Sai Zeng

Using predictive analysis to improve invoice-to-cash collection

It is commonly agreed that accounts receivable (AR) can be a source of financial difficulty for firms when they are not efficiently managed and are underperforming. Experience across multiple industries shows that effective management of AR and overall financial performance of firms are positively correlated. In this paper we address the problem of reducing outstanding receivables through improvements in the collections strategy. Specifically, we demonstrate how supervised learning can be used to build models for predicting the payment outcomes of newly-created invoices, thus enabling customized collection actions tailored for each invoice or customer. Our models can predict with high accuracy if an invoice will be paid on time or not and can provide estimates of the magnitude of the delay. We illustrate our techniques in the context of real-world transaction data from multiple firms. Finally, simulation results show that our approach can reduce collection time up to a factor of four compared to a baseline that is not model-driven.

A Supplier Performance Evaluation Solution for Proactive Supplier Quality Management

Todaypsilas business environment forces companies to rely heavily on suppliers for products, services, and software. The increased reliance on suppliers and the growing complexity of the products, services and extended supply chain raises the importance of having an effective supplier quality management system. One of the keys to an effective supplier quality management system is the ability to provide an end to end supplier performance evaluation system.In this paper, we present a Supplier Performance Evaluation (SPE) solution which has been developed and commercialized that provides effective decision support for sourcing and supplier improvement. More specifically, this solution provides a real-time evaluation of supplier performance by consolidating information across functions, organizations, and various product lines throughout the product lifecycle. Its scoring function combines both subjective and objective metrics using data from surveys, engineering, manufacturing quality, warranty claims and other sources. An embedded alert management function helps to raise the timely awareness of quality issues related to suppliers, or supplied parts. Finally, a supplier performance evaluation example process is illustrated to showcase the usage of the solution.

Predictive modeling for collections of accounts receivable

It is commonly agreed that accounts receivable (AR) can be a source of financial difficulty for firms when they are not efficiently managed and are underperforming. Experience across multiple industries shows that effective management of AR and overall financial performance of firms are positively correlated. In this paper we address the problem of reducing outstanding receivables through improvements in the collections strategy. Specifically, we demonstrate how supervised learning can be used to build models for predicting the payment outcomes of newly-created invoices, thus enabling customized collection actions tailored for each invoice or customer. Our models can predict with high accuracy if an invoice will be paid on time or not and can provide estimates of the magnitude of the delay. We illustrate our techniques in the context of transaction data from multiple firms.

A Model-Driven Development Approach to Integrating Requirements, Design and Simulations in the Early Stages of Product Development

Decisions made in the early stages of the product development lifecycle have significant impact on the downstream activities. However, existing tools supporting decision-making and product verification at these stages are very limited. One of the obvious reasons is the lack of a common understanding between the system-level design activity and the design activities within the various participating engineering disciplines. In this paper, we propose a collaboration solution which we have developed and commercialized based on the model-driven development platform that allows numerous engineers from heterogeneous engineering disciplines to collaborate on the development of a complex system, such as an automobile. It helps engineers apprehend the system holistically and collectively thus make better architectural decisions. More specifically, this solution connects discipline-specific designs and simulations with the system-level requirements that trigger them in order to facilitate the integration of development efforts and to enable system-level evaluation of the design concepts early in the product development processes. Our approach provides an effective way to trace and analyze the impact of requirements and design changes, facilitates reuse of simulation artifacts for the optimization of future product designs, and supports decision-making activities at the system level. We illustrate our approach in the context of a automotive use case involving mechanical, requirement and safety engineers respectively using their own authoring environments but collectively in synch on the total system thanks to an SOA (Service-Oriented Architecture) based integration between their authoring environments.© 2007 ASME

System Data Management: An Inter-Disciplinary Collaboration Architecture for Systems Engineering

This paper presents a novel approach to integrating systems engineering (SE) artifacts and methods with discipline-specific detailed design artifacts and processes, for the purpose of facilitating inter-disciplinary collaboration. In particular, it addresses the lifecycle management of complex products involving mechanical, electrical, electronics and software aspects, and being designed following a formal product development methodology. The primary motivation of the approach is to capture and maintain the traceability between the concrete artifacts stored in discipline-specific “repositories” and the abstract artifacts used to support system-level decisions as well as system integration. The proposed approach acknowledges the fundamental differences that exist between the various engineering disciplines and therefore favors a loose coupling based on a process-centric management of the artifacts traceability links. These considerations lead to an inter-disciplinary collaboration and infrastructure pattern called “system data management” (SDM), with the role of enforcing the integrity of the inter-disciplinary traceability between artifacts. As a byproduct, this approach suggests a novel perspective on product data management (PDM) and software configuration management (SCM) integration that sharply contrasts with point-to-point integration solutions. The authors have implemented a prototype based on a service-oriented architecture (SOA) and existing PDM and SCM technologies.© 2005 ASME

An Evidence Based Analytical Approach for Process Automation: Case Study in Finance and Administration Process Delivery Services

Process Automation is a critical activity to reduce the need for human work in the production of goods and services to make the processes for uniform and efficient. It is always challenging to make informed decisions on areas for automation which addresses time-to-value to the business. In this paper, we present an evidence based analytics approach to identify top opportunities for process automation, and provide objective assessment of benefit to enable process leaders to take informed decisions. This approach is composed of three major steps. The first step is to identify the top hitters of human intensity in the delivery processes through analyzing evidence gathered from activity time-motion monitoring and onsite process deep dive. The second step is to prioritize the opportunities for automation by analyzing technology choices and estimated business impact. The final step is to assess the benefit through deep analysis on the additional evidence gathered on time-motion data on operational procedures through sampling and extrapolation, and degree of automation can be achieved through technology components. A case study in Finance and Administration Process Delivery Services is used to illustrate the core idea of our analytical approach.

A Service-Oriented Architecture for Interdisciplinary Product Design Workflow Choreography

Inter-disciplinary collaboration is critical to the success of processes employed in the development of complex products. A Workflow Management System (WMS) formalizes and automates product development process activities at various stages in the product lifecycle. Existing bridges between a WMS and the application systems involved in product development processes focus on intradisciplinary activities and static binding of artifacts to such activities. In this paper, we describe a novel SOA-based approach to bridge the gap between WMS and loosely-integrated domain-specific application systems focused on interdisciplinary process activities and dynamic binding of artifacts to activities. Our approach builds upon service-oriented inter-disciplinary collaboration architecture. Our solution consists of two components: System Data Management (SDM) and a template for integration between a WMS and the SDM. We illustrate our approach using an automotive use case for requirements verification.Copyright