A model for linking shop floor improvements to manufacturing cost and profitability

Manufacturing units in the so called high-cost countries are struggling under fierce competition on the global market. In order to survive, the factory needs to generate profit to its owners. Profitability can be reached in many different ways apart from only lowering the employees' salaries. It can be improved through increased profit margins (sales in relation to costs) or with an increased capital turnover rate. Finding ways to free capacity and to improve flexibility in order to increase sales is often more interesting to the manufacturing companies than cutting the direct salary costs. A model for analysing profitability of a manufacturing unit is proposed. It is found on a production system analysis and combines in-depth production engineering analysis with economical accounting analysis of the factory. The manual work tasks are of special interest and the productivity of selected bottleneck work areas are analysed thoroughly. The model is intended for use by two industrial analysts during a one-week study. Simulation of different improvement scenarios is carried out and presented to the factory management at the end of the profitability study. A software implementation is required in order to generate the model, collect data and make simulation within the intended time. The implementation is made in spread sheet software using Visual Basic to program interfaces and automatic functions. The primary area of application is the electronics industry in Sweden where the model is used in a research project to strengthen the competitiveness of that industry

Analysis of manufacturing operations using knowledge- Enriched aggregate process planning

Knowledge-Enriched Aggregate Process Planning is concerned with the problem of supporting agile design and manufacture by making process planning feedback integral to the design function. A novel Digital Enterprise Technology framework (Maropoulos 2003) provides the technical context and is the basis for the integration of the methods with existing technologies for enterprise-wide product development. The work is based upon the assertion that, to assure success when developing new products, the technical and qualitative evaluation of process plans must be carried out as early as possible. An intelligent exploration methodology is presented for the technical evaluation of the many alternative manufacturing options which are feasible during the conceptual and embodiment design phases. 'Data resistant' aggregate product, process and resource models are the foundation of these planning methods. From the low-level attributes of these models, aggregate methods to generate suitable alternative process plans and estimate Quality, Cost and Delivery (QCD) have been created. The reliance on QCD metrics in process planning neglects the importance of tacit knowledge that people use to make everyday decisions and express their professional judgement in design. Hence, the research also advances the core aggregate planning theories by developing knowledge-enrichment methods for measuring and analysing qualitative factors as an additional indicator of manufacturing performance, which can be used to compute the potential of a process plan. The application of these methods allows the designer to make a comparative estimation of manufacturability for design alternatives. Ultimately, this research should translate into significant reductions in both design costs and product development time and create synergy between the product design and the manufacturing system that will be used to make it. The efficacy of the methodology was proved through the development of an experimental computer system (called CAPABLE Space) which used real industrial data, from a leading UK satellite manufacturer to validate the industrial benefits and promote the commercial exploitation of the research

Design and discrete event simulation of power and free handling systems

Effective manufacturing systems design and implementation has become increasingly critical, with the reduction in manufacturing product lead times, and the subsequent influence on engineering projects. Tools and methodologies that can assist the design team must be both manageable and efficient to be successful. Modelling, using analytical and mathematical models, or using computer assisted simulations, are used to accomplish design objectives. This thesis will review the use of analytical and discrete event computer simulation models, applied to the design of automated power and free handling systems, using actual case studies to create and support a practical approach to design and implementation of these types of systems. The IDEF process mapping approach is used to encompass these design tools and system requirements, to recommend a generic process methodology for power and free systems design. The case studies consisted of three actual installations within the Philips Components Ltd facility in Durham, a manufacturer of television tubes. Power and free conveyor systems at PCL have assumed increased functions from the standard conveyor systems, ranging from stock handling and buffering, to type sorting and flexible product routing. In order to meet the demands of this flexible manufacturing strategy, designing a system that can meet the production objectives is critical. Design process activities and engineering considerations for the three projects were reviewed and evaluated, to capture the generic methodologies necessary for future design success. Further, the studies were intended to identify both general and specific criteria for simulating power and free conveyor handling systems, and the ingredients necessary for successful discrete event simulation. The automated handling systems were used to prove certain aspects of building, using and analysing simulation models, in relation to their anticipated benefits, including an evaluation of the factors necessary to ensure their realisation. While there exists a multitude of designs for power and free conveyor systems based on user requirements and proprietary equipment technology, the principles of designing and implementing a system can remain generic. Although specific technology can influence detailed design, a common, consistent approach to design activities was a proven requirement In all cases. Additionally, it was observed that no one design tool was sufficient to ensure maximum system success. A combination of both analytical and simulation methods was necessary to adequately optimise the systems studied, given unique and varying project constraints. It followed that the level of application of the two approaches was directly dependent on the initial engineering project objectives, and the ability to accurately identify system requirements

