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Purpose This study aims to comparatively discuss the effect of lean manufacturing (LM) implementation in the manufacturing sectors of developing and developed countries. Design/methodology/approach An in-depth literature review focused on previous research published between 2015 and March 2020. The papers published by the databases such as Google Scholar, Scopus, ProQuest and Web of Science were used in the study. A total of 63 studies that focused on LM application in manufacturing industries in developing and developed countries were used in the research. Findings It was observed that LM improves operational performance for manufacturing organizations in developing and developed countries. Small and medium-sized enterprises in both developed and developing countries have difficulties transforming their organizations into lean organizations compared to large enterprises. Furthermore, the review also found that there seems to have been no paper had reported the negative impact of implementing LM in manufacturing industries in developing and developed countries from 2015 to March 2020. Research limitations/implications The study used research papers written between January 2015 and March 2020 and only considered manufacturing organizations from developed and developing nations. Practical implications The study provides more insight into LM implementation in developing and developed countries. It gives the LM practices and the implications of applying these practices in manufacturing organizations for developing and developed countries. Originality/value A preliminary review of papers indicated that this seems to be the first paper that comparatively studies how LM implementation has affected manufacturing organizations in developed and developing countries. The study also assessed the LM practices commonly used by the manufacturing industries in developing and developed countries.

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.

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Background. Response time is viewed as a key performance indicator in most emergency medical services (EMS) systems. Objective. To determine the effect of increased emergency vehicle numbers on response time performance for priority 1 incidents in an urban EMS system in Cape Town, South Africa, using discrete-event computer simulation. Method. A simulation model was created, based on input data from part of the EMS operations. Two different versions of the model were used, one with primary response vehicles and ambulances and one with only ambulances. In both cases the models were run in seven different scenarios. The first scenario used the actual number of emergency vehicles in the real system, and in each subsequent scenario vehicle numbers were increased by adding the baseline number to the cumulative total. Results. The model using only ambulances had shorter response times and a greater number of responses meeting national response time targets than models using primary response vehicles and ambulances. In both cases an improvement in response times and the number of responses meeting national response time targets was observed with the first incremental addition of vehicles. After this the improvements rapidly diminished and eventually became negligible with each successive increase in vehicle numbers. The national response time target for urban areas was never met, even with a seven-fold increase in vehicle numbers. Conclusion. The addition of emergency vehicles to an urban EMS system improves response times in priority 1 incidents, but alone is not capable of the magnitude of response time improvement needed to meet the national response time targets.

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 multiechelon 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.

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