Eco-efficient supply chain networks: Development of a design framework and application to a real case study

© 2015 Taylor & Francis. This paper presents a supply chain network design framework that is based on multi-objective mathematical programming and that can identify 'eco-efficient' configuration alternatives that are both efficient and ecologically sound. This work is original in that it encompasses the environmental impact of both transportation and warehousing activities. We apply the proposed framework to a real-life case study (i.e. Lindt & Sprüngli) for the distribution of chocolate products. The results show that cost-driven network optimisation may lead to beneficial effects for the environment and that a minor increase in distribution costs can be offset by a major improvement in environmental performance. This paper contributes to the body of knowledge on eco-efficient supply chain design and closes the missing link between model-based methods and empirical applied research. It also generates insights into the growing debate on the trade-off between the economic and environmental performance of supply chains, supporting organisations in the eco-efficient configuration of their supply chains

An Empirical Study of Operational Performance Parity Following Enterprise System Deployment

This paper presents an empirical investigation into whether the implementation of packaged Enterprise Systems (ES) leads to parity in operational performance. Performance change and parity in operational performance are investigated in three geographically defined operating regions of a single firm. Order lead time, the elapsed time between receipt of an order and shipment to a customer, is used as a measure of operational performance. A single ES installation was deployed across all regions of the subject firm\u27s operations.Findings illustrate parity as an immediate consequence of ES deployment. However, differences in rates of performance improvement following deployment eventually result in significant (albeit smaller than pre-deployment) performance differences. An additional consequence of deployment seems to be an increased synchronization of performance across the formerly independent regions

Examining Quality Factors Influencing the Success of Data Warehouse

Increased organizational dependence on data warehouse (DW) systems has drived the management attention towards improving data warehouse systems to a success. However, the successful implementation rate of the data warehouse systems is low and many firms do not achieve intended goals. A recent study shows that improves and evaluates data warehouse success is one of the top concerns facing IT/DW executives. Nevertheless, there is a lack of research that addresses the issue of the data warehouse systems success. In addition, it is important for organizations to learn about quality needs to be emphasized before the actual data warehouse is built. It is also important to determine what aspects of data warehouse systems success are critical to organizations to help IT/DW executives to devise effective data warehouse success improvement strategies. Therefore, the purpose of this study is to further the understanding of the factors which are critical to evaluate the success of data warehouse systems. The study attempted to develop a comprehensive model for the success of data warehouse systems by adapting the updated DeLone and McLean IS Success Model. Researcher models the relationship between the quality factors on the one side and the net benefits of data warehouse on the other side. This study used quantitative method to test the research hypotheses by survey data. The data were collected by using a web-based survey. The sample consisted of 244 members of The Data Warehouse Institution (TDWI) working in variety industries around the world. The questionnaire measured six independent variables and one dependent variable. The independent variables were meant to measure system quality, information quality, service quality, relationship quality, user quality, and business quality. The dependent variable was meant to measure the net benefits of data warehouse systems. Analysis using descriptive analysis, factor analysis, correlation analysis and regression analysis resulted in the support of all hypotheses. The research results indicated that there are statistically positive causal relationship between each quality factors and the net benefits of the data warehouse systems. These results imply that the net benefits of the data warehouse systems increases when the overall qualities were increased. Yet, little thought seems to have been given to what the data warehouse success is, what is necessary to achieve the success of data warehouse, and what benefits can be realistically expected. Therefore, it appears nearly certain and plausible that the way data warehouse systems success is implemented in the future could be changed

Predicting financial distress:A comparison of survival analysis and decision tree techniques

AbstractFinancial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting – edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This analysis is done over a variety of cost ratios (Type I Error cost: Type II Error cost) and prediction intervals as these differ depending on the situation. The results show that decision trees and survival analysis models have good prediction accuracy that justifies their use and supports further investigation

The Impact of Business Intelligence Tools on Performance: A User Satisfaction Paradox?

