Demand Supply Network Research Articles

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a unique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks. Compared to the existing literature mainly concerned with the node connectivity, we focus on developing effective resource allocation methods to prevent these cascading failures from happening and to mitigate/confine them upon occurrence in the network. To prevent cascading failures, we identify some dangerous stress mechanisms, based on which we quantify the robustness of the network in terms of the resource configuration scheme. Afterward, we identify the optimal resource configuration under two resource/load fluctuations scenarios: uniform and proportional fluctuations. We further investigate the optimal resource configuration problem considering heterogeneous resource sharing costs among the nodes. To mitigate/confine ongoing cascading failures, we propose two network adaptations mechanisms: intentional failure and resource re-adjustment, based on which we propose an algorithm to mitigate an ongoing cascading failure while reinforcing the surviving network with a high robustness to avoid further failures.

Maximization of Robustness of Interdependent Networks Under Budget Constraints

We consider the problem of interlink optimization in multilayer interdependent networks under cost constraints, with the objective of maximizing the robustness of the network against component (node) failures. Diverting from the popular approaches of branching process based analysis of the failure cascades or using a supra-adjacency matrix representation of the multilayer network and employing classical metrics, in this work, we present a surrogate metric based framework for constructing interlinks to maximize the network robustness. In particular, we focus on three representative mechanisms of failure propagation, namely, connected component based cascading failure, load distribution in interdependent networks, and connectivity in demand-supply networks, and propose metrics to track the network robustness for each of these mechanisms. Owing to their mathematical tractability, these metrics allow us to optimize the interlink structure to enhance robustness. Furthermore, we are able to introduce the cost of construction into the interlink design problem, a practical feature largely ignored in relevant literature. We simulate the failure cascades on real world networks to compare the performance of our interlinking strategies with the state of the art heuristics and demonstrate their effectiveness.

Software Systems Approach to Multi-Scale GIS-BIM Utility Infrastructure Network Integration and Resource Flow Simulation

There is an increasing impetus for the use of digital city models and sensor network data to understand the current demand for utility resources and inform future infrastructure service planning across a range of spatial scales. Achieving this requires the ability to represent a city as a complex system of connected and interdependent components in which the topology of the electricity, water, gas, and heat demand-supply networks are modelled in an integrated manner. However, integrated modelling of these networks is hampered by the disparity between the predominant data formats and modelling processes used in the Geospatial Information Science (GIS) and Building Information Modelling (BIM) domains. This paper presents a software systems approach to scale-free, multi-format, integrated modelling of evolving cross-domain utility infrastructure network topologies, and the analysis of the spatiotemporal dynamics of their resource flows. The system uses a graph database to integrate the topology of utility network components represented in the CityGML UtilityNetwork Application Domain Extension (ADE), Industry Foundation Classes (IFC) and JavaScript Object Notation (JSON) real-time streaming messages. A message broker is used to disseminate the changing state of the integrated topology and the dynamic resource flows derived from the streaming data. The capability of the developed system is demonstrated via a case study in which internal building and local electricity distribution feeder networks are integrated, and a real-time building management sensor data stream is used to simulate and visualise the spatiotemporal dynamics of electricity flows using a dynamic web-based visualisation.

Multi-Partite Structure of Demand-Supply Network Element

In our recent article we investigate the utilization possibilities of usual time related, demand and supply data sets of an organization. We also create some others from those time related data sets, thanks to what we have the full set of links’ and nodes’ data for further analyses. We also evaluate some usual network analytical approaches to determine the characteristic structure of demand-supply network element (i.e. organization), the relevant network topology and sensitive to failure network skeleton.

Demand supply network opportunity development processes in emerging markets: Positioning for strategy realization in Russia

This paper aims to shed light on the management of supply chains, or rather, demand supply networks (DSN) in emerging markets by foreign companies. According to our definition, specific DSN opportunity development processes are directed towards better functionality and coordination of product, finance and information flows across the DSN. As a consequence, improved performance of the DSN may be achieved, in order to support and realize competitive business strategy and facilitate value creation for customers. To illustrate the concepts and the model, empirical evidence is presented that establishes the role of changing network structures and incumbent capabilities as constraints for DSN opportunity capture, especially in emerging markets. Our results show that DSN positioning strategies designed to overcome these constraints and facilitate opportunity capture, must be proactive, well-timed and innovative.

The IBM advanced planning system for managing next generation demand-supply networks

IBM formed a team in the mid 1990s to develop a next generation demand-supply matching system. Using advanced heuristic algorithms and Linear Programming (LP), this team built a comprehensive system comprising solutions covering the complete spectrum of Supply Chain Planning (SCP). This system's cutting edge innovations and tremendous business impact have generated dozens of intellectual properties and earned major awards in operations research achievement for IBM. Since 2005, this system has also become a daily solver at Analog Devices, Inc. IBM's system fully supports a long term, incremental deployment of advanced SCP functions whenever needed with minimum effort required.

