An integrated core competence evaluation framework for portfolio management in the oil industry

Drawing upon resource-based theory, this paper presents a core competence evaluation framework for managing the competence portfolio of an oil company. It introduces a network typology to illustrate how to form different types of strategic alliance relations with partnering firms to manage and grow the competence portfolio. A framework is tested using a case study approach involving face-to-face structured interviews. We identified purchasing, refining and sales and marketing as strong candidates to be the core competencies. However, despite the company's core business of refining oil, the core competencies were identified to be their research and development and performance management (PM) capabilities. We further provide a procedure to determine different kinds of physical, intellectual and cultural resources making a dominant impact on company's competence portfolio. In addition, we provide a comprehensive set of guidelines on how to develop core competence further by forging a partnership alliance choosing an appropriate network topology

A multiple criteria supplier segmentation using outranking and value function methods

[EN] Suppliers play a key role in supply chain management which involves evaluation for supplier selection problem, as well as other complex issues that companies should take into account. The purpose of this research is to develop and test an integrated system, which allows qualifying providers and also supplier segmentation by monitoring their performance based on a multiple criteria tool for systematic decision making. This proposal consists in a general procedure to assess suppliers based mainly on exploiting all reliable databases of the company. Firstly, for each group of products, their evaluation criteria are defined collaboratively in order to determine their critical and strategic performance, which are then integrated with other criteria that are specific of the suppliers and represent relevant aspects for the company, also classified by critical and strategic dimensions. Two multiple criteria methods, compensatory and non-compensatory, are used and compared so as to point out their strengths, weaknesses and flexibility for the supplier evaluation in different contexts, which are usually relevant in the supply chain management. A value function approach is the appropriate method to qualify providers to be included in the panel of approved suppliers of the company as this process depends only on own features of the supplier. On the other hand, outranking methods such as PROMETHEE have shown greater potential and robustness to develop portfolios with suppliers that should be partners of the company, as well as to identify other types of relationships, such as long term contracts, market policies or to highlight those to be removed from their portfolio. These results and conclusions are based on an empirical research in a multinational company for food, pharmaceuticals and chemicals. This system has shown a great impact as it represents the first supplier segmentation proposal applied to industry, in which decision making not only takes into account opinions and judgements, but also integrates historical data and expert knowledge. This approach provides a robust support system to inform operative, tactical and strategic decisions, which is very relevant when applying an advanced management in practice.This research has been partially developed with the support of the Ministry of Economy and Competitiveness (Ref. ECO2011-27369) and Ministry of Education (Marina Segura, scholarship of Training Plan of University Teaching).Segura, M.; Maroto, C. (2017). A multiple criteria supplier segmentation using outranking and value function methods. Expert Systems with Applications. 69:87-100. doi:10.1016/eswa.2016.10.031S871006

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Technology Transition Performance of the U.S. Department of Defense Small Business Innovation Research Program

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsSustainable public procurement plays an important role in addressing not only environmental but also economic and social issues through government acquisitions from technology-based small suppliers. In this context, the objective of this study is to better understand the holistic public procurement process by assessing the operational efficiency of technology-based small suppliers and associating the economic aspect of public procurement with the social aspect, such as women-owned businesses. To this end, we analyzed U.S. Department of Defense Small Busi-ness Innovation Research grantees by combining network data envelopment analysis with bootstrap truncated regression analysis. Drawing on the analysis results, we found that (1) there is heterogeneity in the performance of research and development, network building, and commercialization sub-processes, and (2) there is a positive relationship between the overall performance and women-owned small suppliers who excel particularly in network building. The former implies that small suppliers may have different expertise in the chain of public procurement; the latter suggests that woman entrepreneurs with a business network may be able to outperform their counterparts in the public procurement market.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

An integrated core competence evaluation framework for portfolio management in the oil industry

