Contact Details

NOTE: if copying this email you will need to re-insert the '@' symbol

Position

Lecturer in Automation and Control

Affiliation

Dr Ajith Kumar Parlikad

Introduction

Ajith Kumar Parlikad is a University Lecturer at Cambridge University Engineering Department. He is based at the Institute for Manufacturing, where he is the Deputy Director of the Distributed Information and Automation Laboratory.

Ajith currently leads research activities on asset management and maintenance at the Institute, with a specific focus on examining how asset information can effectively managed and used to improve asset investment and maintenance decision-making. He currently runs two research projects - one funded by the IMRC on information management strategies, and the other funded by the EPSRC on information quality in asset-intensive organisations - in addition to a number of student projects in this area.

Ajith is a member of Institution of Engineering and Technology (IET), Institute of Asset Management (IAM), a core partner of the International Working Group on "Maintenance for Sustainable Manufacturing (M4SM)", and member of the IFAC Working Group on "Advanced Maintenance Engineering, Services and Technology".

Research Interests

• Asset Management
• Maintenance Engineering
• Decision-making under Uncertainty
• Value of Information
• RFID/Sensors in asset management

Background

Ajith joined Cambridge University to read for his PhD degree, which he successfully completed in August 2006. For his PhD, he developed a methodology for quantifying the benefits of improving product information availability and quality on the effectiveness of product recovery processes. Ajith has also been involved in consulting to logistics companies on RFID.

Before coming to Cambridge, Ajith obtained his bachelors degree in Mechanical Engineering with honours from Govt. Engineering College, Trichur. After working with Steel Industries Kerala Ltd.(SILK) for a couple of months, he did his Masters in Industrial Production & Management at Birla Institute of Technology & Science, Pilani. During the same period, he also worked as an Industrial Engineer at Grasim Industries, which is one of the leading textiles manufacturer in India. He then completed his second Masters in Industrial Engineering at North Carolina State University. During this period he worked on projects with Nortel Networks and IBM eServer manufacturing facilities in Raleigh, NC, USA.

Publications

Click here for a list of Ajith's publications. [144k PDF file]

Click here for a dynamic list of my publications

PhD Opportunities

If you are interested in doing a PhD on any of these topics, please email me.

Asset Management

1. The role of uncertainty in vehicle maintenance

This projects aims to identify the sources of uncertainty in vehicle maintenance decisions, quantify its impact on decision-effectiveness and explore means to reduce this uncertainty.

2. Vehicle health prognostics (predictive maintenance)

This project aims to develop generic techniques to model failures in vehicles, estimate residual life, and to use these models for diagnosis and prognosis.

3. Optimising service decisions through lifecycle tracking and health-monitoring of assets

This involves examining how product usage history and maintenance history information can be used to improve maintenance schedules, spare parts inventory management, etc. This research topic has the potential to generate multiple PhD projects, each examining a specific decision within the asset lifecycle, or one that provides a holistic model for asset lifecycle decisions.

4. Optimising Information Quality for asset management

The objective of the project is to develop quantitative measures for information quality in the context of asset management decisions and to develop methodologies to optimize information quality throughout the asset lifecycle.

5. Optimal design of vehicle health monitoring systems

This project aims to develop a robust methodology to design vehicle health monitoring systems using information quality concepts.

6. Eco-Asset Management

Asset management decision-making almost always focuses on economic optimisation (cost, profit, etc.). This project aims to develop models and techniques to enable asset lifecycle decisions to be made to deliver optimal ecological performance in addition to economic performance. In order to achieve optimality under potentially conflicting constraints, multi-criteria decision-making techniques coupled with traditional decision-theoretic methods will be explored.

7. Return on Investment (ROI) models for vehicle health monitoring

Health monitoring systems come with a cost attached to it. These systems can however reduce costs as well as bring benefits through improved operations and output. Where does the value in health monitoring lie? How can one measure this value? This project aims to develop a tool to identify and quantify ROI for vehicle health monitoring.

8. Asset Lifecycle Simulator

This project aims to develop a simulation tool that can be used to model and analyse lifecycle performance (including physical degradation and failure) of assets.

Value of Information

1. Performance analysis of information systems

This project aims to develop decision-theoretic techniques such as influence diagrams to model and analyse the performance of information systems that support asset management decisions. Such a model should help quantify the performance of various information capture, storage, and retrieval approaches.

2. Information Economics in peer-to-peer logistics networks

This project aims to answer the following questions: (i) what is the economic impact of information sharing on the different partners of a peer-to-peer logistics network? (ii) what is the economic impact of information sharing on the holistic performance of the logistics network? (iii) What is the optimum price of information and what are the factors affecting the price of information?

3. Value of information in distributed decision-making

This project aims to develop models to quantify the value of information in a multi-party distributed decision-making scenario.