opportunities in collaborative production networks

Upon delineating the enterprises that fulfil the specified requirements within the collaborative network for required inputs and operations, our study proceeds to establish an optimal approach to recommend inter-enterprises adaptation scenarios for a new production demand. We have developed a novel optimization model, drawing parallels from the Vehicle Routing Problems (VRP) and enhancing it with a precedence constraint. This addition ensures the algorithm abides by the operation sequence mandated by the new production process, a key distinction from conventional VRPs.

The optimization model is defined by an objective function and a set of parameters and constraints, outlined as follows:

### Objective Function

$$ \min \sum_{i \in I} \sum_{j \in J} c_{ij} \left( \sum_{k \in I} x_{ik}^{j} \right) + \sum_{i \in I} \sum_{k \in I} t_{ik} \left( \sum_{j \in J} x_{ik}^{j} \right) $$

The model aims to minimize the sum of the production cost of operations within an enterprise and the transportation costs between enterprises.

### Parameters

$q_{ij}$: A binary indicator signifying if enterprise i can perform operation j.

$C_{ij}$: The execution cost of operation j at enterprise i, incorporating both production and transportation costs.

$t_{ik}$: The transportation cost between enterprise i and k.

$g_{ik}$: A binary indicator denoting the existence of a feasible route, both physical and collaborative, between enterprises i and k.

### Sets
I: The set of all enterprises

I*: The set of all enterprises with a fictive enterprise

J: The set of required operations

J*: The set of required operations with a fictive operation

## Variables

$$x_{ik}^{j} = \begin{cases} 1 & \text{If company } i \text{ performs operation } j \text{ then company } k \text{ performs next operation} \\ 0 & \text{Else} \end{cases}$$

## Constraints

- Assignment Constraint (1): Each operation j must be executed exactly once within the network of enterprises.

$$\sum_{i \in I} \sum_{k \in I^*} x_{ik}^{j} = 1; \forall j \in J(1)$$

This constraint guarantees that for every operation j there is a unique assignment to an enterprise i and a subsequent operation at enterprise k.

- Capability Constraint (2): An enterprise i can only be assigned an operation j if it can perform it.

$$\sum_{k \in I^*} x_{ik}^{j} \leq q_{ij}; \forall i \in I, j \in J (2)$$

This ensures that the operations are only assigned to enterprises with the necessary capabilities, where $q_{ij}$ is a binary parameter that indicates the ability of enterprise i to perform operation j.

- Precedence Constraint (3): The sequence of operations must be maintained across the enterprises.

$$\sum_{i \in I^*} x_{ik}^{j} = \sum_{l \in I^*} x_{kl}^{j+1}; \forall k \in I, \forall j \in J^* (3)$$

This states that if enterprise k is performing operation j, it must be followed by operation j+1 at some enterprise l to maintain the correct operational sequence.

## Boundary Conditions

These set the starting and ending points of the production sequence.

$$\sum_{i \in I} x_{0i}^{0} = 1$$

$$\sum_{i \in I} x_{i0}^{n} = 1$$

$$x_{00}^{n+1} = 1$$
$$ \sum_{j \in J} \sum_{i \in I^*} x_{0i}^j = 0 $$

These constraints ensure the initiation of the process with a fictive 'start' operation 0 and its conclusion with a fictive 'end' operation n+1, ensuring a well-defined start and finish to the sequence of operations.

These constraints are integral to the model's ability to find an optimal solution that minimizes costs while adhering to the logical flow of the production process. They ensure that only capable enterprises are selected, the sequence of operations is respected, and the entire process flows from a defined start to a finish within the collaborative network.

In this section we present an approach to enhance adaptability, in manufacturing networks. The framework, which is based on ontology plays a role in standardizing communication and organizing knowledge across businesses. This structure is vital for analysing adaptability by identifying candidates within the network who possess the capabilities to meet various requirements. The final step involves using an optimized model that carefully selects the collaboration pathways. This model works in tandem with the adaptability analysis to ensure that the collaborations are not only feasible but also align perfectly with the objectives of the targeted manufacturing system. Together, these components form a robust method for ensuring a resilient and adaptable manufacturing network, primed to meet evolving production demands.

