The necessity of optimum safety stock is really essential for any smart production system. For this reason, the effect of autonomation policy makes a big difference with the basic traditional automation policy. Basically, for a long-run production system, a process may transfer to an ‘out-of-control’ state from an ‘in-control’ state due to labour problems, machinery problems, or any kind of energy problems. During this ‘out-of-control’ state, machines produced imperfect items instead of perfect items. As a result, an inspection is required to identify the imperfect ones. Until now, this inspection has been utilised by human beings through the traditional automation policy and inspection errors may occur. To perform an error-free inspection, an autonomation policy is examined in this model to detect imperfect items from the production process, which makes the process smarter. The defective rate is random and follows a certain distribution. A budget and a space constraints are adopted, which makes the model non-linear with a constraint problem. Contradictory to the existing literature, the demand is price- and quality-sensitive together in a smart production system. To solve this non-linear problem with an optimised value of backorders, number of delivery lots, safety factors, and collection rate, a non-linear optimisation technique (Khun–Tucker optimisation technique) is employed. A numerical example and sensitivity analysis are provided to illustrate the model. The result finds that the optimum autonomation policy can save work-in-process inventory at the optimum value of the decision variable in the proposed model.

This paper develops a sustainable integrated inventory model for maximizing profit with a controllable lead time, discrete setup cost reduction, and consideration of environmental issues. Contrary to the available literature, this paper considers a discrete setup cost for the vendor, thus making the integrated model sustainable. The customer’s demand is assumed to be selling-price dependent to increase the number of sales, and the lead time demand follows a Poisson distribution. The integrated model is used to optimized the total shipment number, volume of shipments, safety factor, investments, selling-price, and probability of moving between the“in-control”to“out-of-control”states. An algorithm is developed to obtain the numerical results. Numerical examples and sensitivity analyses are given to illustrate the model.

For quality improvement purposes often times, a manufacturing unit has to change certain parts of equipment. Any such changes in the assembly line manufacturing system or production process involves a cost known as the setup cost. Minimizing the setup cost and improving the product quality is of prime importance in today's competitive business arena. This paper develops the effects of setup cost reduction and quality improvement in a two-echelon supply chain model with deterioration. The objective is to minimize the total cost of the entire supply chain model (SCM) by simultaneously optimizing setup cost, process quality, number of deliveries, and lot size. Numerical examples are provided to illustrate the model.