Defining CSFs for information systems strategic planning in holding companies: a case study of an Iranian managerial holding company (system group)

Holding companies (HCs) differ in their nature and behavior from other types of companies. Their role is to support, control and budget their subsidiaries. In general, HCs do not compete directly with one another, as it is difficult to find two HCs with the same product and service portfolios. Competition occurs instead at the subsidiary level against companies, which may or may not be part of other HCs, in overlapping markets with similar products and services. This concept of competition, which differs from that of typical commercial companies, is central to the development of HC strategies for supporting and controlling their subsidiaries. Information Systems Strategic Planning (ISSP) attempts to align information systems strategy with business strategy by directing the investment in information systems so as to satisfy strategic goals. Traditionally, ISSP methodologies have addressed the definition of information systems for Strategic Business Units (SBU). This research demonstrates, using a case study of an Iranian Managerial Holding Company, how ISSP can be applied instead to Strategic Business Processes (SBP). It illustrates how to define Critical Success Factors (CSFs) and Information System Needs (ISN) on Strategic Processes instead of Strategic Units. Moreover, this study combines the Balanced Scorecard (BSC) with a statistical questionnaire survey to define strategic processes

A hybrid optimisation method of managing uncertainty in capacity expansion planning

This paper addresses the application of a new fuzzy robust optimisation model in a capacity planning problem with uncertainties in demand and resource constraints. Fuzzy robust optimisation model employs robust optimisation formulation and fuzzy programming to deal with these uncertain variables. A power system capacity expansion plan is used as a case study to test the applicability of the proposed model. The results of this case study show the advantages of the proposed method compared to classical stochastic programming

Hybrid intelligent scenario generator for business strategic planning by using ANFIS

The aim of this study is to investigate a new method for generating scenarios in order to cope with the data shortage and linguistic expression of experts in scenario planning. The proposed hybrid intelligent scenario generator uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to deal with uncertain inputs. In this methodology, the strengths of expert systems, fuzzy logic and Artificial Neural Networks (ANNs) are joined to generate possible future scenarios. The proposed methodology includes four steps: step 1 defines the scope and internal and external variables and step 2 determines rules from experts. Then, step 3 prepares ANFIS system which is conducted by computer programming in Matlab environment. The Last step is sensitivity analysis to study the effects of variation of inputs on outputs. The applicability of the proposed method has been tested against two different case studies

A methodology to define strategic processes in organizations: An exploration study in managerial holding companies

Purpose - To elaborate a methodology enabling organizations to define their strategic processes among other processes. Design/methodology/approach - A case study approach has been chosen due to the nature of this research. Case study research method is qualitative method but it can be positivist. The mix of techniques is appropriate and some degree of quantification is necessary. Three high-level steps are designed and these steps are developed in the managerial holding companies (research case study). Findings - Because of limitations on budget and time, organizations are able to define the processes which are critical to achieve organizations' goals. This methodology has a holistic view in organizations by using balanced scorecard framework. Research limitations/implications - This research is based on a single case. Generalization based on this case should be interpreted cautiously and a limitation of the case study should be kept in mind. Furthermore, the strategy of the research case is a competitive strategy and the strategic processes are chosen according to this strategy. They may be changed based on other strategies. In interpreting the result, these limitations should be kept in mind. Originality/value - Defining the strategic processes helps organizations to use their resources based on their objectives. This paper presents a methodology that improves the ability of organizations in managing and directing their resources efficiently

Towards a framework for strategic knowledge management practice: Integrating soft and hard systems for competitive advantage

