Anticipating a production company's internal and external influencing factors is seen as key driver of the ability to act appropriately to sustain a competitive advantage within a dynamic market environment. In this connection, some factors within the production environment manifest as temporally and structurally recurring patterns (defined as cycles) and are predictable. Modeling and analyzing the cyclic behavior of products, technologies, and manufacturing resources, for example, facilitates a proactive planning approach to production technologies.
This paper uses the example of the commercial vehicle industry to focus on a manufacturer's internal cyclic influencing factors. Based on the results of an industrial case study and a review of existing methods, a conceptual framework is presented for managing the complex interdependencies of the lifecycle of a product, its components, and production technologies.
Cycle-oriented Evaluation of Production Technologies: Extending the Model of the Production Cycle
(2017)
Permanently evaluating and adopting suitable production technologies due to the dynamic environment is a major challenge for producing companies. However, influencing factors that show cyclic behavior can be anticipated and are predictable to a certain extent. Thus, lifecycle models facilitate the forecast of predictable factors and assist in deriving recommendations for action timely. The developed cycle-oriented planning and evaluation approach provides a cycle stage specific technology requirements profile. The conceptual framework ascertains the suitability of established production technologies using fuzzy sets to meet the vagueness inherent in soft requirements. The presented extension of the production cycle model provides a holistic framework to identify deficits concerning properties of established production technologies proactively. This enables a continuous technology evaluation approach resulting in the timely identification of technological need for action.
Methods for managing uncertainty and fuzziness caused by a turbulent and volatile corporate environment play an important role for ensuring long-term competitiveness of producing companies. It is often difficult for practitioners, to choose the optimal approach for modelling existing uncertainties in a meaningful way. This contribution provides a guideline for classification of uncertain information and fuzzy data based on a flowchart and proposes suitable modelling methods for each characterized uncertainty. In addition, a measure for modelability, the degree to which an uncertain or fuzzy parameter can be modelled, is proposed. The method is based on a literature review comprising a discussion of the terms uncertainty and fuzziness.
Cycle management of manufacturing resources: identification and prioritization of investment needs
(2017)
To gain competitive advantages within the growing challenges of the dynamic market environment producing companies must be agile, anticipative and adaptive. Current and future manufacturing requirements need to be fulfilled in the best manner. Consequently, the appropriateness of the applied production technologies has to be analyzed continuously. In order to identify technological need for action timely the interdependencies of temporally and structurally recurring patterns (defined as cycles) within the production environment have to be contemplated. Modeling and analyzing these cycles (e.g. technology lifecycle, manufacturing resource lifecycle) facilitates a proactive planning and an evaluation approach of production technologies. Therefore, this paper presents a conceptual framework supporting the timely adequate identification and evaluation of alternative production technologies to enhance the performance of producing companies.
Conceptualizing disruptive innovation paths, patent zero and patent-data based operationalization
(2024)
Disruptive innovations, as opposed to sustaining innovations, bring change to customers, markets, industries. This change is often quite sudden and drastic. Thus, the early identification of disruptive technologies can be crucial for managers and policy makers alike. In this work we propose a method to support the early identification of disruptive innovations. To do so we conceptualize a 5-phase pathway framework for disruptive innovation, then propose how to operationalize its phases using patent data. We combined the underlying theory by Christensen with different patent indicators from literature to develop an early warning system for disruptive innovations based on the underlying disruptive technologies. Additionally, we introduce the concept of patent zero and provide a first analysis.