# 5 Experimentation

## 5.1 Adaptation Context of "Enterprise 0"

The central theme of our study is the adaptability journey of "Enterprise 0," traditionally a glass beverage bottle manufacturer, as it navigates the challenges posed by the COVID-19 pandemic. Like many in the industry, "Enterprise 0" was thrust into a scenario where maintaining operational continuity meant pivoting production lines towards the emergent demands for sanitary products such as hydroalcoholic gels. This shift was not merely a lifeline for the company's continuity but also served as a litmus test for its capacity to adapt - a potential that had often been underleveraged.

## 5.2 New production requirement description

The hydroalcoholic gel, as specified in the ontology's product domain, is structured into several classes with associated attributes defining its composition. The gel's Bill of Materials enumerates four key raw materials—Glycerol, Hydrogen Peroxide (H2O2), Purified Water, and Isopropanol—and three components which include a plastic (PET) cap, a 100ml plastic (PET and PP) bottle, and a small-sized label.

The production process of the hydroalcoholic gel is delineated through a sequence of four distinct operations, each necessitating specific inputs:

i. Mixing Miscible Liquid-Liquid: This initial operation blends the raw materials—Glycerol, Hydrogen Peroxide, Purified Water, and Isopropanol—without requiring any additional components.

ii. Volumetric Piston Filling: Following the mixture, the second operation involves the use of the plastic bottle, which is filled with the resultant blend from the first operation.
iii. Screw Capping: Subsequently, the filled bottle from the second operation is sealed with the plastic cap, completing the containment of the gel.

iv. Hotmelt Labelling: In the final stage, the now-capped bottle is labelled with a small-sized label, which is adhered to the surface via a hotmelt process.

Each operation intricately builds upon the previous one, leading to the final hydroalcoholic gel product. The initial operation relies solely on the raw materials to create the gel's base mixture. The second operation utilizes this base, transferring it into the bottle that serves as the container. The third operation ensures that the gel is securely enclosed within the bottle by applying the screw cap. Finally, the product's identity and information are conveyed through the application of the label in the fourth operation, marking the completion of the production process.

### 5.3 Acquisition of Collaborative Network Procurement Capabilities

In preparation for its production pivot, "Enterprise 0" embarked on a comprehensive assessment of its procurement capabilities, cataloguing available resources and inputs within its network, including those of its potential collaborators. This inventory process was not about matching needs with provisions but rather compiling a thorough record of what could be sourced. Key items that "Enterprise 0" could procure included high-grade purified water and capsules, as well as the utilization of its current bottling line, capable of filling and sealing 100ml glass bottles, an ideal size for the proposed hydroalcoholic gel product.

Within our collaborative network, "Enterprise 0" contributes a particular set of operational capabilities that are integral to its primary line of beverage production. These include specialized procedures such as the filling of carbonated beverages, encapsulation, and the application of pressure-sensitive labels—operations that are well-established within "Enterprise 0's" manufacturing repertoire. Range of operational capabilities that are certainly useful, they may not comprehensively cover the full spectrum of operations required to produce hydroalcoholic gel. Consequently, to bridge this gap and ensure a holistic fulfilment of the production process, we have consolidated a detailed inventory of the operational capabilities procured by all candidate partners within the network. This collective approach is critical in identifying and mobilizing the necessary resources and processes across the network that, in synergy, can meet the diverse operational demands of the hydroalcoholic gel production.

To augment these existing capabilities, "Enterprise 0" sought out and assembled a consortium of suppliers and manufacturing partners. Each member of this emerging network was selected based on their ability to provide specific inputs or perform operations that could be integral to the new production process. This proactive assembly was pivotal, not for immediate matching with the new product requirements, but to establish a reservoir of potential procurement sources.

### 5.4 Knowledge graph implementation

The ontology was developed using Protégé, with data populated through the "Cellfie" Plugin. Cellfie provides a graphical interface that converts spreadsheet content into OWL ontology within Protégé using a flexible mapping mechanism. This process relies on the Java library "Mapping Master," which uses a domain-specific language (DSL) to transform Cellfie's rule syntax. The data transformation process with Mapping Master involves several steps to ensure accurate mapping from spreadsheets to the ontology. First, the source ontology is loaded, and the spreadsheet containing the data is opened. Mappings are then defined or loaded from a
pre-existing file. These mappings are saved for future use. Once executed, the mappings generate OWL axioms, which are reviewed and saved into the target ontology. The mapping rules are saved in a file for reuse or modification.

In our implementation, spreadsheets were used to define metadata for products, enterprise pools, and available resources, including material and operation taxonomies and their data properties. We then established rules linking the spreadsheets to the predefined ontology and mapped the resulting individuals onto OntoGraf within Protégé. This graph vividly represents the collective capabilities and contributions of the whole network including “Enterprise 0”, offering a structured and insightful visualization of the network’s capacity for adaptation (see figure 10).

<figure><img src="image_11.png" /></figure>
## 5.5 Knowledge graph visualization

In our research, the transition from the ontology implemented in Protégé to a more flexible visualization medium was paramount. For instance, we used the Graph Database Management System (GDBMS) Neo4j. Neo4j was chosen for its ability to represent individuals more flexibly, making it suitable for ontology instantiation. We translated the structured ontology from Protégé into Neo4j using a graph driver, resulting in the creation of our final knowledge graph. This strategic shift was facilitated by a neo4j driver, specifically developed within a Python environment, to seamlessly migrate the knowledge graph from Protégé to Neo4j.

By utilizing the Neo4j platform, with the integration facilitated through a custom-built driver in Python, we were able to not only enhance our understanding of the data but also lay a robust foundation for the subsequent phase of data exploitation. In this phase of our study, we will analyse the knowledge graph in depth, extracting valuable insights to evaluate the adaptability potential in the manufacturing landscape we are investigating.

# 6 Results

## 6.1 Knowledge graph querying

Following the integration of comprehensive data into our ontology, we embarked on a knowledge graph querying process crucial for enabling informed decision-making and gauging "Enterprise 0" adaptability for initiating hydroalcoholic gel production. This was carried out at name, feature, and material levels as per the protocol delineated in section 3.3. Our empirical results revealed "Enterprise 0" has a partial internal adaptability for the hydroalcoholic gel production on operational and input level since it can only fulfil operation 1 and procure the four raw materials. Opting for a collaborative approach, "Enterprise 0" proactively sought to identify external candidates to supplement its capabilities. With this decision process and the subsequent matches. Figure 11 illustrates the successful matches resulting from name, feature, and material matchmaking levels highlighting the iterative filtering process within the targeted enterprise "Enterprise 0" first and then across the collaborative network, for input and resource procurement. The analysis identified external enterprise candidates that could fulfil the remaining required operations (O2, O3 and O4) and the remaining required inputs (P4, P5, P6 and P7) to fulfil all the requirements for the new production of hydroalcoholic gel.

The internal matchmaking within "Enterprise 0" showed that the targeted enterprise could only fulfil the required operation O1 using resource type A1 and concerning the inputs, it can fulfil raw materials only which means required inputs: P4, P5, P6 and P7 (see figure 12).
Since it is a partial internal adaptability, the targeted enterprise decided to pursue and collab with potential candidates within the network. The querying process is sub-divided into three levels: name, feature, the material filtering. Firstly, we start by seeking resources and their procurement enterprises that fulfil the remaining operations: O2, O3, and O4. The figure 13.a shows the first name filtering querying, highlighting the operations that match on name level.

<figure><img src="image_12.png" /><figcaption>**Figure 11:** Matched and missing operation and input requirements for hydroalcoholic gel within the targeted enterprise and then across the whole collaborative network.</figcaption></figure>

<figure><img src="image_13.png" /></figure>
<caption>**Figure 12:** Results for internal matchmaking for operation and input requirements for hydroalcoholic gel within enterprise 0.</caption>

For the required operation O2, that corresponds to OT5 operation type and named as "Volumetric Piston Filling" in the taxonomy, can be procured by enterprise E3 by providing resource type C2 or by enterprise E2 by providing resource type C3. Required operation O3, that corresponds to OT4 operation type and named as "Screw Capping" in the taxonomy, can be procured by enterprise E2 or E1 or E4 by providing resource type B2. And the last required operation O4, that corresponds to OT7 operation type and named as "Hotmelt Labelling" in the taxonomy, can be procured by enterprise E3 by providing resource type D3 or by enterprise E4 by providing resource type D4. For the feature level, we reject the resource type C3 provided by enterprise E2 because the parameter restrictions of resource type C3 do not match with the features of the required inputs of required operation O2 (see figure 13.b). Then the last filtering stage, material level, we reject the resource type D3 provided by enterprise E3 because there is no material compatibility between resource type D3 and inputs required for operation O4 (see figure 13.c).

