Purpose: Healthcare organisations are often described as less innovative than other organisations, since organisational culture works against innovations. In this paper, the authors ask whether it has to be that way or whether is possible to nurture an innovative culture in a healthcare organisation. The aim of this paper is to describe and analyse nurturing an innovative culture within a healthcare organisation and how culture can support innovations in such a healthcare organisation.
Design/methodology/approach: Based on a qualitative case study of a healthcare unit that changed, within a few years, from having no innovations to repeatedly generating innovations, the authors describe important aspects of how innovative culture can be nurtured in healthcare. Data were analysed using inductive and deductive analysis steps.
Findings: The study shows that it is possible to nurture an innovative culture in a healthcare organisation. Relationships and competences beyond healthcare, empowering structures and signalling the importance of innovation work with resources all proved to be important. All are aspects that a manager can influence. In this case, the manager's role in nurturing innovative culture was very important.
Practical implications: This study highlights that an innovative culture can be nurtured in healthcare organisations and that managers can play a key role in such a process.
Originality/value: The paper describes and analyses an innovative culture in a healthcare unit and identifies important conditions and strategies for nurturing innovative culture in healthcare organisations.
Production platforms can be considered as the foundation for the design, development, and reconfiguration of a production system. Even though, the product platform domain has been extensively researched and applied widely in practice, this is not the case in the production domain regarding production platforms and reconfigurable production systems. Therefore, this paper reviews the current product and production platform literature to distinguish the production platform co-development research. A systematic literature review has been carried out to explore the concept of production platforms and pinpoint what research gaps that needs to be bridged.
A well-performing product realisation process in order to introduce new products with high frequency to a low cost, is becoming more of a pre-requisite for manufacturing companies. In a multiple case study, this paper investigates applied industrial practices in production development to support the production realisation process and reports on the current ways of working and challenges therein. The areas of current production development practices, production platforms, standardised work, and knowledge development are explored. Identified challenges towards long-term production development based on the explored areas are presented. The inclusion of future need of production system adaptions from future products is argued for to increase its efficiency. Through including future need of the production system, the notion of considering one product at the time during industrialisation is challenged and a more proactive perspective can be taken. The production platform approach is considered as one enabler for such an improved production development.
The traditional way of developing production systems is often limited by merely considering an imminent new product. The longevity of a production system’s lifecycle is at risk following this approach and may create a focus on the current functionality and capacity rather than on fulfilling future product requirements. Changeable production address this challenge, however, support for production engineers to consider more changeable solutions is lacking. Thus, this paper proposes support for evaluating production capabilities and mapping how new products may impact the production system. The support is developed in two industrial cases which studied current production capabilities and future requirements put on two automatic assembly lines. The support allows for estimates of the cost of repurposing the assembly lines to accommodate the new products and paves the way for seeing beyond the dedicated manufacturing paradigm towards increased levels of changeable production.
Reshoring manufacturing is a strategic decision because of its cost, implications, and complexity. Existing models have largely focused on cost aspects in reshoring decisions and are considered limited in assisting practitioners in the reshoring decision-making process. Variables like cost and quality have been the most important, whereas environment and sustainability seem not a priority, arguing for the myopic nature of these decisions. Therefore, this study employs system dynamics (SD) to expand practitioners’ mental models for the reshoring decision-making process. To do so, first, variables and heuristics are retrieved from the literature. Next, an industry expert is interviewed to have a practitioner’s input. Finally, a descriptive SD model is built by connecting variables and heuristics. The findings indicate that the behavior of the variables in reshoring decisions is dynamic over time. Furthermore, the variables are inter-linked, resulting in non-linear, multi-caused reshoring decisions. The presented SD model allows incorporating the variables that are sometimes difficult to quantify and provides a holistic view of the variables, their relationships, complexities, and the dynamics involved in the reshoring decision-making process. This study contributes to reshoring literature by using SD perspective in the reshoring decision-making process and proposing an SD model for reshoring decision-making. This study assists practitioners in expanding their mental models regarding the reshoring decision-making process. It is further argued that the proposed SD model may work as a generic steppingstone to further develop company-specific feedback-oriented models to support in their reshoring decision-making processes and to support future research on the topic.
