Asset Management Analytics

The objective of this program is to equip participants with the analytical skills required to manage, monitor, and optimize physical and digital assets throughout their lifecycle. The course focuses on enhancing decision-making in asset planning, utilization, maintenance, performance evaluation, and cost optimization. Participants will learn data-driven approaches to maximize asset reliability, reduce operational risks, and improve return on investment.

Category: Analytics

Description

1. Learning Outcomes

Understand asset lifecycle management, performance metrics, and asset valuation.
Analyze asset usage data to identify inefficiencies and improvement opportunities.
Apply analytical methods to optimize asset alloation, utilization, and maintenance.
Use dashboards and reporting tools to monitor asset health and performance.
Perform risk assessment and cost-benefit analysis for assets.
Develop a data-driven asset management strategy aligned with organizational goals.

2. Program Highlights & Key Features

Case studies from manufacturing, energy, and facility management industries.
Hands-on sessions with asset performance datasets and analytical tools.
Practical exercises on asset health scoring and lifecycle optimization.
Expert-led insights on maintenance planning and reliability improvement.
Coverage of modern trends such as IoT-enabled asset tracking and predictive asset intelligence.

Module 1: Introduction to Analytics
Module 2: Basics of Analytics I (Descriptive Statistics)
Module 3: Basics of Analytics II (Inferential Statistics)
Module 4: Linear & Multi-Linear Regression
Module 5: Logistic Regression
Module 6: Clustering
Module 7: Decision Tree
Module 8: Time Series Modeling
Module 9: Predictive Modelling Techniques
Module 10: Maintenance Strategy Optimization
Module 11: Asset Life Cycle Cost Prediction
Module 12: Data Visualization

Introduction to Analytics

Asset Management Principles
Equipment Overview
Operations and Challenges
Conclusion and QnA

Basics of Analytics I (Descriptive Statistics)

Introduction to Data
Measures of Central Tendency – Mean (Arithmetic), Median, Mode
Merits and Demerits of Mean
Standard Deviation and Variance
Skewness and Kurtosis
Probability Distribution
Normal and Standard Normal Distribution
Standardization of Data
Basics of R Code
Understanding Data Types
Arithmetic Operations
Creating Vectors, Matrix, List
Setting Working Directory
Creating Data Frames
Summary Statistics
Conclusion and QnA

Basics of Analytics II (Inferential Statistics)

Hypothesis Testing Concepts
Null and Alternate Hypothesis
Level of Significance
P-Value
Decision Criteria
Statistical Tests – Parametric and Non-Parametric
Large Sample and Small Sample Tests
Z-Test
t-Test
F-Test
Chi-Square Test
ANOVA (Analysis of Variance)
Conclusion and QnA

Linear & Multi-Linear Regression

What is Regression?
Covariance and Correlation
Features of Correlation (r)
Testing the Significance of Correlation Coefficient
Types of Regression Analysis
Purpose of Regression Analysis
Coefficient of Determination (R²)
Adjusted R²
Multiple Linear Regression
Residual Analysis
Multicollinearity
Heteroskedasticity
Typical Applications of Regression
Case Study using R
Conclusion and QnA

Logistic Regression

Logistic Regression Basics
Generalized Linear Model (GLM)
Types of Logistic Regression
Binary Logistic Regression – When and Why
Applications of Logistic Regression
Case Study with R – German Bank Dataset
Conclusion and QnA

Clustering

What is Clustering?
When to Use Cluster Analysis
Applications of Cluster Analysis
Types of Cluster Analysis
K-Means Clustering (In Detail)
Case Study using R
Conclusion and QnA

Decision Tree

What is a Decision Tree?
Why Use Decision Trees?
Types of Decision Trees
Constructing Decision Trees
Random Forest and CART (In Detail)
Case Study using R
Conclusion and QnA

Time Series Modeling

What is Time Series?
Components of Time Series
Forecasting Techniques
Simple Moving Average
Weighted Moving Average
Simple Exponential Smoothing
Double Exponential Smoothing
Triple Exponential Smoothing
Time Series Model Comparison
ACF and PACF Concepts
AR, MA, ARMA Models
ARIMA Model
Industry Use Cases and Applications
Conclusion and QnA

Predictive Modelling Techniques

Hands-On Practical Sessions
Conclusion and QnA

Maintenance Strategy Optimization

Hands-On Practical Sessions

Asset Life Cycle Cost Prediction

Hands-On Practical Sessions

Data Visualization

What is Data Visualization?
Types of Visualization
Common Data Visualization Issues
Data Visualization Tools

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