It is predicated that in the future factories will be more dependent upon complex distributed control software, where execution and communication occur at the edge of the system, close to the machines that are involved in the manufacturing process. This introduces the problem of how to effectively design, deploy and manage such systems. The IEC 61499 function block standard has been given as one way to solve this problem, proposing applications made from a number of modular event driven blocks that encapsulate algorithms potentially in any programming language. At the same time the Internet of Things and Cloud computing fields have encountered the similar problem of encapsulating, managing and distributing scalable applications, and have been investigating software containers as a solution. This paper proposes to combine these two approaches using containers to enable easy distribution and management of software modules on manufacturing devices while taking advantage of the IEC 61499 application model to enable an application orchestration tool. It describes the properties of container technology that can support the creation of function block like components and the proposal is supported by a prototype version of such a system which has already been implemented.
Correct and accurate cost estimations are critical for shipyard companies. Traditionally cost estimations for one-off ship designs are based on expert opinions and statistical-based methods. In this paper, we introduce a method for improving cost estimations in the pre-contract stage of a one-off design shipbuilding project. Pre-contract means that the available information is limited. The introduced method is based on the idea that, although a new vessel has never been built before, cost of similar building assemblies can be found in the production information of earlier produced vessels. The assessment of similarities is performed by means of error-tolerant graph matching. Research to date shows that only taking the basic activities into account, without taking the context into account, does not always lead to a good and reliable result. The example in this paper is based on the ship block assembly process.
Enrique Garcia-Villarreal, Ran Bhamra, Martin Schoenheit
285 - 290
Original Equipment Manufacturers (OEMs) of the medical technology sector in Germany are being challenged with competition from low-wage countries developing, manufacturing, and distributing high-quality products on a global scale. Considering the importance of Sales and Operations Planning (S&OP) in these organisations, the current trend of Smart Manufacturing (Industry 4.0) in Germany, and the dearth of empirical research on both medical technology supply chains and technology-selection processes, this paper presents the outcomes of a study using action research (AR) to develop and practically test a technology-selection framework to support S&OP from both intra-organisational and inter-organisational perspectives. This framework takes into account both OEM and supply chain considerations and proposes an evaluation and design method for the selection of appropriate tools to support S&OP. Additionally, the close cooperation with the case study organisation provided insights into daily operational issues of the members of the supply chain under study.
Julfikar Haider, David Petty, Sofiane Tebboune, Ian Kennedy
291 - 296
This paper summarises the lessons learnt from a study of continuous improvement and information system implementation in a ready-meal food manufacturing company. The objectives of the study were to increase flexibility and capacity within the manufacturing operation by reducing wastage and improving efficiency, and by implementing a more robust Enterprise Resource Planning (ERP) system to enable further growth by improving transaction processing efficiency, customer service, standards and regulatory compliance, traceability, product cost, production and materials planning and performance analysis. The study started with a systematic review of existing activities and practices in the organisation and identified the factors causing waste and inefficiency in manufacturing operations and analysed these to generate potential solutions in the form of worksheets and areas of good practice. In addition, the options for different commercially available ERP packages were assessed to enable an informed choice to be made for implementation in order to realise operational improvement.
Sameh M. Saad, Itzel Noguera, Ramin Bahadori, Sylvana Saad
297 - 302
The aim of this research paper is to analyse the impact of a Consignment Inventory (CI) strategy on patient satisfaction in the pharmaceutical retail market. The consignment inventory solution is implemented in a particular business to business (B2B) company, active in the pharmaceutical retail market in Mexico, where usually companies suffer from a low fulfilment rate which leads to unsatisfied customers and also a reduction in their profits.
An Economic Order Quantity (EOQ) with non-instantaneous receipt mathematical model and simulation techniques are developed to identify the optimal fulfilment rate.
The obtained results of this research paper demonstrated that the implementation of the proposed Consignment Inventory strategy would permit reaching the goal of a much higher fulfilment rate and consequently, higher customer satisfaction.
Integrated Circuits (ICs) are the cardinal elements of modern electrical, electronic and electro-mechanical systems. Amid global outsourcing of ICs' design and fabrication and their growing applications in smart manufacturing or Industrie 4.0, various hardware security threats and issues of trust have also emerged. IC piracy, counterfeiting, and hardware Trojans (HTs) are some of the key hardware threats that merit the attention of manufacturing community. It is worth noting that the lower abstraction levels (ICs) are falsely assumed to operate securely. The proposition, therefore, is that if an operating system (higher abstraction level) is considered to be secure while operating on a compromised IC (lower abstraction level), would it be prudent to regard this implementation as secure? The purpose of this paper is to highlight IC level threats with an emphasis on hardware Trojans that pose a significant threat to smart manufacturing environment in the wake of Industrial Internet of Things (IIoT).
Yuji Yamamoto, Kristian Sandström, Alvaro Aranda Munoz
311 - 316
Although scholars and practitioners are actively discussing the potential benefits of introducing Internet of Thing (IoT) in production, IoT is still as an expensive solution in terms of investment and high technological threshold. Manufacturing companies seek a simpler and lower-cost approach to adopting IoT technologies in production, allowing companies to take advantage of the knowledge and innovation capabilities of people close to shop floor operations. This paper introduces the concept of “Karakuri IoT” – simple and low-cost IoT-aided improvements driven by the people close to shop floor operations. A pre-study is conducted to examine the feasibility of the concept. This paper presents the results of the pre-study.
Andrew Thomas, Richard Barton, Claire Haven-Tang, Paul Byard, Rachel Mason-Jones, Mark Francis
317 - 322
The UK food industry faces significant challenges to remain competitive. With the changing political climate, companies can no longer rely on a low labour cost workforce to maintain low production costs. Smart Production Systems (SPS) is being seen as an alternative towards achieving improvements in productivity. However, little evidence exists on whether UK food companies are prepared for implementing such systems. The purpose of this research is to explore the applicability of SPS in the UK food processing industry and, to identify the preparedness of companies to implement such systems. A triangulated research approach is adopted and includes a questionnaire, interviews and audits of 20 food processing companies. Findings suggest that the current political turbulence in the UK could bring food companies closer to the adoption of SPS, hence it is a good time to consider learnings from other manufacturing sectors and, to outline an SPS implementation strategy.
Mohamed Amin Benatia, Ahmed Remadna, David Baudry, Pierre Halftermeyer, Hugues Delalin
323 - 328
Product traceability can ensure high level of product: quality, integrity and availability. It can be defined as the ability to track & trace individual items throughout their whole lifecycle from manufacturing to recycling. This includes real-time data analytics about actual product behavior (ability to Track) and product historical data (ability to Trace). The need of such systems is becoming increasingly important in multiple domains such as: food, pharmaceutical and cosmetics Supply chains suffering from proliferation of counterfeit items, product diversions and grey market. This paper presents a comparative study between several works on product traceability and proposes a standardized traceability system architecture. We consider a cyber-physical system vision of the supply chain to ensure system integrity and guarantee safety and quality to consumers. We also demonstrate that using Big Data technologies can ensure system interoperability between the different supply chain actors.
Carla G. Machado, Martin Kurdve, Mats Winroth, David Bennett
329 - 334
The traditional view of production systems relies on the organization of physical and information flows enabling customer satisfaction with products or services, following inputs from strategy, policies, rules and principles, supported by tools, systems and methods, and improved through performance management systems. Moving forward to new levels of industrialization, smart manufacturing represents systems integration and automation supported by Cyber-Physical-Systems (CPS) to enable more autonomous, agile and sustainable production processes, which can at the same time be influenced by, as well as influencing the organizational system in real time. As a new managerial topic, this research paper intends to study and systematically organize the literature related to smart manufacturing and production systems design in order to identify whether smart manufacturing can be implemented through the production systems approach and, if so, what are the requirements for implementation and integration of different management systems (e.g. quality, and environment systems).
