Surya Dev Pathak

Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex Adaptive Systems Perspective*

Supply networks are composed of large numbers of firms from multiple interrelated industries. Such networks are subject to shifting strategies and objectives within a dynamic environment. In recent years, when faced with a dynamic environment, several disciplines have adopted the Complex Adaptive System (CAS) perspective to gain insights into important issues within their domains of study. Research investigations in the field of supply networks have also begun examining the merits of complexity theory and the CAS perspective. In this article, we bring the applicability of complexity theory and CAS into sharper focus, highlighting its potential for integrating existing supply chain management (SCM) research into a structured body of knowledge while also providing a framework for generating, validating, and refining new theories relevant to real-world supply networks. We suggest several potential research questions to emphasize how a CAS perspective can help in enriching the SCM discipline. We propose that the SCM research community adopt such a dynamic and systems-level orientation that brings to the fore the adaptivity of firms and the complexity of their interrelations that are often inherent in supply networks.

A holistic view of knowledge integration in collaborative supply chains

New product development (NPD) projects offer a unique opportunity to examine knowledge integration themes in a collaborative network. We propose a finer grained view of knowledge integration mechanisms in collaborative supply chains. Specifically, we propose two different types of mechanisms that are in play within a collaborative supply chain; short-term knowledge sharing and iterative knowledge enrichment. Using data from a large and diverse set of 432 NPD projects, we investigate the effects of knowledge sharing and enrichment between firms and their collaborative network partners, on product concept effectiveness and process performance. Our findings suggest that knowledge sharing and enrichment are significant mechanisms for enterprise-wide knowledge integration in collaborative networks. In addition, upstream knowledge sharing and enrichment has a significant influence on both product concept effectiveness and manufacturing process performance, over and above the effects of downstream knowledge sharing and enrichment. The relationship of upstream and downstream knowledge sharing and enrichment on product concept effectiveness and process performance had varying effects based on the contexts of product type, stage in the product life cycle and project size, indicating a context-specific influence of knowledge strategies. The implication of our results on academic theory and managerial practice is offered.

Modeling of supply chain: a multi-agent approach

Describes the development of an agent-based software system for assisting in decision-making regarding supply chain management and the efficient and effective use of electronic data interchange (EDI) in the automobile industry. Such a system can be applied to different types of industries with some domain-specific modifications. The core architecture is built around the concept of a software-based agent that is programmed internally to interact with other external agents in a pre-defined manner. We are developing a MIC (model-integrated computing) based supply chain management modeling environment. This environment allows domain experts to create models of the software agents to simulate and control the actual online negotiation processes. The modeling environment allows modeling of agent behavior, as well as defining agent-to-agent interaction scenarios.

Simulating growth dynamics in complex adaptive supply networks

This paper discusses an extended adaptive supply network simulation model that explicitly captures growth (in terms of change in size over time, and birth and death) based on Utterbacks (Utterback 1994) industrial growth model. The paper discusses the detailed behavioral modeling of the key components in the model with the help of statechart and decision tree representations. The design of a distributed, multiparadigm, agent-based simulation that addresses the issue of scalability and computational efficiency is presented. The system is targeted to run on a supercomputing grid infrastructure at Vanderbilt University. We present a method for validating this model using an experimental design that models the growth dynamics of the US automobile industry supply network over the past 80 years. The experimental work is now in progress and the results and analysis of this work are presented.

A framework for designing policies for networked systems with uncertainty

This paper presents a framework to design policies for networked systems. The framework integrates model building, stability analysis of dynamic systems, surrogate model generation and optimization under uncertainty. We illustrate the framework using a transportation network benchmark problem. We consider bounded rational users and model the network using software agents. We use Largest Lyapunov exponents to characterize stability and use Gaussian process model as an inexpensive surrogate, facilitating computational efficiency in policy optimization under uncertainty. We demonstrate scalability by solving a traffic grid policy design problem and show how the framework lends itself towards carrying out stability versus performance tradeoffs.

Topic modeling for management sciences: A network-based approach

Big data mining and unsupervised pattern recognition from large corpus of text-based documents has been an active research topic over the past decade. This paper presents a novel sequence of network-based models for identifying high-dimensional clustering patterns between topics for quantitative and predictive modeling of trends in Management Science (an INFORMS Journal) papers over the past 54 years. The proposed methods extrapolate a new spatial dimension from publication records to identify and assess topic inter-dependence and clustering trends over time. First, the optimal number of topics for trend analysis is identified based on spatial clustering patterns using Self-Organizing-Maps (SOM). Next, topic models are used to construct weighted and unweighted complex networks. Based on spatio-temporal clustering trends in the complex networks, the influence, importance and uniqueness of topics are quantified. Finally, the dynamic trends in topic influence are modeled for predictive purposes using Hidden Markov Models (HMM). The proposed methods provide insights into topic type co-existence patterns, topic type rankings, identify ∼40% topics as unique and predict topic importance with average accuracy per topic in the range of 79–84%. Thus, the proposed methods provide the apparatus to translate time-series text-intensive data sets to spatio-temporal models that can provide additional insights on data interdependencies and inter-data influences.

Sensing Abnormal Resource Flow Using Adaptive Limit Process Charts in a Complex Supply Network

Supply networks are becoming increasingly complex with multiple overlapping relationships between firms that may span across industries. Consequently, inventory management is becoming more difficult as managers have to cope with variability in the supply flows that originate from different parts of the network. Managers that quickly sense abnormal flows may intervene and adapt their inventory policies in response to system changes. In this article, we present a framework for sensing abnormal flows originating within the upstream supply network of a focal organization. Our framework combines time series modeling with process charts to identify abnormal flow patterns in the incoming supply streams. It is a flexible framework that uses off-the-shelf technology to provide managers with a process that can be employed for monitoring multiple individual or aggregated data streams originating within any complex system such as complex adaptive supply networks. We illustrate our framework on four years of longitudinal supply data from the second largest food bank in the United States. We identify multiple instances of abnormal supply flows and validate our results through rigorous inventory analysis as well as field-based expert interviews. We discuss the implications of our findings for inventory management in complex supply networks, both from academic and practitioner points of view