Supply Chain Management and Risk Management in an Environment of Stochastic Uncertainty (Retail)

In the context of stochastic uncertainty and the increasing complexity of logistics processes in the retail sector, managers face a problem in obtaining accurate forecasts for the dynamics of changes in key business performance indicators. The purpose of the present work is to assess the impact of risk events and unstable conditions on the level of quality of supply chain services and economic indicators of the retail trade network. Using the anyLogistix software tool, a simulation model was constructed that allows assessing operational risks and their impact on key indicators of the supply chain using the bullwhip effect. Besides, a statistical model of the impact of the ripple effect in the event of failures caused by the occurrence of a man-made risk event and the shutdown of production of one of the suppliers on the financial, customer, and operational performance indicators of the supply chain of grocery retail. The results obtained show that the main factors of changes in the supply chain are operational risks associated with fluctuations in demand and order execution time by the distribution center. With a sufficiently high level of occurrence, their impact on productivity and quality of service is low because they can be eliminated in a short time. The simulation results show that the most tangible risks for the food retail supply chain are supply chain failures, whose consequences require significant coordinating efforts and longer recovery times, as well as additional investments. For example, events, such as a fire in one distribution center and the shutdown of production for 1 week of one of the suppliers of dairy products will lead to the loss of USD 181.75 million by the grocery retailer, which is 3% of the expected revenue. We believe that risk management in supply chains is becoming increasingly complex, and to make effective managerial decisions, it is necessary to constantly improve the tools that combine analytical and optimization methods, as well as simulation modeling.

Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling

Input uncertainty is one of the major obstacles urban building energy models (UBEM) must tackle. The aim of this paper was to quantify the effects of two of the main sources of stochastic uncertainty, namely building occupants and urban microclimate, on electrical and thermal supply system sizing at the district scale. In order to analyze the effects of the former, three different methods of occupant modeling were implemented in a UBEM. The effects of the urban heat island on system sizing were studied through the use of measured temperature data from a weather station in the case study district compared to measured data from a national weather station. The methods developed were used to assess the sizing and costs of centralized and decentralized technologies for a case study in central Zurich, Switzerland. The choice of occupant modeling approach was found to affect the district’s total annualized costs for space heating and cooling by ±5%, whereas for the costs of electricity the variation was ±8%. Regarding outdoor temperature, the effects on the heating demands proved be negligible, however the costs of the cooling alternatives were found to vary by about 4% at the district scale due to the effect of urban climate, for individual buildings this deviation was as high as 40%.

Quantifying Uncertainty in Infectious Disease Mechanistic Models

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on SARS-CoV-2. We describe the statistical uncertainty as belonging to three categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.

Optimization of searching for moving targets of a multifunctional radar station

A method for optimizing the process of searching for an unknown number of moving targets in conditions of stochastic uncertainty is proposed, which allows to significantly reduce the average time of their finding by a multifunctional radar device equipped with an antenna array. More effective is the optimal managed search, in which the order of viewing different directions is determined in the search process depending on the results of already performed views. One of the important areas of further improvement of radar technology is the transition to antenna arrays with digital beamforming based on adaptive signal processing directly in the elements of the digital antenna array (DAA).

A FORMALIZED ALGORITHM FOR OPTIMIZING THE DECISION-MAKING PROCESS IN CONDITIONS OF STOCHASTIC UNCERTAINTY

One of the main tasks of the decision support theory is the study of methods and tools for solving the problem of minimizing the negative consequences and risks in choosing strategic directions for the development of the studied system - the object of management. The formal algorithm of the optimization in conditions of the decision-making process stochastic uncertainty, and realization of steady states in system is investigated. The purpose of the algorithm model is to provide the predicted dynamics, compensation of structural, parametric uncertainty of the control system. The ambiguity of the choice the alternative solutions and as a consequence - the inadequacy of the mathematical model, due to the significant amount of stochastic and functional relationships, different ways of presenting input data, the impossibility formalizing the studied processes. Solutions in conditions of partial or complete uncertainty can be found by searching for elements of a set the alternatives, each of which with some probability may be the optimal solution. If statistical observations of the studied object or management process are incomplete, insufficiently formalized, or impossible at all, then the uncertainty of the decision to predict the directions of their possible development is clear. The decision-making process in conditions of uncertainty is proposed to be divided into stages: specification and formalization of the decision-making model; choice methods and algorithms for constructing alternatives taking into account the peculiarities of the chosen decision-making model. Parametric uncertainty is described as an interval estimate of possible values of the studied parameter. The interval can be strictly limited by numerical values, or with not clear limits - descriptive qualitative variables. Modeling of the control process in conditions of stochastic uncertainty is based on the definition of the object under study as a complex system. A promising area of research on this topic is a mathematical description of the value distribution function within the interval, which can be formalized on the basis of expert estimates, or as a heuristic probability distribution function of unpredictable events.