deep learning based production forecasting in manufacturing a packaging equipment case study

Abstract We propose a Deep Learning (DL)-based approach for production performance forecasting in fresh products packaging. On the one hand, this is a very demanding scenario where high throughput is mandatory; on the other, due to strict hygiene requirements, unexpected downtime caused by packaging machines can lead to huge product waste. Thus, our aim is predicting future values of key performance indexes such as Machine Mechanical Efficiency (MME) and Overall Equipment Effectiveness (OEE). We address this problem by leveraging DL-based approaches and historical production performance data related to measurements, warnings and alarms. Different architectures and prediction horizons are analyzed and compared to identify the most robust and effective solutions. We provide experimental results on a real industrial case, showing advantages with respect to current policies implemented by the industrial partner both in terms of forecasting accuracy and maintenance costs. The proposed architecture is shown to be effective on a real case study and it enables the development of predictive services in the area of Predictive Maintenance and Quality Monitoring for packaging equipment providers

Understanding new venture market application search processes: A propositional model.

Technology-based ventures are confronted with complex decisions on how to apply their technology platform in highly uncertain and ambiguous market environments. Based on four case studies, a dynamic decision model is developed in which we highlight the similarities between the search and learning processes in venture development contexts and in new product development contexts. This entrepreneurial search and learning process is understood as consisting of sequences of episodes – characterized by uncertainty and ambiguity - and scripts – i.e. approaches to market application search. The model implies that a venture's adaptability - i.e. its ability to move efficiently and effectively between these episodes and their related scripts - influences its survival.Case studies; Decision; Decisions; Learning; Market; Model; Processes; Product; Product development; Research; Sequences; Similarity; Studies; Technology; Uncertainty;

JTEC Panel report on electronic manufacturing and packaging in Japan

This report summarizes the status of electronic manufacturing and packaging technology in Japan in comparison to that in the United States, and its impact on competition in electronic manufacturing in general. In addition to electronic manufacturing technologies, the report covers technology and manufacturing infrastructure, electronics manufacturing and assembly, quality assurance and reliability in the Japanese electronics industry, and successful product realization strategies. The panel found that Japan leads the United States in almost every electronics packaging technology. Japan clearly has achieved a strategic advantage in electronics production and process technologies. Panel members believe that Japanese competitors could be leading U.S. firms by as much as a decade in some electronics process technologies

Towards a model for managing uncertainty in logistics operations – A simulation modeling perspective

Uncertainty rules supply chains. Unexpected changes constantly occur on all levels; strategically through globalization, introduction of novel technology, mergers and acquisitions, volatile markets, and on an operational level through demand fluctuations, and events such as late arrival of in-bound material, machine equipment breakdown, and quality problems. The problem with uncertainty is increasing as the focus on cost reductions and efficiency in the industry tends to stretch supply chains to become longer and leaner, thus making them more vulnerable to disturbances. The aim of this thesis is to explore strategies for evaluating and managing uncertainties in a logistics context with the objectives; “to propose a method for modeling and analyzing the dynamics of logistics systems with an emphasize on risk management aspects”, and “to explore the impact of dynamic planning and execution in a logistics system”. Three main strategies for handling uncertainties are being discussed; robustness, reliability, and resilience. All three strategies carry an additional cost that must be weighed against the cost and risk of logistical disruptions. As an aid in making this trade-off, a hybrid simulation approach, based on discrete-event simulation and Monte Carlo simulation, is proposed. A combined analytical, and simulation approach is further used to explore the impact of dynamic planning and execution in a solid waste management case. Finally, a draft framework for how uncertainty can be managed in a logistics context is presented along with the key reasons why the proposed simulation approach has proven itself useful in the context of logistics systems

One MES model in Digital Manufacturing

Digital manufacturing is base for Industry 4.0, based on advanced digital-oriented technologies, smart products (advanced production mode and new characteristics), and smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative manufacturing, using it exchange also for digital platforms of design of the innovative products. In this paper we are show developed model of Serbian digital factory with selected examples for the MES area

