Dr Parminder Singh Kang

Job: Research Fellow

Faculty: Technology

School/department: School of Engineering and Sustainable Development

Research group(s): Lean Engineering Research Group, Advanced Manufacturing Processes and Mechatronics Centre

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 207 8089

E: pkang@dmu.ac.uk

W: http://uk.linkedin.com/pub/parminder-singh-kang-phd-m-sc-b-tech-miet/46/74b/96/

 

Personal profile

Dr Parminder Singh Kang is working as a Research Fellow at De Montfort University (DMU), Leicester, UK. His main responsibilities are providing the academic lead and day to day management of externally funded research projects. Dr Kang received B-Tech degree (Computer Science and Engineering) in 2006 form Punjab Technical University, India. He received M.Sc. degree (IT) in 2008 and completed his PhD (Improving Manufacturing Systems Using Integrated Discrete Event Simulation and Evolutionary Algorithms) in Manufacturing Science in 2012 at DMU, UK. He is member of institution of mechanical engineers (IMechE), Institution of Engineering and Technology (IET) and The American Society of Mechanical Engineers (ASME). He has over 5 years of experience working on externally funded collaborative industrial R&D cross-disciplinary project in the area of organizational operations optimization. His research interests are; evolutionary algorithms, combinatorial optimization, simulation modelling, Lean/Six Sigma, autonomous decision making and application of these techniques (integrated approaches) in industrial/service process improvement and operations optimization.

Having educational background and experience in the area of computer science and engineering and manufacturing science Dr Kang’s key competencies are in the area of;

Operations Management and Manufacturing Science

- Lean Tools and Techniques, Designing and Modelling (Mathematical and Simulation), Process Improvement, Lead Time Reduction, Cost Reduction, Setup reduction, buffer sizing, Bottleneck Detection and Analysis,    Sequencing, Scheduling (finite capacity scheduling) and Buffer Management, Multi-Objective Combinatorial Optimization, Evolutionary Algorithms, Autonomous planning, scheduling and decision making, Problem Solving and Root Cause Analysis.

- Experience working with simulation packages (Simul8, Simio and Arena) and Finite Capacity Scheduling (Preactor).

Computer Science and Engineering;

Knowledge and experienced in R&D software development for large collaborative research projects, development of Evolutionary Algorithms, Artificial Intelligence Techniques and Autonomous Systems development and using these techniques in process improvement and optimization, algorithm and data structure design for complex systems.

- Competent in a number of Programming Languages; C/C++, C#, Visual Basic and Java, Handling .COM objects, Advanced multithreading, advanced shell scripting and data base design and management skills.

