Demand forecasting and smoothing capacity planning for products with high random demand volatility

For those products that are heavily competitive in the marketplace, demand volatility and unpredictability have been growing. This has resulted in a sizeable deviation in demand forecasts when using the traditional forecasting methods. Accordingly, this study aims to develop a real option approach-based forecasting model for predicting demand during the upcoming planning horizon for products with high random volatility on demand. The real option approach can effectively deal with the long-term trends and random variation involved in a given demand stochastic diffusion process. Additionally, this study proposes taking Monte Carlo simulation as a numerical method to solve the demand-forecasting model. Monte Carlo simulation not only can accurately approximate almost any type of stochastic processes, but also can competently handle the path-dependant relationship existing between successive demands. Subsequently, these demand forecasts are used to determine the provisioned smoothing capacity during the upcoming planning horizon. To this end, this study also proposes several effective and practical smoothing capacity-planning approaches in accordance with the specified production strategy. Based on a numerical example, the integrated planning approach can obtain a plausible result.     

An ABC-based cost model with inventory and order level costs: a comparison with TOC

Activity-Based Costing (ABC) was developed to address the deficiencies of traditional accounting systems in the modern business environment by helping managers understand product and customer profitability and identify priority areas for process improvements. In this study, activity-based costing (ABC) concepts are integrated with a Mixed Integer Program (MIP) for order management and profitability analysis in the case of a firm facing demand in excess of capacity. The model considers unit-level, batch, and order-related costs within a mixed-integer programming model representing the firm's operating structure. The performance criterion of profit and service levels during the 20-period planning horizon is used for model evaluation in a comparison with the results from a Theory of Constraints (TOC) formulation. The analysis of these competing models will provide guidelines for applications of order management models with simultaneous consideration of production planning and profitability analysis, as well as help managers understand product and customer profitability, and identify priority areas for process improvements. The results indicate that the ABC-based model is more effective in increasing profitability and reducing inventory levels when compared with the TOC-based formulation, making better use of overhead cost information in the selection of orders.     

OPPS-PRI 2.0: an open and optimized process planning system for prismatic parts to improve the performance of SMEs in the machining industry

The needs of customers are now highly specific and continually changing, although they still want to have high-quality and low-cost products. Companies must respond rapidly to the demands of customers and improve their productivity. They must also provide a suitable product variety in order to survive and be competitive in the market. They attempt to find strong and flexible solutions to this problem. One of the keys for improving productivity and responsiveness is computer-aided process planning (CAPP). The automatic generation or preparation of effective and optimal process plans within a short period of time is vitally important in manufacturing, since small- and medium-sized enterprises (SMEs) have recently become a central part of the manufacturing industry of developing countries. The use of new technology and CNC machine tools has dramatically increased in developing countries, while complete CAD/CAM integration through CAPP is still not seen as ‘realizable’. The productivity and responsiveness of SMEs are the two fundamental issues to be improved to provide qualitative success as well as quantitative success and competitiveness. CAPP is the key technology for the increased productivity and responsiveness in SMEs, which are very sensitive to the diffusion of process planning know-how. SMEs have started to believe that it is necessary to use CAPP systems to improve the productivity of their CNC machine tools and systems. Therefore, the main objective of this research is to focus on the development of an open process planning system that can produce optimal process plans and machining codes for SMEs in the machining industry. The present paper introduces the updated version of a process planning system called OPPS-PRI 2.0 (Optimised Process Planning System for PRIsmatic parts) which was developed for SMEs.     

Performance evaluation and capacity planning in a metallurgical job-shop system using open queueing network models

In this paper, we apply performance evaluation and capacity allocation models to support decisions in the design (or redesign) and planning of a job-shop queueing network of a metallurgical plant. Approximate parametric decomposition methods are used to evaluate system performance measures, such as the expected work-in-process (WIP) and production leadtimes. Based on these methods, optimisation models are then applied for the allocation (or reallocation) of capacity to the stations of the job-shop network. These models are also used to generate approximate trade-off curves between capacity investment and WIP or leadtime, which are valuable for a production manager to estimate how much capacity should be allocated to the stations to reach some targeted performance measures. These curves are also useful for the sensitivity analysis of the solutions to changes in the input parameters, such as the variability of the product demands, the mix of the production and the throughput rate of the network.     

A system of safety management practices and worker engagement for reducing and preventing accidents: An empirical and theoretical investigation

Objective

The overall research objective was to theoretically and empirically develop the ideas around a system of safety management practices (ten practices were elaborated), to test their relationship with objective safety statistics (such as accident rates), and to explore how these practices work to achieve positive safety results (accident prevention) through worker engagement.

Method

Data were collected using safety manager, supervisor and employee surveys designed to assess and link safety management system practices, employee perceptions resulting from existing practices, and safety performance outcomes.

Results

Results indicate the following: there is a significant negative relationship between the presence of ten individual safety management practices, as well as the composite of these practices, with accident rates; there is a significant negative relationship between the level of safety-focused worker emotional and cognitive engagement with accident rates; safety management systems and worker engagement levels can be used individually to predict accident rates; safety management systems can be used to predict worker engagement levels; and worker engagement levels act as mediators between the safety management system and safety performance outcomes (such as accident rates).

Implications

Even though the presence of safety management system practices is linked with incident reduction and may represent a necessary first-step in accident prevention, safety performance may also depend on mediation by safety-focused cognitive and emotional engagement by workers. Thus, when organizations invest in a safety management system approach to reducing/preventing accidents and improving safety performance, they should also be concerned about winning over the minds and hearts of their workers through human performance-based safety management systems designed to promote and enhance worker engagement.