(655h) Quantitative Operability Analysis of Process Supply Chains | AIChE

(655h) Quantitative Operability Analysis of Process Supply Chains


Swartz, C. L. E. - Presenter, McMaster University
Wang, H., McMaster University

The North American forest products industry (FPI) is primarily commodity-based, and is facing challenges from low-cost competitors, leading to a sustained decline in demand and over-capacity of equipment (Thorp, 2005). As a consequence, capital and operational costs, as well as R&D activities have been reduced, resulting in obsolete pulp and paper mills. A possible strategy to improve the struggling business model is through revenue diversification, in which value-added specialty products are produced along with conventional pulp and paper products (Orzechowska, 2005). In order to achieve this, new process technologies need to be incorporated into existing pulp and paper mills, and essentially transform them into integrated forest biorefineries (IFBR). The obsolete mills can be retrofitted and transformed into more sustainable IFBRs to promote product variety. The additional capital cost incurred is justified by having improved profitability and greater robustness against market volatility (Wising and Stuart, 2006).

A key consideration in the transformed FPI paradigm is that product, process and supply chain designs have adequate flexibility and robustness to changes in market conditions. This is applicable both at the strategic design level, where market changes sustained over long periods are considered, as well as at the tactical/operation level, where responsiveness to shorter-term variability is required.

Process plants operate in a continually changing environment and are required to operate satisfactorily under sustained changes as well as short-term fluctuations. Operability of a chemical process reflects the ability of a system to perform satisfactorily under the conditions away from the nominal operating and/or design conditions (Grossmann and Morari, 1984). Two considerations of operability are flexibility and dynamic responsiveness.  Flexibility reflects the ability of a system to remain feasible in the face of sustained changes, while dynamic responsiveness refers to the ability to respond rapidly to short-term fluctuations. Systematic and quantitative analyses for process operability have been proposed (Grossmann and Morari, 1984; Swaney and Grossmann, 1985), as well as the incorporation of operability considerations in optimization-based plant design (Mohideen et al., 1996; Baker and Swartz, 2004).

Analogous to chemical processes, supply chain processes are required to satisfactorily meet customer demand or transition between operating policies rapidly to exploit external opportunities. The above considerations have motivated an investigation into an optimization based framework for the design of operable supply chains. A quantitative framework to analyze supply chain flexibility was developed and demonstrated through case studies (Wang et al.,  2013).  Quantitative measures of responsiveness have also been established and included in supply chain design and analysis (You et al., 2008; Mastragostino and Swartz, 2014).  The focus of the present work is to combine both flexibility and responsiveness considerations within a single supply chain optimization framework.  This is particularly useful within the IFBR context, in which demand variation in commodity and value-added products occurs at different frequencies.  Sustained uncertain demand levels in the former are handled through flexibility considerations, while supply chain dynamics are taken into account with consideration of short-term fluctuations in value-added product demand.

The proposed framework offers insights when examining the relations and trade-offs between flexibility, responsiveness, and profitability.  It can be applied not only to the design of new process supply chain networks, but also to existing supply chains for performance assessment based on economics and operability criteria. While motivated by FPI transformation, the methodology outlined is widely applicable to general process supply chain networks.


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