Jeffrey Tew

Mutually Coordinated Visualization of Product and Supply Chain Metadata for Sustainable Design

In this paper, we present a novel visualization framework for product and supply chain metadata in the context of redesign-related decision scenarios. Our framework is based on the idea of overlaying product-related metadata onto interactive graph representations of a supply chain and its associated product architecture. By coupling environmental data with graph-based visualizations of product architecture, our framework provides a novel decision platform for expert designers. Here, the user can balance the advantages of a redesign opportunity and manage the associated risk on the product and supply chain. For demonstration, we present ViSER, an interactive visualization tool that provides an interface consisting of different mutually coordinated views providing multiple perspectives on a particular supply chain presentation. To explore the utility of ViSER, we conduct a domain expert exploration using a case study of peripheral computer equipment. Results indicate that ViSER enables new affordances within the decision making process for supply chain redesign.

A Framework for Visualization-Driven Eco-Conscious Design Exploration

A large portion of design activity involves applying previous design knowledge in order to solve new problems. Therefore, facilitating eco-conscious exploration of archived designs is needed for advancing sustainable product design. It is thus necessary to create integrated exploration tools that share common data representations for design and sustainability-related product metadata. This can allow designers to observe covariations in design data and develop engineering intuition with regards to environmental sustainability performance. In this work, we present a framework for relating sustainability and product metadata using taxonomy-based representations of lifecycle data. This facilitates simultaneous visualization of environmental indicators along with part similarities. To demonstrate this framework, we implement shapeSIFT, an interactive multidimensional visualization tool for eco-conscious design exploration. shapeSIFT uses a visual analytics-based approach to represent part metadata and environmental indicators. This facilitates query-based dynamic exploration of part repositories.

HOLA: Heuristic and opportunistic link selection algorithm for energy efficiency in Industrial Internet of Things (IIoT) systems

Internet of Things (IoT) promises to be a key enabler in Smart Manufacturing and Smart Supply Chain. The IoT systems are responsible for enabling and improving the operational efficiencies of factories, plant floors, including assembly plants. These systems are characterized by reliable sensing and reporting of multiple parameters within the factory floor. Such sensing activities offer safe, efficient and optimized performance of not only the machines manufacturing the products, but also the workforce operating them. Industrial IoT (IIoT) systems could suffer from high and uneven energy consumption due to the nature of the network deployment. Such behavior is unacceptable as it not only increases the carbon footprint of the plant, but also makes the planned maintenance of IoT devices for battery replacement a huge challenge. In this paper, we propose a heuristic and opportunistic link selection algorithm, HOLA, which not only reduces the overall energy consumption of the IoT network but also balances it across the network. HOLA achieves this energy-efficiency by opportunistically offloading the IoT device data to smart-devices being carried by the workforce in the factory settings. Further, these smart-devices with multiple radio links such as Bluetooth, Wi-Fi, and 3G/4G LTE heuristically determine the best link to transmit the data to the Cloud based on the quality and energy cost of the link. Our experimental and simulation studies validate that HOLA can improve the energy efficiency of IIoT systems by reducing the overall energy consumption and balancing it across the network.

Reducing inventory cost for a medical device manufacturer using simulation

Seeking to enter new geographic markets where expected margins are relatively tight, a manufacturer of medical devices must reduce inventory and related costs in its finished goods supply chain. The manufacturers supply chain includes four echelons - factories, distribution centers, regional salespeople (also known as “vans”), and customers. The amount of inventory typically held and corresponding reorder policies near the customer end of this supply chain are not known. A simulation approach was selected to provide insight into those inventory levels based on assumed reorder policies. Analysis conducted using a simulation model implemented using SimPy point to significant potential savings, with the value of inventory-related savings over a four year period approaching

Evaluating cost-to-serve for a retail supply chain

200 million.

D-PUF: An Intrinsically Reconfigurable DRAM PUF for Device Authentication and Random Number Generation

Driven by decreasing inventory storage space in stores and a corresponding need to increase delivery frequency, a major retailer is considering adding cross dock nodes, between distribution centers and stores, to its supply chain network. Currently, distribution centers serve stores directly. The retailer would like to understand if introducing an additional node allows for cost-effectively increasing delivery frequency. In the proposed scenario, the additional node would receive products from both the distribution center and upstream suppliers to serve the stores. Implemented as a discrete-event simulation, this cost-to-serve model compares the scenarios by applying costs to simulated logistics events and resource levels. Results suggest introducing new nodes is cost neutral, even considering the reduced transportation costs.

Supply chain immune system: concept, framework, and applications

Physically Unclonable Functions (PUFs) have proved to be an effective and low-cost measure against counterfeiting by providing device authentication and secure key storage services. Memory-based PUF implementations are an attractive option due to the ubiquitous nature of memory in electronic devices and the requirement of minimal (or no) additional circuitry. Dynamic Random Access Memory-- (DRAM) based PUFs are particularly advantageous due to their large address space and multiple controllable parameters during response generation. However, prior works on DRAM PUFs use a static response-generation mechanism making them vulnerable to security attacks. Further, they result in slow device authentication, are not applicable to commercial off-the-shelf devices, or require DRAM power cycling prior to authentication. In this article, we propose D-PUF, an intrinsically reconfigurable DRAM PUF based on the idea of DRAM refresh pausing. A key feature of the proposed DRAM PUF is reconfigurability, that is, by varying the DRAM refresh-pause interval, the challenge-response behavior of the PUF can be altered, making it robust to various attacks. The article is broadly divided into two parts. In the first part, we demonstrate the use of D-PUF in performing device authentication through a secure, low-overhead methodology. In the second part, we show the generation of true random numbers using D-PUF. The design is implemented and validated using an Altera Stratix IV GX FPGA-based Terasic TR4-230 development board and several off-the-shelf 1GB DDR3 DRAM modules. Our experimental results demonstrate a 4.3×-6.4× reduction in authentication time compared to prior work. Using controlled temperature and accelerated aging tests, we also demonstrate the robustness of our authentication mechanism to temperature variations and aging effects. Finally, the ability of the design to generate random numbers is verified using the NIST Statistical Test Suite.

Architecting Fail-Safe Supply Networks

ABSTRACT Behind every product and service is a supply chain. These supply chains are extremely complex and vulnerable to multifarious risks that threaten their performance and stability, ranging from simple machine or truck breakdowns to catastrophic natural disasters. This paper presents a novel framework for supply chain risk management based on the analogy of the biological immune system, which has multiple layers of increasing sophistication to neutralise a variety of risks. The various layers include: physical barriers, innate response, and adaptive response. The working of the framework is explained with an illustrative semiconductor supply chain. Applications of this framework include short-term disruption response and long-term immunisation, providing planner and manager level insights respectively.