Using Overall Equipment Effectiveness for Manufacturing System Design

Different metrics for measuring and analyzing the productivity of manufacturing systems have been studied for several decades. The traditional metrics for measuring productivity were throughput and utilization rate, which only measure part of the performance of manufacturing equipment. But, they were not very helpful for “identifying the problems and underlying improvements needed to increase productivity” [1]. During the last years, several societal elements have raised the interest in analyze the phenomena underlying the identification of productive performance parameters as: capacity, production throughput, utilization, saturation, availability, quality, etc. This rising interest has highlighted the need for more rigorously defined and acknowledged productivity metrics that allow to take into account a set of synthetic but important factors (availability, performance and quality) [1]. Most relevant causes identified in literature are: The growing attention devoted by the management to cost reduction approaches [2] [3]; The interest connected to successful eastern productions approaches, like Total Productive Maintenance [4], World Class Manufacturing [5] or Lean production [6]; The importance to go beyond the limits of traditional business management control system [7]; For this reasons, a variety of new performance concepts have been developed. The total productive maintenance (TPM) concept, launched by Seiichi Nakajima [4] in the 1980s, has provided probably the most acknowledged and widespread quantitative metric for the measure of the productivity of any production equipment in a factory: the Overall Equipment Effectiveness (OEE). OEE is an appropriate measure for manufacturing organizations and it has being used broadly in manufacturing industry, typically to monitor and control the performance (time losses) of an equipment/work station within a production system [8]. The OEE allows to quantify and to assign all the time losses, that affect an equipment whilst the production, to three standard categories. Being standard and widely acknowledged, OEE has constituted a powerful tool for production systems performance benchmarking and characterization, as also the starting point for several analysis techniques, continuous improvement and research [9] [10]. Despite this widespread and relevance, the use of OEE presents limitations. As a matter of fact, OEE focus is on the single equipment, yet the performance of a single equipment in a production system is generally influenced by the performance of other systems to which it is interconnected. The time losses propagation from a station to another may widely affect the performance of a single equipment. Since OEE measures the performance of the equipment within the specific system, a low value of OEE for a given equipment can depend either on little performance of the equipment itself and/or time losses propagation due to other interconnected equipments of the system. This issue has been widely investigated in literature through the introduction of a new metric: the Overall Equipment Effectiveness (OTE), that considers the whole production system as a whole. OTE embraces the performance losses of a production system both due to the equipments and their interactions. Process Designers need usually to identify the number of each equipments necessary to realize each activity of the production process, considering the interaction and consequent time losses a priori. Hence, for a proper design of the system, we believe that the OEE provides designer with better information on each equipment than OTE. In this chapter we will show how OEE can be used to carry out a correct equipments sizing and an effective production system design, taking into account both equipment time losses and their propagation throughout the whole production system. In the first paragraph we will show the approach that a process designer should face when designing a new production system starting from scratch. In the second paragraph we will investigate the typical time-losses that affect a production system, although are independent from the production system itself. In the third part we will define all the internal time losses that need to be considered when assessing the OEE, along with the description of a set of critical factors related to OEE assessment, such as buffer-sizing and choice of the plant layout. In the fourth paragraph we will show and quantify how time losses of a single equipment affects the whole system and vice-versa. Finally, we will show through the simulation some real cases in which a process design have been fully completed, considering both equipment and time losses propagation

Designing a service parts quality system for rapid customer response

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2002."June 2002."Includes bibliographical references (leaves 57-60).by Randall Bauman.S.M.M.B.A

A fault-tolerant multiprocessor architecture for aircraft, volume 1

A fault-tolerant multiprocessor architecture is reported. This architecture, together with a comprehensive information system architecture, has important potential for future aircraft applications. A preliminary definition and assessment of a suitable multiprocessor architecture for such applications is developed