No abstract

A major inhibitor to supply chain customer value delivery is uncertainty, which yields increased total operational costs. Given transport often connects companies to their customers, effective transport operations can enable the delivery of customer value. Our aim is to establish a supply chain uncertainty model that explicitly incorporates transport operations. Our model has its origin from previous works on the logistics triad. We identify that uncertainty can potentially originate from five different sources: shipper, customer, control systems, carrier and externally. The results of this paper inform further empirical research into the area of transport uncertainty and hence logistics flexibility as a means for accommodating such uncertainty. The research also highlights the root causes of inefficiency within freight transport, which often has a causal relationship with environmental performance.

Delays to road freight vehicles impose a very high cost on the nation. Delayed arrival time can occur for a variety of reasons. This paper presents the findings of a Highways Agency funded study, which has investigated the user valuations of three different kinds of delay:• A delay resulting from an increased journey time, with fixed departure time • An increase in the spread (or range) of arrival times for a fixed departure time • A schedule delay where the departure time is effectively put back.The paper summarises the findings of the study, which centred on an interview survey of forty shippers, hauliers and third party logistics operators. Respondents were asked to consider one of their freight flows on the trunk road network in detail. Various reasons why respondents value a high degree of predictability of journey times on the trunk road network are identified and discussed. The paper then moves on to present and discuss user valuations of each kind of delay, estimated using the Leeds Adaptive Stated Preference (LASP) methodology.

Purpose -The purpose of this paper is to provide rankings of quality of peer reviewed periodicals for faculty research use, rankings of usefuless of both peer reviewed and non-peer reviewed periodicals for teaching and outreach purposes, and rankings of usefulness of search engines for finding articles. Design/methodology/approach -An online survey was conducted during the Autumn Semester of 2006. 82 periodicals were identified as relevant to the Supply Chain Management field. Respondents were asked to rank their top used journals in terms of use for teaching, outreach and research. Findings -A total of 143 surveys were completed. Journal of Business Logistics, Harvard Business Review, and International Journal of Physical Distribution and Logistics Management were the top three ranked journals in the composite index. EBSCO Business Source Premier, ProQuest, and Google Scholar were the top three search engines used to find logistics and SCM articles. Research limitations/implications -A clear global consensus is forming on the top journals for SCM. However, the emergence of Operations Management/Operations Research Journals into the rankings is an important trend. There was also the absence of previously ranked journals as the field evolved from transportation and distribution to logistics to SCM. Originality/value -This paper is the largest survey completed to date in the field and substantially updates previous surveys. All academics who are active in SCM will find value in this paper.

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost.