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.

In the current socio-economic situation, smart products are essential for daily life. Energy is a very much related matter to smart products. To buy a smart product, people mostly care about that smart product’s energy consumption and the price. There is always a tug-of-war between the price of the product and the energy consumption of that product. An energy-efficient smart production system is described in this study where the production is variable, and in the out-of-control state, it produces defective products. For prevention of the out-of-control state, preventive maintenance and restoration are used within the smart production system. The rework policy helps to profit from the defective products, and the warranty policy helps to motivate the users. This model applies an improved strategy to the production process and develops a new product that needs to be marketed. Finally, this model plays a vital role in creating smart products with moderate energy consumption at a minimal cost. The mathematical model is a non-linear profit maximization problem that is solved both analytically and numerically. The classical optimization technique founds optimum solutions. Different numerical examples and sensitivity analysis with graphs are used to validate the mathematical model.

Remanufacturing is getting attention nowadays due to increasing waste and corresponding emissions. One of the important factors of remanufacturing is the quality of the remanufactured products. The collection and distribution of used products require proper management. Based on this situation, this study discusses a hybrid closed-loop supply chain management in cooperation with a hybrid production system. The vendor comes up with the policy of sharing remanufacturing responsibility by sharing the technology license with other supply chain players. The carbon cap restricts emissions from the entire hybrid production system of the vendor. Other factors of this proposed study are service by the retailer and quality, gift policy, and customer awareness by the vendor. This study examines the scenario under random market demand. Classical optimization provides the solution under the Stackelberg game policy where the vendor acts as leader and the retailer & third party act as followers. This paper considers two scenarios: Scenario A for a continuous distribution and Scenario B for no specific distribution. A comparison is drawn between various motivating factors-based policies to control supply chain management.