International Journal of Physical Distribution & Logistics Management

Gino Marchet; Sara Perotti; Riccardo Mangiaracina

2012 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600031211219645

Purpose – The purpose of this paper is to present a parametric model to assess the impacts of information and communication technology (ICT) applications on company freight transportation processes (i.e. “company” perspective). The aim is to support both internal monitoring procedures and the decision‐making process of ICT adoption, as well as increase managers' awareness of these solutions in improving their business. The model has been applied to inter‐modal terminal processes, which are particularly critical due to the high number of players involved and the need for integrating different modes of transport throughout the whole delivery process. Design/methodology/approach – The present study is part of a broader research on the topic of ICT adoption in the freight transportation industry. Based on the outcome of the previous stages of this research, the impact of ICT applications on inter‐modal processes has been modeled using an activity‐based costing approach. Interviews were conducted with both inter‐modal terminal managers and technology providers to collect the required inputs and validate the model. A case study has been performed to apply the model and a further sensitivity analysis has been carried out. Findings – The application of the model to the examined inter‐modal terminal showed that the most significant “as is” costs are those connected to handling activities. Three different ICT scenarios have been also explored. Based on the company “as is” scenario, the model provides an assessment of how and when a positive return on investment can be achieved. Results proved that the benefits deriving from ICT adoption are considerable and depend on the level of technology adopted. Originality/value – The paper addresses an identified need in the literature of quantifying the impact of ICT for freight transportation. It is one of the few attempts to model costs and benefits of ICT for freight transportation, taking into account the major factors involved. Additionally, the model can be a valuable support to practitioners in evaluating their investments, as well as monitoring their company current performance.

Michael S. Garver; Zachary Williams; G. Stephen Taylor; William R. Wynne

2012 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600031211219654

Purpose – Much of the research conducted in logistics/SCM has focused on satisfaction/retention of customers. This has left a critical gap for managers: before customers can be satisfied and ultimately retained, a purchase choice of logistics services has to occur. To date, very little research has addressed how logistics customers make purchase choice decisions about logistics services. The purpose of this paper, using logistics research methods, is to introduce adaptive choice modelling (ACM) to address this gap and put forth a research method that is useful for academic researchers and logistics/SCM managers. Design/methodology/approach – This paper provides an overview of ACM, along with a discussion of its important research advantages, limitations, and practical applications. Additionally, an empirical demonstration of this research technique is provided to illustrate how academic researchers and logistics managers can use ACM to better understand the decision‐making process of customers when selecting logistics services. Findings – In order to demonstrate this research technique, a research project was designed and implemented that analyzed the choice process of consumers selecting parcel carriers to ship a textbook. The results show that price, speed of delivery, and tracking are the three most important variables in the selection decision. The results also show that consumers are not homogeneous, but can be divided into five distinct need‐based segments. Recognizing and understanding the nature of these segments should help managers better meet the needs of parcel shippers. Research limitations/implications – The main research limitation with this study is that it is based on a convenience sample; thus future research will need to replicate this study to confirm the research findings. However, the ultimate purpose of the study is to present a new research method and discuss how to apply this method, so that logistics/SCM practitioners and academic researchers can better understand customers of logistics/SCM services. Thus, while the nature of the sample is a limitation, it should be viewed in this context. Originality/value – While conjoint analysis has existed for decades, this technique has rarely been implemented by logistics/SCM researchers and practitioners. Instead, logistics/SCM researchers and practitioners have focused more on retention methods and have virtually ignored modelling the actual purchase choice of logistics/SCM services. New advancements in conjoint analysis, specifically the ACM approach, have many important and unique advantages and applications for logistics/SCM researchers and practitioners. ACM has not been used in a logistics/SCM context.

Malini Natarajarathinam; Jennifer Stacey; Charles Sox

2012 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600031211219663

Purpose – The purpose of this paper is to develop efficient heuristics for determining the route design and inventory management of inbound parts which are delivered for manufacturing, assembly, or distribution operations and for which there is limited storage space. The shipment frequencies and quantities are coordinated with the available storage space and the vehicle capacities. Design/methodology/approach – Two heuristics that generate near optimal solutions are proposed. The first heuristic has an iterative routing phase that maximizes the savings realized by grouping suppliers together into routes without considering the storage constraint and then calculates the pickup frequencies in the second phase to accommodate the storage constraint. The second heuristic iteratively executes a routing and a pickup frequency phase that both account for the storage constraint. A lower bound is also developed as a benchmark for the heuristic solutions. Findings – Near optimal solutions can be obtained in a reasonable amount of time by utilizing information about the amount of storage space in the route design process. Practical implications – The traditional emphasis on high vehicle utilization in transportation management can lead to inefficient logistics operations by carrying excess inventory or by using longer, less efficient routes. Route formation and pickup quantities at the suppliers are simultaneously considered, as both are important from a logistics standpoint and are interrelated decisions. Originality/value – The two proposed heuristics dynamically define seed sets such that the solutions to the capacitated concentrator location problem (CCLP) are accurately estimated. This increased accuracy helps in generating near‐optimal solutions in a practical amount of computing time.

Nagihan Çomez; Timothy Kiessling

2012 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600031211219672

Purpose – The purpose of this paper is to study joint inventory and pricing strategy for a continuous inventory review system. While dynamic pricing decisions are often studied in the literature along with inventory management, the authors' aim in this study is to obtain a single long‐run optimal price; also to gain insight about how to obtain the optimal price and inventory control variables simultaneously and then the benefits of joint optimization of the inventory and pricing decisions over the sequential optimization policy often followed in practice. Design/methodology/approach – A general ( R;Q ) policy system with fixed cost of ordering is modelled and then the case where unsatisfied demand is lost is studied. General forms of both the additive and multiplicative demand models are used to obtain structural results. Findings – By showing optimality conditions on the price and inventory decision variables, two algorithms on how to obtain optimal decision variables, one for additive and another for multiplicative demand‐price model are provided. Through extensive numerical analyses, the potential profit increases are reported if the price and inventory problem are solved simultaneously instead of sequentially. In addition, the sensitivities of optimal decision variables to system parameters are revealed. Practical implications – Although there are several studies in the literature investigating emergency price change models, they use arbitrary exogenous prices menus. However, the value of a price change can be better appreciated if the long‐run price is optimal for the system. Originality/value – Very few researchers have investigated constant price and inventory optimization, and while there are several past studies demonstrating the benefits of dynamic pricing over a static one, there still are not many findings on the benefit of joint price and inventory optimization.