Optimization of preventive maintenance and spare parts stocking using real-time IoT data in a manufacturing plant

Project Abstract

Optimization of preventive maintenance and spare parts stocking using real-time IoT data in a manufacturing plant examines an integrated framework that leverages real-time sensor data, predictive analytics, and inventory optimization to reduce downtime, extend equipment life, and minimize carrying costs. The study addresses the growing need for proactive maintenance strategies in complex production environments characterized by high asset heterogeneity, variable demand, and stringent service level requirements. A mixed-methods approach combines data-driven predictive maintenance models with stochastic and integer programming for spare parts optimization, underpinned by a digital twin of the plant’s production lines and maintenance workflows. Real-time data streams from vibration, temperature, pressure, and energy consumption sensors, along with machine logs and maintenance history, are collected, cleaned, and fused to generate actionable health indicators and Remaining Useful Life (RUL) estimates for critical components. The predictive models, including survival analysis, machine learning classifiers, and deep learning architectures, are trained on historical failure events and operational conditions, achieving improved accuracy in failure forecasting and maintenance timing. The maintenance planning module integrates these forecasts with a multi-criteria optimization framework that balances preventive maintenance schedules, reliability targets, and production imperatives, while incorporating constraints such as maintenance windows, labor availability, and risk of cascade failures. In parallel, the spare parts stocking model optimizes inventory levels by considering lead times, obsolescence risk, demand variability, and service level constraints, using a two-stage stochastic programming approach augmented with scenario-based Monte Carlo simulations to quantify the value of information from IoT data. The study implements a digital twin environment to simulate different maintenance and inventory policies, enabling scenario analysis and policy tuning before live deployment. A case study in a mid-to-large-scale manufacturing plant demonstrates reductions in unplanned downtime, maintenance costs, and stockouts, while improving overall equipment effectiveness (OEE) and service levels. Key findings indicate that real-time IoT-enhanced visibility enables earlier interventions, more precise RUL estimation, and dynamic, condition-based stocking strategies that adapt to changing production demands. The research also investigates data governance, cyber-physical security, and the organizational change required to adopt such an integrated maintenance and inventory system. The results are validated through a combination of retrospective data analysis, pilot implementation results, and sensitivity analyses that assess policy robustness under varying failure rates and demand scenarios. The study contributes to the literature by presenting a holistic framework that fuses IoT analytics, predictive maintenance, and inventory optimization within a digital twin context, offering practical guidance for engineers and managers seeking to elevate maintenance performance, spare parts readiness, and operational resilience in modern manufacturing environments. Recommendations for implementation include data architecture design, model interpretability, change management, and a roadmap for incremental deployment with measurable KPIs such as reduction in downtime, maintenance cost per hour, stockout frequency, and total cost of ownership.

Project Overview

What This Project Is About

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A plain-language overview of how maintenance and inventory decisions can be smarter by using data from sensors and devices in a factory. The project explores how real-time information from equipment and parts can guide when to service machines and how many spare parts to keep, reducing downtime and excess stock.

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The Problem It Addresses

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Many factories face unexpected machine failures and stockouts of critical parts, which disrupt production and raise costs. Relying on memories or schedules alone often leads to unnecessary maintenance or delayed repairs. This project aims to reduce downtime and optimize parts inventory using live data.

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Objectives of the Project

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1. Identify key maintenance indicators from real-time data.
2. Develop a simple model to decide when to service equipment.
3. Create a basic system to predict spare parts needs.
4. Evaluate potential reductions in downtime and inventory costs.
5. Provide actionable guidelines for implementation in a small-to-medium factory.

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What You Will Do Step by Step

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1. Review simple literature on maintenance and inventory basics.
2. Map your factory’s critical machines and parts.
3. Install or access sensors and data sources from equipment.
4. Collect short-term data and identify common failure signals.
5. Build an easy decision framework for maintenance timing.
6. Create a basic parts forecasting approach using past usage and current trends.
7. Test the framework in a simulated or small pilot setting.
8. Analyze results, discuss limitations, and outline practical steps for rollout.

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Expected Outcome

A straightforward method and simple tools that help decide when to perform maintenance and how many spare parts to keep, leading to less downtime and lower inventory costs. The project should produce clear recommendations for implementation in real factories.