Current research lacks details on how SMMEs are able to capitalize on how their IT-solutions supports data-driven decision-making. Such details are important for being able to support further development of SMMEs and assuring their sustainability and competitive edge. Prosperous SMMEs are vital due to their economical and societal importance. To alleviate the lack of details, this paper presents the results of four case studies towards SMMEs partly aimed at investigating their current state of data-driven decision-making. The findings reveal that IT-solutions in some areas are either underdeveloped or unexplored. Instead, the SMMEs tend to focus on traditional manufacturing techniques, continuous improvements in the manufacturing process, and manual support routines and thereby neglects opportunities offered in relation to e.g. incident management, product quality monitoring, and the usage of KPIs not directly linked to manufacturing.
The efficiency of an organizational supply chain system depends upon several factors. One of the major factors is inventory management. Efficient inventory management can generate more profit. To have an efficient control of a huge amount of inventory items, the traditional approach is to classify the inventory into different groups. Inventory classification using ABC analysis is one of the most widely employed techniques in organizations. However, ABC analysis is based on only single measurement. It has been recognized that other criteria are also important in inventory classification. In this work, A Multi-Criteria Decision Making (MCDM) technique-based approach is proposed for Multi-Criteria Inventory Classification (MCIC). To cope with multiple criteria, use of TOPSIS, a MCDM technique, is proposed for the MCIC. The methodology is illustrated using a case example from literature. Comparisons of the proposed approach with some previous methods are illustrated based on a classical MCIC problem.
Growing competition market situations have emerged the requirement of the real-time data, understanding data behaviour, and maintenance actions in the manufacturing system. The future decision-making process in manufacturing needs to be more flexible to adapt to various methods for maintenance decision support systems (MDSS). This paper classifies various application areas of MDSS through a systemic literature review. Specifically, it identifies the relationship between the machine maintenance areas and the processes in which it integrates different tools and techniques to develop MDSS. The accumulated information helps in analyzing trends and shortcomings to concentrate the efforts for future research work. The reviewed papers are selected based on the contents, application tool assessments and clustered by their application areas. Furthermore, it proposes a structure outlined based on the functional knowledge as well as the information flow design during the development of MDSS, along with the relationship among application areas.
Zhe Zhong, Salman Saeidlou, Mozafar Saadat, Ahmed Abukar
355 - 360
Over the past decade, the rapid growth of big data has led manufacturing intelligence to become one of the most popular topics in the area of advanced manufacturing. Although some of the current internet and computer network technologies enable collaborative enterprises to share manufacturing knowledge, they were unsuccessful in maximizing the potential predictive decision-making ability of using their historical data. The aim of this paper is to demonstrate the development of an intelligent predictive model, in order to predict the conformity of production orders. A manufacturing ontology was built, based on the historical data of a real industrial case study. The framework of the knowledge-based predictive model was drawn by a classification tree, which includes solutions to the predictive questions. The elements of the decision tree were transformed into SWRL rules to be input to Pellet reasoner, so that the intelligent machines can automatically infer knowledge from the ontology.
Zhen Hao, Ahmed Abukar, Mozafar Saadat, Salman Saeidlou
361 - 366
At present, estimating the deliverables of products in the manufacturing industry mainly depends on the knowledge of experts, but the knowledge of experts is sometimes difficult to obtain and often limited. The main purpose of this paper is to provide a method that can predict the deliverability of a product based on machine learning and data science technologies. The data used is real industry data and the machine learning algorithm used does fit the data set. The results of the model illustrate that using machine learning algorithms to predict deliverability is feasible. The machine learning algorithm achieves precision and recall percentages respectively. However, the findings also address some limitations. The machine learning algorithm has requirements on the form of the data. If the new data and the historical data have different forms, the machine learning algorithm cannot generalize well on new data.
Tehseen Aslam, Anna Syberfeldt, Amos Ng, Leif Pehrsson, Mathias Urenda-Moris
369 - 374
Virtual production development is adopted by many companies in the production industry and digital models and virtual tools are utilized for strategic, tactical and operational decisions in almost every stage of the value chain. This paper suggest a testbed concept that aims the production industry to adopt a virtual production development process with integrated tool chains that enables holistic optimizations, all the way from the overall supply chain performance down to individual equipment/devices. The testbed, which is fully virtual, provides a mean for development and testing of integrated digital models and virtual tools, including both technical and methodological aspects.
