Girish Upreti

Life Cycle Assessment of Nanomaterials

Nanotechnology has wide range of applications. This paper emphasizes the need to conduct “life cycle”-based assessments as early in the new product development process as possible, for a better understanding of the potential environmental and human health consequences of nanomaterials over the entire life cycle of a nano-enabled product. This is further supported through an illustrative case study on automotive exterior body panels, which shows that the perceived environmental benefits of nano-based products in the Use stage may not adequately represent the complete picture, without examining the impacts in the other life cycle stages, particularly Materials Processing and Manufacturing. Nanomanufacturing methods often have associated environmental and human health impacts, which must be kept in perspective when evaluating nanoproducts for their “greenness.” Incorporating life cycle thinking for making informed decisions at the product design stage, combining life cycle and risk analysis, using sustainable manufacturing practices, and employing green chemistry alternatives are seen as possible solutions.

Employee turnover forecasting for human resource management based on time series analysis

ABSTRACT In some organizations, the hiring lead time is often long due to responding to human resource requirements associated with technical and security constrains. Thus, the human resource departments in these organizations are pretty interested in forecasting employee turnover since a good prediction of employee turnover could help the organizations to minimize the costs and impacts from the turnover on the operational capabilities and the budget. This study aims to enhance the ability to forecast employee turnover with or without considering the impact of economic indicators. Various time series modelling techniques were used to identify optimal models for effective employee turnover prediction. More than 11-years of monthly turnover data were used to build and validate the proposed models. Compared with other models, a dynamic regression model with additive trend, seasonality, interventions, and a very important economic indicator effectively predicted the turnover with training R2 = 0.77 and holdout R2 = 0.59. The forecasting performance of optimal models confirms that time series modelling approach has the ability to predict employee turnover for the specific scenario observed in our analysis.

Production sequencing in a fifth wheel industry: Case study

Fifth wheel is a type of hitch mounted in the bed of a truck. Fifth wheel coupling provides the link between a trailer and the towing truck. Lean methodology is applied to handle production sequencing at fifth wheel industry. Due to high variation of final products and non-constant demand, there is no specific methodology to determine the quantity of production and its order. This results into overproduction and high number of inventory between welding and assembly, and also finished product. We propose a sequencing algorithm between painting and welding area that helps to reduce inventory and movements between welding and assembly. An evaluation is made on the feasibility of coordinating welding production and painting of parts in sequence. The simulation of current and future state indicates shorter lead time and lower work in process.