(614e) Operability Considerations In Process Supply Chain Design for Forest Industry Transformations

The forest products industry (FPI) is primarily commodity based and facing challenges as a result of global competition and a sustained reduction in demand of key sector products.  This has led to proposals for a shift towards revenue diversification of existing pulp and paper mills, through the production of biofuels or high-value specialty products alongside conventional commodity products.  The strategy will result in the transformation of existing pulp and paper mills into integrated forest biorefineries (IFBR), which implies an increase in capital expenditure compensated for by the ability for plants to manufacture high value products.  A key consideration within this new business paradigm is that the process and supply chain have sufficient capability to be resilient against market volatilities such as demand, supply or price fluctuations.  The present research draws on concepts of operability analysis associated with process plant operation and design.          

Process plants operate in a continually changing environment and are required to operate satisfactorily under sustained changes as well as short-term fluctuations. Process plant operability reflects the ability of a system to  function satisfactorily under the expected range of process conditions.  Two notions of operability are flexibility and dynamic operability.  Flexibility reflects a system's ability to remain feasible in the face of sustained changes, while dynamic operability refers to a system’s ability to respond rapidly to short-term fluctuations.  Systematic, quantitative approaches for flexibility and dynamic operability analysis of process plants have been proposed (Grossmann and Morari, 1983), as well as the incorporation of operability considerations in optimization-based plant design (Mohideen et al., 1996; Baker and Swartz, 2004; Sakizlis et al., 2004).

The above considerations have motivated an investigation into an optimization based framework for the design of operable supply chains.  Notions of operability that are commonly used within a supply chain context include lead time, order fill rate, on-time deliveries and delivery flexibility.  A comprehensive review of supply chain performance metrics is given in Beamon (1999).  Of note is that agile supply chains (Christopher and Towill, 2001) include elements of both static and dynamic operability.  In a recent study, You and Grossmann (2008) developed a framework which allows for network design, within a bi-criterion optimization, that considers both net present value and lead time of the supply chain design.  In an earlier study, Sabri and Beamon (2000) developed a multi-criterion optimization framework for supply chain planning which considers net present value, customer service level, and distribution and plant flexibility.  

The present study considers three key objectives:  (i) development of an optimization based framework to quantify the operability of a process supply chain design, (ii) utilization of the operability metrics in an optimization framework for the design of operable process supply chain systems, and (iii) development of appropriate case studies which are relevant to the forest products industry.  The literature on biorefinery supply chain optimization is primarily concerned with high level strategic decisions.  However, of relevance to the present study is the work of Eksioglu et al. (2009) who developed an optimization framework that captures deterioration, supply seasonality and supply availability of the biomass-to-biorefinery supply chain.  The supply chain model developed in our study can be characterized by time discretization at the operational level to capture response dynamics, flexible manufacturing plants, manufacturing and transportation delays and uncertain demand patterns.  A goal of the current study is to develop a tool that can be used to investigate the long term sustainability of new business paradigms within the forest products industry.  The paper will present the context of the study in relation to developments within the forest products industry, draw parallels between process plant and supply chain operability, and present an optimization-based framework for operability analysis of process supply chains.   Preliminary results will be presented, and insights regarding the relationship between effective revenue diversification and operability will be discussed. In addition, key challenges and future research directions will be identified.

References:

Baker, R. and Swartz, C.L.E. (2004). Simultaneous solution strategies for inclusion of input saturation in the optimal design of dynamically operable plants. Optimization and Engineering, 5, 5-24.

Beamon, B.M. (1999). Measuring supply chain performance. International Journal of Operations and Production Management, 19, 275-292.

Christopher, M. and Towill, D. (2001). An integrated model for the design of agile supply chains. International Journal of Physical Distribution and Logistics Management, 31, 235-246

Eksioglu, S.D., Acharya, A., Leightley, L.E. and Arora, S. (2009). Analyzing the design and management of biomass-to-biorefinery supply chains. Computers and Industrial Engineering, 57,1342–1352.

Grossmann. I.E. and Morari, M. (1983).  Operability, resiliency and flexibility – Process design objectives for a changing world.  Proc. Int. Conf. on Foundations of Computer-Aided Process Design, A.W. Westerberg and H.H. Chien, eds., 931-1030.  

Mohideen, M.J., Perkins, J.D. and Pistikopoulos, E.N. (1996). Optimal design of dynamic systems under uncertainty. AIChE Journal, 42, 2251-2272.

Sabri, E.H. and Beamon, B.M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28, 581-598

Sakizlis, V., Perkins, J.D. and Pistikopoulos, E.N. (2004). Recent advances in optimization-based simultaneous process and control design. Comp. Chem. Eng., 28, 2069-2086.

You, F. and Grossmann, I.E. (2008). Design of responsive supply chains under demand uncertainty. Comp. Chem. Eng., 32, 3090–3111.