Most deterministic optimization models use average values of nondeterministic variables as their inputs. It is, therefore, expected that a model that can accept the distribution of a random variable, while this may involve some more computational complexity, would likely produce better results than the model using the average value. Artificial neural network (ANN) is a standard technique for solving complex stochastic problems. In this research, ANN and adaptive neuro-fuzzy inference system (ANFIS) have been implemented for modeling and optimizing product distribution in a multi-echelon transshipment system. Two inputs parameters, product demand and unit cost of shipment, are considered nondeterministic in this problem. The solutions of ANFIS and ANN were compared to that of the classical transshipment model. The optimal total cost of distribution using the classical model within the period of investigation was 6,332,304.00. In the search for a better solution, an ANN model was trained, tested, and validated. This approach reduced the cost to 4,170,500.00. ANFIS approach reduced the cost to 4,053,661. This implies that 34% of the current operational cost was saved using the ANN model, while 36% was saved using the ANFIS model. This suggests that the result obtained from the ANFIS model also seems marginally better than that of the ANN. Also, the ANFIS model is capable of adjusting the values of input and output variables and parameters to obtain a more robust solution.

Certain inventory items are living organisms, for example livestock, and are therefore capable of growing during the replenishment cycle. These items often serve as various saleable food items downstream in supply chains. The purpose of this paper is to develop a lot sizing model for growing items if the supplier of the items offers incremental quantity discounts. A mathematical model is derived to determine the optimal inventory policy which minimises the total inventory cost in both the owned and rented facilities. A solution procedure for solving the model is developed and illustrated through a numerical example. Sensitivity analysis is performed to demonstrate the response of the order quantity and total costs to some key input parameters. Incremental quantity discounts result in reduced purchasing costs; however, ordering very large quantities has downsides as well. The biggest downsides include the increased holding costs, the risks of running out of storage capacity and item deterioration since the cycle time increases if larger quantities are purchased. Owing to the importance of growing items in the food supply chains, the model presented in this article can be used by procurement and inventory mangers when making purchasing decisions.

The impact of Lean Manufacturing (LM) implementation on organizational performance is an ongoing discussion. The effect of implementing LM tools on operational performance across various industries in Zimbabwe, a country with an unstable real gross domestic product is evaluated. A structural model of LM that is aligned with the Toyota Production System (TPS) house was proposed. A structured survey questionnaire was used for the collection of data in identified companies. Of the 600 companies contacted, 214 useful responses were obtained implying a response rate of 35.6%. The structural and operational models were tested using the Statistical Package for Social Sciences and SmartPLS 3. The result indicated that operational performance was improved by implementing the selected LM tools.