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

A review of energy simulation tools for the manufacturing sector

Manufacturing is a competitive global market and efforts to mitigate climate change are at the forefront of public perception. Current trends in manufacturing aim to reduce costs and increase sustainability without negatively affecting the yield of finished products, thus maintaining or improving profits. Effective use of energy within a manufacturing environment can help in this regard by lowering overhead costs. Significant benefit can be gained by utilising simulations in order to predict energy demand allowing companies to make effective retrofit decisions based on energy as well as other metrics such as resource use, throughput and overhead costs. Traditionally, Building Energy Modelling (BEM) and Manufacturing Process Simulation (MPS) have been used extensively in their respective fields but they remain separate and segregated which limits the simulation window used to identify energy improvements. This review details modelling approaches and the simulation tools that have been used, or are available, in an attempt to combine BEM and MPS, or elements from each, into a holistic approach. Such an approach would be able to simulate the interdependencies of multiple layers contained within a factory from production machines, process lines and Technical Building Services (TBS) to the building shell. Thus achieving a greater perspective for identifying energy improvement measures across the entire operating spectrum and multiple, if not all, manufacturing industries. In doing so the challenges associated with incorporating BEM in manufacturing simulation are highlighted as well as gaps within the research for exploitation through future research. This paper identified requirements for the development of a holistic energy simulation tool for use in a manufacturing facility, that is capable of simulating interdependencies between different building layers and systems, and a rapid method of 3D building geometry generation from site data or existing BIM in an appropriate format for energy simulations of existing factory buildings

Application of Collaborative Robots for Increasing Productivity in an Eyeglasses Lenses Manufacturer

This research focuses on a framework for making decisions when adopting collaborative robots (cobots) to collaborate with or replace human workers. Top management at a real-life case study firm that manufactures a variety of eyeglasses lenses wants to implement cobots in the sorting process since such a repetitive task has been shown to have a significant negative influence on workers' ergonomic ailments. Its current procurement decision-making process focuses solely on financial perspectives without taking into account any other significant criteria. Therefore, the purpose of this study is to investigate the elements that are crucial in deciding whether to use cobots in manufacturing lines., Multivariate statistical methods, comprising the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), are applied to analyse the elements that are associated with the latent variables such as safety, ergonomics, productivity, quality, system, internal organisation and external organisation. In addition, alternative deployments of cobots in the case study are validated through the ARENA simulation software. More specifically, the results showed that using cobots in the workplace might boost output while lowering WIP, waiting times, the number of tasks in queue, and the workforce. In addition, cobots may reduce employee ergonomic risk and enhance workplace safety

A collaborative decision support system framework for vertical farming business developments

The emerging industry of vertical farming (VF) faces three key challenges: standardisation, environmental sustainability, and profitability. High failure rates are costly and can stem from premature business decisions about location choice, pricing strategy, system design, and other critical issues. Improving knowledge transfer and developing adaptable economic analysis for VF is necessary for profitable business models to satisfy investors and policy makers. A review of current horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. Data from the literature alongside lessons learned from industry practitioners are centralised in the proposed DSS, using imprecise data techniques to accommodate for partial information. The DSS evaluates business sustainability using financial risk assessment. This is necessary for complex/new sectors such as VF with scarce data

Daydreaming factories

Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions