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In this chapter, an air cargo shipment planning problem is considered by including individual risk factors of any sub-contracted agents. Due to competitive market conditions, air cargo forwarders are advised to remain flexible in operations. A mixed integer linear programming formulation including the potential for divisible activities is developed to model the shipment planning problem. To solve this complicated problem, we employ an ant colony optimization (ACO) methodology. Numerical examples are generated using data from both the extant literature and from a global air cargo company, allowing investigation of the viability of the novel methodology. We find that the algorithm/methodology provides effective solutions for small problem sizes.

The purpose of this paper is to develop propositions explaining the influence of individual goals and social preferences on human decision making in transport planning. The aim is to understand which individual goals and social preferences planners pursue and how these influence planners’ decisions.

Propositions are developed based on investigation of decision making of transport planners in a Dutch logistics service provider using multiple data collection methods.

The study shows how decision making of transport planners is motivated by individual goals as well as social preferences for reciprocity and group identity.

Further research including transaction data analysis is needed to triangulate findings and to strengthen conclusions. Propositions are developed to be tested in future research.

Results suggest that efforts to guide planners in their decision making should go beyond traditional (monetary) incentives and consider their individual goals and social preferences. Moreover, this study provides insight into why transport planners deviate from desired behaviour.

While individual decision making plays an essential role in operational planning, the factors influencing how individuals make operational planning decisions are not fully understood.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

During the past ten years, product proliferation, the trend toward larger facilities serving more markets, dramatic increases in the cost of labour and of plant and equipment and customers requiring faster, more dependable deliveries of smaller quantities have greatly increased management's awareness of the need for product flow planning and control. It is now well recognised that the movement and storage of goods is a vital link between production and marketing which is capable of having a significant impact on the overall profitability of the firm and, therefore, must be carefully planned and controlled.

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.

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.

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.

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.

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.

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.

This purpose of this paper is to present a methodology for optimally planning long‐haul road transport activities through proper aggregation of customer orders in separate full‐truckload or less‐than‐truckload shipments in order to minimize total transportation costs.

The model is applied to a specific Italian multi‐plant firm operating in the plastic film for packaging sector. The method, given the order quantities to be shipped and the location of customers, aggregates shipments in subgroups of compatible orders resorting to a heuristic procedure and successively consolidates them in optimized full truck load and less than truck load shipments resorting to a Genetic Algorithm in order to minimize total shipping costs respecting delivery due dates and proper geographical and truck capacity constraints.

The paper demonstrates that evolutionary computation techniques may be effective in tactical planning of transportation activities. The model shows that substantial savings on overall transportation cost may be achieved adopting the proposed methodology in a real life scenario.

The main limitation of this optimisation methodology is that an heuristic procedure is utilized instead of an enumerative approach in order to at first aggregate shipments in compatible sets before the optimisation algorithm carries out the assignments of customer orders to separate truckloads. Even if this implies that the solution could be sub‐optimal, it has demonstrated a very satisfactory performance and enables the problem to become manageable in real life settings.

The proposed methodology enables to rapidly choose if a customer order should be shipped via a FTL or a LTL transport and performs the aggregation of different orders in separate shipments in order to minimize total transportation costs. As a consequence, the task of logistics managers is greatly simplified and consistently better performances respect manual planning can be obtained.

The described methodology is original in both the kind of approach adopted to solve the problem of optimising orders shipping in long‐haul direct shipping distribution logistics, and in the solution technique adopted which integrates heuristic algorithm and an original formulation of a GA optimisation problem. Moreover, the methodology solves both the truckload assignment problem and the choice of LTL vs FTL shipment thus representing an useful tool for logistics managers.

To succeed in today’s global marketplace, organizations are looking at streamlining their supply chain through the successful deployment of information technology. This paper, by means of a case study, discusses how a manufacturing company implemented a transportation planning and optimization system to enhance their downstream supply chain operations. The application development framework is used to analyze the implementation process. The findings of this case study will benefit companies seeking to create a competitive advantage in the marketplace through advanced physical distribution capabilities.

Distribution requirements planning (DRP) is one of the scheduling methods used in logistics systems. A generalised version is presented of a DRP system designed to enhance scheduling flexibility of currently used DRP systems and to deal with multi‐sourcing trans‐shipment problems in a multi‐echelon logistics system. The required information inputs, capabilities and advantages of this generalised DRP system are described in detail. Finally, the future research direction to improve the adaptability of DRP in distribution systems is discussed.

The criticality of late deliveries in transportation lies in the threat of considerable multi-level supply chain costs. This study aims to reveal the dynamic capabilities playing a facilitating role in preventing delay, thus providing timely delivery, as well as developing an understanding of how and when those capabilities are activated within the supply chain network.

An exploratory study was conducted involving 16 semi-structured expert interviews with the representatives of logistics service providers and shippers. Following an interpretive phenomenology framework, the prevention phenomenon was explained.

Findings revealed two preventive capability categories in delay prevention: (1) proactive capabilities, referring to the enabling actions planned before departure, and (2) reactive capabilities, referring to actions planned after departure. Findings pinpoint that, in addition to the proactive capabilities, reactive capabilities enabled by innovative problem-solving actions are crucial for adapting to a dynamically changing environment in prevention. Moreover, this study shows that prevention capabilities are characterized by tangible and intangible resources and integration of resources with external links which constitute a delay prevention network within a wider service ecosystem.

This study stands out with its specific focus on delay prevention capabilities and enabling actions from the perspectives of logistics service providers and shippers. The premises of the resource-based view are combined with dynamic capabilities theory, leading to a proposed time-based taxonomy of proactive and reactive capabilities in supply chains, aimed at creating value and strengthening resilience.

Vendor managed inventory (VMI) is a model for supply chain collaboration gaining ground in multiple industries around the world. Forecasting is one of several means to predict future demand. The purpose of this paper is to identify what types of advance demand data would be valuable to the supplier for successful replenishment planning in cases of VMI, frequency and means of information exchange.

The work is based on a literature review and a multiple case study where both successful and unsuccessful VMI collaboration programs are studied. In all cases, the supplier or the customer was a manufacturer. The discussion is based on case‐observations of causes and effects that are compared with existing literature.

It is found that current inventory status and sales forecasts are the most important kinds of information to be made available to the supplier in a VMI relationship. This also conforms with the general perception of the literature review. Some kinds of information are found valuable while other kinds are found not to be of importance. It was also found that kinds of information differ with the type of supply chain activities being performed by the customer, i.e. there is a different kind of information recommended when the customer makes to stock than when he is a wholesaler or a manufacturer making to order. This observation has not been discussed in the literature reviewed.

The value of generalization from case studies is subject to general discussion. The cases applied in this research encompass Norwegian suppliers and dyads at the upstream end of the value chain. Geographically related cultural differences might limit the applicability of this work.

The findings support the existing literature and this paper takes a practitioner's perspective on information sharing. The aim is to offer a comprehensive yet educational view of information sharing and VMI.