The manufacturing industry faces the challenge of providing copious product variety at a competitive price. This development has escalated into the point where SMEs are becoming in need to consider product mix as a relevant aspect for automation selection despite low volumes. Apparently such a manually operated production cell has productivity limitations in addressing these increasing demands of mass customization and competitive prices. Therefore, this paper proposes using discrete- event simulation (DES) to assist the decision-making process (DMP) for implementing a new automation technology within a production cell and showcase key performance indicator (KPI) identification using simulation. Two modeling scenarios were designed and contrasted to showcase implementing automation. One consists of a manually operated assembly line, and the other represents a semi- automated assembly line of the same process but with robots in specific areas of the production line. The results indicate that the comparative study between the two scenarios of a manually operated assembly cell and a semi-automated one can provide valuable insights into the DMP. The proposed approach has shown several influencing factors to consider in the DMP. The choice of prioritizing which element should have precedence depends on the requirement specifics. The insights from the study also indicate the requirement of further research in this context, considering different parameters apart from the current research and understanding their influence on the DMP. Moreover, acknowledging the secondary aspects concerning this study context, such as ergonomics, space utilization, workplace safety, and sustainability, require further investigation.
This paper examines potential opportunities at two SMEs (small to medium-sized enterprises) to improve the decision making process for change in their manufacturing organizations. Present procedures of the decision making process for manufacturing system development have been studied by applying feedback systems thinking. A framework for systemic change management is proposed utilizing a bottom-up perspective to acknowledge individual competence and creativity. In conclusion applying system principles facilitates an environment for proactive developments towards a learning organization.
Improvement work in manufacturing industry usually focuses on the utilisation of equipment. System dynamics simulation is a potential tool for increasing the utilisation of systems. By using group model building and simulation it facilitates a common view and better informed decisions for change. However, a gap between theory and practice of how to implement these projects is identified, consequently the major question for this thesis. The approach for solving this problem used industrial case studies with action research character; including modelling and interviews affecting the actors in the studied systems. Together with literature studies these efforts contribute with identifying how system dynamics projects can be performed for manufacturing systems development. It is shown that the support for how to implement system dynamics projects is unsatisfying and general. During the research progress a framework of guidelines has crystallised in order to bridge the presented gap of this thesis. Finally, the results are considered to make it easier to support manufacturing systems development using system dynamics.
Managing maintenance in manufacturing within an economical short-termism framework and taking the consequential long-term cost effects into account is hard. The increasing complexity of managing maintenance and its impact on the business results calls for more advanced methods to support long-term development through effective activities in the production system environment. This problem-based design science research has evolved into the novel concept of a hybrid simulation-based optimization (SBO) framework which integrates multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively. The objective is to support managers in their decision-making on the strategic and operational levels for prioritizing activities to develop maintenance and production performance.
To exemplify the hybrid SBO framework this research presents an SD model for the study of the dynamic behaviors of maintenance performance and costs, which aims to illuminate insights for the support of the long-term strategic development of maintenance practices. The model promotes a system view of maintenance costs that includes the dynamic consequential costs as the combined result of several interacting maintenance levels throughout the constituent feedback structures. These levels range from the applied combination of maintenance methodologies to the resulting proactiveness in production, such as the ratio between planned and unplanned downtime, in continuous change based on the rate of improvements arising from root-cause analyses of breakdowns. The model creation and validation process have been supported by two large maintenance organizations operating in the Swedish automotive industry. Experimental results show that intended changes can have both short-term and longterm consequences, and that the system may show both obvious and hidden dynamic behavioral effects.
