Arief Adhitya

Decision support for integrated refinery supply chains: Part 1. Dynamic simulation

Supply chain studies are increasingly given top priority in enterprise-wide management. Present-day supply chains involve numerous, heterogeneous, geographically distributed entities with varying dynamics, uncertainties, and complexity. The performance of a supply chain relies on the quality of a multitude of design and operational decisions made by the various entities. In this two-part paper, we demonstrate that a dynamic model of an integrated supply chain can serve as a valuable quantitative tool that aids in such decision-making. In this Part 1, we present a dynamic model of an integrated refinery supply chain. The model explicitly considers the various supply chain activities such as crude oil supply and transportation, along with intra-refinery supply chain activities such as procurement planning, scheduling, and operations management. Discrete supply chain activities are integrated along with continuous production through bridging procurement, production, and demand management activities. Stochastic variations in transportation, yields, prices, and operational problems are considered in the proposed model. The economics of the refinery supply chain includes consideration of different crude slates, product prices, operation costs, transportation, etc. The proposed model has been implemented as a dynamic simulator, called Integrated Refinery In-Silico (IRIS). IRIS allows the user the flexibility to modify not only parameters, but also replace different policies and decision-making algorithms in a plug-and-play manner. It thus allows the user to simulate and analyze different policies, configurations, uncertainties, etc., through an easy-to-use graphical interface. The capabilities of IRIS for strategic and tactical decision support are illustrated using several case studies.

A model-based rescheduling framework for managing abnormal supply chain events

Enterprises today have realized the importance of supply chain management to achieve operational efficiency, cut costs, and maintain quality. Uncertainties in supply, demand, transportation, market conditions, and many other factors can interrupt supply chain operations, causing significant adverse effects. These uncertainties motivate the development of decision support systems for managing disruptions in the supply chain. In this paper, we propose a model-based framework for rescheduling operations in the face of supply chain disruptions. A causal model, called the composite-operations graph, captures the cause-and-effect among all the variables in supply chain operation. Its subgraph, called scheduled-operations graph, captures the causal relationships in a schedule and is used for identifying the consequences of a disruption. Rescheduling is done by searching a rectifications-graph, which captures all possible options to overcome the disruption effects, based on a user-specified utility function. In contrast to heuristic approaches, the main advantages of the proposed model-based rescheduling method are the completeness of solution search and flexibility of the utility function. The proposed framework is illustrated using a refinery supply chain example.

Decision support for integrated refinery supply chains: Part 2. Design and operation

Abstract Supply chain management has continually attracted much attention as companies are constantly looking into areas where they can cut costs and improve profit margin while maintaining customer satisfaction. Optimizing design and operation of the supply chain is vital for this purpose. Simulation models that capture the dynamics and uncertainties of the supply chain can be used to effectively conduct design and operation optimization studies. In Part 1 of this two-part paper, we proposed an integrated refinery supply chain dynamic simulator called Integrated Refinery In-Silico (IRIS). Here, we demonstrate the application of IRIS to provide decision support for optimal refinery supply chain design and operation based on a simulation–optimization framework. Three case studies are presented: identifying the optimal strategy to deal with supply disruptions, optimization of design decisions regarding additional capacity investments, and optimization of policies’ parameters. These decisions are optimized for two objectives: profit margin and customer satisfaction. The framework consists of a linkage between IRIS and a non-dominated sorting genetic algorithm, implemented in a parallel computing environment for computational efficiency. Results indicate that the proposed framework works well for supporting policy and investment decisions in the integrated refinery supply chain.

An Online Decision Support Framework for Managing Abnormal Supply Chain Events

Enterprises today have acknowledged the importance of supply chain management to achieve operational efficiency, and cutting costs while maintaining quality. Uncertainties in supply, demand, transportation, market conditions, and many other factors can interrupt supply chain operations, causing significant adverse effects. These uncertainties motivate the development of simulation models and decision support system for managing disruptions in the supply chain. In this paper, we propose a new agent-based online decision support framework for disruption management. The steps for disruption management are: monitoring the KPIs, detecting the root cause for the deviation of KPIs, identifying rectification strategies, finding the optimal rectification strategy and rescheduling operates as necessary in response to the disruption. The above framework has been implemented as a decision support system and tested on a refinery case study.

