(648d) Scheduling of Business Transactional Processes in a Digital Supply Chain | AIChE

(648d) Scheduling of Business Transactional Processes in a Digital Supply Chain


Perez, H. - Presenter, Carnegie Mellon University
Amaran, S., The Dow Chemical Company
Erisen, E., Dow Inc.
Wassick, J., The Dow Chemical Company
Grossmann, I., Carnegie Mellon University
There is a need for a holistic approach towards supply chain optimization, involving material, information, and financial flows. Up to this point, the focus on supply chain optimization has been primarily on material flows (raw materials, intermediates, and products) [1] [2] [3]. Some studies have shown that failing to integrate material flows with other flows (i.e. financial ones) leads to suboptimal or even infeasible supply chain operation [4] [5]. Following this pattern, it is expected that integrating information flows should result in improved supply chain performance. However, both modeling and integrating information flows have received little if any attention by the optimization community. These flows, which are captured in Enterprise Resource Planning (ERP) systems and structured as business processes, are essential for supply chain performance. We aim at modeling these business processes mathematically and optimize from an operational standpoint. To the knowledge of the authors, the approach presented here is novel as previous work has not been reported for optimizing the operations of business transactional processes with supply chains. Thus, the work presented paves to way for integrating business processes in supply chain optimization.

The Order-To-Cash business process is modeled as a scheduling problem where human and automated agents process orders from the moment a customer places an order until the goods are delivered to the customer and invoice payment is received. Five Mixed-Integer Linear Programming (MILP) scheduling models are applied to the OTC process: 1) general precedence model, 2) queue slot model, 3) continuous-time Resource-Task Network (RTN) model, and discrete-time 4) RTN and 5) State-Task Network (STN) models [3]. The novelty of this approach is in using techniques from the Process Systems Engineering (PSE) community to model and optimize information flows within business processes for supply chain operations. The models account for the allocation of human resources in processing orders in the OTC process. The optimization objective is to improve company profit and customer experience by increasing order fulfillment and reducing backlogs. Three case studies are presented to compare the performance and scaling of the five scheduling models. The discrete-time STN shows the best performance in terms of scaling, scheduling up to hundreds of orders in a deterministic OTC system. The results show the promise of using mathematical programming to improve supply chain performance by optimizing business processes. The models also allow to identify bottlenecks in the business processes and determine where additional resources should be allocated. Furthermore, the models can be used as valuable tools to assist customer service representatives in defining realistic promise-to-delivery dates for their customers.


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