THE RISK PLANNING METHODOLOGICAL PRINCIPLES FOR HOUSING INVESTMENT

In many countries wordwide there is an urgent need to increase the housing level. However, a significant increase in this level is hindered by a number of factors. Among these factors, the significant risk of housing construction investing is essential. Reducing the riskiness of housing construction investing requires, among other things, the use of scientifically grounded risk planning methods in such investment. With taking this into account, the purpose of this work is to develop methodological principles for the housing investing risk planning. The study subjects were the patterns of forming the housing construction investing risks. The methodology of this study involved the use of systems analysis, economic and mathematical modeling, tools of decision theory, and technical and economic calculations. Among the results of the study, the formed input information array needed for planning the housing investing risks should be noted. This array includes the following main blocks: information on the available regulatory and legal support for housing construction; information on potential developers, intermediaries, and other entities investing in housing construction (particularly on the occurrence of risky events’ frequency and scale in their activities); information on potential investment objects (such as their preliminary estimated costs, consumer characteristics, construction terms); information on the concluding agreements’ conditions and procedure between the housing construction investment subjects. The indicator system for retrospective assessment in housing construction investing risk is proposed. In particular, the following indicators groups are included in this system: actual frequency indicators of risky events’ occurrence in the investment entities’ activity in the previous period(s); specific indicators of the risky events’ occurrence scale in the investment entities’ activities in the previous period(s); relative indicators of the risky events’ occurrence scale in the investment entities’ activities in the previous period(s). Also, this indicators system was supplemented by a number of generalizing indicators. The sequence of the risk planning process in housing construction investing for all the main participants of the process is proposed. For the investors particularly, this sequence contains the following sequential actions: a set of situations in which the construction object may appear is formed; the probability for each of these situations is estimated; the expected value of the economic benefit from the housing purchase in each situation is set; the mathematical expectation calculation of the value of economic benefits from the housing purchasing is made; the coefficient of variation is calculated according to the average linear deviation of economic benefits from the housing purchasing; the estimated market value of a residential real estate object is calculated taking into account the risk factor; the profitability index of the particular dwelling purchase operation is calculated. The practical implication of the developed methodological principles of the risk planning in housing construction investing in the practice of the construction subjects will increase the approved management decisions validity.

Dynamic Cooperative Model for Ranking Construction Risks using Monte Carlo Simulation

Construction projects suffer from diverse uncertainties that hinder the key objectives’ achievement. These uncertainties represent risks that may appear through the project life cycle. This paper introduces a quantitative model to estimate and rank risks dynamically during the risk planning phase. Such ranking would help decision-makers appropriately respond to and/or control construction risks. The model provides proper risk contingency reserves for both project time and cost that meet decision-makers' selected confidence levels using Monte Carlo Simulation (MCS). In order to quantify the project uncertainty, severities of residual risks are determined and allocated at the project's activities-level using a planning/scheduling spreadsheet model and a MCS tool suitable for spreadsheets. The model is able to calculate the contribution of each risk from the determined contingency at both the project level for both the time and cost at the decision-maker confidence level.The model represents a direct implementation for a Risk Planning Contingency Model (RPCM); which involves four modules as follows: (1) Risk Register (RR), (2) Risk Allocator (RA), (3) Risk Simulator (RS), and (4) Contingency Calculator (CC). These modules are hosted in a critical path model scheduling spreadsheet to facilitate risk management. In addition, a simulation engine add-in is used for analyzing the probability distribution for the project time and cost outcomes. In order to verify the proposed model, the process and analysis have been applied to a case study project. The results show that the RPCM is capable to rank and estimate the residual risks in an easy, fast, and effective way.

A conceptual framework for improving effectiveness of risk management in supply networks

PurposeThe purpose of the paper is to develop a conceptual framework for improving the effectiveness of risk management in supply networks following a critical literature review.Design/methodology/approachA critical review of 91 scholarly journal articles published between 2000 and 2018 supports the development of an integrated conceptual framework.FindingsThe findings emphasize that supply chain integration (SCI) can have both a positive and negative impact on the effectiveness of risk management in supply networks. It is possible to have a positive effect when SCI can be used to develop competencies in joint risk planning within the organization and with wider supply network members and, in turn, to develop collaborative risk management capabilities. Supply network characteristics can influence whether and the extent to which SCI has a positive or negative impact on risk management effectiveness.Research implicationsThe conceptual framework can be used to empirically assess the role of SCI for effective risk management. Dynamic evaluation of the effectiveness of risk management and potential redesign of the supply network by considering other contingent factors are some future research avenues.Practical implicationsThere is a need for developing specific competencies in risk planning within organizations and joint risk planning with supply network members which, in turn, can help develop collaborative risk management capabilities to improve the effectiveness of risk management in supply networks. Network characteristics will influence whether and the extent to which SCI results in the effectiveness of risk management.Originality valueMoving beyond recent (systematic) reviews on supply chain risk management, this study develops a novel conceptual framework interlinking SCI and the effectiveness of risk management while considering network characteristics.