The application of MOO distinguishes this work from previous research efforts that have mixed SD and DES. It presents a unique methodology to support more quantitative and objective-driven decision making in maintenance management, in which the outcome of an SD+MOO strategy selection process forms the basis for performance improvements on the operations level. This is achieved by framing the potential gains in operations in the DES+MOO study, as a result of the applied strategy in the SD model. All in all, this hybrid SBO framework allows pinpointing maintenance activities based on the analysis of the feedback behavior that generates less reactive load on the maintenance organization.
Purpose – This paper applies systems thinking modelling to enhance the dynamic understanding of how to nurture an innovative culture in healthcare organisations to develop the innovation system in practice and speed up the innovative work. The model aims to provide a holistic view of a studied healthcare organisation’s innovation processes, ranging from managerial values to its manifestation in improved results.
Design/methodology/approach – The study is based on empirical material from a healthcare unit that, within a few years, changed from having no innovations to repeatedly generating innovations. The study uses the modelling language of causal loop diagrams (CLDs) in the system dynamics methodology to identify the key important aspects found in the empirical material.
Findings – The proposed model, based on the stories of the interviewees, explores the dynamics of inertia when nurturing an innovative culture, identifying delays attributed to the internal change processes and system relationships. These findings underscored the need for perseverance when developing an innovative culture in the entrepreneurial phases.
Practical implications – The approach of using systems thinking to make empirical healthcare research results more tangible through the visual notations of CLDs and mental simulations is believed to support exploring complex phenomena to induce and nurture both individual and organisational learning.
Originality/value – The results from this approach provide deepened analysis and provoke the systems view to explain how the nurturing of the culture can accelerate the innovation processes, which helps practitioners and researchers to further expand their understanding of their healthcare contexts.
Modeling projects, in order to build richer understanding of the dynamics of real-world phenomena in manufacturing systems, benefit from utilizing System dynamics group model building. This paper describes a project utilizing such method in order to identify the interrelated dynamics of aging machinery equipment, competence development, and level of automation for accurate manufacturing systems development. These central aspects were identified by the project group during modeling and were considered vital in order to approach the proper Machine Strategy for the system of interest. Aspects of attention in the study also considered participants’ learning of the system of interest, participants’ perception upon model results, and the comparison between utilizing group model building and the traditional modeler-client approach. It is shown that System dynamics group model building has potential use in manufacturing, and indeed that more efforts are needed for successful use in projects. For that reason the need of a framework for supporting system dynamics projects in manufacturing is identified.
The purpose of this paper is to investigate the economic sustainability implications of reconfigurable modularization and changeability in semi-automatic assembly systems using a system dynamics perspective. Through our applied research, using a multiple case study approach, we assess the potential and drawbacks of reconfigurable modularization to advance sustainable practices in the manufacturing industry with the purpose of improving overall long-term resource allocation in product realization processes. The traditional approach of developing and industrializing one product at a time is becoming obsolete due to factors such as more frequent product introductions, technological innovations, and sustainability requirements. This is due to the increasing trends of product variety and customization, which often necessitate costly modifications to production systems throughout their life cycles. To address these challenges, scholars advocate for the adoption of reconfigurable modular architectures in product and production system designs, facilitated through product platforming. However, when it comes to studies of the long-term economic impacts from the effects in operations, meaning the economic sustainability implications for the production system throughout its life cycle, there is limited research examining the economic rationale for this approach. Therefore, this paper proposes a systematic examination of the economic sustainability implications of reconfigurable modularization in semi-automatic assembly systems using a system dynamics perspective. By leveraging a system dynamics simulation, we structure and investigate the potential economic short- and long-term tradeoffs between the benefits and drawbacks of reconfigurable modularization derived from empirical findings across four case studies. The novelty of this study highlights not only the investment costs and related engineering implications and their costs but also the estimated operation costs encompassing multiple product introductions expected during the life cycle of a production system. We believe that such an approach offers valuable insights into how reconfigurable modularization can be useful from an economic sustainability viewpoint within semi-automatic assembly systems, thereby contributing to the ongoing industrial transformation towards sustainability.