Publications and outputs 

  • Continuous Process Improvement Implementation Framework Using Multi-Objective Genetic Algorithms and Discrete Event Simulation
    Continuous Process Improvement Implementation Framework Using Multi-Objective Genetic Algorithms and Discrete Event Simulation Kang, Parminder Singh; Bhatti, R. S. Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems. Design/methodology/approach This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources. Findings Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework. Originality/value Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Passenger Departure Process Modelling to Investigate the Effect of Variability for a Major International Airport
    Passenger Departure Process Modelling to Investigate the Effect of Variability for a Major International Airport Al-Dhaheri, Abdulla; Kang, Parminder Singh; Al-Halafi, M. The special environment of any airport, especially a major international hub made process improvement and analysis difficult. This is due to the variability induced by several factors and which could potentially change, dramatically, at short notice. This made current research paper significantly different from previous simulation based approaches applied to improve the airport operations. Also, large, cumulative variations in demand set in an environment where rapid expansion of the airport is taking place also created major difficulties because of the shifting flow of passengers. This research paper demonstrated the application of discrete event simulation model of the airport much more accurate and detailed than those described in previous studies of passenger departure processes. This paper proposes a detailed modelling approach for departure process and several recommendations are made for further work to improve the airport check-in process operations.
  • Applying lean principles to health economics transactional flow process to improve the healthcare delivery
    Applying lean principles to health economics transactional flow process to improve the healthcare delivery Alrashed, I.A.; Kang, Parminder Singh Abstract: Defects reduction and end-to-end process improvement are key to successful delivery of key services such as healthcare. This research paper investigates the implication of Lean management for healthcare service improvement. Transactional flow process is one of the key processes within the Saudi Arabian healthcare system. Transactional flow process in health economics needs to be defects free to insure an accurate healthcare delivery. This paper identifies and investigates two transactional flows within the health economics department. The anticipated outcome of this research paper is identification of two value streams and critical analysis of the Lean tools to improve the overall performance. The Publisher's final version can be found by following the DOI link. Open access publication
  • Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines
    Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines Aboutaleb, Adam; Kang, Parminder Singh; Hamzaoui, Raouf; Duffy, A. P. Flexible flow lines use flexible entities to generate multiple product variants using the same serial routing. Evaluative analytical models for the throughput rate of asynchronous serial flow lines were mainly developed for the Markovian case where processing times, arrival rates, failure rates and setup times follow deterministic, exponential or phase-type distributions. Models for non-Markovian processes are non-standalone and were obtained by extending the exponential case. This limits the suitability of existing models for real-world human-dependent flow lines, which are typically represented by a normal distribution. We exploit data mining and simulation modelling to derive a standalone closed-form formula for the throughput rate of normally distributed asynchronous human-dependent serial flow lines. Our formula gave steady results that are more accurate than those obtained with existing models across a wide range of discrete data sets. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers
    Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers Raju, Y.; Kang, Parminder Singh; Moroz, Adam; Clement, Ross; Hopwell, Ashley; Duffy, A. P. Accurate forecasting of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. This paper is an attempt to understand the cause for the high level of variability such as weather, holidays etc., in demand of SME wholesalers. Therefore, understanding the significance of unidentified factors may improve the forecasting accuracy. This paper presents the current literature on the factors used to predict demand and the existing forecasting techniques of short shelf life products. It then investigates a variety of internal and external possible factors, some of which is not used by other researchers in the demand prediction process. The results presented in this paper are further analysed using a number of techniques to minimize noise in the data. For the analysis past sales data (January 2009 to May 2014) from a UK based SME wholesaler is used and the results presented are limited to product ‘Milk’ focused on café’s in derby. The correlation analysis is done to check the dependencies of variability factor on the actual demand. Further PCA analysis is done to understand the significance of factors identified using correlation. The PCA results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The correlation of the above three factors increased relative to monthly and becomes more stable compared to the weekly and daily demand.
  • Discrete Event Simulation to Reduce the Effect of Uncertainties on Project Planning
