Ignacio E. Grossmann

R. R. Dean University Professor, Department of Chemical Engineering, Carnegie Mellon University






A Digital Twin Framework for Business Transactional Processes in Digital Supply Chains

We present an integrated framework for building a virtual replica of the business transactional processes in a supply chain. The framework contains two main modules: a simulation module and an optimization module. In the simulation module, the business processes are modeled as networks of queues through which requests (either internal or external to the enterprise) can flow. The digital replica of the business processes creates value by providing a simulation platform upon which to 1) test optimization strategies, 2) forecast potential delays in requests based on the current state of the real process and the historical data, 3) identify and mitigate bottlenecks, and 4) provide more accurate fulfillment dates to customers. Opportunities for the optimization of business processes exist in both their design and their operation. The integration between simulation and optimization can be performed sequentially (e.g. optimize first and then apply to the simulations) or in real-time (e.g. run optimization algorithms in a feed-back loop as the simulation is being executed). As an integrated simulation and optimization environment, the framework bridges and extends the literature in business process simulation and business process optimization, building upon previous work by the authors that was restricted to only business process scheduling.

The proposed framework is developed in the Julia programming language, which brings the advantages of using several simulation and optimization libraries available within the Julia environment. These include discrete event simulation via SimJulia and mathematical optimization via JuMP. As an integrated platform, the framework minimizes the need for creating interfaces between different programming languages and databases required for simulation and optimization platforms. The framework is general in that it allows building simulation and optimization models for business process based on four key inputs: 1) the network structure of the activities in the business process, 2) the assignment of resources to activities, 3) the resource capacities, and 4) the probabilistic distributions of the processing times for each activity. The latter can be specific to the resource type, order type, and customer. An case study is shown in which the capabilities of the framework are showcased.


Ignacio E. Grossmann is the R. R. Dean University Professor in the Department of Chemical Engineering, and former department head at Carnegie Mellon University. He obtained his B.S. degree at the Universidad Iberoamericana, Mexico City, in 1974, and his M.S. and Ph.D. at Imperial College in 1975 and 1977, respectively. He is a member and former director (2005-2015) of the “Center for Advanced Process Decision-making,” an industrial consortium that involves about 20 petroleum, chemical, engineering, and software companies. He is a member of the National Academy of Engineering, and associate editor of AIChE Journal. He has received the following AIChE awards: Computing in Chemical Engineering, William H. Walker for Excellence in Publications, Warren Lewis for Excellence in Education, and Research Excellence in Sustainable Engineering. In 2015 he was the first recipient of the Sargent Medal by the IChemE. He has honorary doctorates from Abo Akademi in Finland, University of Maribor in Slovenia, Technical University of Dortmund in Germany, University of Cantabria in Spain, and from the Russian Kazan National Research Technological University. He has been named Thomson Reuters Highly Cited Researcher in 2014-2016. He has authored more than 500 papers, several monographs on design cases studies, and the textbook Systematic Methods of Chemical Process Design, which he co-authored with Larry Biegler and Art Westerberg. He has also organized the Virtual Library on Process Systems Engineering. Grossmann has graduated 56 Ph.D. and 11 M.S. students.