PrimeNumerics since its inception has been developing the Statistical Data Models.

Our Statistical Data Model Library provides the most comprehensive array of data analysis, data management, data visualization, and data mining solutions. Techniques include the widest selection of predictive modeling, clustering, classification, and exploratory techniques in one platform.

This is tried and true analytics platform which delivers successful business results for our customers.

Good data and sophisticated analysis only get you so far. For your best decisions, you must be empowered to interpret and act upon your data trends in a manner relevant to your business goals, customized for your company, in your industry, in your town, with your government policies and your business rules.

Our Statistical Data Model Library is easy-to-use analytic solution, incorporates the innovative Rules Builder that enables you to apply both business rules and analytic models to your business processes. Forecast, plan, and anticipate quality issues like never before. You can provide scoring solutions, meet supplier demand, and develop strategic options with unmatched business relevance, all with defined user roles and accompanying privileges company-wide.

ModelBusiness Situation Basic Techniques
PN-001Automotive Car Warranty claims Prediction.Non homogeneous poisson process
PN-002Analysis of an accelerated life test for electrical motor insulation.The purpose of the test is to estimate the median life of the insulation at the design operating temperature .It is the process of testing a product by subjecting it to conditions (stress, strain, temperatures etc.) in excess of its normal service parameters in an effort to uncover faults and potential modes of failure in a short amount of time.By analyzing the product's response to such tests, we can make prediction about the service life and maintenance intervals of a product.Accelerated life model
PN-003Analysis of failure of mica capacitor from circuit bords and predicted number of capacitors failing .Binomial Model
PN-004Analyze of recurrence data when the recurrence ages are grouped into intervals,instead of being exact ages and predict the mean function cumulative of time. Mean Cumulative Function (MCF) provides a valid statistical inference on recurrent adverse .Mean Cumulative Function
PN-005Analysis of the lifetimes of locomotive engine fans and predict the lifetime of engine fansBayesian method
PN-006Analysis of Survival Time of Semiconductor in Two Batches.Bayesian Analysis
PN-007Analyze of recurrence data on repairs and mean cumulative function for the number or cost of repairs for a population of repairable systems.Mean Cumulative Function
PN-008Analysis of right censored data from a single population and predict the lifetimes of diesel engine fansWeibull model
PN-009Analyzed failure data for the dielectric insulation of generator armature bars(analyze two combined Failure Modes)Reliability method
PN-010Analyze the failure of bearing and predict the life of failureWeibull Regression Model
PN-011Analyze the number of cycles to fatigue failure of specimens of a certain alloyThree parameter weibull analysis
PN-012Analysis of component reliability and predict the number of components removed for maintenance.Generalised additive model
PN-013Forecast the automotive sales.ARIMA model
PN-014Analyze the data represent failure times of machine parts, some of which are manufactured by manufacturer A and some by manufacturer B. predict the lifetime ( which gives information of quality).generalized linear model
PN-015Analysis of recurrence data from repairable systems. Repair data analysis differs from life data analysis, where units fail only once. As a repairable system ages,it accumulates repairs and costs of repairs. cost of repairs for a population of repairable systemsMean Cumulative Function (MCF)
PN-016Analyze the capacitor failure and quality of capacitor.During the manufacture of a metal-oxide semiconductor (MOS) capacitor, causes of failures were recorded before and after a tube in the diffusion furnace was cleaned.Pareto Analysis
PN-017 Method to model insurance claims dataMarkov Chain Monte Carlo
PN-018Analysis of Pump failure data . The number of failures and the time of operation are recorded for pumps. Each of the pumps is classified into one of two groups corresponding to either continuous or intermittent operationMarkov Chain Monte Carlo
PN-019Analyzing Recurring Failures.Replacement of components such as Good as New or Bad as Old? Non Pomogeneous Poisson Process
PN-020Analysis of pump failurePoisson and Nomal Model
PN-021Comparing the repair performance of two systems.evaluate whether the population repaire(or cost) rate increasing or decreasing with age estimate average number or cost of repair per unist on warranty or over design life of the product,comparaing two or more sets of data from different disigns production peroids ,maintanance policies enviroment operating condintions etc. predict turure number and cost of repairs .Mean Cumulative Function
PN-022Predicts total maintenance cost ($) for next six months using time series method. This prediction will help planner (manager) to allocate the maintenance budget for next six month.Forecasting Total Maintenance Cost
PN-023Pareto analysis is used to identify critical problem by number of failures or by maintenance cost or by repair time.Pareto Analysis
PN-024Moving range chart is tests for special causes to make an individual measurements chart more sensitive to special causes of variation.Moving Range Chart
PN-025The accelerated life test of an insulating fluid and are the times to electrical breakdown of the fluid under different high voltage levelsWeibull Analysis
PN-026This method to model insurance claims data of automotive carBayesian Analysis
PN-027The number of failures of pumps and the time of operation are recorded of pumps. Predict the failure of pump.Nonlinear Poisson Regression Random Effects Model
PN-028Analysis of Microprocessor Failure dataCox proportional hazards model
PN-029Analysis of failure of Motor of scooter with operating temperature as a covariateCox proportional hazards model
PN-030Analysis of Gas Turbine failure dataReliabilty method
PN-031Analyzed data from an accelerated test on the lifetimes of Air gap capacitors as a function of operating voltage and temperature. It is the process of testing a product by subjecting it to conditions i.e. temperatures, voltage in excess of its normal service parameters in an effort to uncover faults and potential modes of failure in a short amount of timeWeibill Analysis
PN-032Monitor the number of defects per motor.The motor of the same model are inspected, and the number of defects per motor is recorded.C-CHART
PN-033Monitor the number of failing circuits from the circuit manufacturing
PN-034Analyzed data from a strain-controlled fatigue test on specimens of a type of superalloy.Weibull Regression Model
PN-035Monitor number of failing circuits produced by the circuit manufacturing process.p-Chart
PN-036Estimating cost of product in manufacturing Simple Linear Regression
PN-037Forecast Repaircost of Motor Failure .Multiple Linear Regression Method
PN-038Forecast the mean residual life of an assetMean Residual Life Function
PN-039Analysis of failure cost by failure code for particular asset.Cause and cost Chart
PN-040Analysis of Risk Priority Number (RPN) data to evaluate risk level of component for perticular asset.Failure Mode Efect Analysis
PN-041Monitor the Total claim cost,Labor cost ,complaints of customers in warranty of carsTile Graph
PN-042Forecast the Total Maintenance cost Multiple Linear Regression
PN-043Analysis of failure occurs in Wind TurbinePareto analysis
PN-044Automotive car insurance claim predictionPoisson regresssion
PN-045Analysis of motor failure with different degradation stageWeibull model
PN-046Analysis of wind turbine failure data and identify critical problem by failure of bearingsPareto Analysis
PN-047Nurse scheduling ProblemOptimization
PN-048Job shop scheduling ProblemOptimization
PN-049Depot location problemOptimization
PN-050Predict the Probability of defualtLogistic Regression
PN-051Failure predictionSimilarity based modeling
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