Advanced operational policies under multi-stage production management

Abstract

Capability to respond quickly to the varying customer demands and product market conditions is the major driving force behind a successful production system. The customer satisfaction has a huge impact on the firm's performance and future standings in the market. High levels of customer satisfaction can be attained by acquiring information about the customer preferences and associating them with the production management perspectives of the production system. This enhances the need of technological transformation and advanced production management strategies. To achieve this goal, the efficient production system acquires advanced machines and robots to support the workforce in producing good quality products with minimal effort and time. However, there always exists a risk of human encounter with these moving machines and occupational accidents. Along with the profitability enhancement objective, the production systems have to modify their production policies in compliance with the customer preferences and workplace safety. This is often achieved on the expense of the firm's profitability. The technological transformation and operational growth combine to develop an efficient production management. The outcomes of these factors must be modeled, analyzed, and adjusted to achieve corporate objectives of the production system. Particularly, the advanced manufacturing includes the production setups that formulate and deploy effective use of the information throughout the predicted product life. The objective of the production management is to attain the capability of quickly responding to the demand variations while maintaining economic as well as environmental excellence. Regarding product manufacturing, a wide variety of industrial products are manufactured in any multi-stage production systems. Real-life production systems are imperfect in nature, and imperfect quality products are also produced along with the perfect quality products. This imperfect proportion is not always constant, it is random or uncertain. The process quality can be improved by adopting process improvement strategies through discrete investments in various aspects of the production system. Effective operational policies are required to achieve finest trade-off among corporate objectives of the production system. In this context, this study is aimed at providing productive operational policies to achieve economic advancement, shop floor invulnerability, environmental protection, and customer gratification for an imperfect multi-stage production management. To achieve the major goals of this research work, this study is conducted in two phases. The first phase develops an optimization model for economic advancement of an imperfect multi-stage production system by obtaining optimal batch size, backorder size, production rate, and number of machines at each production stage. Different cases are studied under the influence of uncertain information about the model specifications along with the environmental accountability involving carbon emissions tax and energy costs. A solution algorithm is developed to solve the model cases by the combined application of analytical optimization technique and iterative method. Experimental analysis of the model verifies practical applicability of the model by providing important managerial insights. The second phase of this study analyzes wide ranging operational policies for the corporate advancement by considering shop floor protection, customer retention, and profitability. A multi-objective optimization model is developed with the aim of improving workplace safety, customer satisfaction index, and total profit of an imperfect multi-item multi-stage production system by suggesting optimal production practices, discrete investments for process advancement, and resource conservation policies. The constrained multi-objective non-linear optimization model with imprecise parametric information is solved through a combination of a modified chance constrained optimization technique and weighted fuzzy goal programming approach. The experimental analysis is performed considering a portion of real data from a manufacturing company and the numerical results are analyzed. The sensitivity analysis of the key parameters is carried out and significant managerial implications are devised to illustrate the practical implication of the proposed production models.