Abstract
In many manufacturing systems human resources are essential in some cognitive intensive tasks while the more repetitive ones are assigned to automatic systems. If on the one hand, automation has a deterministic pace; humans are known by a flexible and variable work manner. Therefore, a reliable description of both hardware and human components is required for designing such manufacturing systems. The purpose of this paper is to investigate the impact of the variable throughput of a manual process in a production flow that contains automatic processes upstream and downstream. With regard to the description of human behavior, two sources of variability were considered: natural and abnormal variability. Natural variability refers to the differences in terms of processing times that can be found among individuals. Organizational aspects such delays in shift changing and breaks along the shift, are referred as abnormal variability, and were also investigated by means of an analytical and simulation models.

Abstract

Abstract
Controlling, maintaining, and improving quality is a central topic in manufacturing. Total Quality Management (TQM) provides several tools and techniques to deal with quality related topics, which are not always applicable. With the increased use of Information Technology (IT) in manufacturing there is a higher availability of data with great potential of further improvements. At the same time this results in higher requirements for data storage and processing with demanding, time consuming sessions for interpretation. Without suitable tools and techniques knowledge remains hidden in databases. This paper presents a methodology to help analyzing root causes of nonconformities (NCs) through a pattern identification approach. Hereby a methodology of Knowledge Discovery in Databases (KDD) is adapted and used as a quality tool. As the core element of the KDD methodology, the data mining, a well-known statistical measure from the field of economics—the Herfindahl–Hirschman Index (HHI)—is integrated. After presenting the theoretical background a new methodology is proposed and validated through an application case of the automotive industry. Results are obtained and presented in the form of patterns in matrices. They suggest that concentration indices may indicate possible root causes of NCs and invite for further investigations. © Springer International Publishing Switzerland 2013.

Abstract

Abstract
Performance assessment is critical in today's competitive environments, where companies need to establish trade-offs between key competitive dimensions. The complexity of these environments calls for new approaches to performance assessment. Thus, in this work, we propose a novel conceptual framework for performance assessment in manufacturing environments combining different production strategies. Focus is laid on MTO/ETO combined environments and a three-stage problem analysis is considered. Firstly, a hybrid SD-DES-ABS model approach addresses the needs of a system that handles different types of orders, processes and workforce allocation requirements; secondly, the model results for different demand scenarios are assessed using a one-way ANOVA analysis followed by a Tukey - Kramer's test, with pairwise comparisons for assessment of significant performance variations under different system operating policies. A full factorial Design of Experiments (DOE) analysis follows, for determining the relevant process parameters influencing the system performance. As an example of application of the proposed framework, we consider the case of an advanced manufacturing company, whose manufacturing environment encompasses combined MTO/ETO production strategies.

Abstract
In a high-volume production environment it is particularly important to ensure that key process variables and parameters are within the specification limits. Often, adjustments and changes to the process are required to ensure all applicable quality requirements. In most cases, problems are not caused by an isolated factor. In fact, they are the result of interactions between several factors, including quality ingredients, parameter settings and other processing conditions. In the biscuit manufacturing industry, the thickness and weight of the biscuit are two of the most important product's quality characteristics. Regardless of the product purchased by the customer, the declared weight is considered a specification, so any deviation of these characteristics from their nominal values leads to a change in the weight of the packages. In this paper, we explore a six-sigma approach in the improvement of an industrial biscuit production.