Article ID: | iaor20061326 |
Country: | United Kingdom |
Volume: | 105 |
Issue: | 7 |
Start Page Number: | 876 |
End Page Number: | 899 |
Publication Date: | Sep 2005 |
Journal: | Industrial Management & Data Systems |
Authors: | Caputo Antonio C., Fratocchi L., Pelagagge P.M. |
Keywords: | distribution, scheduling, artificial intelligence: decision support |
Purpose – To present a decision support system (DSS) enabling the analysis of the cost-effectiveness of direct-shipping long-haul road transport policies, including full truck load (FTL) and less than truck load (LTL) modes, and to select the optimal carrier. Design/methodology/approach – Analytical estimation of transportation costs is provided in a framework including an interactive computer procedure and a dedicated database structure capable of characterizing the logistics system. Findings – Main criticalities of manual logistic planning are: sub-optimal selection of carrier and excessive use of LTL transport, while the optimal FTL vs LTL trade-off is not fully explored in practice. Research limitations/implications – This is an analysis tool of user-defined scenarios and does not provide the automatic synthesis of shipments planning. Admittedly, this model does not attempt to optimize the shipping strategy, but to quantitatively assess the effects of the adopted decisions. Practical implications – Alternative shipping policies can be compared to perform what-if analyses and explore the outcome of alternative decisions (FTL vs LTL shipping modes) even in terms of transportation expenditures. Allows rapid selection of the optimal motor carrier and assesses the extra cost due to a sub-optimal choice. Gives the experienced manager a framework for critical assessment of shipping decisions, suggesting improvement areas for cost reduction. Originality/value – With respect to other software tools for carrier selection provides explicit analysis of extra costs incurred by manual planning, thus becoming a strategic tool for logistic decision making. Furthermore, enables managerial insights to be gained and makes manual planning more effective.