Tender Evaluation for the Telecommunication Industry using the Analytic Network Process

In the past few decades, tender evaluation has consistently dominated most of organizations\u27 top strategic priorities. Additionally, the field of tender evaluation has generated a vast amount of research efforts, wherein most of these efforts center on methods enabling consideration of all affecting criteria together to make an appropriate decision. Despite the great deal of advances in the methods of tender evaluation based on technical view, there still lacks comprehensive and organizational-driven decision making tools to support organizations during the crucial task of choosing a suitable tender that best meets their business and technical needs.;Tender evaluation has a strategic role in the success of large enterprises in the telecommunication market. It is a complex, multi-person, multi-criteria process. The criteria used to evaluate a tender contain quantitative which are easy to measure and qualitative attributes which most available methods fail to deal with them. In this study, a model is developed using Analytic Network Process (ANP) in a Benefit, Opportunity, Cost, and Risk (BOCR).;The essence of this approach is decomposition of a complex problem into a hierarchy with objective at the top of the hierarchy, criteria and sub-criteria at levels and sub-levels of the hierarchy, respectively, and decision alternatives at the bottom of the hierarchy. Factors at given hierarchy level are compared in pairs to assess their relative preference with respect to each of the factors at the next upper level. These can support complex problems that would be otherwise difficult to handle. This method is capable of handling discrete criteria of both quantitative and qualitative in nature and provides complete ordering of the alternatives.;The primary feature of this methodology is its ability to simultaneously consider all types of criteria for tender evaluation in telecommunication companies. The criteria defined for the model using Delphi method from experts in the field and are general to all telecommunication tenders. The developed model is used in an empirical study on an ongoing tender in a mobile telecom service provider company to analyze the tenderers\u27 data and evaluate and rank them. The result of this model is compared to the company\u27s evaluation result which is obtained from traditional Texas Instruments Matrix method. The proposed model shows the ranking of the tenderers in different BOCR merits separately as local priorities to help the evaluators make a more efficient decision. A sensitivity analysis on the empirical study was conducted to show how the rankings of the tenderers are changing by changing the weights of the BOCR merits.;The research work presented here may be used by telecommunication professionals and managers to aid in making appropriate decisions on tender evaluation process and determinate strategies for reducing the risk of this process

Method for Business Process Management System Selection

In recent years business process management (BPM) and specifically information systems that support the analysis, design and execution of processes (also called business process management systems (BPMS)) are getting more attention. This has lead to an increase in research on BPM and BPMS. However the research on BPMS is mostly focused on the architecture of the system and how to implement such systems. How to select a BPM system that fits the strategy and goals of a specific organization is largely ignored. In this paper we present a BPMS selection method, which is based on research into the criteria that are important for organizations, which are going to implement a BPMS

Dynamic technological capability (DTC) model for the next generation of technology evolution

