Knowledge management in multinational companies: the repatriates’ role in the competitive advantage in subsidiaries

This paper addresses the topic of knowledge management in multinational companies (MNCs). Its purpose is to examine the role of expatriates in knowledge acquisition and transfer within MNCs. Specifically it focuses on knowledge acquisition and transfer from one MNC head office located in Germany to two Portuguese subsidiaries as a basis for competitive advantage in their Portuguese subsidiaries. A qualitative research methodology is used, specifically through an exploratory case study approach, which examines how international assignments are important for the role of expatriates In knowledge acquisition and transfer between foreign head offices and their Portuguese subsidiaries. The data were collected through semi structured interviews to 10 Portuguese repatriates from two Portuguese subsidiaries of one foreign MNC. The findings suggest that the reasons that lead to expatriating employees from Portuguese subsidiaries to foreign head offices are connected to (1) knowledge management strategies to development the subsidiary’s performance; (2) new skills and knowledge acquisition by future team leaders and business/product managers in Portuguese subsidiaries; (3) procuring knowledge, from agents in head office, to be disseminated amongst co-workers in Portuguese subsidiaries; (4) acquiring global management skills, impossible to acquire locally and; (5) developing global projects within MNC. Also our results show that knowledge acquisition and transfer from foreign head office, through subsidiaries’ expatriates, contributes directly to the Portuguese subsidiaries’ innovation, improved performance, competitive advantage and growth in the economic sectors in which they operate. Moreover, evidence reveals that expatriation is seen as a strategy to fulfil some of the main organisational objectives through their expatriates (e.g., create new products and business markets, develop and incorporate new organisational techniques and processes, integrate global teams within multinational corporation with a responsibility on the definition of global objectives). The results obtained suggest that expatriates have a central role in acquiring and transferring strategic knowledge from MNC head office to their subsidiaries located in Portugal. Based on the findings, the paper discusses in detail the main theoretical and managerial implications. Suggestions for further research are also presented. The study’s main limitation is the small size of the sample, but its findings and methodology are quite original and significant.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Six sigma methodologies: implementation and impacts on Portuguese small and medium companies (SMES)

Artigo elaborador no âmbito dos trabalhos decorrentes à dissertação de Mestrado do Aluno David Leite, no Mestrado em Gestão Integrada Qualidade, Ambiente e Segurança, ESTGF-IPP, Oreintados pelos Professores Luís Fonseca (ISEP-IPP) e Vanda Lima (ESTGF-IPP).

Size matters when we talk about corporate social responsibility: the case of portuguese companies

Social responsibility arises as a measure of the corporate policy that goes beyond the production activity, thus covering also the social and environmental activities. This issue has been gaining importance over time. The number of companies that are implementing socially responsible actions has been growing steadily even though it is still centered on large and medium sized companies. Social responsibility is an accessible theme to any company of any size, once the social and environmental issues are resolved through concrete actions, but in the small and micro-sized companies there are factors that inhibit such actions. Some of these factors are: reduced investment capacity; minimal expectations of medium to long term returns; or even the short-term management focus that is aimed at survival. Such facts can be verified in the case of Portuguese companies, where out of the 41 companies certified by SA 8000 only 5 are micro-sized or small and even these are linked to large organizations.

Optimal generation scheduling of wind-CSP systems in day-ahead electricity markets

This paper presents a coordination approach to maximize the total profit of wind power systems coordinated with concentrated solar power systems, having molten-salt thermal energy storage. Both systems are effectively handled by mixed-integer linear programming in the approach, allowing enhancement on the operational during non-insolation periods. Transmission grid constraints and technical operating constraints on both systems are modeled to enable a true management support for the integration of renewable energy sources in day-ahead electricity markets. A representative case study based on real systems is considered to demonstrate the effectiveness of the proposed approach. © IFIP International Federation for Information Processing 2015.

Expatriates recruitment and selection for long-term international assignments in portuguese companies

Long-term international assignments’ increase requires more attention being paid for the preparation of these foreign assignments, especially on the recruitment and selection process of expatriates. This article explores how the recruitment and selection process of expatriates is developed in Portuguese companies, examining the main criteria on recruitment and selection of expatriates’ decision to send international assignments. The paper is based on qualitative case studies of companies located in Portugal. The data were collected through semi-structured interviews of 42 expatriates and 18 organisational representatives as well from nine Portuguese companies. The findings show that the most important criteria are: (1) trust from managers, (2) years in service, (3) previous technical and language competences, (4) organisational knowledge and, (5) availability. Based on the findings, the article discusses in detail the main theoretical and managerial implications. Suggestions for further research are also presented.

Defining Electricity Tariffs Using the Knowledge About the Consumers Profiles in ELECON Project

The increasing importance of the integration of distributed generation and demand response in the power systems operation and planning, namely at lower voltage levels of distribution networks and in the competitive environment of electricity markets, leads us to the concept of smart grids. In both traditional and smart grid operation, non-technical losses are a great economic concern, which can be addressed. In this context, the ELECON project addresses the use of demand response contributions to the identification of non-technical losses. The present paper proposes a methodology to be used by Virtual Power Players (VPPs), which are entities able to aggregate distributed small-size resources, aiming to define the best electricity tariffs for several, clusters of consumers. A case study based on real consumption data demonstrates the application of the proposed methodology.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Environmental management and mudit training at the New University of Lisbon by University-Industry co-operation

Environmental Training in Engineering Education (ENTREE 2001) - integrated green policies: progress for progress, p. 329-339 (Florence, 14-17 November 2001; proceedings published as book)

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Data Extraction Tool to Analyse, Transform and Store Real Data from Electricity Markets

The study of electricity markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring process produced. Currently, lots of information concerning electricity markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge to define realistic scenarios, which are essential for understanding and forecast electricity markets behavior. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of electricity markets and of the behaviour of the involved entities. In this paper an adaptable tool capable of downloading, parsing and storing data from market operators’ websites is presented, assuring constant updating and reliability of the stored data.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Elspot: Nord Pool Spot Integration in MASCEM Electricity Market Simulator

The energy sector in industrialized countries has been restructured in the last years, with the purpose of decreasing electricity prices through the increase in competition, and facilitating the integration of distributed energy resources. However, the restructuring process increased the complexity in market players' interactions and generated emerging problems and new issues to be addressed. In order to provide players with competitive advantage in the market, decision support tools that facilitate the study and understanding of these markets become extremely useful. In this context arises MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), a multi-agent based simulator that models real electricity markets. To reinforce MASCEM with the capability of recreating the electricity markets reality in the fullest possible extent, it is crucial to make it able to simulate as many market models and player types as possible. This paper presents a new negotiation model implemented in MASCEM based on the negotiation model used in day-ahead market (Elspot) of Nord Pool. This is a key module to study competitive electricity markets, as it presents well defined and distinct characteristics from the already implemented markets, and it is a reference electricity market in Europe (the one with the larger amount of traded power).