Logistics Research

The real challenge for managers is to develop and implement a suitable supply chain performance framework that not only helps in making right decisions but also facilitates the benchmarking of their internal supply chain. The main purpose of this study was to develop a framework based on the performance metrics such as (1) total length of the supply chain, (2) supply chain inefficiency ratio and (3) supply chain working capital productivity. Case study approach is used to benchmark the SCM performance of two paint companies. Further, in order to examine the relationship between SCM practices and SCM performance measures, an empirical analysis has been done by formulating research hypothesis. Results show strong support for linkage between SCM practices and selected performance metrics.

Logistics networks need to handle globally growing transportation volumes in cost efficient and sustainable configurations. This research explores the impact of the oil price on the optimal degree of centralization of logistics networks through the fluctuation of transportation costs. The impact of the degree of centralization on greenhouse gas emissions of transportation is evaluated. Investigations are conducted by means of a model consisting of logistics network, logistics costs, carbon dioxide emissions and an empirically determined relationship of oil price, fuel price and transportation costs. First, the dependency of transportation costs in European road transports from oil prices is derived. Then, the optimal configuration of the logistics network is determined for a range of oil prices. Sensitivity analysis on the value of traded goods is performed. Results show an existing dependency of the European logistics network structure limited to oil prices above 150 USD/barrel. Dependency on the oil price increases for high-value goods compared to low-valued goods. Carbon dioxide emissions diminish with lower centralization degree, as an effect of lower total transport distance.

In this study, we present a novel approach for continuous detection, localization, and identification of parcels and bins in automated facility logistics systems. It presents a distinct departure from the traditional system design: light barriers and barcode readers are substituted by low-cost cameras and few RFID readers. By combining vision-based systems and RFID systems, this approach can compensate for the drawbacks of each respective system. For example, only the vision system is used for localization. The main part of our paper describes computer-graphics methods specific to the given problem to both track and read visual markers attached to parcels or bins. In addition, we use information from the RFID system to narrow the decision space for detection and identification. From an economic point of view, this approach can lower the costs of changing a material flow system.

Logistics collaboration has emerged a prevalent strategy to mitigate challenge individuals and organizations encounter. A successful collaboration, however, depends on certain trustworthy behaviors partner exhibit. To that end, understanding aspects constituting behavioral uncertainty and mechanisms by which such aspects affect partner trust is a necessary. This necessity counts on emergent behavioral trust uncertainties, constituted by partner’s actions and interactions occurring during collaboration. While this is a necessary requirement, most of the studies in the literature lack to take into account the influence of behavioral uncertainty on collaboration and partner trust. To that effect, this paper uncovers outlined limitation by establishing behavioral factors influencing partner trust in operational stage of logistics collaboration. To accomplish this objective, a systematic literature review (SLR) is deployed to consolidate research domains of logistics, supply chain, collaboration, and trust. SLR proceeds by defining a review protocol, followed by a search process conducted in 5 databases using 20 search terms on articles published between 2001 and 2015 inclusively. Among findings this SLR has revealed are four behavioral factors and thirteen criteria proposed to affect partner trust. Additionally, these factors constitute success and measurable criteria needed for empirical investigation which may employ experimental and/or case-study methods. Moreover, synthesized factors extend further an understanding of behavioral trust in ad hoc collaborative networks, a large part of which being supported by networks of humans and computers.

One of the most important problems in the petroleum industry is the well-known petrol station replenishment problem with time windows, which calls for the determination of optimal routes by using a fleet of tank trucks to serve a set of petrol stations over a given planning horizon. In this paper, we introduce a model and solve a specific problem that originates from a real-life application arising in the fuel distribution where specific attention is paid to tank trucks with compartments and customers with different types of products and time windows. Literally, we call the resulting problem the multi-compartment vehicle routing problem with time windows (MCVRPTW). To solve the MCVRPTW, we begin by describing the problem, providing its mathematical formulation and discussing the sense of its constraints. As the problem is NP-hard, we propose an efficient tabu search algorithm for its solution. We introduce the Kolmogorov–Smirnov statistic into the framework of the tabu search to manage the neighbourhood size. We evaluate the performance of the algorithm on a set of vehicle routing problems with time windows instances as well as other realistic instances. Our results are compared to CPLEX, to the heuristics reported in the literature and also to those extracted from the company plans.

Regarding the amount of processed information, there are two basic approaches to handle today’s increasing requirements for facility logistics systems. First, the collection of all achievable information about the system and its load in order to predict future system states accurately; theoretically, this could lead to optimal results. Second, the use of rather simple heuristics with less need for information but higher flexibility and robustness instead. This approach is for instance being realized with the Internet of Things in facility logistics. This paper analyses the dependency of throughput time and capacity utilization on available information for two limiting cases. While a ‘best possible’ case assumes that the material flow control has all relevant information about the system and loads, a ‘worst reasonable’ case considers the same for very limited information. The influence of layout, throughput, and element availabilities on this relationship is analyzed for both cases on the basis of a scalable generic test system and steady flow situations. In total, nine simulation studies are conducted and analyzed regarding throughput time and element utilization as performance indicators. Implications for the importance of information on performance depending on system complexity are discussed, and directions for further research are provided.

This case study uses a surface mine to investigate multidisciplinary logistics analysis methods for improving refinery operations. Existing resource scheduling, inventory forecasting, and economic production quantity procedures have not been able to identify how to improve productivity. The objective was to locate and demonstrate proven techniques from operations research (and other related disciplines) which could be applied to solve logistics problems. Historical operations data along with a new sample (n = 140) were utilized for the analysis. Preliminary parametric tests failed, but later a multiple server queue model was developed by integrating nonparametric techniques, waiting line theory, stochastic probabilities, and break-even scenario analysis. Quantitative and qualitative data were analyzed, resulting in a solution to increase truck arrival rates by 10% which was projected to increase refinery utilization by 7–77%, thereby generating a potential productivity savings of $161,223.31 per year.

The assessment of greenhouse gas (GHG) emissions of supply chain activities is performed to create transparency across the supply chain and to identify emission-cutting opportunities. Literature provides several generic and case study approaches to estimate GHG emissions. But research often focuses on products. This paper sheds light on how the greenhouse performance of a fast-moving consumer goods (FMCG) distribution network depends on several (FMCG specific) variables to set up a “CO2 network footprint”. Within a quantitative computational study, the distribution network footprint of an existing FMCG manufacturer is analyzed. Three options being fundamentally able to reduce total GHG emissions are identified: number of distribution centers, performance of the engaged logistics service provider and shipment structure. First, transportation processes for the investigated FMCG manufacturer are analyzed to derive GHG emissions caused by different distribution shipments. Second, initial data are manipulated to simulate variable changes, that is, different logistics structures. Third, results are reported and analyzed to show up how different changes in logistics structures may reduce GHG, without technological propulsion or use of regenerative energy.