While Business Intelligence (BI) initiatives have been a top-priority of CIOs around the worldfor several years, accounting for billions of USD of IT investments per annum (IDC), academicresearch on the actual benefits derived from BI tools and the drivers of these benefits remainsparse.This paper reports the findings of an exploratory, cross-sectional field study investigatingthe factors that define and drive benefits associated with the deployment of dedicated BI tools.BI is broadly defined as an analytical process which transforms fragmented data ofenterprises and markets into action-oriented information or knowledge about objectives,opportunities and positions of an organization; BI tools are software products primarilydesigned and deployed to support this analytical process (e.g. data warehouse software, datamining software, digital dashboards applications).Building upon DeLoneand McLean’s (1992; 2002; 2003) information systems successmodel, we develop, test and refine a BI quality and performance model adapted for the specificpurpose, application, user group and technology of BI tools. The ultimate performancepredictors in this model are user satisfaction and the impact of BI tools on managerial decisionquality, both of which are determined by data quality.Partial Least Square (PLS) modeling is used to analyze data collected in a surveyadministered to IT executives of large Australian Stock Exchange (ASX) listed companies.The results confirm some of the theoretical relationships established in – especially theoriginal – DeLone-McLean model in the specific context of BI. More importantly, the resultsalso confirm the important role of explicit BI management as antecedent of benefits derived fromBI tools, and the key impact of data quality on managerial decision making and organizationalperformance.However, the results also reveal a ‘user satisfaction paradox’: In contrast to thepredictions derived from the DeLone-McLean model, organizational performance is negativelyassociated with user satisfaction with BI tools. Financial performance data collected for ex-post verification of this unexpected result confirm this paradox. We discuss BI-specificinterpretations of these unexpected findings and provide avenues for future research

Data Warehouse Success Lead towards Supply Chain Efficiency

Data warehouse management is crucial challenge due to which most of the supply chain companies are facing the issues of data management. These challenges effect adversely on data warehouse success and ultimately effect negatively on supply chain efficiency. Different studies are carried out by different researchers on the area of supply chain, however, these studies are missing with the element of data warehouse management. Therefore, the objective of the study is to examine the factors that influence on data warehouse success and supply chain efficiency. Data were collected from warehouse employees working in Indonesian supply chain companies. Results of the study shows that data warehouse success is based on various factors such as system quality, information quality, service quality and relationship quality. These factors have positive association with data warehouse success and data warehouse success increases the supply chain efficiency. Thus, companies should focus on these elements to promote data warehouse success. This study is helpful for practitioners to promote supply chain efficiency through data warehouse success

The Effect of Corporate Cultural Factors on the Data Warehousing Success: An Empirical Investigation

This study is aimed at identifying the impact of corporate cultural factors on successful development of data warehousing in the United Arab Emirates. The theoretical framework of the study is formulated based on analysis of related literature coupled with the information gained from interviewing data warehousing experts. Five hundred and eighty data warehouse users in 34 companies were surveyed to obtain their perceptions of the extent that each of 132 items had actually contributed to their firms’ DW success at different phases of development. Rigorous multivariate statistical analysis procedure has been followed to design and construct an overall model of DW success. The model has proven that all its independent variables have significant influence on the DW overall success and that corporate cultural factors have dominant impact on this success throughout the different phases of DW development

Tourists behavior during their trip: How they use and offer recommendations?

The rise of new technologies has changed the way tourists trust in eWOM to choose a restaurant. There is a growing use of opinion and price comparison websites, where opinions and ratings can be shared with other users. In addition, the spreading of false or paid comments has made this type of webs seek the generation and maintenance of trust. However, there are few studies that analyse how to generate trust in these webs and its effect in the intention of the consumer to participate in WOM behaviours, once the tourist is already in its tourist destination. Therefore, this research analyses the influence of recommendations on the generation of tourists’ trust in the review websites of restaurant industry while they are in the destination. A regression analysis of data from 439 tourists has revealed that the perceived credibility, the quality of the information and the quality of the web affect trust in review websites. This fact encourages the contracting of restaurant services and communication among consumers, both in a traditional way (WOM) and through the review websites (eWOM), while the tourist is in the tourism destination.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Logistics 4.0 in warehousing: a conceptual framework of influencing factors, benefits and barriers

Purpose: In the last decade, the Industry 4.0 paradigm had started to rapidly expand to the logistics domain. However, Logistics 4.0 is still in an early adoption stage: some areas such as warehousing are still exploring its applicability, and the technological implementation of this paradigm can become fuzzy. This paper addresses this gap by examining the relationship among influencing factors, barriers, and benefits of Logistics 4.0 technologies in warehousing contexts. Design/methodology/approach: Starting from a Systematic Literature Review (SLR) approach with 56 examined documents published in scientific journals or conference proceedings, a conceptual framework for Logistics 4.0 in warehousing is proposed. The framework encompasses multiple aspects related to the potential adopter’s decision-making process. Findings: Influencing factors toward adoption, achievable benefits, and possible hurdles or criticalities have been extensively analyzed and structured into a consistent picture. Company’s digital awareness and readiness result in a major influencing factor, whereas barriers and criticalities are mostly technological, safety and security, and economic in nature. Warehousing process optimization is the key benefit identified. Originality/value: This paper addresses a major gap since most of the research has focused on specific facets, or adopted the technology providers’ perspective, whereas little has been explored in warehousing from the adopters’ view. The main novelty and value lie in providing both academics and practitioners with a thorough view of multiple facets to be considered when approaching Logistics 4.0 in logistics facilities