The model and the planning method of volume and variety assessment of innovative products in an industrial enterprise

Anisimov, Vladimir; Anisimov, E. G.; Saurenko, T. N.; Sonkin, M. A.

doi:10.1088/1742-6596/803/1/012006

Cited by 22 publications

(9 citation statements)

References 6 publications

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“…In this case, the removal of the inherent uncertainty of the information situation is ensured by applying the principle of "maximum entropy" of the first and second kinds. For the formation of conditionally deterministic models, the entropy was used, defined by the ratio (10). For the formation of stochastic models, Shannon entropy was used.…”

Section: Discussionmentioning

confidence: 99%

Models for predicting damage due to accidents at energy objects and in energy systems of enterprises

et al. 2019

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Achieving energy security by preventing and timely eliminating the consequences of accidents at energy facilities and in energy supply systems of enterprises is one of the important tasks of energy management. The basis for planning appropriate energy security measures is the prediction of damage from these accidents. The purpose of forecasting is to assess the possibility of an accident occurring at some point in time and leading to a particular damage, and to assess the magnitude of this damage. The article proposed methodological approaches to the construction of mathematical models of such prediction. In this case, as an indicator of damage, the economic losses caused by these accidents are taken. The simulation is based on the representation of this indicator in the form of a step change function of the magnitude of losses in the event of an accident. Depending on the amount of information available in the period prior to forecasting, the mathematical representation of the forecasting problem is reduced to the construction of conditionally determined or stochastic models. Conditionally determined models allow obtaining acceptable damage estimates with a short period of retrospection and small amounts of information, and stochastic models with significantly large amounts. At the same time, the principle of “maximum uncertainty” formalized in the form of maximum entropy is the basis for removing uncertainty in the construction of both conditionally determined and stochastic models. Its use has allowed increasing the objectivity of forecasts by minimizing the subjective information used in modeling. The proposed approaches to the construction of mathematical models for predicting accidents at energy facilities and power supply systems of enterprises are the basis for creating specific techniques for solving relevant energy management tasks both at the micro level at the scale of individual enterprises and at the macro level at the scale of industries, regions and the state as a whole.

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Section: Discussionmentioning

confidence: 99%

Models for predicting damage due to accidents at energy objects and in energy systems of enterprises

et al. 2019

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“…The initial information for solving this problem is a set of data on the values of x j , t j accumulated during the period preceding the forecast, as well as a priori data on the possible nature of the forecasted processes, obtained on the basis of expert assessments and (or) management experience of similar economic, social, organizational and technical systems [7][8][9][10].…”

Section: Methodical Approach To Building Conditionally Determined Modmentioning

confidence: 99%

Models of forecasting destructive influence risks for information processes in management systems

Anisimov

Anisimov²,

Saurenko³

et al. 2019

ICS

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Introduction: One of the side effects of introducing modern information technologies in the management of economic, social,organizational and technical systems is the stronger dependence of the management quality on intentional or accidental destructiveinfluences which violate the integrity, confidentiality and availability of the information used. This determines the relevanceof developing appropriate information security systems. The substantiation of the development of such systems requires solvingthe problems of comparative assessment of the destructive impact risks and the cost of their prevention. Purpose: Predicting thedanger of a destructive impact on information processes in control systems. Method: The prediction is based on representingthe destructive effects in the form of a random sequence of events which lead to disruptions in the information processes. Theconsequences of failures are also represented by certain random variables. Results: Methodical approaches are proposed in orderto build models for predicting temporal and volumetric characteristics of damage from destructive influences on information processesin the management of economic, social, organizational and technical systems. In these models, we suggest to assess thedanger of destructive impacts by the probability of the onset of a destructive event at a certain time moment, and by the amountof damage caused by it. The basis for the construction of prediction models is the presentation of damage indicators in the formof step functions of time. The constructive representation of these functions is based on the conditional deterministic approach.The completeness of a priori information usage in determining specific parameters of the damage functions is ensured by applyingthe maximum uncertainty principle. The measure for the uncertainty is entropy. The conditional deterministic approachfor higher uncertainty levels was developed in a stochastic approach. On its basis, classes of stochastic models were proposed,corresponding to various information situations. These models allow you to estimate not only the expected values of damageindicators due to the failure in taking measures to ensure information security while managing targeted systems, but also theirprobabilistic characteristics. Practical relevance: The proposed approaches are the basis for the creation of particular models andtechniques in the interests of well substantiated decisions on the formation of the structure of the organization and managementof information security subsystems.

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“…In the pursuit of smart manufacturing, to satisfy the customer needs with quality, responsiveness and cost-effectiveness become the major challenge of nowadays factories [1]. The market demands are uncertain [2] and prone to be influenced by the composite effects of the driven forces, including the following: Market saturation: most known potential customers have already had the similar kind of product, and the new market segments are still not thriving to prove be the promising revenue fountain [3]. Product innovation: the customers hardly pay more for these products with slim marginal utility [4].…”

Section: Introductionmentioning

confidence: 99%

Smart Material Planning Optimization Problem Analysis

Lee¹,

Jan²

2019

Advanced Analytics and Artificial Intelligence Applications

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Mostly, the concept of smart manufacturing is addressed based upon how to effectively facilitate the production activities by using the automation equipment; however, causing the fluctuation of production may frequently root to the uncertain incoming sales orders. These uncertain factors may be influenced by various economic parameters, such as changes within trade regulations, competitor innovations, and changes within the market. In order to reduce the difference between the forecasted demand versus actual demand and to minimize risk, these factors need to be taken into account and be fully investigated. The current widely applied forecast methods are factory capacity-driven and based on the trend against the activity history. When the uncertainty comes from the external, then the forecasts derived from these models cannot provide convincing insights to let the firms make decisions confidently. Many previous prestigious studies focused on the problemsolving optimization mathematic methods and articulated the causality among latent factors; few have addressed to a holistic framework that the firms can practice on. This study presents a clear operable step-by-step framework to manage and cushion the impact from the external uncertain factors. It also introduces three novel and feasible production planning models with the consideration of the economic parameters. The empirical case was a multi-nation machinery-making firm who has adopted the proposed framework to optimize the material forecasts pursuing their smart manufacturing goals.

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The model and the planning method of volume and variety assessment of innovative products in an industrial enterprise

Cited by 22 publications

References 6 publications

Models for predicting damage due to accidents at energy objects and in energy systems of enterprises

Models for predicting damage due to accidents at energy objects and in energy systems of enterprises

Models of forecasting destructive influence risks for information processes in management systems

Smart Material Planning Optimization Problem Analysis

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