The central question of this thesis is how should the managers and technologists of technological organisations decide on how to invest in the co-evolution of technologies and adapt their influences to the evolution of their organisational capabilities by knowing the benefits, opportunities, costs and risks of such an investment? In the context of this research the main drivers are recognized as: - Variation in the accuracy and quality of technology - Changing market and instability in the demand for technology - Huge cost with less revenue from the technology - Increasing influence of regulations The issue of particular interest within this question includes creating a solution method for decision makers so that they can create value for their organisations by making a less risky investment decision in technology evolution, under the conditions that will be relevant to the next generation of technologies. The research work uses a case study approach within the context of the UK mobile industry in order to answer the basic and problem-oriented questions, by which; 1. the characteristics of the future technological evolutions within which the next generation of technologies must be operated are identified. 2. related theories are identified in respect of the value creation for organisations with evolving capabilities in response to the dynamic environment. 3. emphasis is placed upon the contribution of the technology co-evolution towards the evolution of organisational capabilities, as a result of a critical view of the concept of dynamic capabilities. 4. a basis is developed for the need of a solution method, consistent with the characteristics of the next generation of technologies, which respond to the current limitation of the theory of the dynamic capabilities, that must be overcome to achieve new requirements of the technology evolution. The output from the research work includes: I. A new framework, which exploits distinct technological roles: component, product and applications, support and infrastructure and integrates these technological capabilities from internal and external industries, following the four stages evolutionary cycle, including variation/reconfiguration, selection/search/learning, replication/leveraging, retention/integration. In this research, this new framework is called an evolutionary framework. II. A new set of 52 factors which are organized with respect to their clusters: technological evolution (TE), organisational evolution (OE), resource evolution (RE); their drivers: accuracy and quality of technology, market demand for technology, cost of technology, self and governmental regulations; and their merits: benefits, opportunities, costs, risks. In this research, this new set of factors is called an evaluation method. The fusion of the above concept and method places a new model, called the Dynamic Technological Capability model, within the context of technological organisations such as the UK mobile operators. The basis of the DTC model is that the exogenous industries are forcing the technology co-evolution, even if the previous generation of technologies remained unsuccessful in the dynamic market. To overcome the problems of making a less risky investment decision in the next generation of technology under such circumstances, the decision makers must have a model through which they can take measures of the investment decisions in the form of the benefits, opportunities, costs and risks values before making any investment decision. These novel aspects of the DTC model are illustrated by applying it to the UK mobile operators: Vodafone, Orange and O2, for the process of making an investment decision in the next generation of Location Based Services (LBS), called Assisted-Global Positioning System (A-GPS) technology

Integration of an Improved Grey-Based Method and Fuzzy Multi-Objective Model for Supplier Selection and Order Allocation

For multi-attribute decision making (MADM) problems, a grey based approach (LI) had been developed to evaluate, rank and select the best suppliers. The method calculates a grey possibility degree between compared suppliers alternatives set and positive ideal referential alternative. The drawback of the method is that the negative ideal referential alternative is not considered in evaluating and ranking of the alternatives. Moreover, the method can only consider interval fuzzy number as input data and real number is neglected. Based on this model and other MADM methods, all demand was sold by the best supplier. In other cases, if the best supplier cannot satisfy all demand, multi-objective programming is used to formulate the problem and assign optimum order quantities to the best suppliers (multi-sourcing). Some techniques, such as goal programming (GP) approach, ε-Constraint method, Reservation level (RL) driven Tchebycheff procedure (RLTP) method had been proposed to solve the multi-objective models. It may be a problem that these techniques traced back to more than 10 years ago. Therefore, there may be still the need to produce a new technique in order to solve the multi-objective models. In this study, to overcome the first drawback, the LI method was improved based on the concepts of technique for order preference by similarity to ideal solution (TOPSIS) to consider both the positive and the negative ideal referential alternative for evaluation of the suppliers. The improved version of the LI method is called the I.LI method. Based on the concepts of TOPSIS, the chosen alternative should have the shortest distance from the positive ideal solution and the farthest from the negative ideal solution. Moreover, in order to solve the problems, a new grey based method (NG) based on the TOPSIS concepts was proposed that can easily consider both interval fuzzy number and real number simultaneously. Afterwards, an innovative comparative approach was proposed to compare the three MADM methods, the LI, the I.LI and the NG methods, and to show that which method is more optimal than the other methods. Subsequently, in this thesis, an integration of the NG method and fuzzy multi-objective model was suggested for multi-sourcing and multi-product supplier selection problem. The score of suppliers calculated by the NG method was served as coefficients in one objective function of the multi-objective model. In this fuzzy multi-objective model, the products are divided into two independent and dependent products so that (1) the price breaks (discounts) depend on the size of the order quantities, (2) independent products’ sales volume affect the prices and discounts of the dependent products and (3) all products must be sold as a bundle. Finally, to overcome the third problem, a new weighted additive function, which is able to consider relative importance of each objective as well as condition of fuzzy situation, is proposed to solve the fuzzy multi-objective model and assign optimum order quantities to the suppliers evaluated and ranked by the NG method. The results of the innovative comparative approach showed that the result of the NG method is more optimal than the I.LI method and the latter is more optimal than the LI method. Therefore, the NG method was selected to be integrated with the fuzzy multi-objective model. Also, the fuzzy multi-objective model was solved by the new weighted additive function, and the results demonstrated that besides considering the relative importance of the objectives, the new technique is also able to consider the condition of fuzzy situation