Purpose: This paper aims to address the limitations of current knowledge management (KM) models by presenting a strategic knowledge management (SKM) framework based on a unique configuration of literature concerned with optimising learning and knowledge creation at the interface between human (soft) and information and communication technology (hard) networks. Design/methodology/approach: This paper revisits the key tenets and most frequently cited models in the existing literature, summarises their common elements, clarifies the interrelationships between the hard and soft KM processes and practices and systemically incorporates these previously separate and independent elements into a new integrated conceptual framework. Then, it identifies key organisational factors which could facilitate this integration and leverage the value generated from different systems embedded in this model. Findings: The paper highlights the key elements and applications of a new SKM conceptual model for actively and purposefully integrating explicit and tacit knowledge embedded within organisation systems and broader social and business intelligence networks. Practical implications: The application of the thinking, organising principles and management practices derived from the SKM framework with its unique characteristics that are hard to substitute or imitate may support improvement and/or innovation of processes, products, services and brands contributing to sustainable competitive advantage of the firm. Originality/value: While both hard and soft KM systems have been individually identified by previous studies as integral to KM, the research is amongst the first attempts to explore how to integrate both systems within a strategic KM framework with supporting organisational design principles for creating firm competitive advantage

Intelligent robust optimisation method for power capacity expansion in Western Australia (WA)

The aim of this study is to identify an optimised capacity expansion plan for W A's electricity system that meets its future demand and also reduces the Greenhouse gas emission. The proposed framework develops a scenario generator by using Artificial Neural Networks (ANNs) and fuzzy logic. Then, an optimisation method called "intelligent robust optimisation" finds a robust investment option based on different WA's future scenarios. The results of case studies show the robust expansion plan for W A based on the future growth in demand and considering other constraints forced by government

Adaptive Neuro-Fuzzy Inference System for generating scenarios in business strategic planning

The aim of this study is to investigate a new method for generating scenarios in order to cope with the data shortage and linguistic expression of an expert in scenario planning. This study incorporates the concepts of neural network and fuzzy logic. The proposed methodology includes: (1) defining the scope and internal and external variables (2) determining rules from experts (3) preparing ANFIS system and (4) generating probable scenarios based on training algorithm. Following this structure, it is possible to generate the feasible scenarios with their associated degree of probabilities. The applicability of the proposed method has been tested against a case study

Supplier portfolio selection based on the monitoring of supply risk indicators

This paper introduces a dynamic optimisation model for a manufacturer's optimal selection of a portfolio of suppliers. The model uses a set of indicators that measure risks imposed by suppliers on the manufacturing supply chain. These indicators measure financial stability, production stability, product quality and cost of suppliers. The model uses a combined simulation-optimisation framework to select suppliers and allocate orders to them based on real-time monitoring of supply risk indicators. This model uses a multi-period order allocation approach based on the viewpoint of a manufacturer in a manufacturing supply chain system. A system dynamics model simulates the interrelations and feedbacks among parties in the supply chain, i.e., suppliers, the manufacturer, and the manufacturing product market. It models the effect of supply risk indicators on a manufacturer's profit over a planning horizon. The result of the simulation is fed to a portfolio optimisation model to determine an optimal supplier order allocation based on the manufacturer's propensity for risk. The model informs the manufacturer to rebalance its supply portfolio in response to early changes in supply risk indicators over a planning horizon. The results show that supplier portfolio selection based on this framework provides higher expected profit and less risks to the manufacturer over the planning horizon. For instance, in our numerical example, the high-level risk averse decision maker made a profit of 5.4% and a risk of 1% less than those of low-level risk averse decision maker at the end of the planning horizon

A novel decision-making approach for supplier selection under risks

Despite the huge amount of research in the area of supplier selection studying the effect of various risks on a system disruption, less focus has been laid on the proactive management of risk exposure to minimize the undesirable effects of such risks on the system in a timely manner. As such, a real-time monitoring of market indicators gains significance noting that managerial actions can be taken in response to the changes in the business environment in order to prevent disruption effects and limit unfavorable outcomes. This approach is able to dynamically update and so maintain a current perspective on the supplier status. This paper introduces a novel simulation-optimization approach to select a set of suppliers aiming to minimize the supply chain risk and maximize its profit based on a set of risk indicators affecting the supply chain, including financial health, production progression, and quality performance. We analyze the interrelationships among risk sources by modeling the supply chain dynamics through a system dynamics simulation. The results of simulation further apply in a portfolio optimization to maximize the profit and minimize the risks. To demonstrate the application of the model, a case study is developed based on the supply chain of a company with three possible suppliers and one final product. The results of this model support decision makers in determining the optimal supplier selection and order allocation based on their expected profit and their propensity for risk