A data and modelling framework for strategic supply chain decision-making in the petro-chemical industry.

[摘要] ENGLISH ABSTRACT: The research was initiated by an opportunity within the petro-chemical company Sasol to explore, improve and integrate various analytical techniques used in the modelling, design and optimisation of supply chains. Although there is already a strong focus on the use of analytical applications in this environment, the lack of both modelling integration and analytical data availability has led to less than optimal results.This document presents an exploration into the supply chain planning landscape, and inparticular strategic planning in the petro-chemical environment. Various modelling methodologies and techniques that support strategic supply chain decision-making are identified, followed by an in-depth analysis of the data requirements for effectively constructing each of these models.Perhaps the biggest hurdle in the continual use of modelling techniques that support strategic supply chain decision-making, remains the extent of the data gathering phase in any such project. Supply chain models are usually developed on an ad hoc project basis, each time requiring extensive data gathering and analysis from transactional data systems.The reason for this is twofold: 1) transactional data are not configured to meet the analyticaldata requirements of supply chain models, and 2) projects are often done in isolation,resulting in supply chain data that end up in spreadsheets and point solutions.This research proposes an integrated data and modelling framework, that aspires to thesustainable use of supply chain data, and continual use of modelling techniques to supportstrategic supply chain decision-making. The intent of the framework is twofold: 1) to enablethe design of new supply chains, and 2) to ensure a structured approach for capturing historical supply chain activities for continued review and optimisation.At the heart of the framework is the supply chain analytical data repository (SCADR), adatabase that maintains supply chain structural and managerial information in a controlleddata model. The motivation behind developing a database structure for storing supplychain data is that a standard encoding method encourages data sharing among differentmodelling applications and analysts.In the globalised environment of the 21•t century, companies can no longer ensure its marketposition solely by its own functional excellence ... in the new economy, whole business ecosystems compete against each other for global survival (Moore, 1996). This motivates theever-increasing importance of supply chain management, which necessitates the use ofadvanced analytical tools to assist business leaders in making ever more complex supplychain decisions.It is believed that the integration of information requirements for multiple optimisation/modelling initiatives in a structured framework (as presented in this research) will enablesustainability and improved strategic decision-making for the petro-chemical supply chain.

[发布机构] Stellenbosch University