Dynamic technological capability (DTC) model for the next generation of technology evolution

The central question of this thesis is how should the managers and technologists of technological organisations decide on how to invest in the co-evolution of technologies and adapt their influences to the evolution of their organisational capabilities by knowing the benefits, opportunities, costs and risks of such an investment? In the context of this research the main drivers are recognized as: - Variation in the accuracy and quality of technology - Changing market and instability in the demand for technology - Huge cost with less revenue from the technology - Increasing influence of regulations The issue of particular interest within this question includes creating a solution method for decision makers so that they can create value for their organisations by making a less risky investment decision in technology evolution, under the conditions that will be relevant to the next generation of technologies. The research work uses a case study approach within the context of the UK mobile industry in order to answer the basic and problem-oriented questions, by which; 1. the characteristics of the future technological evolutions within which the next generation of technologies must be operated are identified. 2. related theories are identified in respect of the value creation for organisations with evolving capabilities in response to the dynamic environment. 3. emphasis is placed upon the contribution of the technology co-evolution towards the evolution of organisational capabilities, as a result of a critical view of the concept of dynamic capabilities. 4. a basis is developed for the need of a solution method, consistent with the characteristics of the next generation of technologies, which respond to the current limitation of the theory of the dynamic capabilities, that must be overcome to achieve new requirements of the technology evolution. The output from the research work includes: I. A new framework, which exploits distinct technological roles: component, product and applications, support and infrastructure and integrates these technological capabilities from internal and external industries, following the four stages evolutionary cycle, including variation/reconfiguration, selection/search/learning, replication/leveraging, retention/integration. In this research, this new framework is called an evolutionary framework. II. A new set of 52 factors which are organized with respect to their clusters: technological evolution (TE), organisational evolution (OE), resource evolution (RE); their drivers: accuracy and quality of technology, market demand for technology, cost of technology, self and governmental regulations; and their merits: benefits, opportunities, costs, risks. In this research, this new set of factors is called an evaluation method. The fusion of the above concept and method places a new model, called the Dynamic Technological Capability model, within the context of technological organisations such as the UK mobile operators. The basis of the DTC model is that the exogenous industries are forcing the technology co-evolution, even if the previous generation of technologies remained unsuccessful in the dynamic market. To overcome the problems of making a less risky investment decision in the next generation of technology under such circumstances, the decision makers must have a model through which they can take measures of the investment decisions in the form of the benefits, opportunities, costs and risks values before making any investment decision. These novel aspects of the DTC model are illustrated by applying it to the UK mobile operators: Vodafone, Orange and O2, for the process of making an investment decision in the next generation of Location Based Services (LBS), called Assisted-Global Positioning System (A-GPS) technology

ADOPTION OF EMERGING TECHNOLOGY TOOLS IN LOGISTICS INDUSTRY: PRIORITIZATION USING ANP AND BOCR METHODS

This study aims to prioritize technology tools in the logistics industry. It focuses on four key technology trends: Augmented Reality (AR), the Internet of Things (IoT), Big Data, and Robotics and Automation (R&A). The objective is to determine the prioritization and ranking of these technologies in the logistics sector using the Analytic Network Process (ANP) model and analyzing using the Benefits, Opportunities, Costs, and Risks (BOCR) model. The study identified specific criteria and sub-criteria to evaluate the technologies, and experts from the fields provided judgments based on these criteria. Applying the ANP and BOCR models, the research presents the ranking of the technology trends, highlighting their importance and potential impact on the logistics industry. The findings of this research help to gain further knowledge of the technology adoption in the logistics sector and provide valuable insights for industry professionals and decision-makers

Industry 4.0 enabling sustainable supply chain development in the renewable energy sector:A multi-criteria intelligent approach