Due to increasingly customized manufacturing and stricter requirements on sustainability, it is challenging to achieve energy efficient optimization for machining processes. This paper presents a novel Cyber Physical System and Big Data enabled machining optimization system to address the above challenge. An effective evolutional algorithm based on Fruit Fly Optimization is applied to generate an adaptive energy efficient schedule, and improve schedule when there are significantly varying working conditions and adjustments on the schedule are necessary. Practical case studies presented in this paper demonstrate the effectiveness and great potential of applicability of the system into practice.
The manufacturing industry has a duty to minimize their environmental impact and more and more legislations include environmental impact evaluations from a life cycle perspective to avoid burden shift. Current manufacturing industry increase their use of computer-based simulations for optimizing production processes. In recent years, a number of studies have been published, combining simulations with life cycle assessments (LCA), to evaluate and minimize the environmental impact of production activities, as part of improving the production processes. Still, current knowledge concerning simulations for LCA is rather scattered. Therefore, this paper reviews relevant literature covering simulation based LCA for production development. The results of the review and cross comparison of papers are structured following the 6 categories in line with the ISO standard definition of LCA (goal formulation, scope definition, environmental impact assessment, data quality, level of modelling details, and model validation) and report the strengths and constraints of the reviewed studies.
Ning Tian, Kai Cheng, Jiaqi Liu, Yanghui Zou, Lisong Zhang
387 - 392
The combustion gas tunnel is one of the main equipment used in the ground-test of the thermal protection system of supersonic aircraft. The assessment of the thermal protection system requires high uniformity of the flow field in the test system. The degree of matching between the pressure in the test cabin and the outlet pressure of the nozzle is a key factor affecting the uniformity of the flow field. The combustion reaction equations and CFD-FASTRAN are used to simulate the thermal environment of the gas tunnel. The gas component derived from the combustion reaction can provide a mixed gas model for numerical simulation. The shock wave characteristics of gas jet on the surface of a hemispherical-nose and cone body model are analyzed. The effects of different cabin pressures on the thermal environment of the model were analyzed. A new method for matching cabin and jet pressures is proposed.
Hengyuan Ma, Wei Liu, Xionghui Zhou, Qiang Niu, Chuipin Kong
393 - 398
Higher quality requirements and lower production time are the main thrust behind the development of tool path optimization approach. In this paper, a new NC code optimization system based on virtual manufacturing technology is proposed, which includes three key modules: geometric simulation, physical simulation and NC code optimization. In the geometric simulation module, a general and extensible NC code parsing framework is built and space partitioning modeling approach with voxels is employed to extract cutting parameters in instantaneous machining. While in the physical simulation module, a database recording pre-calibrated cutting force models is established and machining mechanics are predicted. Finally, NC codes are optimized by scheduling the values of feedrate and spindle speed which targets at constant cutting forces and less machining time. Later experiments illustrate that after multi-criteria optimization, the fluctuation of cutting force is reduced and efficiency is improved, which well demonstrates the feasibility and effectiveness of the system.
Julian Marc Schlosser, Serkan Mouchtar, Robert Schneider, Jochen Schanz, Wolfgang Rimkus, David K. Harrison, Martin Macdonald, Muditha Kulatunga
399 - 404
In the present work an effective parameter identification for the evaluation of a failure model for FE-simulations has been carried out. This failure model is suitable to calculate different fracture elongation values among occurring triaxiality of a high-strength aluminium sheet metal alloy (AA7075). Various specimen geometries have been selected to achieve different loading states (triaxiality). For biaxial strains a forming limit curve (FLC) is converted by using mathematical formulations. To measure the equivalent local strains in the event of fracture an optical measurement system has been installed and adapted to a tensile testing machine. The failure curve and the material model which includes the extrapolated flow curve are implemented into the FE-simulation model. In order to improve the accuracy of the failure-curve a parameter optimization has been carried out. It shows that by using the optimised failure curve a high correlation between experimental and calculated force-displacement curves for any given specimen geometry can be achieved.