The proponents of resource-based theory argue that efficient management of core competence portfolio provides sustainable competitive advantages. However, literature demonstrates little evidence regarding (i) how to identify core competence, specifically for a company operating in the oil sector, (ii) how to identify tangible and intangible resources related to the core competence of the company, and (iii) how to manage a company’s competence portfolio more efficiently by forging network alliances with collaborating firms. Drawing upon resource-based theory this paper presents a core competence evaluation framework for managing the competence portfolio of an oil company. The paper introduces a network typology to illustrate how to form different types of strategic alliance relations with partnering firms to manage and grow the competence portfolio. The framework is tested using a case study approach involving face-to-face structured interviews with twenty-five divisional managers of a large oil company in the Middle East. We identified purchasing, refining and sales and marketing as strong candidates to be the core competencies of the company. However, despite the company’s core business of refining oil, the core competencies were identified to be their research and development and performance management (PM) capabilities. We further provide a procedure to determine different kinds of physical, intellectual and cultural resources making a dominant impact on company’s competence portfolio. In addition, we provide a comprehensive set of guidelines on how to develop core competence further by forging a partnership alliance choosing an appropriate network topology. The paper makes many contributions to the field of strategic management and core competence evaluation in the oil sector. The guidelines provided can assist practitioners with devising appropriate network relationships with partnering companies in order to outsource, divest, protect and/or develop their core competence portfolio

A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency – combination of fuzzy QFD and DEA

© 2018 Elsevier Ltd Resources of an organisation (people, time, money, equipment, etc) are never endless. As such, a constant and continuous challenge for decision makers is to decide which projects should be given priority in terms of receiving critical resources in a way that the organisation's productivity and profitability is best guaranteed. Previous literature has already developed a plenitude of project portfolio selection methodologies ranging from simple scoring to complex mathematical models. However, most of them too often fail to propose one integrated and seamless method that can simultaneously take into account three important elements: (1) prioritisation of selection criteria over each other, (2) uncertainty in decision-making, and (3) projects interdependencies. This paper aims to fill this gap by proposing an integrated method that can simultaneously address all these three aspects. The proposed method combines Quality Function Development (QFD), fuzzy logic, and Data Envelopment Analysis (DEA) to accounts for prioritisation, uncertainty and interdependency. We then apply this method in a numerical example from a real world case to illustrate the applicability and efficacy of the proposed methodology

Decision theory in sustainable supply chain management: a literature review

Purpose – This study aims to aid theory building, the use of decision theory (DT) concepts in sustainable supply chain management (SSCM) research is examined. Design/methodology/approach – An abductive approach considers two DT concepts, Snowden’s Cynefin framework for sense-making and Keeney’s value-focussed decision analysis, in a systematic literature review of 160 peer-reviewed papers in English. Findings – Around 60 per cent of the papers on decision-making in SSCM come from operational research (OR), which makes explicit use of DT. These are almost all normative and rationalist and focussed on structured decision contexts. Some exceptions seek to address unstructured decision contexts via Complex Adaptive Systems or Soft Systems Methodology. Meanwhile, a second set, around 16 per cent, comes from business ethics and are empirical, behavioural decision research. Although this set does not explicitly refer to DT, the empirical evidence here supports Keeney’s value-focussed analysis. Research limitations/implications – There is potential for theory building in SSCM using DT, but the research only addresses SSCM research (including corporate responsibility and ethics) and not DT in SCM or wider sustainable development research. Practical implications – Use of particular decision analysis methods for SSCM may be improved by better understanding different decision contexts. Social implications – The research shows potential synthesis with ethical DT absent from DT and SCM research. Originality/value – Empirical behavioural decision analysis for SSCM is considered alongside normative, rational analysis for the first time. Value-focussed DT appears useful for unstructured decision contexts found in SSCM. Originality/value – Empirical, behavioural decision analysis for SSCM is considered alongside normative rational analysis for the first time. Value-focussed DT appears useful for unstructured decision contexts found in SSCM

Identifying optimal technological portfolios for European power generation towards climate change mitigation: A robust portfolio analysis approach

Here, an integrative approach is proposed to link integrated assessment modelling results from the GCAM model with a novel portfolio analysis framework. This framework comprises a bi-objective optimisation model, Monte Carlo analysis and the Iterative Trichotomic Approach, aimed at carrying out stochastic uncertainty assessment and enhancing robustness. The approach is applied for identifying optimal technological portfolios for power generation in the EU towards climate change mitigation until 2050. The considered technologies include photovoltaics, concentrated solar power, wind, nuclear, biomass and carbon capture and storage, for which different subsidy curves for emissions reduction and energy security are considered. © 2019 Elsevier LtdThe most important part of this research is based on the H2020 European Commission Project “Transitions pathways and risk analysis for climate change mitigation and adaptation strategies—TRANSrisk” under grant agreement No. 642260