The Effects of JIT on the Development of Productivity Norms

Low inventory, or just-in-time (JIT) manufacturing systems, enjoy increasing application worldwide, yet the behavioral effects of such systems remain largely unexplored. Operations Research (OR) models of low inventory systems typically make a simplifying assumption that individual worker processing times are independent random variables. This leads to predictions that low-inventory systems will exhibit production interruptions. Yet empirical results suggest that low-inventory systems do not exhibit the predicted productivity losses. This paper develops a model integrating feedback, goal-setting, group cohesiveness, task norms, and peer pressure to predict how individual behavior may adjust to alleviate production interruptions in low-inventory systems. In doing so we integrate previous research on the development of task norms. Findings suggest that low-inventory systems induce individual and group responses that cause behavioral changes that mitigate production interruptions

SALBPGen - A systematic data generator for (simple) assembly line balancing

Assembly line balancing is a well-known and extensively researched decision problem which arises when assembly line production systems are designed and operated. A large variety of real-world problem variations and elaborate solution methods were developed and presented in the academic literature in the past 60 years. Nevertheless, computational experiments examining and comparing the performance of solution procedures were mostly based on very limited data sets unsystematically collected from the literature and from some real-world cases. In particular, the precedence graphs used as the basis of former tests are limited in number and characteristics. As a consequence, former performance analyses suffer from a lack of systematics and statistical evidence. In this article, we propose SALPBGen, a new instance generator for the simple assembly line balancing problem (SALBP) which can be applied to any other assembly line balancing problem, too. It is able to systematically create instances with very diverse structures under full control of the experiment's designer. In particular, based on our analysis of real-world problems from automotive and related industries, typical substructures of the precedence graph like chains, bottlenecks and modules can be generated and combined as required based on a detailed analysis of graph structures and structure measures like the order strength. We also present a collection of new challenging benchmark data sets which are suited for comprehensive statistical tests in comparative studies of solution methods for SALBP and generalized problems as well. Researchers are invited to participate in a challenge to solve these new problem instances.manufacturing, benchmark data set, assembly line balancing, precedence graph, structure analysis, complexity measures

Design and commission of an experimental test rig to apply a full-scale pressure load on composite sandwich panels representative of aircraft secondary structure

This paper describes the design of a test rig, which is used to apply a representative pressure load to a full-scale composite sandwich secondary aircraft structure. A generic panel was designed with features to represent those in the composite sandwich secondary aircraft structure. To provide full-field strain data from the panels, the test rig was designed for use with optical measurement techniques such as thermoelastic stress analysis (TSA) and digital image correlation (DIC). TSA requires a cyclic load to be applied to a structure for the measurement of the strain state; therefore, the test rig has been designed to be mounted on a standard servo-hydraulic test machine. As both TSA and DIC require an uninterrupted view of the surface of the test panel, an important consideration in the design is facilitating the optical access for the two techniques. To aid the test rig design a finite element (FE) model was produced. The model provides information on the deflections that must be accommodated by the test rig, and ensures that the stress and strain levels developed in the panel when loaded in the test rig would be sufficient for measurement using TSA and DIC. Finally, initial tests using the test rig have shown it to be capable of achieving the required pressure and maintaining a cyclic load. It was also demonstrated that both TSA and DIC data can be collected from the panels under load, which are used to validate the stress and deflection derived from the FE model

Field Induced Jet Micro-EDM

Electrical discharge machining (EDM) is of the potential of micro/nano meter scale machining capability. However, electrode wear in micro-EDM significantly deteriorates the machining accuracy, thus, it needs to be compensated in process. To solve this problem, a novel micromachining method, namely field induced jet micro-EDM, is proposed in this paper, in which the electrical field induced jet is used as the micro tool electrode. A series of experiments were carried out to investigate the feasibility of proposed method. Due to the electrolyte can be supplied automatically by the capillary effect and the electrostatic field, it is not necessary to use pump or valves. The problem of electrode wear does not exist at all in the machining process because of the field induced jet will be generated periodically. It is also found that the workpiece material can be effectively removed with a crater size of about 2 micrometer in diameter. The preliminary experimental results verified that the field induced jet micro-EDM is an effective micromachining method

A Use of Theory of Constraints Thinking Processes for Improvements in the Merged Beams Experiment at Oak Ridge National Laboratory.

Thinking exercises used in the Theory of Constraints (TOC) were used to find and remove constraints at the Merged Beams Experiment at Oak Ridge National Laboratory. The goal of this project was to significantly reduce the amount of time used to take a certain type of measurement during an experimental cycle. After the TOC exercises were used, a basic plan for change was discovered. Preliminary data were taken to establish a baseline of performance from which changes were made. Post-Modification was analyzed showing the project was a success. The overlying reasoning for this exercise was to prove successfully that continuous improvement techniques used in the manufacturing industry can also be successful in a research environment. After overcoming the differences in the goals between each environment, it can be concluded that this reasoning is justified

Analyzing RFID Data For The Management Of Reusable Packaging

A common issue that most automotive manufacturers have to face in production logistics is the efficient handling of a considerable number of cost-intensive pallets, trays, boxes and similar reusable packaging goods. As empirical studies show, deficiencies in monitoring, controlling and optimizing packaging material are widespread within this industry. In this contribution a case study is used to investigate the potential of supporting these managerial tasks with a combined use of RFID infrastructures and Business Intelligence (BI) infrastructures. This includes a derivation of relevant RFID reader locations, the identification of further relevant data sources as well as crafting concrete analysis and reporting scenarios based on the paradigm of multidimensional data modeling. The results are used to design a concept for a BI and RFID based system architecture. They highlight the need to include data management systems that bring data integration capabilities and that are capable of tracking historical data – as a possible component of a wider BI infrastructure for manufacturing and logistics

Data based root cause analysis for improving logistic key performance indicators of a company's internal supply chain

The manufacturing industry faces an increasingly complex and dynamic environment due to shorter product life cycles, advanced production structures and expanding customer services. It is imperative that logistic key performance indicators (KPIs) be considered along with product costs and product quality to obtain a competitive advantage. Numerous companies possess an internal supply chain that fails to meet logistic performance goals set by the management. The measurables for logistic performance include logistic KPIs such as delivery time as well as cost relevant figures including work-in-process or the utilization of employees. In a case of unsatisfactory logistic KPIs, it is pertinent to identify the root causes before attempting to rectify the situation. Increasing digitalization within industry means a substantial volume of confirmation data is available regarding the core processes of a company's internal supply chain. This study discloses a model-based analysis of confirmation data to identify the root causes of unsatisfactory logistic KPIs. A framework for the analysis is constructed by defining generic cause-and-effect relationships between the relevant logistic KPIs and influencing as well as disturbing factors. The results produced by the model-based analysis and the interpretation of the confirmation data show the occurring cause-and-effect relationships for particular use cases and deduce the root causes for insufficient logistic KPIs. From there, companies can develop and implement suitable steps to increase the logistic KPIs by focusing on the newly-identified root causes instead of non-related, but recurring, complications. A case study is included to show the practicality of the presented method. The root cause analysis provides the basis for advanced logistics controlling systems to automatically identify weak-points and propose counteractive measures and therefore continuously improve and adapt the supply chain to changing conditions