The aim of this paper is to provide a multi-criteria decision-making intelligent approach based on Industry 4.0 and Triple Bottom Line principles for sustainable supply chain development in the renewable energy sector. In particular, the solar photovoltaic energy supply chain is used as a case study, encompassing the entire energy production process, from supply to disposal. An exhaustive literature review is conducted to identify the main criteria affecting social, economic and environmental sustainability in the photovoltaic energy supply chain, and to explore the potential impact of Industry 4.0 on sustainability. Subsequently, three Fuzzy Inference Systems combining quantitative and qualitative data are built to calculate the supply chain's social, economic and environmental sustainability. Experts' opinions are used to identify the impact of Industry 4.0 technologies on the three pillars of sustainability for each supply chain stage. Finally, a novel sustainability index, Sustainability Index 4.0, is formulated to compute the overall sustainability of the photovoltaic energy supply chain in seven countries. The results show the applicability and usefulness of the proposed holistic model in helping policy makers, stakeholders and users to make informed decisions for the development of sustainable renewable energy supply chains, taking into account the impact of Industry 4.0 and digital technologies

The Analytic Network Process Applied in Supply Chain Decisions, in Ethics, and in World Peace

The Analytic Hierarchy/Network Process which was developed by Dr. Thomas Saaty “has revolutionized how we resolve complex decision problems” (INFORMS, 2008). The Analytic Network Process (ANP) is applied herein in the context of supply chain decision making; then as a tool to bridge the separation thesis between business and ethics and show that ethical decision and business decisions are interrelated and can and should be jointly considered; and finally to guide the G-2 powers in their efforts to improve relations. In the first supply chain model a Metrics Arrow of relevant performance metrics that follow the temporal flow of the product is presented and used to select a third party logistics provider. The ANP model also provides managerial insight into the interdependencies of the performance metrics. The second model deals with selecting which green supply chain initiative a company should implement. A generalized framework is developed and then customized and applied in a specific case study of a TV audio video producer’s supply chain. Two ethics cases are analyzed in the first chapter on ethics to demonstrate the benefits of using a rigorous prioritization process, the ANP, to make ethical decisions. This chapter is intended to act as introduction of the ANP to the ethics community and focuses on the benefits of using the ANP. Next, a complex model that uses a stakeholder theory approach is used to address the ethical issues of hydraulic fracturing. The benefits to the natural gas industry to participate in an integrative stakeholder approach are demonstrated. As another demonstration of the ANP a complex decision with a direct influence on peace and stability in the world is the relationship between the two superpowers the People’s Republic of China and the United States is analyzed. As improvements have been made in the relationship between the two countries there are critical decisions that must be faced in the near future. This model suggests which of five initiatives if addressed will be most beneficial to both countries. In the final chapter the main findings are summarized and future research is suggested

Inventory Management Via Topsis Analytical Hierarchy Process (AHP) Method Embedded With Economic Order Quantity

Inventory management is the process of keeping track of all the material on the manufacturing industry has in its industry stock. Effective inventory management aligns all inventory types to the efficient creation of the production process to finished products and delivery to the customer's satisfaction. Ineffective inventory management either beyond having too much inventory or too little inventory, poor inventory management causes inefficiencies of production activity. This will be having reordering inventory from suppliers at last minute or increase risk of mistakes non-fulfilled customer orders on time. The consequence of poor inventory management can cause customers to withdraw orders or industry pay compensation due to order delivery date no achieve as agreement. Due to the poor inventory management problem, this project purpose to use economic order quantity (EOQ) application in a rubber manufacturing company and optimization of the economic order quantity (EOQ) with Technique for Order Preference by Similarity to Ideal Solution Analytic Hierarchy Process (TOPSIS-AHP). Nowadays, the choice of suppliers and the supplier material performance assessment are very important. This become the major challenges in the manufacturing industry which mainly faced by supply chain managers or purchaser. The progress to evaluating each supplier and selecting the best supplier are become complicated tasks. In the decision-making process, there are different criteria andalternatives must take into consideration. The objective is to determine and select the best supplier of inventory by using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The economic order quantity (EOQ) model was used to calculate the best quantity of the material. Therefore, at the end of this project concluded the best supplier is supplier A with its material. The result is consistent in all methodologies. The economic order quantity (EOQ) to order upon purchasing of new inventory is 2847 k