The product realisation process is one of several formalized supports for industrial actors to excel in concurrent engineering procedures. To satisfy customers today mass customization is increasingly in need, requiring delicate modular architectures, both in product designs and production. Emerging is also the digitalized co-platforming era of automating the synchronization of product and production platforms. Yet, in all these processes, humans as agents have different roles, objectives, and mental models that governs their decision-making, being the bearer of separate ideas on what to optimize from their end. In product development large sensitivity is given to customer demands and trends to design attractive products, while less attention may be placed on evaluating the increase of variation into the production flows from new products, potentially increasing the workload and complexity of assembly systems, as well as, the subsequent material logistics. In production, much effort is invested to increase standardization, increase the pace, and minimize the manufacturing cost, with the objective to minimize required changes to the current production system. Consequently, it is a hard problem to satisfy all criteria at once, and how to solve it has no clear answer. Therefore, this study has applied qualitative System Dynamics modelling, also often referred to as systems thinking, to investigate how these opposing views were represented at an industrialized house builder. The purpose was to explore and model the perspectives and mental models of two leading roles to model their conflicting objectives. As a result, an overall model of main interactions of product and production development is proposed to support interpreting the findings, visualize the identified conflicting dynamics, and work as a vehicle for analysis.
Abstract: The purpose and novelty with this recently started research is the introduction of a modelling concept that aims to include the interdependencies maintenance have with financial figures, customer behavior, and production, using systems thinking. It suggests on a path forward in acknowledging short- and long term effects from maintenance on the production system and its financial results. Using systems thinking modelling enables learning on consequences from strategies and policies on the studied system; enabling evaluation of future scenarios supporting decision makers in defining sustainable strategies of action on the policy-level. This paper provides a brief outline of the thoughts behind the research project and points the direction for future research by first introducing aspects regarding the problem and possibilities to address, then briefly introduce different modelling approaches that in part address the problem, which is summarized into a path forward, and finally includes an example of a model by the author of a machine strategy problem that connects the physical assets and actions with financial costs.
The problem of maintenance consequential costs has to be dealt with in manufacturing and is core of this paper. The need of sustainable partnership between manufacturing and maintenance is addressed. Stuck in a best practice thinking, applying negotiation as a method based on power statements in the service level agreement, the common best possible achievable goal is put on risk. Instead, it may enforce narrow minded sub optimized thinking even though not intended so. Unfortunately, the state of origin is not straightforward business. Present maintenance cost modelling is approached, however limits to its ability to address the dynamic complexity of production flows are acknowledged. The practical problem to deal with is units put together in production flows; in which downtime in any unit may or may not result in decreased throughput depending on its set up. In this environment accounting consequential costs is a conundrum and a way forward is suggested. One major aspect in the matter is the inevitable need of shift in mind, from perspective thinking in maintenance and manufacturing respectively towards shared perspectives, nourishing an advantageous sustainable partnership.
The expected demographic changes, and especially the rise in life expectancy, will considerably increase elderly patients' demand for healthcare. There are different strategies that can offer better care for these patients, reduce their unnecessary visits to the emergency departments, and in consequence, reduce the number of hospitalizations and days at the hospital. This study employed system dynamics to analyze the economic and quality-related effects of different closer care strategies such as investments in care coordinators and mobile health clinics, as well as to offer proactive care in the primary care facilities for elderly patients. The results indicate that a combination of the different strategies will support better care for patients, will reduce hospital costs and will reduce the existing pressure on the emergency department. The paper also reflects on the process followed to conduct the study and the lessons learned.
