Transforming failure into success through organisational learning: An analysis of a manufacturing information system

This paper describes the idiosyncracies of a case study company, through highlighting issues and problems experienced during their attempts to evaluate, implement and realise the holistic implications of a manufacturing information system. Although the Information System (IS) was operational for a period of time, it was eventually deemed a failure. The reason for this was that a range of human and organisational factors prevented the organisation from embracing the full impact of the system. The eventual success of their information system was realised through a bespoke implementation, based upon a traditional systems development lifecycle that indirectly addressed learning issues following the earlier failed deployment. The paper highlights key issues relating to business success and failure, and then contrasts them alongside the presented case study. In doing so, the authors conclude by proposing methods through which manufacturing information systems can be transformed for business success. This is described achievable through both a realisation in the positioning of the organisation relative to technology management, and the related mapping of human and technological constructs that support information systems related succes

A Dynamic Knowledge Management Framework for the High Value Manufacturing Industry

Dynamic Knowledge Management (KM) is a combination of cultural and technological factors, including the cultural factors of people and their motivations, technological factors of content and infrastructure and, where these both come together, interface factors. In this paper a Dynamic KM framework is described in the context of employees being motivated to create profit for their company through product development in high value manufacturing. It is reported how the framework was discussed during a meeting of the collaborating company’s (BAE Systems) project stakeholders. Participants agreed the framework would have most benefit at the start of the product lifecycle before key decisions were made. The framework has been designed to support organisational learning and to reward employees that improve the position of the company in the market place

An Ontology for Defect Detection in Metal Additive Manufacturing

A key challenge for Industry 4.0 applications is to develop control systems for automated manufacturing services that are capable of addressing both data integration and semantic interoperability issues, as well as monitoring and decision making tasks. To address such an issue in advanced manufacturing systems, principled knowledge representation approaches based on formal ontologies have been proposed as a foundation to information management and maintenance in presence of heterogeneous data sources. In addition, ontologies provide reasoning and querying capabilities to aid domain experts and end users in the context of constraint validation and decision making. Finally, ontology-based approaches to advanced manufacturing services can support the explainability and interpretability of the behaviour of monitoring, control, and simulation systems that are based on black-box machine learning algorithms. In this work, we provide a novel ontology for the classification of process-induced defects known from the metal additive manufacturing literature. Together with a formal representation of the characterising features and sources of defects, we integrate our knowledge base with state-of-the-art ontologies in the field. Our knowledge base aims at enhancing the modelling capabilities of additive manufacturing ontologies by adding further defect analysis terminology and diagnostic inference features

Organisational learning - a critical systems thinking discipline

Original Paper European Journal of Information Systems (2001) 10, 135–146; doi:10.1057/palgrave.ejis.3000394 Organisational learning—a critical systems thinking discipline P Panagiotidis1,3 and J S Edwards2,4 1Deloitte and Touche, Athens, Greece 2Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK Correspondence: Dr J S Edwards, Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. E-mail: [email protected] 3Petros Panagiotidis is Manager responsible for the Process and Systems Integrity Services of Deloitte and Touche in Athens, Greece. He has a BSc in Business Administration and an MSc in Management Information Systems from Western International University, Phoenix, Arizona, USA; an MSc in Business Systems Analysis and Design from City University, London, UK; and a PhD degree from Aston University, Birmingham, UK. His doctorate was in Business Systems Analysis and Design. His principal interests now are in the ERP/DSS field, where he serves as project leader and project risk managment leader in the implementation of SAP and JD Edwards/Cognos in various major clients in the telecommunications and manufacturing sectors. In addition, he is responsible for the development and application of knowledge management systems and activity-based costing systems. 4John S Edwards is Senior Lecturer in Operational Research and Systems at Aston Business School, Birmingham, UK. He holds MA and PhD degrees (in mathematics and operational research respectively) from Cambridge University. His principal research interests are in knowledge management and decision support, especially methods and processes for system development. He has written more than 30 research papers on these topics, and two books, Building Knowledge-based Systems and Decision Making with Computers, both published by Pitman. Current research work includes the effect of scale of operations on knowledge management, interfacing expert systems with simulation models, process modelling in law and legal services, and a study of the use of artifical intelligence techniques in management accounting. Top of pageAbstract This paper deals with the application of critical systems thinking in the domain of organisational learning and knowledge management. Its viewpoint is that deep organisational learning only takes place when the business systems' stakeholders reflect on their actions and thus inquire about their purpose(s) in relation to the business system and the other stakeholders they perceive to exist. This is done by reflecting both on the sources of motivation and/or deception that are contained in their purpose, and also on the sources of collective motivation and/or deception that are contained in the business system's purpose. The development of an organisational information system that captures, manages and institutionalises meaningful information—a knowledge management system—cannot be separated from organisational learning practices, since it should be the result of these very practices. Although Senge's five disciplines provide a useful starting-point in looking at organisational learning, we argue for a critical systems approach, instead of an uncritical Systems Dynamics one that concentrates only on the organisational learning practices. We proceed to outline a methodology called Business Systems Purpose Analysis (BSPA) that offers a participatory structure for team and organisational learning, upon which the stakeholders can take legitimate action that is based on the force of the better argument. In addition, the organisational learning process in BSPA leads to the development of an intrinsically motivated information organisational system that allows for the institutionalisation of the learning process itself in the form of an organisational knowledge management system. This could be a specific application, or something as wide-ranging as an Enterprise Resource Planning (ERP) implementation. Examples of the use of BSPA in two ERP implementations are presented

On the application of model-based definition strategies to the delivery of technical training

ASME 2017 12th International Manufacturing Science and Engineering Conference (MSEC2017). June 4-8, 2017, Los Angeles, CA, USAThe application of computer technology to engineering and manufacturing domains has drastically transformed the way products and systems are designed and produced. However, a major drawback of CAD/CAM/CAE systems is the steep learning curve required to understand and master their extensive and increasingly complex set of functionalities. In this paper, we present a new approach to deliver CAD training materials that is inspired by Model-Based Definition (MDB) strategies, where annotated 3D models become the center of the training process. In our system, textual 3D annotations are connected to a Product Lifecycle Management (PLM) system to provide access to interactive video tutorials which are linked to specific features of a CAD model. As a proof of concerto to validate this approach, a plugin for a commercial CAD package was developed that enhances the functionality of standard 3D annotation mechanisms and enables users to interact with the technical training materials directly within the CAD interface. New data structures were implemented to support the connection and integration with PLM systems. A group of tutorials are described to illustrate the system architecture and implementation details

Improving the accessibility of modelling for management learning : a systems thinking approach using ithink

This thesis describes research aimed at increasing the accessibility of modelling to the general manager as a tool to promote organisational learning and improve managerial performance. An exploratory approach was adopted and a wide-ranging investigation of the whole process of modelling and its relevance to learning was carried out. A review of individual learning, organisational learning and modelling techniques in management, led to the identification of system thinking as a modelling methodology whose role in promoting learning warranted further research. Two major pieces of fieldwork were conducted. Firstly, the process of training managers in systems thinking was studied. Secondly, a case study of the adoption of systems thinking by a large manufacturing company was carried out. During the course of this work, a number of training case studies and a supply chain management training workshop, based upon the use of a generic supply chain model, were developed. This fieldwork identified model conceptualisation as a major area of difficulty for novice modellers. In order to provide assistance in this area, a new framework for model conceptualisation, based upon the use of archetypes and generic models, was developed. During the course of this work an exploration of the relationship between qualitative and quantitative modelling was carried out. This resulted in the development of simulation models of a number of the system archetypes. Additionally, a computerised Delphi-based knowledge acquisition tool was developed. The purpose of this tool was to allow a large group of geographically dispersed people to become directly involved in the modeffing process. In conclusion, this thesis has suggested that there are substantial benefits to be gained from encouraging managers to become modellers. It has also confirmed the potential of systems thinking to support modelling for learning

A machine learning approach for predictive warehouse design

Warehouse management systems (WMS) track warehousing and picking operations, generating a huge volumes of data quantified in millions to billions of records. Logistic operators incur significant costs to maintain these IT systems, without actively mining the collected data to monitor their business processes, smooth the warehousing flows, and support the strategic decisions. This study explores the impact of tracing data beyond the simple traceability purpose. We aim at supporting the strategic design of a warehousing system by training classifiers that can predict the storage technology (ST), the material handling system (MHS), the storage allocation strategy (SAS), and the picking policy (PP) of a storage system. We introduce the definition of a learning table, whose attributes are benchmarking metrics applicable to any storage system. Then, we investigate how the availability of data in the warehouse management system (i.e. varying the number of attributes of the learning table) affects the accuracy of the predictions. To validate the approach, we illustrate a generalisable case study which collects data from sixteen different real companies belonging to different industrial sectors (automotive, manufacturing, food and beverage, cosmetics and publishing) and different players (distribution centres and third-party logistic providers). The benchmarking metrics are applied and used to generate learning tables with varying number of attributes. A bunch of classifiers is used to identify the crucial input data attributes in the prediction of ST, MHS, SAS, and PP. The managerial relevance of the data-driven methodology for warehouse design is showcased for 3PL providers experiencing a fast rotation of the SKUs stored in their storage systems

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS