Geometric approach to machine exploitation efficiency : modelling and assessment

Abstract

This article presents a new approach to the exploitation assessment of machines and devices. A key aspect of this approach is the construction of the assessment model based on the geometric representation of measures associated with each other, which covers the full specifics of the exploitation process. This approach is successfully implemented by the Overall Equipment Effectiveness (OEE) model, which is fully susceptible to the geometric modelling process due to the three-way system of assessed exploitation aspects. The result of this approach is the vectored OEE model and its interpretation in terms of time series of changes in values of components. Methods of determining vector calculus measures were developed, including the second-order tensor and gradient. This is the subject of the variability of the reliability conditions of machines or production processes. It allows for the realisation of an exploitation assessment based on dynamic changes in the values of their components in the time domain. This is a significant difference to the classical static approach to such an assessment. The developed new geometric OEE model was confirmed by verification tests using the LabView software, based on two parallel data sets obtained with analytical and simulation methods using the FlexSim software.

Application of Principle Component Analysis and logistic regression to support Six Sigma implementation in maintenance

“…In general, Six Sigma projects follow the DMAIC cycle with five phases: Define, Measure, Analyze, Improve, and Control. According to [58] DMAIC "is one of the well-known manifestations of the scientific method applied to problem-solving". DMAIC cycle includes a number of different statistical and management tools used in each phase such as Voice of the Customer (VOC) tools, Critical to Quality (CTQ), Quality Function Deployment (QFD), SIPOC (Suppliers-Inputs-Process-Outputs-Customers), Failure Mode and Effect Analysis (FMEA), Root Cause Analysis (RCA), Measurement System Analysis (MSA), and statistical process control (SPC) [65,69,91,99].…”

The Theory of Exploitation as a Support for Management Accounting in an Enterprise

“…They also drew attention to the relationship between the unit costs of operation and the physical processes of wear taking place in the objects-expressed as the intensity of damage. Undoubtedly, there is close feedback between the operation of the operation system in the enterprise and management accounting-i.e., data from the operation system are one of the foundations of management accounting and decision making, which in turn determine the effectiveness and efficiency of the operation system [35]. For example, an increase in the operating costs of an "old" technical facility may result in a decision to withdraw it from the system and replace it with a new one (or subject it to modernization).…”

Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth

“…Demand forecasting is a critical component of a business's operations and its ability to gain a competitive advantage in the market. Reliable and precise forecasts help businesses better adjust to market needs and meet customer demands more effectively [1,2]. In addition, they support process optimization, cost minimization, and reduce inventory problems [3][4][5].…”

“…Demand is affected by numerous, often unidentified or unknown factors, such as seasonality, promotional effects, social events, new trends, unexpected crisis, terrorism, changes in weather conditions, commercial behavior of competitors in the market, etc. [1,4,5]. The problem lies both in the complexity of supply chains [10,11] and in the companies having limited access to information, often narrowed only to sales observations [1,12].…”

“…[1,4,5]. The problem lies both in the complexity of supply chains [10,11] and in the companies having limited access to information, often narrowed only to sales observations [1,12]. Data use is also limited due to different formats of archived data, lack of data integration tools, time of data collection, and timeliness of observations [13][14][15].…”