Decision support for green supply chain operations by integrating dynamic simulation and LCA indicators: diaper case study.

As the issue of environmental sustainability is becoming an important business factor, companies are now looking for decision support tools to assess the fuller picture of the environmental impacts associated with their manufacturing operations and supply chain (SC) activities. Lifecycle assessment (LCA) is widely used to measure the environmental consequences assignable to a product. However, it is usually limited to a high-level snapshot of the environmental implications over the product value chain without consideration of the dynamics arising from the multitiered structure and the interactions along the SC. This paper proposes a framework for green supply chain management by integrating a SC dynamic simulation and LCA indicators to evaluate both the economic and environmental impacts of various SC decisions such as inventories, distribution network configuration, and ordering policy. The advantages of this framework are demonstrated through an industrially motivated case study involving diaper production. Three distinct scenarios are evaluated to highlight how the proposed approach enables integrated decision support for green SC design and operation.

Performance analysis of a multi-plant specialty chemical manufacturing enterprise using an agent-based model

Modern day manufacturing enterprises consist of networks of worldwide production sites, each of which has its own supply chain. There are complex interactions between the decisions at various levels of such enterprises that lead to intricate dynamics. To make holistic decisions, it is necessary to measure and analyze performance of the enterprise and its constituents under various conditions. Such performance analysis calls for appropriate modeling and simulation tools. Agent-based modeling has been demonstrated as a promising approach for modeling such complex networks of distributed actors. In this paper, we demonstrate how an agent-based model can be developed to explicitly capture the interactions among the various constituents including the plants, functional departments, and external entities. As an illustrative case, an agent-based model of a lube additive manufacturing supply chain is introduced and the performance of the system studied under a significant range of behaviors, business policies, and environmental events.

Sustainability trends in the process industries: A text mining-based analysis

Sustainability is widely recognized as one of the most important challenges facing the world today. Companies publish sustainability reports that present their efforts and achievements in meeting sustainability goals and targets. In this paper, text mining is used to identify sustainability trends and practices in the process industries. Four main sectors of the industry are studied: oil/petrochemicals, bulk/specialty chemicals, pharmaceuticals, and consumer products. Our study reveals that the top sustainability focuses of the four sectors are very similar: health and safety, human rights, reducing GHG, conserving energy/energy efficiency, and community investment. Sector-specific sustainability issues have also been identified, for example oil spill prevention in the oil/petrochemicals sector and access to medicine in the pharmaceuticals sector. Environment is identified to be the predominant sustainability aspect in the process industries. The text mining methodology, results, and findings are detailed in the paper.

Quantifying the effectiveness of an alarm management system through human factors studies

Abstract Alarm systems in chemical plants alert process operators to deviations in process variables beyond predetermined limits. Despite more than 30 years of research in developing various methods and tools for better alarm management, the human aspect has received relatively less attention. The real benefit of such systems can only be identified through human factors experiments that evaluate how the operators interact with these decision support systems. In this paper, we report on a study that quantifies the benefits of a decision support scheme called Early Warning, which predicts the time of occurrence of critical alarms before they are actually triggered. Results indicate that Early Warning is helpful in reaching a diagnosis more quickly; however it does not improve the accuracy of correctly diagnosing the root cause. Implications of these findings for human factors in process control and monitoring are discussed.

Green Supply Chain Design and Operation by Integrating LCA and Dynamic Simulation

Abstract With sustainability increasingly becoming an important business factor, companies are now looking for methods and tools to help assess the fuller picture of the environmental impacts associated with their manufacturing and supply chain activities. Life cycle assessment (LCA) is a widely-used technique for measuring the environmental costs assignable to a product or service. However, LCA takes a high-level view and assumes a fixed supply chain structure and operation. It does not explicitly consider the effect of supply chain design and practices which can be a significant contributor to the overall environmental impacts. This paper presents an approach integrating LCA indicators and dynamic simulation for green supply chain design and operation. Environmental impact indicators are incorporated into a dynamic model of the supply chain along with profit and customer satisfaction, so that sustainability of various design and operational decisions can be assessed comprehensively. The application and benefits of the proposed approach are demonstrated using two case studies.

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.