This paper presents results from a simulation case study analyzing care strategies for elderly patients in a regional healthcare system (HCS) in Sweden. Three strategies to reduce emergency visits, hospitalisations, and stays were evaluated: care coordinators at emergency departments, mobile health clinics visiting fragile patients in their homes, and proactive primary care. Using system dynamics modelling and empirical data, the impact on the regional HCS was explored considering the reduced care demand and demographic changes. Subsequently, the impact on the population's health status was assessed. Combining strategies yielded the best outcome, but improving patients' health status may increase long-term care demand. The study emphasizes the importance of implementing these strategies to offer better care for elderly patients and reduce healthcare costs. Findings highlight the potential long-term effects of improving health status and the need for a comprehensive approach to address the evolving care demands of an ageing population.
Development of manufacturing systems is dependent on human decision making. One important factor in the decision making process is the organisational ability to transform available information into useful knowledge. The ability is generally limited by the organisation's level of competence and use of methods. However, real systems are not simple and straightforward but dynamically complex and difficult to interpret in order to perform successful change. One tool for diagnosing and solving complex business problems is system dynamics. It is interesting for its capability to acknowledge dynamic complexity.
This paper presents a framework of guidelines that facilitates implementing a system dynamics project for manufacturing systems development. It is the result of industrial case studies, supporting verification of the framework contents. This is presented in order to improve using system dynamics as a decision support in manufacturing. And it may bridge a gap between academic theory and industrial practice.
Lack of time due to daily problems in need of attention restrains proper assessments of improvement opportunities. There is neither proper support at hand to deal with the dynamic complexity of human activity and systems in use. This paper explores if system dynamics simulation can be used to model tooling problems on a management problem level at a manufacturer and evaluates its use. System dynamics is a methodology designed to aid understanding of dynamically complex problems and increases decision making impact. The results focus on the achieved models which prove to have sense behaviour despite lack of thorough data. In conclusion the applied method provides with an analysis of complex problem situations applicable for a decision support, otherwise performed through good guessing. Main characteristics from reality have been included in model and an experimental laboratory to test future policies on achieved.
Maintenance can be performed in multiple procedures, and it is hard to justify investments in preventive work. It is a complex equation between the inherent complexity of maintenance and its tight dependencies with production, but also the aspect of direct cost and consequential costs from activities. A model is presented that quantify dynamics of maintenance performance in order to enable a systems analysis on the total of consequences from different strategies. Simulation offers experimenting and learning on how performance is generated. The model is based on parts of previous research on maintenance modelling, system dynamics, maintenance theory, and mapping of practical information flows in maintenance. Two experiments are presented that both take off from a reactive strategy of maintenance performance, and implement two different strategies for preventive maintenance. Using the model enriches the analysis on how the aspects of maintenance performance work together with different maintenance strategies.
Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research.
Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement.
Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.
Industrial maintenance includes rich internaldynamic complexity on how to deliver value. While the technical development hasprovided with applicable solutions in terms of reliability and condition basedmonitoring, managing maintenance is still an act of balancing, trying to pleasethe short-termism from the economic requirements and simultaneously address thenecessity of strategic and long-term thinking. By presenting an analysis tojustify maintenance studying system behavior, this paper exemplifies thecontribution of the combined approach of a system dynamics maintenanceperformance model and multi-objective optimization. The paper reveals howinsights from the investigation, of the near optimal Pareto-front solutions inthe objective space, can be drawn using visualization of performance ofselected parameters. According to our analysis, there is no return back to thesingle use of system dynamics; the contribution to the analysis of exploringsystem behavior, from applying multi-objective optimization, is extensive.However, for the practical application, the combined approach is not areplacement – but a complement. Where the interpretation of the visualizedPareto-fronts strongly benefits from the understanding of the model dynamics, inwhich important nonlinearities and delays can be revealed, and thus facilitateon the selected strategical path for implementation.
Identifying sustainable strategies to develop maintenance performance within the short-termism framework is indeed challenging. It requires reinforcing long-term capabilities while managing short-term requirements. This study explores differently applied time horizons when optimizing the tradeoff between conflicting objectives, in maintenance performance, which are: maximize availability, minimize maintenance costs, and minimize maintenance consequence costs. The study has applied multi-objective optimization on a maintenance performance system dynamics model that contains feedback structures that explains reactive and proactive maintenance behavior on a general level. The quantified results provide insights on how different time frames are conditional to enable more or less proactive maintenance behavior in servicing production.
