Jesper Kronborg Jensen

Product carbon footprint developments and gaps

Purpose – Over the last decade, multiple initiatives have been undertaken to learn how to capture the carbon footprint of a supply chain at a product level. The purpose of this paper is to focus on the process of standardization to secure consistency of product carbon footprinting (PCF) and to outline how the current developments in PCF support the need for a standardized method to measure and report environmental performance in supply chains. Design/methodology/approach – This paper is based on a literature review and a review of international standards for PCF which brings knowledge of PCF to the existing literature of green supply chain management. Findings – The multiple initiatives for standardization each improve the understanding of standardized methods of conducting PCF. At the same time, however, important differences exist between the standards in terms of the modelling framework to be used when conducting a PCF, and a paradox exists concerning methods for securing future standardization of PCF....

Cardiac troponin I for the prediction of functional recovery and left ventricular remodelling following primary percutaneous coronary intervention for ST-elevation myocardial infarction

Objective To investigate the ability of cardiac troponin I (cTnI) to predict functional recovery and left ventricular remodelling following primary percutaneous coronary intervention (pPCI) in ST-elevation myocardial infarction (STEMI). Design Post hoc study extending from randomised controlled trial. Patients 132 patients with STEMI receiving pPCI. Main outcome measures Left ventricular ejection fraction (LVEF), end-diastolic and end-systolic volume index (EDVI and ESVI) and changes in these parameters from day 5 to 4 months after the index event. Methods Cardiac magnetic resonance examination performed at 5 days and 4 months for evaluation of LVEF, EDVI and ESVI. cTnI was sampled at 24 and 48 h. Results In linear regression models adjusted for early (5 days) assessment of LVEF, ESVI and EDVI, single-point cTnI at either 24 or 48 h were independent and strong predictors of changes in LVEF (p<0.01), EDVI (p<0.01) and ESVI (p<0.01) during the follow-up period. In a logistic regression analysis for prediction of an LVEF below 40% at 4 months, single-point cTnI significantly improved the prognostic strength of the model (area under the curve = 0.94, p<0.01) in comparison with the combination of clinical variables and LVEF at 5 days. Conclusion Single-point sampling of cTnI after pPCI for STEMI provides important prognostic information on the time-dependent evolution of left ventricular function and volumes. Trial Reg No The study was part of a randomised controlled trial http://www.clinicaltrials.gov; Unique identifier: NTC00326976.

Chasing value offerings through green supply chain innovation

Purpose – The need for innovations to achieve economically viable and green supply chains has been illuminated in recent literature. Closed‐loop supply chains are part of green supply chain management and include traditional forward supply‐chain activities as well as additional activities of the reverse supply chain. Extant supply chain literature calls for a chain perspective in order to avoid sub‐optimization in the chain since changes at one stage can affect the performance at other stages. The purpose of the paper is to analyze how implementation of green supply chain innovations can enhance value offerings along the supply chain.Design/methodology/approach – This research is based on an explorative, single‐embedded case study of a food supply chain. The case is concerned with implementation of green supply chain innovation in terms of biogas technology. The single case comprises four actors in the food supply chain: a retailer, an industrial bakery, a mill, and a farmer. Data collection is based on s...

Offshoring and backshoring manufacturing from a supply chain innovation perspective

ABSTRACT This paper compares the practice of offshoring and backshoring of manufacturing from a supply chain innovation (SCI) perspective. The purpose is to advance the understanding of whether and how companies pursuing different globalisation strategies work differently with the SCI components, technology, and business processes. The results, based on a questionnaire survey in Denmark on relocation of manufacturing, indicate that companies reshoring manufacturing have invested more in manufacturing innovation and, to a higher degree, have reorganised their manufacturing resources compared with companies that either have offshored their manufacturing or maintained it at home.

Dynamic supply chain design: a Delphi study of drivers and barriers

Supply chain management has both in academia and practice proven its important role to sustain and further develop companies’ competitive advantages. This is with initiatives that focus on cost-efficiencies and turnover improvement. During the last two decades, companies have faced complexity in their supply chains currently with increased global operations. The dynamic business environment forces companies to secure a competitive (re-) design of their supply chains. This paper seeks to advance the understanding on the drivers and barriers for such designs. In total, 39 experts (30 from industrial enterprises, 4 senior supply chain consultants and 5 supply chain management professors) have participated in a four-step Delphi study and have identified main drivers for dynamic supply chain design as being cost reduction, delivery reliability and change in demand for agility. The main barriers are identified as forecasting being too weak, supply chain complexity and product portfolio complexity. In addition, an explorative factor analysis has been carried out to identify how drivers and barriers can be grouped together. Finally, the paper compares the drivers and barriers underlining different competitive strategies.

Practitioners understanding of big data and its applications in supply chain management

Big data poses as a valuable opportunity to further improve decision making in supply chain management (SCM). However, the understanding and application of big data seem rather elusive and only partially explored. The purpose of this paper is to create further guidance in understanding big data and to explore applications from a business process perspective.,This paper is based on a sequential mixed-method. First, a Delphi study was designed to gain insights regarding the terminology of big data and to identify and rank applications of big data in SCM using an adjusted supply chain operations reference (SCOR) process framework. This was followed by a questionnaire-survey among supply chain executives to elucidate the Delphi study findings and to assess the practical use of big data.,First, big data terminology seems to be more about data collection than of data management and data utilization. Second, the application of big data is most applicable for logistics, service and planning processes than of sourcing, manufacturing and return. Third, supply chain executives seem to have a slow adoption of big data.,The Delphi study is explorative by nature and the questionnaire-survey rather small in scale; therefore, findings have limited generalizability.,The findings can help supply chain managers gain a clearer understanding of the domain of big data and guide them in where to deploy big data initiatives.,This study is the first to assess big data in the SCOR process framework and to rank applications of big data as a mean to guide the SCM community to where big data is most beneficial.

Big Data and its Applications in Supply Chain Management: Findings from a Delphi Study

Big data and its applications have increasingly received interest by both scholars and practitioners. However, there is still missing evidence regarding how big data is understood as well as its applications in supply chain management (SCM). Empirical contributions are especially limited. This study seeks to address this gap through an explorative Delphi study to understand the terminology of big data and its application in the SCM processes of sourcing, manufacturing, service, logistics, planning, and return. The findings reveal that big data is mostly concerned with data collection and logistics, service, and planning processes are the most applicable processes for deploying big data analytics in SCM. Furthermore, a range of applications have been identified and ranked within each process.