VALUE CO-CREATION THROUGH HORIZONTAL COLLABORATION : A Case Study of a Service Partner Network in Manufacturing Industry

Today’s firms operate in complex, networked value systems, where they are connected to other actors over multiple ties. Strategic value nets are intentionally formed business networks. The network actors share common goals and co-create value through collaborative value activities. The way how a strategic value net configures and coordinates its resources, capabilities and value activities is essential for the net’s competitiveness. Moreover, the net’s ability to dynamically restructure its operations and react to change is even more important. The purpose of this study is to provide understanding on how horizontal collaboration creates value in a predominantly vertically structured service value net in manufacturing industry, and thereby contributes to the service value net’s operations. The study identifies practices and motives for horizontal collaboration, and analyses the pre-requisites, enablers, barriers and limitations to horizontal collaboration in the studied context. The research was conducted as an explorative, single case study. The case is a service partner network of a multi-national engineering company, where the global partner network sells and provides product life cycle services to end customers. This study addresses collaboration between service partners. The applied research method was qualitative, and the research approach followed systematic combining. The empirical data was collected in ten semi-structured interviews. The findings indicate that a good vertical relationship is a pre-requisite for horizontal collaboration. Horizontal relationships are primarily competitive and characterized by mistrust. The manufacturer has a central role in facilitating horizontal networking and increasing transparency in the network’s operations. Transparency is the key to initiate trust building, balance competitive tensions and create collaborative culture in the network. The research indicates that horizontal collaboration strengthens the value net’s shared identity and clarifies the strategic intent. Horizontal practices co-create value in daily customer service requests by providing short-term support to network members in terms of resources and knowledge, but also enable the network to learn and develop together in the long term. Finally, the study proposes that a cross-dimensional, collaborative foundation helps the network adjust to change. This thesis contributes to previous research on management of strategic value nets and collaboration in B2B service systems. Furthermore, the study provides new insights by addressing a secondary value creation logic in a strategic net

Integration Of Information Technology And Simulation For Managing Manufacturing-Logistics Network

This paper helps investigate the integration of information technology (IT) and simulation in order to help understand how to set reliable delivery dates in manufacturing-logistics networks.  This integration is necessary to assist organizations involved in the network to help plan and control their operations more efficiently.  The authors reviewed previous work in the areas of IT-oriented logistics, real time simulation, and due date assignment.  We describe a general architecture for a real-time simulation-based system and create a prototype based on our architecture.  An application of the prototype in a small manufacturing company is employed.  The prototype implemented was for a local manufacturer of made-to-order safety windows with three manufacturing plants and a one office location, all within a 50-mile radius.  The prototype for this company was developed utilizing a Boreland’s Delphi 3 application development tool.  The reason that a distributed simulation model was not implemented was that we focused on validating the most critical components of our architecture using a simple prototype as our initial validation effort.  The following possible advantages of our architecture were identified: *        The architecture can use the most up-to-date operational data to make decisions regarding delivery date assignment and network management.*        The architecture can support both central and distributed environments. *        The prototype developed based on the architecture could assign tight delivery dates.*        The prototype could be used to maintain or increase the level of on-time deliveries by monitoring the operations.*        The prototype could generate options on delivery dates and cost based on routings/priority and transportation service options.&nbsp

Characteristics of redistributed manufacturing systems: a comparative study of emerging industry supply networks

This paper explores the characteristics of redistributed manufacturing systems within the context of emerging industry supply networks (EI SNs), with a particular focus on their structure, operations and reconfiguration dynamics. A number of factors have resulted in the redistribution of manufacturing. Within Emerging Industries, advances in process and information technologies, have changed the physical and information characteristics of components and products, and the viable production economies of scale. Further, the emergence of new specialised companies fulfilling key research, production or service roles have changed industry structure and operations, and the conventional model of value creation. Six industrial systems are examined using an Industrial System mapping methodology providing a basis for cross-case analysis, selected on the basis of representing alternative and novel evolution paths that may provide insights into the characteristics of EI SNs within a redistributed manufacturing context. Cross-case analysis suggests several generic aspects to EI SNs, including the blurring of traditional industry boundaries and the critical requirement to manage uncertainty. Alternative forms of EI SNs are observed supporting particular EI evolution paths. Further, more adaptive SNs support increased product variety, with lower inventory models enabled by enhanced production and distribution flexibility, often located closer to demand.The authors would like to acknowledge UK Research Council EPSRC, the industrial collaborators who provided access to their organisations, and their supply network, industrial and institutional partners.This is the author accepted manuscript. The final version is available from Taylor & Francis via http://dx.doi.org/10.1080/00207543.2016.121476

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

The Obama Administration’s Proposal to Establish a National Network for Manufacturing Innovation

[Excerpt] In his FY2013 budget, President Obama proposed the creation of a National Network for Manufacturing Innovation (NNMI) to help accelerate innovation by investing in industrially relevant manufacturing technologies with broad applications, and to support manufacturing technology commercialization by bridging the gap between the laboratory and the market. The NNMI proposal calls for the establishment of up to 15 Institutes for Manufacturing Innovation (IMI) funded through a one-time infusion of $1 billion in mandatory funding to the Department of Commerce’s National Institute for Standards and Technology (NIST) and carried out over a period of 10 years. Each IMI would be comprised of stakeholders from industry (including large companies and small- and medium-sized manufacturing enterprises), academia, federal agencies, and state government entities. According to the proposal, each IMI is to be competitively selected, serve as a regional hub for manufacturing innovation (as well as part of the national network), and have a unique focus area (e.g., an advanced material, manufacturing process, enabling technology, or industry sector). The NNMI would be managed collaboratively by NIST, the Department of Defense, Department of Energy, National Science Foundation, and other agencies

Operations management in high value manufacturing

This paper explores the issue of high value manufacturing from an operations management perspective and discusses the critical role that the operations management community must play in moving manufacturing organisations to 'higher value ground'. An exploratory study was carried out in two phases. In Phase 1 existing literature was examined following a systematic review process. This was followed by a stakeholder analysis that included manufacturing companies, government agencies and consultancies and case studies of three organisations that had recently repositioned their businesses. Phase 2 comprised of a focus group based industry consultation exercise. The aim of the second phase was to validate and refine the findings from the initial phase and to generate the key ideas that would inform a future research agenda. This paper provides the foundation for further research by identifying those operational issues that are affecting industry as it seeks to move to higher value ground

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Visualising product-service system business models

Copyright © 2014. Copyright in each paper in this conference’s proceedings is the property of the author(s). Permission is granted to reproduce copies of these works for purposes relevant to the above conference, provided that the author(s), source and copyright notice are included on each copy. For other uses, including extended quotation, please contact the author(s).The paper addresses the issue of how to visualise innovative business models at various stages of the design and development process. The focus is on a particular type of business model, defined Product-Service Systems (PSSs), characterised by an integrated product-service offering, but can be generalised to other business model innovations. The paper presents a visualisation system based on a formalised business model ontology and a set of visualisation tools, and discusses how it can be used to enhance internal and external communication and improve dialogue and co-design activities inside the company and with external stakeholders