Supporting Demand Supply Network Optimization with Petri Nets

The present study implements a generic methodology for describing and analyzing demand supply networks, that is, networks from a company’s suppliers to its customers. There can be many possible demand supply networks with different logistics costs for a product. Therefore, we introduce a Petri Net-based formalism, and a reachability analysis based algorithm that finds the optimum demand supply network for a user-specified product structure. The method has been implemented and is currently in production use inside all Nokia business groups. It is used in demand supply planning of both network elements and handsets. An example of the method’s application to a concrete Nokia product is included.

Investigating the effect of product variants, and demand distributions on the optimal demand supply network setup

In today's marketplace, consumers are looking for product variety and low prices. In this paper, we investigate how product variation and demand distributions affect demand supply network optimality in a real-life based setting. The research methodology was based on a novel Petri net formulation, which employs reachability analysis to demand supply network optimisation. We found that in our case study localised final assembly is justified with one or two product variants, but a centralised production should be favoured with three or more product variants. We also noted the sensitivity of this result to transport cost/inventory carrying cost tradeoff.

Demand planning approaches to aggregating and forecasting interrelated demands for safety stock and backup capacity planning

Results of demand planning serve as the basis of every planning activity in a demand–supply network and ultimately determine the effectiveness of manufacturing and logistic planning, such as capacity and safety stock planning, in the network. The uncertainty of demand signals that are propagated and magnified over the network becomes the crucial cause of ineffective operation plans. With the globalization of demand–supply networks and the desire for a more integrated operation plan, demand planning is now one of the greatest challenges facing manufacturers. To manage the demand variability, appropriate demand aggregation and statistical forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time-series model as a study vehicle to investigate the effects of aggregating two interrelated demands. It is shown that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, the paper further explores the properties of the aggregated time series and provides guidelines for practitioners to determine proper aggregation and forecasting approaches. An important finding of this research is that demand aggregation is far more effective than statistical forecasting in operations planning for any two demands with low positive correlation or negative correlation.

Planning nervousness in a demand supply network: an empirical study

PurposePlanning processes along a demand supply network in an environment characterized by rapid market fluctuations and product changes are studied. The relationship between demand planning and the bullwhip effect is investigated by comparing planning accuracy in different demand supply network echelons and locating where there is most nervousness.Design/methodology/approachThe current demand supply planning process flow was described based on interviews with key decision‐makers throughout the demand‐supply network from retailers to second tier suppliers. A data analysis of the quality of plans for demand and supply was generated in each decision‐making point by collecting planning and actual data of two products.FindingsThe results show that planning accuracy varies between the parties in the supply chain. The connection between planning nervousness and the bullwhip was investigated in detail through a vendor‐managed inventory (VMI) model in the chain. Planning nervousness causes bullwhip, as the changes in demand were amplified in the used information sharing process in VMI. In product introduction phase, the phenomenon was emphasized.Practical implicationsTo stabilize and simplify planning the process should be differentiated according to product life‐cycle phases. One proposal is to improve communication practices with suppliers, especially to stabilize demand information sharing with VMI‐suppliers.Originality/valueThe structure of the electronics supply chain makes planning processes challenging. In this research we were able to follow the data flow and planning process throughout the supply chain, which is not often the case.

Challenges for going downstream

Manufacturing companies are increasingly offering services related to the products that they supply and building capabilities to exploit better the installed base. This move downstream requires new skills to understand and manage the demand and supply networks of industrial services. However, not much research has been published on demand and supply management of such services. As it appears that leading edge companies are ahead of the academic work on industrial service provision, we approached the topic through an explorative study and interviewed representatives from five companies to determine their conceptions of the demand–supply network management of industrial services. The interviews showed that, although companies are providing industrial services, many aspects related to the supply and demand of the services are still poorly understood. This paper presents the issues raised by the company representatives that should be addressed with further research.

Learning in the new business school setting: a collaborative model

The claim is made that many of today’s evolutions in business management, characterized by new ways of knowledge creation in a collaborative, multi‐disciplinary environment, are not yet adequately reflected in business college education. The paper puts business education within the framework of demand chain management and supply networks, which will dictate modern business management. Secondly, the management process is revisited in terms of dynamic performance measurement and the link with double‐loop learning. Then the paper will go on discussing the multi‐functionality required and how this is (not) reflected in many business curricula. Finally, the author addresses the need to expose business students to modern business ERP (Enterprise Resource Planning), and collaborative commerce‐software interfaces, in conjunction with a critical analysis of the underlying methodologies used. Furthermore, the need to collaborate more closely within the business school setting and outside with business practitioners is highlighted.