    Discrete Event Simulation to Reduce the Effect of Uncertainties on Project Planning Kang, Parminder Singh; Aboutaleb, Adam; Duffy, A. P.; Embley, Tim; Glenn, Jame; Adams, Colin Planning is a vital decision making activity that influences the future of an organization by determining what tasks are to be performed, who required resources are and in what sequence. Organizations often follow a rigorous process to plan and deliver projects optimally based on the given resource and budget constraints. However, uncertainties increase the complexity of the planning process and contribute towards increased cost, delays and resource allocation issues. Therefore, it is important to understand the uncertainties and constraints associated with project activities and their effect on both business processes and organizational goals. Understanding the causal relationships between activities and constraints could allow organizations to operate more effectively and efficiently even in uncertain environments and lead to a more informed decision making process. This paper exemplifies the use of discrete event simulation tool to develop a strategically focused project delivery plan founded on the assessment of uncertainties that could arise during the delivery of the project. Proposed methodology follows a structured and systematic approach in order to identify the factors that can affect the delivery of the project and evaluate solutions that may mitigate or reduce the risk to As Low As Reasonably Practical (ALARP). The main objective is to complement the existing project planning activities rather than replace the existing tools.
  • Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization
    Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization Kang, Parminder Singh; Aboutaleb, Adam; Silva, Christopher Ugo; Duffy, A. P.; Erhart, Alexandre; Todeschini, Victor The quest for manufacturing process improvement and higher levels of customer satisfaction mandates that organizations must be equipped with advanced tools and techniques in order to respond towards ever changing internal and external customer demands by maintaining the optimal process performance, lower cost and higher profit levels. A manufacturing process can be defined as a collection of activities designed to produce a specific output for a particular customer or market. To achieve internal and external objectives, significant process parameters must be identified and evaluated to optimize the process performance. This even becomes more important to deal with fierce competition and ever changing customer demands. This paper illustrates an integrated approach using design of experiments techniques and discrete event simulation (Simul8) to understand and optimize the system dynamic based on operational control parameter evaluation and their boundary conditions. Further, the proposed model is validated using a real world manufacturing process case study to optimize the manufacturing process performance. Discrete event simulation tool is used to mimic the real world scenario, which provides a flexible and powerful way to comprehensively understand the manufacturing process variations and allows controlled 'What-If´ analysis based on design of experiments approach. Finally, this paper discusses the potential applications of the proposed methodology in the cable industry in order to optimize the cable manufacturing process by regulating the operational control parameters such as dealing with various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations.
  • Using Agent-Based Simulation to Investigate Daily Order Variation of a B2B Fresh Food Supplier
    Using Agent-Based Simulation to Investigate Daily Order Variation of a B2B Fresh Food Supplier Clement, Ross; Kang, Parminder Singh; Hopewell, Ashley; Duffy, A. P. Agent-based simulation has been used to simulate customers of a B2B fresh food supplier, in order to examine why total orders vary considerably on a day by day basis. Different types of virtual customers can be included in the simulation, ordering products using different strategies including their own demand prediction. This simulation suggests that customers changing the day of their order is the largest cause of daily order variance.
  • Knowledge Engineering Based Forecasting to Improve Daily Demand Prediction for Refrigerated and Short Shelf-Life Food Supply Chains
    Knowledge Engineering Based Forecasting to Improve Daily Demand Prediction for Refrigerated and Short Shelf-Life Food Supply Chains Kang, Parminder Singh; Clement, Ross; Hopewell, Ashley; Duffy, A. P.; Garicia-Taylor, Marilu The accuracy of demand forecasting for companies in the food industry is highly important, especially for those that deal with products that require refrigeration or that have short shelf-life, given the fact that the freshness and overall quality of the products offered can affect the profit margins for business and the health of the consumers (Doganis et al., 2006). Furthermore, Agrawal and Schorling (1996) as cited by Chen and Ou (2008) highlighted that having easy access to accurate and up-to-date information about demand forecasting is vital for any company aiming to maintain high levels of competitiveness in their market sector. This is even more important for fresh foods wholesalers, whose profit is directly affected by wasted or unsold products and unsatisfied customers (unfulfilled demand), especially when storage facilities are limited.
  • Classification and Clustering Approaches to Understanding Customer Ordering by Customers of a Fresh Food Supplier
    Classification and Clustering Approaches to Understanding Customer Ordering by Customers of a Fresh Food Supplier Clement, Ross; Kang, Parminder Singh; Duffy, A. P.; Hopewell, Ashley Purpose: This paper looks at characterization of B2B customers of a fresh food wholesale company supplying SME clients in terms of their weekly orders of a variety of fresh products. Customers whose orders can be predicted (days of the week order is placed, size of order) can easily be supplied without risk of waste due to the wholesaler ordering stock that is not sold to customers before it must be disposed of. Greater understanding of customer order patterns is necessary to improve demand prediction and reduce waste. Research Approach: Extensive real-world data from a fresh food wholesaler has been analysed in bulk. Customers’ weekly orders have been classified into one of nine classes depending on how each week’s order compares to the previous week. Equal order amounts on the same day (or days) of the week as the previous week are the most predictable class. Varying order amounts for orders placed on different days of the week are a much less predictable class. Other classes represent customers who either cease ordering after having made previous orders, or who place an order after not ordering in previous weeks. K-means clustering has also been used to extract clusters of customers showing similar ordering patterns from the customer base. These functions have been integrated into a data visualization tool which displays the clusters in terms of the frequency of occurrence of order classes, and their standard deviation within the clusters.

