Research on Marketing Prediction Model Based on Markov Prediction

Many activities in modern business marketing management are random and repetitive. The marketing effect is constantly influenced by a variety of factors such as changing market supply and demand, customers’ purchase intentions, and national financial policy. As a result, Markov analysis can be used to analyze the status and trend of some variables, that is, to predict the future status and trend of a variable based on its current status and trend, in order to forecast possible changes in the future and take appropriate countermeasures. The mathematical model of product marketing prediction is presented in this paper by establishing the probability matrix of product state transition and analyzing and calculating with the Markov chain, resulting in a practical and reliable theoretical basis for economic prediction. After using the Markov analysis method, a suitable mathematical model can be created based on market investigation and statistics, which is extremely useful for making reasonable predictions about the market’s future development trend and improving marketing effectiveness.

Operational Parametric Based Reliability Estimation & Justification of Failure Criterion of Hydro Power Plant: A Case Study

Generating more Power are complex at cheaper cost, also continuous energy supplied are important Hydro power generation is one of the most successful renewable energy resources for the production electrical energy without any environmental hazard and presently it providing more than 86% of all electricity generated by renewable sources worldwide and accounts for about 20% of world electricity. To increase the percentage of green energy in account of world electricity generation the analysis must be performed to get the information about the working conditions of each component in plants so that the required maintenance action should be taken. Maintenance and operation of a hydro power plant is very complicated and the process to calculate and analyzing its compatibility and reliability is very important. In this work introducing a Markov model to evaluate the reliability parameter of THPS-I Sirmour, Rewa. For this work the operational data regarding failure and maintenance time taken to repaired and analysis of all parts of generating unit of the power plant for period of 2010-2015 is considered. The availability and reliability of individual unit of power plant is evaluated by taking into account different reliability Parameters, namely failure rate (λ), repair rate (µ), MTTR, MTTF, MTBF through the collected data and tabulating the required information for the analysis. By this analysis work we can improve reliability of all the components of each unit of power plant. The sub-unit that is commonly failed during operation is like- penstock, butter fly valve, spiral case, turbine, generator, excitation system, speed governor etc. Reliability plays a key role in the cost-effectiveness of systems Keywords: Hydro power plant, Reliability evaluation, Reliability parameters, Markov analysis, Total schedule outage hrs and Total forced outage hrs.

Markov Decision Process approach in the estimation of raw material quality in incoming inspection process

The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtime due to defective raw materials. Prediction of the raw material acceptance rate can regulate the raw material supplier selection and improve the manufacturing process by filtering out non-conformities. This paper presents a raw material acceptance prediction model (RMAP) developed based on the Markov analysis. RFID tags are used to track the parts throughout the process. A secondary dataset can be derived from the raw RFID data. In this study, a dataset is simulated to reflect a typical incoming inspection process consisting of six substations (Packaging Inspection, Visual Inspection, Gauge Inspection, Rework1, and Rework2) are considered. The accepted parts are forwarded to the Pack and Store station and stored in the warehouse. The non-conforming parts are returned to the supplier. The proposed RMAP model estimates the probability of the raw material being accepted or rejected at each inspection station. The proposed model is evaluated using three test cases: case A (lower conformities), case B (higher conformities) and case C (equal chances of being accepted and rejected). Based on the outcome of the limiting matrix for the three test cases, the results are discussed. The steady-state matrix forecasts the probability of the raw material in a random state. This prediction and forecasting ability of the proposed model enables the industries to save time and cost.

Mean Field Equilibrium in Multi-Armed Bandit Game with Continuous Reward

Mean field game facilitates analyzing multi-armed bandit (MAB) for a large number of agents by approximating their interactions with an average effect. Existing mean field models for multi-agent MAB mostly assume a binary reward function, which leads to tractable analysis but is usually not applicable in practical scenarios. In this paper, we study the mean field bandit game with a continuous reward function. Specifically, we focus on deriving the existence and uniqueness of mean field equilibrium (MFE), thereby guaranteeing the asymptotic stability of the multi-agent system. To accommodate the continuous reward function, we encode the learned reward into an agent state, which is in turn mapped to its stochastic arm playing policy and updated using realized observations. We show that the state evolution is upper semi-continuous, based on which the existence of MFE is obtained. As the Markov analysis is mainly for the case of discrete state, we transform the stochastic continuous state evolution into a deterministic ordinary differential equation (ODE). On this basis, we can characterize a contraction mapping for the ODE to ensure a unique MFE for the bandit game. Extensive evaluations validate our MFE characterization, and exhibit tight empirical regret of the MAB problem.

Markov Analysis: Strategi Mempertahankan Brand Image Produk Unggulan Sumatera Utara

Tujuan Penelitian adalah Untuk mempertahankan brand Image UMKM Produk Unggulandi Sumatera Utara. Ada beberapa langkah yang dilakukan dalam melaksanakan penelitian ini yaitu, a) melakukan wawancara terkait strategi pemasaran yang selama ini dilakukan, b) melakukan wawancara terkait brand image yang digunakan, c) melakukan analisis markovd) menerapkan strategi analisis markov untuk mempertahankan brand Image. Strategi yang dimaksud adalah strategi mempertahankan brand Image. Salah satu strategi yang dilakukan adalah Analisis Markov. Metode pengumpulan data dalam penelitian ini menggunakan kuesioner yang dibagikan kepada 50 responden yang merupakan konsumen UMKM Produk Unggulan Sumatera Utara yaitu Syrup Markisah dan Ulos Khas Sumatera Utara, dengan indikator yaitu: Kualitas Produk, Harga, Packaging dan Mudah didapati. Dengan Skala Likert sebagai skala pengukuran. Jenis data pada penelitian ini adalah Data Primer dan metode yang digunakan untuk menganalisis adalah Metode Analisis Markov dengan data Deskriptif Kuantitatif. Dan dari hasil penelitianini menunjukkan bahwa dari kelima merk ulos yang penulis teliti, yang mengalami penambahan jumlah konsumen terbesar adalah merek O dengan pergeseran dari 3 orang (6%) naik menjadi 16 orang (32%) hal ini dikarenakan merek A unggul dalam kualitas produk yang nyaman saat digunakan. Sedangkan yang mengalami jumlah penurunan konsumen terbesar adalah merek S dengan pergeseran dari 25 orang (50%) turun menjadi 16 orang (32%) hal ini dikarenakan bahan yang digunakan terlalu kasar dan cukup mahal. Sedangkan untuk syrup markisah terjadi pergerseran merk antara Pohon Pinang dan Sarang Tawon.

MODELING OF THE MANUFACTURING SYSTEMS STATE IN THE CONDITIONS OF THE LEAN PRODUCTION

This article investigates the hierarchy of the manufacturing system, which consists of a set of interrelated processes aimed at converting information, knowledge, energy, materials, and other resources into value for the consumer based on the principles of lean production. Modern manufacturing systems are becoming more and more complex to manage. The problems that need to be solved are associated with a significant number of time-varying parameters, large time delays, high non-linearity of processes, and a complex relationship between input and output parameters. Depending on the parameters of internal components and characteristics of external conditions, the state of manufacturing systems can change in an unpredictable manner. The paper considers many types of discrete states in which the system can be. The estimation of the probability of finding the manufacturing system in any of the given states was carried out using discrete Markov analysis. The article also presents the results of studies of possible transitions between states in which the production system is presented in the form of a transition matrix.