From PSE to PSE²—Decision support for resilient enterprises

Abstract

In recent years, the process systems engineering (PSE) community has recognized the need to address chemical enterprises comprising globally distributed, but strongly interacting, facilities. We examine this extension of PSE, which we call the PSE of enterprise (PSE²), as it relates to the five traditional PSE areas of system representation, modeling and simulation, synthesis and design, planning and scheduling, and control and supervision. We illustrate the strong structural, operational, and methodological parallels between PSE and PSE² in this study.

Introduction

A typical enterprise in the chemical industry comprises a network of production and inventory facilities (including distribution centers). The addition of raw material suppliers, third-party logistics service providers (3PLs), product distribution centers, and/or customers completes its supply chain. In principle, the entities of an enterprise could belong to multiple supply chains, where each supply chain is for one or more of the products that the enterprise produces. To keep things simple, in this paper, we use the term “enterprise” loosely to mean only the subset of entities that are in a single supply chain of interest. As materials flow from the suppliers to the customers via the various facilities in a supply chain, finances flow in the opposite direction, and information flows in both directions.

Managing an enterprise requires a host of strategic, tactical, and operational decisions related to sourcing, manufacturing, storage, logistics, and distribution. These decisions may occur at various levels such as enterprise, zone/region, site, plant, etc., but they are all aimed towards the goal of ensuring the success of the enterprise such as maximizing its shareholder value. However, the advent of globalization is making supply chain management more and more complex. As new markets open up, existing markets saturate and new sources of raw materials and demands arise, enterprises locate new facilities (production, storage, distribution, etc.), shut down old ones, identify new business partners, enter into new alliances, learn to operate with new rules, regulations, and cultures, etc. Clearly, the result is an increasingly ever-changing business environment, where the aforementioned factors add to the inherent complexities of managing a business and further exasperate optimal decision-making. These supply chain complexities result in a heightened exposure to various operational risks and disruptions that arise from uncertainties in day-to-day operations such as variations in supply, demand, production, transportation, and cost, as well as from natural or man-made adverse events such as earthquakes and terrorist attacks. Adequate risk management is critical to ensure the continuity of profitable operations. Against this backdrop, the most important challenge facing enterprises is the science of extracting timely wisdom (or optimal decisions) from the mountains of instantaneous data.

While most decisions in a chemical supply chain are in principle similar to those in a non-chemical supply chain, several distinguishing features and issues are noteworthy. In contrast to other supply chains, such as automobile, electronic, general retail, etc., the concepts of “discrete parts” and “assembly” are alien to chemical manufacturing. Chemical supply chains involve a huge variety of non-discrete, immiscible, often incompatible and non-substitutable, and huge-volume materials, each of which has its own unique characteristics. The industry is the largest consumer of its own products and many of its businesses are high-volume and low-margin. In addition, downstream plants may supply raw material to upstream plants. Huge inventories that are so critical to continuity and profitability; need for safety-first; high sensitivity to energy prices, sociopolitical uncertainties, environmental regulations; and extensive trading are the other key features of chemical supply chains, which easily set them apart from other supply chains. Furthermore, maritime transport is the workhorse of chemical supply chains and the hazardous nature and huge volumes of chemicals necessitate the use of highly expensive and sophisticated transport equipment and storage facilities, which require complex and expensive cleaning procedures and maintenance, and result in long lead times. The logistics costs in chemical supply chains are the highest among all asset-intensive supply chains (12% of revenues for chemicals vs. 10% for pharmaceuticals and 9% for automotive) and can be as high as 20–30% of the purchase cost (Karimi, Srinivasan, & Han, 2002).

Interestingly, strong structural and operational similarities exist between a chemical process and a chemical supply chain, which have led the PSE community to extend (Grossmann, 2005) its interest and methodologies to supply chain management in recent years. Several recent reviews on various aspects in supply chain management exist in the literature. McDonald and Reklaitis (2004) presented a comprehensive review on the design and operation of supply chains with a greater emphasis on financial strategies. They discussed several issues such as international tax planning, means of financing, transfer pricing, and uncertainties. In addition, they discussed various modeling and solution strategies such as simulation–optimization, real options, multi-stage models, among others. Sahinidis (2004) reviewed optimization under uncertainty, which is an inherent complexity in supply chain decision-making. Shah (2005) reviewed opportunities in process supply chains. Srinivasan, Karimi, and Vania (2006b) presented opportunities for PSE in decision support for the chemical industry. In this work, we demonstrate that the broad set of concepts that form the bedrock of PSE in the context of traditional chemical processes can be naturally extended to the realm of enterprises.