Click here to view a full listing of Dr Parminder Kang's publications and outputs.

Research interests/expertise

  • Application of Lean manufacturing techniques to high variety/low volume manufacturing environments and services operations.
  • Evolutionary algorithms based combinatorial optimization in Industrial/Service process improvement.
  • Applications of simulation and mathematical modelling.
  • Interdisciplinary research to provide solution for real world engineering problems; Evolutionary Algorithms, advanced database, Simulation and ICT approaches to solve real world problems; such as job sequencing, batch size optimization, planning and scheduling, buffer management.
  • Application of autonomous systems in manufacturing operations planning and scheduling.
  • Root Cause analysis based problem solving combined with multi-objective evolutionary optimization, especially in the highly variable environments.
  • Operations planning within industrial and service organizations, improving process competencies and autonomous decision making.
  • Automatic and optimum design of operational systems with particular emphasis on highly variable environments.
  • Application of my distinctive knowledge in a wide range of industrial sectors to address problems such as; theatre, healthcare, creative product design, manufacturing, logistics, distribution and materials processing.

Qualifications

  • Ph.D. in Manufacturing Science
  • M.Sc. Information Technology (Distinction)
  • B-Tech Computer Science and Engineering

Courses taught

  • ENGT5103 – Engineering Business Environment & Energy Studies
  • CSCI 1412 Computer Technology
  • TECH 2018 Multimedia and the Internet
  • TECH 1001 Electronic Production and Publishing
  • IMAT 5122 Computer Systems and Networks

Honours and awards

  • Preactor User Group Meeting Presentation; 18th June 2015; Topic: Autonomous Production Planning and Scheduling 
  • Presenter at 2014 Preactor User Group Meeting; 18 – 19th June 2014 (http://www.preactor.com/Home.aspx) Topic: Autonomous scheduling for industrial applications; http://www.reactivescheduling.com 
  • June 2009 – June 2012 -  De Montfort University, Leicester, UK -  Ph.D. Bursary Student
  • March 2012 -  De Montfort University, Leicester, UK – Laxton Travel Funding Award

Membership of professional associations and societies

  • Member of American Society of Mechanical  Engineers [ASME]
  • Member of Institution of Mechanical Engineers [MIMechE]
  • Member of Institution of Engineering and Technology [MIET]

Externally funded research grants information

CURRENT PROJECTS:

Project Title: Development of an Innovative Autonomous & Intelligent Demand Management Tool for Refrigerated and Shelf-Life Constrained Food Supply Chains (AIDMT)
Project Type: Collaborative R&D, 23792-161273
Role: Researcher/Day-to-Day Management

Project Title: Development of an Innovative Autonomous Model Development Tool for boosting Manufacturing Process Competencies (AMDT)
Project Type: Collaborative R&D, 18834-132285
Role: Researcher/Day-to-Day Management

FINISHED PROJECTS:

Project Title: Development of an Autonomous Systems Development Tool for application within Manufacturing Operations-Planning (ASDT)
Project Type: Collaborative R&D, 5908-45002
Role: Researcher and  Developer/Day-to-Day Management

Project Title: Achieving vertically-integrated carbon-fibre reinforcement design and manufacture demonstrators for structural manufacturing and construction 3-D composites (AVISC)
Project Type: Collaborative R&D, 13451-87159
Role: Researcher/Day-to-Day Management

Project Title: Development of Metadata-based Animation & Art Production Improvement Tool (MAAPIT)
Project Type: Collaborative R&D, 12160-76206
Role: Researcher/Day-to-Day Management

Project Title: Reducing Road Freight Empty Running (REFER) - R&D, TSB CB157C
Role: Researcher

Project Title: Accelerated Process Excellence using Virtual Discreet Event Simulation (VDEPS)
Project Type: Collaborative R&D, K1532G
Role: Researcher/ Programmer  

Project Title: Reducing Theatre Production Cost and Lead Times using Advanced Technology
Project Type: Collaborative R&D,AJ151K
Role: Researcher/ Programmer

Project Title: Improving Customer Demand and Cost Forecasting Methods
Project Type: Collaborative R&D, H0254E
Role: Programmer

Professional esteem indicators

  • Member of XI International Conference Committee on Computational Complexity and Intelligent Algorithms (ICCCIA 2014)
  • Member of The IEEE International Conference reviewing  committee on Industrial Engineering and Engineering Management (IEEM)
  • Reviewer of European journal of Operational Research, Journals of Computers and Operations Management and International Journal of Production Research.
  • Presenter at 2014 Preactor User Group Meeting; 18 – 19th June 2014 (http://www.preactor.com/Home.aspx) Topic: Autonomous scheduling for industrial applications
  • Preactor User Group Meeting Presentation; 18th June 2015; Topic: Autonomous Production Planning and Scheduling 

Case studies

1. Autonomous Finite Capacity Scheduling (AutoPlan) - DMU research boosts productivity for one of the world's biggest steel companies:

See more at: http://www.dmu.ac.uk/about-dmu/news/2015/february/dmu-research-boosts-productivity-for-one-of-the-worlds-biggest-steel-companies.aspx

 

Parminder-Kang

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