Penalty and reward contracts between a manufacturer and its logistics service provider

Contracts are used to coordinate disparate but interdependent members of the supply chain. Conflicting objectives of these members and lack of coordination among the members lead to inefficiencies in matching supply with demand. This study reviews different types of contracts and proposes a methodology to be used by companies for analyzing coordinating contracts with their business partners. Efficiency of the contract is determined by comparing the performance of independent companies under the contract to the supply chain performance under the central decision maker assumption. We propose a penalty and reward contract between a manufacturer and its logistics service provider that distributes the manufacturer’s products on its retail network. The proposed contract analysis methodology is empirically tested with transportation data of a consumer durable goods company (CDG) and its logistics service provider (LSP). The results of this case study suggest a penalty and reward contract between the CDG and its LSP that improves not only the individual firm’s objective functions but also the supply chain costs. Compared to the existing situation, the coordination efficiency of the penalty and reward contract is 96.1 %, proving that optimizing contract parameters improves coordination and leads to higher efficiencies

Using simulation gaming to validate a mathematical modeling platform for resource allocation in disasters

The extraordinary conditions of a disaster require the mobilisation of all available resources, inducing the rush of humanitarian partners into the affected area This phenomenon called the proliferation of actors, causes serious problems during the disaster response phase including the oversupply, duplicated efforts, lack of planning In an attempt to reduce the partner proliferation problem a framework called PREDIS (PREdictive model for DISaster response partner selection) is put forward to configure the humanitarian network within early hours after disaster strike when the information is scarce To verify this model a simulation game is designed using two sets of real decision makers (experts and non-experts) in the disaster Haiyan scenario The result shows that using the PREDIS framework 100% of the experts could make the same decisions less than six hours comparing to 72 hours Also between 71% and 86% of the times experts and non-experts decide similarly using the PREDIS framewor

An exploration of big data practices in retail sector

Connected devices, sensors, and mobile apps make the retail sector a relevant testbed for big data tools and applications. We investigate how big data is, and can be used in retail operations. Based on our state-of-the-art literature review, we identify four themes for big data applications in retail logistics: availability, assortment, pricing, and layout planning. Our semi-structured interviews with retailers and academics suggest that historical sales data and loyalty schemes can be used to obtain customer insights for operational planning, but granular sales data can also benefit availability and assortment decisions. External data such as competitors’ prices and weather conditions can be used for demand forecasting and pricing. However, the path to exploiting big data is not a bed of roses. Challenges include shortages of people with the right set of skills, the lack of support from suppliers, issues in IT integration, managerial concerns including information sharing and process integration, and physical capability of the supply chain to respond to real-time changes captured by big data. We propose a data maturity profile for retail businesses and highlight future research directions

Search algorithms in the aftermath of a disaster

Presentatio

Collaboration in urban distribution of online grocery orders

Purpose: Population growth, urbanisation and the increased use of online shopping are some of the key challenges affecting the traditional logistics model. The purpose of this paper is to focus on the distribution of grocery products ordered online and the subsequent home delivery and click and collect services offered by online retailers to fulfil these orders. These services are unsustainable due to increased operational costs, carbon emissions, traffic and noise. The main objective of the research is to propose sustainable logistics models to reduce economic, environmental and social costs whilst maintaining service levels. Design/methodology/approach: The authors have a mixed methodology based on simulation and mathematical modelling to evaluate the proposed shared logistics model using: primary data from a major UK retailer, secondary data from online retailers and primary data from a consumer survey on preferences for receiving groceries purchased online. Integration of these three data sets serves as input to vehicle routing models that reveal the benefits from collaboration by solving individual distribution problems of two retailers first, followed by the joint distribution problem under single decision maker assumption. Findings: The benefits from collaboration could be more than 10 per cent in the distance travelled and 16 per cent in the time required to deliver the orders when two online grocery retailers collaborate in distribution activities. Originality/value: The collaborative model developed for the online grocery market incentivises retailers to switch from current unsustainable logistics models to the proposed collaborative models

Traveler satisfaction in rapid rail systems: the case of Istanbul Metro

Multi-faceted characteristics of urban travel have an impact on the passengers' overall satisfaction with the transport system. In this study, we investigate the interrelationships among traveler satisfaction, travel and traveler characteristics, and service performance in a multimodal network that comprises of a trunk line and its feeder lines. We analyze the factors influencing the choices of access to rail transit stations and the satisfaction of transit travelers with the rapid rail transit systems. We quantitatively study these relationships and demonstrate the complexity of evaluating transit service performance. Since the interrelationships among variables affecting this system are mainly stochastic, we analyze the satisfaction with transit system problem using a Bayesian Belief Network (BBN), which helps capture the causality among variables with inherent uncertainty. Using the case of Istanbul, we employ the BBN as a decision support tool for policy makers to analyze the rapid rail transit services and determine policies for improving the quality and the level of service to increase the satisfaction with transit system. In the case study, satisfaction with accessibility and access mode variables are found to be more effective variables than total travel time for travel time satisfaction, confirming the significant role of access in multimodal travels

International competitiveness power and human development of countries

Human development should be the ultimate objective of human activity and its aim should be healthier, longer, and fuller lives. It is expected that if the competitiveness of a country is suitably managed, human welfare will be enhanced as a consequence. The research described here seeks to explore the relationship between the competitiveness of a country and its use for human development. For this purpose, 45 countries were evaluated using data envelopment analysis, where the global competitiveness indicators are taken as input variables and the human development index indicators as output variables. A detailed analysis is also conducted for the emerging economies

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey