Qinhua Wang

Supply Chain Finance

In an era of globalization, it is almost impossible for a company to stay competitive with its supply chain locating in a single country ( A. Sabbaghi & N. Sabbaghi, 2004 , Global supply-chain strategy and global competitiveness. International Business & Economics Research Journal, 3 (7), 63–76). On the other hand, increasing global sourcing and selling brings substantial challenges on the material flow, information flow, and financial flow along lengthened supply chains. Many researches in the past decades have illustrated the benefits of supply chain visibility by streamlining information flow ( Eppen, 1979 , Effects of centralization on expected costs in a multilocation newsboy problem. Management Science, 25 (5), 498–501; Lee & Whang, 2000 , Information sharing in a supply chain. International Journal of Manufacturing Technology and Management, 1 (1), 79–93; Vickery, Jayaram, Droge, & Calantone, 2003 , The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships. Journal of Operations Management, 21 (5), 523–539; Zhang & Zipkin, 1990 , A queueing model to analyze the value of centralized inventory information. Operations Research, 38 (2), 296–307). It is well known that an informational centralized supply chain outperforms its decentralized counterpart through reducing bullwhip effect (Chen, Drezner, Ryan, & Simchi-Levi, 2000, Qualifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46 (3), 436–443; Lee, Padmanabhan, & Whang, 1997 , Information distortion in a supply chain: The bullwhip effect. Management Science, 34 (4), 546–558).

Financial supply chain management

To effectively manage a supply chain, we should no longer focus on physical flow and informational flow only, but also the financial flow. The financial supply chain management (FSCM) extracts capital tied to the raw material, work-in-process, inventory in transit, and accounts receivable (accounts payable). In this paper we review a set of supply chain financing solutions tailored by the financial institutions, and introduce a few quantitative works from the operations research society. The survey reveals the benefits of actively managing the financial supply chain and highlights the quantitative decision-making process in FSCM. Moreover, we propose several interesting problems in FSCM which are remained to be explored.

Towards a flexible business process modeling and simulation environment

Business process is crucial to the success of any business. Business process modeling enables a common understanding and analysis of a business process, and simulation is an effective way to diagnose and evaluate complex business processes. There are lots of software tools in market for business process modeling and simulation, however, a common issue for these tools is the conflict between usability and flexibility. The improvement of usability often means the reduction of flexibility. This paper introduces an IBM asset named supply chain process modeler (SCPM), which aims at providing a tailored business process modeling and simulation environment for business users. SCPM tries to achieve a better trade-off between usability and flexibility with two major efforts, a process repository for process modeling, and a flexible environment for process simulation. A case study from real consulting practice is also included to illustrate how SCPM works for business process simulation.

An innovative stochastic dynamic model to three-stage supply chain finance

In this paper, we formulate a stochastic dynamic program for the three-stage supply chain finance problem from the perspective of banks. The objective is to maximize the expected income for the bank under three types of risks, i.e., suppliers credit risk, buyers credit risk and inventory-in-transit risk. We contribute to the literature with an innovative mathematical model of supply chain finance problem, which is an emerging and eye-catching business itself. Solving for optimal policy of our problem will help banks tradeoff income against risks.

The Internet of Things as an Enabler to Supply Chain Innovation

The Internet of Things (IoT) connects physical world to the cyber-world, which may impose predicable and unpredictable changes to enterprise business models and our everyday life. In this paper, we propose a model to describe the impact of IoT on the material, information and capital flowas well as carbon footprint through the supply chain, and further support the model with possible business scenarios. Besides, we raise several issues that may interest scholars and practitioners.

A solution for reallocating public bike among bike stations

Public bike system for proximity travel is widely used in urban cities all over the world. Main reasons for launching public bike program is to reduce carbon footprint so as to prevent greenhouse effect and to solve Last-Mile problem in order to make convenient for citizens. However, we may encounter awkward cases in peak hours (from 7:00 to 9:00 and 17:00 to 19:00) that no bike is available at some stations or no vacant place is left to put bikes. The actual solution in most cities is in a traditional way based on call centers. A personnel taking charge of bikes in a certain zone reallocates bikes among bike stations based on notification of bike users by telephone call when bike stations are full or empty. This inefficient solution will reduce bike utilization and even annoy bike users. In this paper, we propose a system & methodology to intelligently guide personnel to reallocate bikes among stations, aiming at overcoming the above awkward cases and maximizing bike utilization.

Big data fueled process management of supply risks: sensing, prediction, evaluation and mitigation

Supplier risks jeopardize on-time or complete delivery of supply in a supply chain. Traditionally, a company can merely do an ex-post evaluation of a suppliers performance, and handles emergencies in a reactive rather than a proactive way. We propose an agile process management framework to monitor and manage supply risks. The innovation is two fold - Firstly, a business process is established to make sure that the right data, the right insights, and the right decision-makers are in place at the right time. Secondly, we install a big data analytics component, a simulation component and an optimization component into the business process. The big data analytics component senses and predicts supply disruptions with internally (operational) and external (environmental) data. The simulation component supports risk evaluation to convert predicted risk severity to key performance indices (KPIs) such as cost and stockout percentage. The optimization component assists the risk-hedging decision-making.

A Lagrangean-based heuristic for a two-stage facility location problem with handling costs

This paper considers a multi-product and two-stage facility location problem with three layers of nodes: plants with limited production capacities, capacitated DCs to be located, and customers with known demands per product. The goal is to minimize a global cost including DC opening costs, transportation costs, and handling costs. The latter are not simply proportional to the amounts of products traversing DCs. They are modeled in a realistic way by associating each cross-docking task occurred at DCs with a limited set of handling modules. Each module for some task is a combination of manpower and equipment with a handling capacity and a handling cost, for instance a set of forklifts with their drivers to accomplish unloading task. The handling cost of a module is incurred as soon as the module is used, even for a fraction of its handling capacity. In order to obtain good lower and upper bounds, an heuristic based on Lagrangean relaxation is designed. The computational results show that our approach provides high quality solutions and outperforms CPLEX.

Integrate ERP system into business process management system

Enterprise resource planning (ERP) systems present a holistic view of the business from single information and IT architecture. But ERP systems are comprehensive, huge and complex systems and warrant careful planning and execution not ensure the successful implementation. A business process is set of interrelated activities which have definable inputs result in output that adds value from a custom perspective. Business Process Management (BPM) is a structured approach to understand, analyze, support, and continuously improve fundamental process. In order to improve the success rate of ERP in project implementation, enterprises often implement BPM before or during the ERP implementation. In this paper, a framework for integration ERP system into BPM system is proposed. The framework closes the gap between ERP system and BPM system, enables full life-cycle of process centric enterprise system implementation.

Towards automatic incremental synchronization of multiple process models via information flow optimization

Process model is one of the foundation stones of business process management(BPM). In a lot of industrial practices of BPM, we encounter the requirements to synchronize process models. Although there have already been many research initiatives and mature solutions for two-model synchronization, multi-model synchronization is still a difficult question. Major challenges include how to define multi-model synchronization semantics and how to propagate changes across the multiple models in order to realize incremental synchronization. This paper proposes a solution in which the modification propagation is considered as information flow. After defining synchronization semantics, we work out a propagation scope for each tracked model change. Then an algorithm is designed to generate an propagation plan for all model changes, with the objective to optimize the information flow. The multiple process models get synchronized after all modifications are propagated according to the plan. A case from an industrial practice is used to demonstrate the semantics and algorithm of incremental synchronization of multiple process models.