Calls for papers
International Journal of Logistics Systems and Management
Special Issue on: "Modelling Supply Chain Planning Problems by Integrating Data Analysis and Optimisation Techniques"
Dr. P. D. D. Dominic, Universiti Teknologi Petronas, Malaysia
Dr. P. Parthiban, National Institute of Technology, India
Prof. Mark Goh, University of South Australia, Australia
Today’s corporations are struggling with their supply chains. Supply base globalisation on the one hand, and product/channel diversification on the other, mean that supply chains are now more complex than ever. Many companies report rising inventory levels and increased service pressure at the same time as they are impacted by rising fuel and commodity costs.
A continuous quest for improved performance is the linchpin of success in many best-in class organisations. These organisations exhibit proven ability to strive and excel in all spheres of activities. These activities cut across all of the organisations’ functions and processes stretching from sourcing to customer service. Otherwise known as supply chain, “excellent design and efficient execution of these activities and processes” charts a company’s success. Excellent supply chain management helps leading companies around the world achieve better service, lower costs, lower inventory, and ultimately competitive advantage. We need breakthrough success enabled through efficient supply chain planning processes. There is a need for designing, modelling and executing the various supply chain planning issues and problems.
Supply chain planning comprises five top elements such as demand planning and forecasting, sales and operations planning, inventory planning, production planning and logistics planning. Facilitating the planning functions based on the transformation process from data to information to knowledge is a supreme concern for every supply chain. Many organisations are being swamped with data and volumes of contradictory information, but with limited real usable knowledge.
Statisticians focus on data accuracy, database administrators emphasise data completeness and operation researchers target optimisation. But the integration of the above is missing. So, current isolated islands of data analysis and optimisation techniques should be connected, and an integrated and systematic union of data analysis and optimisation techniques for modelling supply chain planning problems is a current need. The objective of this special issue is to address this need.
The integration of data analysis and optimisation techniques can be used to assist decision-makers at all management levels with three levels of supply chain planning problems:
- Strategic management level: supply chain network planning, design and optimisation
- Tactical management level: collaborative demand planning and forecasting and replenishment planning and coordination planning
- Operational management level: production planning, routing of products and vehicles by logistics planning and organisation of returns and services
Suitable topics include but are not limited to:
- Modelling systems which employ integration of data analysis and optimszation techniques in order to provide decision support for
- Strategic supply and distribution network planning
- Demand planning and forecasting
- Inventory planning and optimisation
- Transportation and logistics planning and optimisation
- Sales and operations planning
- Purchasing and procurement
- The data analysis techniques can include
- Multi-variate analysis
- Multi-criteria decision making analysis
- The optimisation techniques can include
- Mathematical modelling
- Data mining in globalised supply chain
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper was not originally copyrighted and if it has been completely re-written).
All papers are refereed through a peer review process. A guide for authors, sample copies and other relevant information for submitting papers are available on the Author Guidelines page.
Submission deadline: 30 October, 2012