Managing maintenance within an economical short-termism framework, without considering the consequential long-term cost effect, is very common in industry. This research presents a novel conceptual system dynamics model for the study of the dynamic behaviors of maintenance performance and costs, which aims to illuminate insights for the support of the long-term, strategic development of manufacturing maintenance. By novel, we claim the model promotes a system's view of maintenance costs that include its dynamic consequential costs as the combined result of several interacting maintenance levels throughout the constituent feedback structures. These range from the applied combination of maintenance methodologies to the resulting proactiveness in production, which is based on the rate of continuous improvements arising from the root cause analyses of breakdowns. The purpose of using system dynamics is to support the investigations of the causal relationships between strategic initiatives and performance results, and to enable analyses that take into consideration the time delays between different actions, in order to support the sound formulation of policies to develop maintenance and production performances. The model construction and validation process has been supported by two large maintenance organizations operating in the Swedish automotive industry. Experimental results show that intended changes can have both short and long-term consequences, and that obvious and hidden dynamic behavioral effects, which have not been reported in the literature previously, may be in the system. We believe the model can help to illuminate the holistic value of maintenance on the one hand and support its strategic development as well as the organizational transformation into proactiveness on the other.
In our increasingly globalised economy, managing continuous change and remaining competitive has become a central issue for organisations in the industrial sector. Building a sustainable competitive advantage through effective decision making and the use of decision making tools has been widely studied [1,2]. The success of a company will be dependent on the skills of the workers, their capacity for learning, and adapting to special and evolving client necessities. Culture change via, communication and participation are the elements of change identified for engineering companies [3]. Thus, the main objective of this research is to understand the behaviour of commitment, the variables that influence it and the variables that are influenced by it. Commitment is considered a key factor due to its influence on performance. The methodology that was followed was based on the modelling methodology proposed by Sterman [4]. The first step was the problem definition, the second step was data collection. The purpose was to define the feedback loops of which the conceptual model (CM) is composed. Thirdly, conceptual model definition was developed. As a result, the outcome that is achieved through this research is a conceptual model. The main function of this model is to facilitate the understanding of the behaviour of commitment through Systems Thinking tools. This research contributes to both Strategic Human Resource Management (SHRM) and Systems Thinking (ST) fields of study. The most notable contribution for ST is the fact of combining more than one input source (Literature + Group Model Building + prior research) for the conceptual model definition. The combination of these input sources for an ST model is not common in the scientific community. Moreover, the use of ST in SHRM is limited.
Nowadays customer needs are changing rapidly, resulting in shorter product life cycles and a need for a higher product introduction rate. This requires manufacturers to introduce new products whilst keeping production efficiency at a satisfactory level and production costs low. Based on these challenges, there is a need to consider both production efficiency and potential assembly line investment costs during the planning of new product introductions. Hence, this paper aims to support decision-making regarding whether to introduce and produce a new product in an already existing assembly line or to invest in a new assembly line. To its support, a tool which illustrates how to support manufacturing investment decisions through line balancing techniques has been developed. The tool was based on theoretical findings from two literature reviews, investigating assembly line balancing techniques and assembly line investment costs, and through data collected in a single case study, including how a company is currently supporting investment decisions and performing line balancing. The case study was conducted with a large Swedish company from the automotive industry. Data was collected through semi-structured interviews, document studies and a focus group. The proposed decision-supporting tool conducts line balancing for both combined and separate assembly lines, and converts the results into costs. These costs are then compared with the potential investment costs of either producing in an already existing assembly line or investing in a new assembly line. The final output is a summarization of the potential costs related to both alternatives which provides the user with the most economically beneficial alternative by taking both production efficiency and investment costs into consideration.