Evaluating eREVERSE auctions (EeRA): A research note

This eGISE network paper seeks to evaluate issues relating to the implementation of electronic reverse auctions (eRA) within local government procurement processes. The adoption of an eRA invites pre-qualified suppliers to compete with each other for a specified good or service. Consequently, there is a unique opportunity for the buyer to receive a reduced cost through the successful bidder. However, the literature identifies a number of adverse effects within these arrangements depending upon the nature of the buyer/supplier relationship. The objectives of the research involves identifying a set of business scenarios to demonstrate the impact of different eRA strategies in this respect. This will be achieved through a structured case analysis approach to enable qualitative data to be modelled through a visual toolset simulation. It is believed the outcome of the investigation will provide valuable insights into the complexities associated with the eProcurement process

Handling Concept Drift for Predictions in Business Process Mining

Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies

Effectiveness of R&D project selection in uncertain environment: An empirical study in the German automotive supplier industry

This paper presents results of an empirical large-scale study on uncertainty reduction of R&D projects and R&D project selection. The empirical field is the German automotive supplier industry. We explore R&D project selection practices in this specific industry and briefly contrast our findings with the academic research and management literature in this field. We concentrate on answering three research questions (with focus on questions no. 1 and 2): I. Which information and related uncertainties are crucial for the product selection decision to the R&D decision makers? II. How do R&D decision makers today cope with typical challenges related to reducing uncertainty? Where do they face major problems and how effective are they? III. What are major implications for managing the Fuzzy Front End (FFE) of innovation process in industry practice and respectively for further academic research in this field? Key findings are that on the one hand certainty about fields of product applications, target markets and production feasibility are most important criteria for initial product selection decisions. On the other hand market and cost related uncertainties (e.g. sales volume, product price, cost per unit) cannot be satisfyingly reduced in practice before project approval for development or definite termination of projects. Although different uncertainty profiles exist within the process of project evaluation, most companies do not systematically choose available product selection methods and tools according to specific uncertainty situations. Intuition still plays a major role in R&D product selection. Some first conclusion drawn from this research are: A sufficient level of resources (including financial and methodological know-how), a systematic use of suitable project selection instruments, and a fit with the company specific as well as the OEMs' product/brand strategies can be potential levers for more effective uncertainty reduction before product decision. --

An application of hybrid life cycle assessment as a decision support framework for green supply chains

In an effort to achieve sustainable operations, green supply chain management has become an important area for firms to concentrate on due to its inherent involvement with all the processes that provide foundations to successful business. Modelling methodologies of product supply chain environmental assessment are usually guided by the principles of life cycle assessment (LCA). However, a review of the extant literature suggests that LCA techniques suffer from a wide range of limitations that prevent a wider application in real-world contexts; hence, they need to be incorporated within decision support frameworks to aid environmental sustainability strategies. Thus, this paper contributes in understanding and overcoming the dichotomy between LCA model development and the emerging practical implementation to inform carbon emissions mitigation strategies within supply chains. Therefore, the paper provides both theoretical insights and a practical application to inform the process of adopting a decision support framework based on a LCA methodology in a real-world scenario. The supply chain of a product from the steel industry is considered to evaluate its environmental impact and carbon ‘hotspots’. The study helps understanding how operational strategies geared towards environmental sustainability can be informed using knowledge and information generated from supply chain environmental assessments, and for highlighting inherent challenges in this process

Situating the Next Generation of Impact Measurement and Evaluation for Impact Investing

In taking stock of the landscape, this paper promotes a convergence of methods, building from both the impact investment and evaluation fields.The commitment of impact investors to strengthen the process of generating evidence for their social returns alongside the evidence for financial returns is a veritable game changer. But social change is a complex business and good intentions do not necessarily translate into verifiable impact.As the public sector, bilaterals, and multilaterals increasingly partner with impact investors in achieving collective impact goals, the need for strong evidence about impact becomes even more compelling. The time has come to develop new mindsets and approaches that can be widely shared and employed in ways that will advance the frontier for impact measurement and evaluation of impact investing. Each of the menu options presented in this paper can contribute to building evidence about impact. The next generation of measurement will be stronger if the full range of options comes into play and the more evaluative approaches become commonplace as means for developing evidence and testing assumptions about the processes of change from a stakeholder perspective– with a view toward context and systems.Creating and sharing evidence about impact is a key lever for contributing to greater impact, demonstrating additionality, and for building confidence among potential investors, partners and observers in this emergent industry on its path to maturation. Further, the range of measurement options offers opportunities to choose appropriate approaches that will allow data to contribute to impact management– to improve on the business model of ventures and to improve services and systems that improve conditions for people and households living in poverty.

Determining sourcing strategies : A decision model based on activity and cost driver information.

Determing sourcing strategies for different material groups provides a major challenge to most companies. There has been little research on the choice of the optimal number of different suppliers for a given product group and the determination of their market shares. In this paper we propose a mathematical programming model using activity based costing information to determine optimal order splitting among suppliers on the basis of the different costs associated with the purchasing decision. We argue that sourcing strategies should be based on the minimisation of the total cost of ownership resulting from external purchases. The model is applied to the case of ball bearings at Cockerill Sambre S.A., a Belgian multinational company in the steel industry.Model; Sourcing; Strategy;

Knowledge acquisition in supply chain partnerships: The role of power

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Knowledge is recognised as an important source of competitive advantage and hence there has been increasing academic and practitioner interest in understanding and isolating the factors that contribute to effective knowledge transfer between supply chain actors. The literature identifies power as a salient contributor to the effective operation of a supply chain partnership. However, there is a paucity of empirical research examining how power among actors influences knowledge acquisition and in turn the performance of supply chain partners. The aim of this research is to address this gap by examining the relationship between power, knowledge acquisition and supply chain performance among the supply chain partners of a focal Chinese steel manufacturer. A structured survey was used to collect the necessary data. Two conceptually independent variables – ‘availability of alternatives’ and ‘restraint in the use of power’ – were used to assess actual and realised power, respectively. Controlling for contingencies, we found that the flow of knowledge increased when supply chain actors had limited alternatives and when the more powerful actor exercised restraint in the use of power. Moreover, we found a positive relationship between knowledge acquisition and supply chain performance. This paper enriches the literature by empirically extending our understanding of how power affects knowledge acquisition and performance