<figure><img src="image_14.png" /><figcaption>**Figure 13.a:** External operation matchmaking on name level.</figcaption></figure>

<figure><img src="image_15.png" /></figure>
<figure><img src="image_16.png" /><figcaption>**Figure 13.c:** External operation matchmaking on material level.</figcaption></figure>

It is the same logic with input requirements where the goal is to find the enterprise candidates within the network that will collab with the targeted enterprise to fulfil the remaining required inputs: P1, P2n and P3. First filtering stage is name level where we identified four enterprises that can fulfil the name level match for input requirements. For the required input P1, which corresponds to the PET and PE made bottle of 100ml, matches with three procured input type PT1 (PET and PE bottle of 100ml), PT3 (PET bottle of 200ml), and PT12 (glass bottle of 100ml). Enterprise E6 procures input type PT1 and PT3 and enterprise E4 procures input type PT1 and PT12. For required input P2, which corresponds to a PET made screw cap, it matches with input type PT11 which is a screw cap. We identified two enterprises that procure required input P2: E4 and E5. The last required input is P3, which is defined as a small size label, that matches with input type PT8 which is also a label. The enterprise identified to procure the required input P3 is E3 (see figure 14.a).

<figure><img src="image_17.png" /></figure>
<caption>**Figure 14.a:** External input matchmaking on name level.</caption>

The second step is the feature filtering. We reject in this stage the procured input PT3 because the feature of the input does not match with the required ones of input P1. The input type PT3 is a 200ml while the required input P1 is a 100ml volume (see figure 14.b).

<figure><img src="image_18.png" /><figcaption>**Figure 14.b:** External input matchmaking on feature level.</figcaption></figure>

Then, the last step is the material filtering, and, in this stage, we reject the procured input PT12 because it is a bottle made of PET only while we required a bottle made of the two materials PET and PE; and finally, we get our last enterprise candidates and their procured inputs in order to fulfil the remaining input requirements (see figure 14.c).

<figure><img src="image_19.png" /><figcaption>**Figure 14.c:** External input matchmaking on material level.</figcaption></figure>
## 6.2 Optimization model

Following a comprehensive adaptability analysis within "Enterprise 0's" network, our optimization model undertook a detailed exploration of cost-efficient inter-adaptation strategies, specifically analysing production and transportation expenditures. The evaluation incorporated seven distinct enterprise candidates, scrutinizing their individual operational costs and capabilities to supply necessary inputs. Notably, the model orchestrated a strategic sequence of operations across the network, commencing with "Enterprise 0" which possessed all initial inputs. The pathway proceeded to "Enterprise 3" for bottling, supplied by "Enterprise 6" with the nearest provision of plastic bottles. Subsequently, the process advanced to "Enterprise 2" for capping, utilizing caps sourced from "Enterprise 5." The final stage of labelling was carried out upon returning to "Enterprise 3," thereby completing the manufacturing cycle of the hydroalcoholic gel network (see figure 15.b). This meticulously optimized route resulted in a total cost of 55 euros, reflecting not only the financial prudence of the approach but also the operational synergy achieved by collaboration. This detailed mapping of inter-enterprise workflows and cost analysis illustrates the robust adaptability and resilience of the network, emphasizing the effectiveness of our method in enhancing cooperative production capabilities and gives insights about inter-adaptation cost estimation. Figure 15.a visually captures these dynamics, representing "Enterprise 0" as "Enterprise 1" to factor in the computational inclusion of a fictive enterprise. This serves to underscore the practical application of our optimization method, demonstrating its value in reinforcing the collective capability of the network to efficiently meet new production demands.

<figure><img src="image_20.png" /><figcaption>Figure 15.a: Results from the optimization model computation.</figcaption></figure>

<figure><img src="image_21.png" /></figure>
<caption>**Figure 15.b: Inter-adaptation recommendations from the optimization model.**</caption>

## 7 Implications and limitations

In our research, we present a method to enhance resilience in collaborative manufacturing systems by integrating an ontological model with an optimization algorithm. This approach contributes to an analysis of adaptability within manufacturing environments. Through our case study with "Enterprise 0," which transitioned to hydroalcoholic gel production, we demonstrated the practical application of this methodology. The ontological model provided insights into the capabilities and gaps within "Enterprise 0" and its network, helping to identify potential avenues for adaptation. Subsequently, the optimization algorithm is adept at estimating co-production costs, encompassing both manufacturing and transportation expenses, to provide enterprises with the comprehensive financial insights necessary for strategic planning and decision-making. This case study underscored our method's capacity to streamline processes and enhance the resilience of the manufacturing network.

This article will have theoretical, practical and policy implications.

## Theoretical implications

This research advances the theoretical understanding of resilience in manufacturing systems by introducing a holistic approach that integrates resilience theory, collaborative network dynamics, and ontology-based analysis. While traditional approaches often focus on individual components of the system, our study emphasizes the importance of viewing resilience at the network level, where the adaptability of interconnected firms plays a crucial role. By applying ontology-based analysis, we provide a structured method to assess adaptability within manufacturing networks. This approach contributes to the existing theoretical models by highlighting the relational aspects of resilience, offering a more comprehensive view than conventional methods. Additionally, our findings suggest that resilience should be seen as an ongoing process, requiring continuous adaptation rather than being treated as a fixed state. This perspective encourages further exploration of resilience as a dynamic concept within the broader discourse on manufacturing systems.

## Practical implications

The framework developed in this study equips decision-makers with a powerful tool to fully exploit the adaptive potential of their manufacturing systems. By identifying candidates that align with production requirements and providing a structured adaptation scenario, the framework effectively filters viable options and facilitates the estimation of co-production costs within the manufacturing network. The goal of the ontology in this context is not to provide an
exhaustive, detailed understanding of every aspect of the production network, but rather to
cover essential information with a high degree of granularity. This allows decision-makers to
filter candidates efficiently based on key criteria.

This approach is particularly valuable in environments characterized by frequent and
unpredictable disruptions, as it enables a systematic evaluation of the network's strengths and
vulnerabilities. Moreover, it is equally applicable for capitalizing on strategic opportunities,
even in the absence of unexpected events.

Through the integration of the ontological model, managers gain a structured understanding of their production network, which empowers them to optimize resource allocation and streamline operations. The cost estimation capabilities embedded within the optimization algorithm further enhance decision-making by providing a clear financial framework. This allows organizations to make informed choices that balance operational efficiency with financial sustainability.

The case study of "Enterprise 0" illustrates the practical application of these tools, showing how the framework can be employed not only to maintain production during disruptions but also to strengthen overall network resilience over time. By offering a strategic view of the adaptability of the manufacturing system, the framework supports operational continuity ensuring that organizations are better equipped to navigate future challenges.

## Policy implications

From a policymaking perspective, this research emphasizes the need for policies that prioritize adaptive methods in manufacturing systems, rather than focusing solely on specific technologies. The COVID-19 pandemic, as a black swan event, revealed the limitations of traditional, rigid manufacturing structures. Future disruptions are likely to be just as unpredictable, making it essential for policymakers to encourage methods that enhance flexibility and resilience across the entire manufacturing ecosystem.

To achieve this, policies should support the development and implementation of adaptive methods that allow manufacturing systems to respond to a wide range of disruptions. This includes promoting frameworks that integrate dynamic decision-making processes, collaborative networks, and flexible production planning. While technologies such as reconfigurable manufacturing systems (RMS) and dynamic supply chain management tools are valuable, the focus should be on fostering methods that enable manufacturers to pivot effectively in response to unforeseen challenges, regardless of the specific nature of the disruption.

## Limitations

While this study makes significant contributions to both theory and practice, it is important to acknowledge its limitations. One key limitation lies in the focus on external adaptability, primarily addressing inter-organizational cooperation and resource allocation across the network. Although this focus is critical for enhancing overall network resilience, it leaves out the internal adaptability measures that are equally important, such as process reconfiguration within individual enterprises.

## 8 Conclusion and future perspectives

In conclusion, our study presents a practical and theoretically grounded approach for enhancing resilience in collaborative manufacturing systems, combining ontological analysis with
optimization strategies to equip manufacturing entities with tools for navigating complex and dynamic production environments. While this approach shows promise, it also opens avenues for further research and refinement.

Future studies should focus on integrating additional internal factors into the framework, allowing for a more comprehensive approach to resilience. Moreover, the current application of our method was limited to the manufacturing domain, specifically within a single case study. Although the initial results are encouraging, further validation is needed across different sectors and diverse operational contexts to confirm its broader applicability with real world cases.

Expanding the ontological model to include additional domains, such as regulatory compliance and environmental sustainability, could further enhance its utility, particularly in industries that operate under strict regulatory requirements. By continuing to explore and apply this method, we can contribute to the development of more adaptable and resilient manufacturing practices that benefit both industry and society.

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