Dairy is a key contributor to the Kenyan economy and it helps in poverty alleviation and food nutrition in rural and urban areas. Dairy processing acts as a link between dairy farmers and the consumers in Kenya. However, milk processors have been facing numerous challenges that consequently affect their performance. Poor quality of milk, post-harvest losses resulting from inadequate cooling plants and seasonality of milk production in the country are among the problems the milk processors face. Raw milk, the main inventory for milk processors, is highly perishable and requires processing immediately after collection. However, this has not been the case. In fact, 54% of the invested capacities of milk processors remain underutilized. This calls for leanness in inventory that will ultimately reduce waste and improve firms’ performance through reduced complaints. This study was guided by resource orchestration and lean theory. The study was a census and the unit of analysis was all milk processors in Kenya licensed by the Kenya Dairy Board. A pilot study was conducted to enhance the questionnaire’s quality. A Cronbach Alpha of 0.8 was used. Before the regression was run, diagnostic tests were conducted to check for assumptions of linearity. An analysis of variance was conducted to test the variables’ significance and, ultimately, a multiple regression analysis was run. Maintain zero inventory and use of technology to order inventory and maintain zero inventory had positive and significant coefficients while collaboration with suppliers and members in a supply chain had a negative insignificant coefficient. The study concludes that zero inventory and use of technology to order have significant positive correlation coefficients. This implies that the two can help a firm reduce customer complaints and in return improve performance.
Abstract Though production facilities are advancing day by day, some imperfection in manufacturing process still remains. This paper develops a manufacturing system with rework process of defective items. We have assumed that the defective items are repairable and a portion of the reworked items is considered to be scrap. In model, it is proposed that demand of items, proportion of defective products and scrap rate are randomly distributed with known probability density function. In the model it is also assumed that the supplier of raw materials offers a delay period to the manufacturer and manufacturer extends a similar delay policy to his customers. The main objective of the system is to minimize the manufacturer's overall cost and to determine manufacturer's optimal replenishment policy. The optimal replenishment policies are also discussed with the help of some theorems. Numerical examples are illustrating the theoretical results and sensitivity of important cost parameters is discussed.
Locating and allocating distribution centres optimally is a vital decision in supply chain management. Doing so will improve the efficiency of the logistics system as well as reduce its operating costs significantly. In this study, we develop a mixed-integer mathematical model to determine the optimal locations and allocations of distribution centres, which minimise the operational cost. The performance of this model is then tested using data collected from a leading building products distribution company in the USA. The computational results were analysed to determine whether the company can reduce its transportation and operational costs significantly by implementing new optimal distribution locations. Results show that the company can reduce its transportation costs 20% by implementing the location and allocations given by the proposed mathematical model.
The objective of this research is to study the interactions of deterministic and stochastic optimisation models in presence of information sharing in a supply chain with a varying level of error in demand forecast. In a two-echelon supply chain there are two different strategies, deterministic or stochastic, for supplier and manufacturer. All the four combinations (stochastic vs. deterministic for supplier and manufacturer) of the decision-making strategies in a two-echelon supply chain have been considered and the performance is evaluated. The results show that stochastic optimisation is efficient in lowering the operational costs and bull-whip effect in most cases. However, in cases where the trend in demand variation is smooth, using deterministic strategy by both stakeholders is beneficial if the demand forecast accuracy is more than 50%. If both stakeholders use stochastic strategy when the information accuracy is less than 50% coupled with more variation in demand, information sharing is not beneficial.
Synchronisation among supply chain (SC) stages is essential, but difficult to achieve, which results in a bullwhip effect. This paper proposes an interactive fuzzy-based genetic algorithm approach ...
This paper presents idea, methodology, features, functionality and benefits to different departments of a customised application software solution named as e-Aushadhi developed by Center of Development of Advance Computing (CDAC), India. e-Aushadhi automates the workflow of various process of procurement, management and distribution of various drugs, sutures and surgical required by different state warehouses across India. It further aims to improve the drug warehouse (DWH) management system and facilitate to provide cost-effective essential drugs to common people by keeping an eye on the flow of drugs, vaccines and other health sector goods.
In today's volatile business environment, a single supply chain (SC) for a firm can be quite risky. The loss resulting from any disruptive operations, in a case with a single SC, can be much higher than costs of having a few resilient supply chains. For a firm to provide its customers with the desired responsiveness, a few supply chains should be developed. However, due to scarcity of resources, it would be impossible to indiscriminately provide top quality service to all customers. This study uses the Delphi method and analytic hierarchy process (AHP) to rank a firm's customers and to determine the implied resource requirements to fulfil customer orders. Using this information, a customer priority index (CPI) matrix is constructed. The CPI matrix is subsequently divided into four quadrants and for each quadrant, a unique SC strategy is formulated, considering the customer's importance and degree of scarcity of resources.
This work describes the development of a model to address sustainability in supply chain networks in comparing environmental impact to cost optimisation. It takes into account the effect of inventory management policies on energy consumption in transportation and storage. This work first focusses on a base case with one product type, involving one transportation link (from the supplier to the distributor) and one storage node. In addition, an extended case is considered, including additionally the transportation from the distributor to the customers. Based on real data, our results show that the most energy efficient solution is not necessarily detrimental to the economic profits. Also, a higher performance does not always mean a higher energy use: increasing the service level can indeed decrease the energy use. Finally, our analysis shows that the model can be extended to an entire supply chain network.
