The impact of learning opportunities and decision authority on occupational health

In an investigation of 418 employees in the Norwegian Postal Service, employees with high learning opportunities and high decision authority were found to be better off on psychological functioning, health and organizational outcome variables than employees with low scores on these variables. Decision authority and learning opportunities had specific and independent impact on subjective health, psychological functioning, coping style and organizational outcome variables. There were, however, also interaction effects between demands, learning opportunities, and decision authority on subjective health. Learning opportunities and decision authority were operationalized with a questionnaire, supplemented with questions on the opportunities to learn skills beyond the present job situation. It is suggested that this is a particularly important dimension for coping with the present day rapid changes in working life, where the objective for many workers will be to broaden their repertoire and competence to increase their flexibility in the labour market.     

The Job Demands-Job Control Model and absence behaviour: results of a 3-year longitudinal study

Empirical results of earlier studies only marginally supported the relevance of Karasek's Job Demands-Job Control Model for absence behaviour. Since longitudinal studies with respect to these relations were largely lacking, a four-wave panel study was carried out using data from 1755 male employees of a technical maintenance firm in the public sector. Job demands, job control, physical working conditions, and the employee's age, education, and health were measured in one year and absenteeism in the same year and in the next 3 years. Data were analysed with linear regression and Poisson regression techniques. The Poisson regression technique was superior to the linear regression technique in explaining absence. Age, health and prior absence were the best predictors of later absence behaviour. With respect to the Job Demands-Job Control Model, the main findings of the study were (1) that job control was significantly associated with a low number of simultaneous and later absence days, and (2) that, contrary to expectations, job demands were also related to a low number of simultaneous and later absence days. These results hold when age, health, education, prior absence, and working conditions are controlled for. Job control and job demands did not predict later absence frequency. In the discussion it is suggested that a high level of job demands may not only be harmful for the well-being of employees but also work as 'a pressure to attend'.     

Supply chain management: a supplier perspective

A new way of researching into supply chain management is introduced by adopting a supplier perspective. Details are given of mixed integer linear planning and simulation models. The planning model takes into consideration firm and forecast orders (customer's forecast purchasing orders) and the behaviour of the supplier's suppliers and suppliers' subcontractors. The simulation model takes into account the dynamic behaviour of the supply chain and includes the planning production behaviour of a supplying company based on the planning model. The supplier manager can use the simulation model to determine what kind of parameters most affect company performances and then propose new management rules. Quantitative results that prove the benefits of integrating forecast orders for an aeronautic supplier have been provided.     

Neural networks in production planning and control

The purpose of this paper is to present a selection of the most significant ANN (artificial neural network) applications used to solve PPC (production planning and control) problems. Therefore, the paper itself is a reasoned classification rather than a simple survey. The PPC domain is divided into various areas, and for each area one or more examples of ANNs are discussed, together with the benefits and drawbacks of their application to that particular area. Finally, two case studies developed by the authors are reported and discussed to analyse the main implementation problems to be considered when using ANNs.     

Discriminant coordinates analysis for multivariate functional data

Abstract

One of the basic statistical methods of dimensionality reduction is analysis of discriminant coordinates given by Fisher (1936 Fisher, R. A. 1936. The use of multiple measurements in taxonomic problem. Annals of Eugenics 7 (2):179–88. doi:10.1111/j.1469-1809.1936.tb02137.x.[Crossref] , [Google Scholar]) and Rao (1948). The space of discriminant coordinates is a space convenient for presenting multidimensional data originating from multiple groups and for the use of various classification methods (methods of discriminant analysis). In the present paper, we adapt the classical discriminant coordinates analysis to multivariate functional data. The theory has been applied to analysis of textural properties of apples of six varieties, measured over a period of 180?days, stored in two types of refrigeration chamber.     

The impact of learning opportunities and decision authority on occupational health

In an investigation of 418 employees in the Norwegian Postal Service, employees with high learning opportunities and high decision authority were found to be better off on psychological functioning, health and organizational outcome variables than employees with low scores on these variables. Decision authority and learning opportunities had specific and independent impact on subjective health, psychological functioning, coping style and organizational outcome variables. There were, however, also interaction effects between demands, learning opportunities, and decision authority on subjective health. Learning opportunities and decision authority were operationalized with a questionnaire, supplemented with questions on the opportunities to learn skills beyond the present job situation. It is suggested that this is a particularly important dimension for coping with the present day rapid changes in working life, where the objective for many workers will be to broaden their repertoire and competence to increase their flexibility in the labour market.     

Decentralized WIP-oriented manufacturing control (DEWIP)

DEWIP is a manufacturing control system for job shop environments aiming at achieving short and reliable lead times by establishing WIP control loops between the manufacturing work centres. The paper describes the mode of function, the setting of parameters and simulation results of the new manufacturing control system. The setting of parameters is done with the aid of the funnel model and the theory of logistic operating curves, both developed at the Institute of Production Systems at the University of Hanover. The simulation is conducted using industrial data and makes it possible to assess DEWIP with regard to lead times, WIP level, performance and schedule reliability. DEWIP is compared both with an uncontrolled process and with the manufacturing control systems Load oriented order release (LOOR), Conwip and Polca. The results suggest that DEWIP and the models employed for the setting of parameters are suitable for job shop production and therefore offer a valuable alternative to prevailing centralized manufacturing control systems.     

Robustness evaluation of multisite distributed schedule with perturbed virtual jobshops

A method for evaluating the robustness of medium term multisite distributed schedule is presented. The method of robustness evaluation is based on steps and tools associated with the conceptual model of a MultiSite Reactive Production Activity Control (MSR-PAC). The MSR-PAC makes it possible to study the sensitivity of the scheduled plans in presence of perturbations. These must be evaluated before being dispatched to the shop-floors. At present, to the best of the authors' knowledge, there is no means to make this evaluation in the distributed systems such as extended enterprise. The monitoring of errors is based on the discrepancy between the two flow-shape functions that model respectively the dynamics of the scheduled manufacturing orders and the state of the perturbed production. The MSR-PAC is based on a multisite agent system and on the monitoring of the perturbed virtual jobshops. The method can also be used for controlling short term distributed production activities.     

Layered supervisory control architecture for reconfigurable automation

The development of control systems and supporting software remains one of the greatest obstacles to the widespread implementation of highly adaptive reconfigurable automation technology. This paper will present the structure of a modular automation control system designed to support rapid reconfiguration and redeployment of automation components. The paper will first present the concept of a layered system architecture and the use of modular automation components to support the construction of a wide variety of automated systems. This layered system architecture is then applied to the proposed modular supervisory control system responsible for configuring and driving automated production resources. Each of the control components comprising this modular control system will be presented.     

FORRIDGE - Principles of good practice in material flow

The post World War II Industrial Revolution has driven production away from the old 'economies of scale' to the new 'economies of scope'. Apparently Japanese led, the associated roots of present good practice on material flow can be traced back in the USA to Jay Forrester and in the UK to Jack Burbidge. Specifically, in the supply chain scenario, Forrester exposed the importance of system structure in relating and integrating information flow with material flow. Similarly Burbidge led the way in exploiting cycle time reduction, synchronization of orders throughout the chain, simplified product structures, and streamlined component flows. This paper unites their contributions to material flow engineering and shows how present day system design methodologies incorporate their ideas on best practice. These principles are particularly relevant to the product delivery process (PDP) which governs material flow in supply chains.