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Presents a multiple‐objective mathematical programming model to
co‐ordinate logistics decisions with those on the interface between the
production and marketing departments. The model can help decide on an
overall budget to request from senior management for logistics and these
interfaces, and in systematically allocating the funds between
transport, inventory and production. In so doing, this multi‐period
model specifies the timing and quantity of raw‐material purchases, and
the location and timing of production activities and distribution flows.
The budget for expenditures on logistics and its interface activities is
taken as an objective to minimise, instead of as a given dollar level to
be satisfied. A second objective is to maximise the profit of logistics
and its related interfaces. Trade‐offs between these two conflicting
aims yields the decision maker′s "best compromise" solution.

PurposeA program of shipment consolidation (SCL) is the purposeful intervention by management to regularly combine several small shipments so that a larger load may be dispatched on the same vehicle. SCL decisions traditionally have been based upon minimization of total logistics costs (inventory plus transportation). The paper aims to answer the following research question: given the environmental implications of vehicle emissions as a function of load weight, are the familiar SCL policies still optimal?Design/methodology/approachNonlinear expressions relating carbon dioxide (CO2) emissions to vehicle weight, and parameterized by trip length and average travel speed, were derived from published experimental data. Those expressions were included in a simulation model that assessed the environmental impact, in addition to the logistics cost, of the policies concerning when to release a consolidated load.FindingsFor short holding times, the quantity policy performs best in terms of both logistics cost and pollution reduction. In the case of low‐order arrival rates and long holding times, the time policy is best at reducing emissions and logistics costs. However, the best dispatch policy conflicts in terms of pollution reduction and logistics cost minimization for the following cases: moderate holding times and long holding times combined with high‐order arrival rates. In these cases, it is necessary to consider the speed of travel, trip length and unit cost of emissions when evaluating the policies.Research limitations/implicationsA carbon trading market appears to be on the horizon in several industries, which will establish a price per unit weight for CO2 emissions and make it beneficial to minimize the total cost (including emissions) of the network. This research only considers CO2 pollution, but future investigations could also consider other pollutants such as nitrogen oxides, carbon monoxide and volatile organic carbons.Practical implicationsSCL policies can include a "green logistics" component that is based on empirical data.Originality/valueOne undesirable consequence of transportation by truck is CO2 emissions. However, the impact can be lessened, while still emphasizing total logistics cost per load, with our simulation‐based results for shipment release policies.

This research compares the logistics systems of Asia and Europe and categorises them into distinct levels of logistics excellence. First, the context in Asia and in Europe is summarized. Then, attributes of a world‐class logistics system are proposed. By applying cluster analysis to data from authoritative sources, we objectively segregate European and Asian logistics systems into three logistics tiers. There are several surprises, the main one being that the UK is classified Tier 2 (not as favourable as Tier 1). A prioritized set of attributes that the UK could improve on to qualify for the Tier 1 group is suggested. Sensitivity analyses are conducted to determine changes to the classifications. After finding that the top‐ranking logistics systems of Europe and Asia are from Denmark and Singapore, respectively, those two countries are studied in detail to draw logistics lessons applicable elsewhere.

Decision analysis in management science employs concepts from economics such as utility functions and indifference curves. A utility function U models the "satisfaction" that a customer obtains from logistics service. Here U depends on two attributes (lead time, fill rate) whose values more directly represent customer service. The shipper can, at additional cost, improve either or both of these attributes. Constructs and maximizes various utility functions U given a total budget B for distribution service. Finds that without increasing the budget overall logistics service can often be improved from the customer's point of view. Whether U is additive or multiplicative, a customer's utility resulting from the optimal lead time and fill rate is typically 20 per cent higher than when those attribute levels are set intuitively (without reference to customer preferences and tradeoffs expressed by U). Gives some introduction to decision analysis (certainty equivalent, risk aversion, …) to aid in understanding the functional forms employed for U and methods of solution, rendering the paper more self‐contained.

ABSTRACTMost research on lot sizing has been for the case of a manufacturing system. In this paper, analogous issues are studied for a distribution network. Specifically, we consider the choice of shipment quantities within distribution requirements planning (DRP). A simulation model of DRP in a multi‐echelon, rolling‐schedule environment is used to examine, in conditions of both certain and uncertain demand, the performance of five lot‐sizing rules.We conducted a full‐factorial experiment in which four additional parameters were varied: distribution network structure (two options), demand distribution (three options), forecast error distribution (three options), and ordering cost (three values), as suggested by the consulting study which motivated our research.We found that for DRP, contrary to the "shop floor" wisdom on MRP, the choice of lot‐sizing method can be important. Generally the Silver‐Meal and Bookbinder‐Tan heuristics were significantly better than the other methods.