Abstract
In today’s competitive business environment, the cost of a product is one of the most important considerations for its sale. Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product. Logistics is one such factor. Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses. Clearly, the logistics sector faces several dilemmas from order attributes to environmental changes in this regard. This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold. Consequently, the methodology to optimise delivery cost and its impact on environmental focus by reducing CO2 emissions has gained relevance. The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery. Shipment Consolidation has been categorized in three order scheduling approaches: Time based consolidation, Quantity based consolidation, and a Hybrid (Time-Quantity) based consolidation. In this paper, a new Hybrid Consolidation approach is presented. Using the Hybrid approach, it has been shown that order delivery can be facilitated by taking into account not only the order pick up time, but also the total order quantity. These results have shown that if a time window is available in respect of the order delivery time, then the order can be delayed from pickup to consolidate it with other orders for cost optimization. This hybrid approach is based on four consolidation principles, two of which work on fixed departure and two, on demand departure. Three of these rules have been implemented and tested here with an application case study. Statistical analysis of the results is illustrated with different planning evaluation indicators. The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule. Testing with bigger data sets is required.
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