Assessment of Risk and Uncertainty Factors Inherent in Nigerian Highway Project Using Analytical Hierarchical Process

The study presents a framework through which risk and uncertainties in Nigeria highway projects can be appraised using the analytical hierarchical process model. Pairwise comparison matrix was performed on eleven (11) risk and uncertainty factors that affect highway project performance through a questionnaire survey conducted among two hundred and four (204) respondents, which involved various stakeholders in the highway construction industry using Saaty’s AHP rating scale. The relative weights (significance/impact level) of each of the highway risk factors were estimated during the AHP model development. The results in descending order of risk factors priorities are Standard & Regulations (S&R), Construction (C), Project Staff (PS), Project Sponsor (PSp), Design (D), Project Finance (PF), Economic (E), Equipment (EQ), Environmental & Geotechnical (En&G), Site Location (SL) and Subcontractor (S) with impact levels of 16.6%, 14.2%, 13.9%, 13%, 12.7%, 12.2%, 10.8%, 9.8%, 6.2%, 4.2%, and 3.8% respectively. The model was validated using the statistical consistency test, with the model showing a consistency ratio equal to 0.1. The model was then applied to five (5) highway construction projects which had been constructed to predict the ones with the most and least risks. The result was in tandem with that which was given by the project managers from experiences on the project. This study showed that the Analytical Hierarchical Process (AHP) decision support model can effectively be used for risk assessment and prioritization of highway construction projects for efficient resource utilization in Nigeria.

The Effect of Risk and Uncertainty Factors on Managerial Decision Making

This study evaluated the effect of risk and uncertainty factors on financial decision making. The long-term goals and ways for achievement are constantly attached with uncertainty since we don’t know the circumstances, either positive or negative, which happen later. Uncertainty is a key logical factor that influences the dynamic. The reason for this investigation is to check how risk and factors of uncertainty impact the financial aspects of a firm. The risk factors incorporate, financial risk, market fluctuations hazards, fluctuation of unfamiliar and loan costs. Uncertainty factors incorporate political, monetary and environmental uncertainty. The results reveal that management knowledge and expertise related to these factors are utmost important for effective decision making and sustainable growth.

Identification of Differences in Perception of Uncertainty Factors in the Consumer Behavior of Millennials in E-commerce

E-commerce offers huge potential in terms of online sales, but also poses certain risks for consumers and retailers, in particular cybercrimes, hacker attacks, spam, but also a lack of personal interactions and information asymmetries. These risks may lead to consumer uncertainty. This means that consumers feel insecure when buying products online compared with buying the same products in brick-and-mortar stores. This uncertainty when shopping online discourages many consumers from taking part in online transactions. Previous research points to the fact that consumer uncertainty stems from perceived information asymmetries due to hidden information and moral hazard problems. Perceived information asymmetry can be defined as the situation in which the buyer thinks that the seller has a greater amount or quality of information about products, their properties, and ultimately sales practices. Keywords: e-commerce, consumer behaviour, new generation of customers, uncertainty.

Implementation of MCDM-Based Integrated Approach to Identifying the Uncertainty Factors on the Constructional Project

Presently, environmental management for companies emphasizing environmental protection has become one of the most critical issues for customers, shareholders, governments, employees, competitors, and global pressures requiring organizations to produce environmentally-friendly products and services. This challenge has created a new concept called green supply chain management in business, which combines environmental thinking with the supply chain. Selection of suppliers by considering risk criteria is a category that has attracted the attention of a large number of researchers in order to select the best suppliers according to uncertain factors. In this research, we aim to select a green supplier considering risk factors using a new MCDM approach under uncertainty. For this selection problem, HF-MAIRCA, a new multicriteria sorting method for many alternatives, has been developed. This is used for sorting the alternatives into predefined, ordered supplier categories. This sorting method can be applied to different environmental problems that have a large number of alternatives. As a result of Iran’s case study, the result shows that materials flexibility and materials quality are essential criteria for green supplier selection.

SUPPLY CHAIN UNCERTAINTY: AN EMPIRICAL STUDY OF INDONESIA’S AGRO-INDUSTRY

Supply chain management (SCM) is a multi-stakeholder network for managing the flow of raw materials, finished products, information and money. The supply chain’s network refers to the interdependencies of the processes and activities. Taking this into account, stakeholders deal with an environment of volatility, uncertainty, complexity and ambiguity (VUCA). The dynamic nature of the supply chain’s environment implies uncertainty in the upstream and downstream sides. Drawing from the literature, manufacturers need to mitigate any uncertainty in their supply chains, which can consist of supply, demand, and technology uncertainties. However, the previous literature neglected any discussion of supply chain uncertainty in the context of the agro-industry. Hence, to answer this gap, this study aimed to investigate the supply chain’s environmental uncertainty factors in the agro-industry’s sectors. Accordingly, this study obtained 30 respondents from the agro-industry in Indonesia. This study reported that the agro-industry in Indonesia has uncertainties about supply, demand and technology. It is faced with the inability of suppliers to consistently deliver raw materials, in terms of their quantity and quality. Meanwhile, demand uncertainty is caused by the fluctuations in customers’ demands and the industry’s low forecasting accuracy. Moreover, the rapidly changing technology has implications for uncertainty in services and product standards; making it difficult for manufacturers to anticipate the changes. This uncertainty in the supply chain’s environment needs to be controlled by the agro-industry through supply chain integration

Sequence learning-based schedule prediction for flexible manufacturing systems under uncertainties

This study presents a method of production schedule prediction for flexible manufacturing systems with consideration of the uncertainty factors including limited machine capacity, diverse processing time and unplanned waiting time. The proposed method can predict product-level schedules using sequence learning, which derives data-learned models to predict production sequence proactively and granularly at the product-level. A decision tree technique is applied to derive such predictive models to pre-trace the locations of individual products allocated to each workstation. A deterministic technique is also applied to estimate waiting and production time per product as well as total production time consumed to fabricate all products assigned by work orders.