scholarly journals Graph Database to Enhance Supply Chain Resilience for Industry 4.0

2022 ◽  
Vol 15 (1) ◽  
pp. 0-0
Keyword(s):  
Supply Chain ◽  
Data Analysis ◽  
Inventory Management ◽  
Industry 4.0 ◽  
Query Language ◽  
Graph Model ◽  
Modeling Framework ◽  
Supply Chain Resilience ◽  
Graph Data ◽  
Wide Range

Supply chain network in the automotive industry has complex, interconnected, multiple-depth relationships. Recently, the volume of supply chain data increases significantly with Industry 4.0. The complex relationships and massive volume of supply chain data can cause visibility and scalability issues in big data analysis and result in less responsive and fragile inventory management. The authors develop a graph data modeling framework to address the computational problem of big supply chain data analysis. In addition, this paper introduces Time-to-Stockout analysis for supply chain resilience and shows how to compute it through a labeled property graph model. The computational result shows that the proposed graph data model is efficient for recursive and variable-length data in supply chain, and relationship-centric graph query language has capable of handling a wide range of business questions with impressive query time.

scholarly journals A Model of Cytotoxic Dose-Response Nonlinearities Arising from Adaptive Cell Inventory Management in Tissues

Dose-Response ◽  
2005 ◽  
Vol 3 (4) ◽  
pp. dose-response.0 ◽  
Author(s):  
Louis Anthony (Tony) Cox
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Stem Cell ◽  
Inventory Management ◽  
Decision Rules ◽  
Error Rates ◽  
Average Error ◽  
Cytokine Network ◽  
Modeling Framework ◽  
Wide Range

Why do low-level exposures to environmental toxins often elicit over-compensating responses that reduce risk to an organism? Conversely, if these responses improve health, why wait for an environmental challenge to trigger them? This paper presents a mathematical modeling framework that addresses both questions using the principle that evolution favors tissues that hedge their bets against uncertain environmental challenges. We consider a tissue composed of differentiated cells performing essential functions (e.g., lung tissue, bone marrow, etc.). The tissue seeks to maintain adequate supplies of these cells, but many of them may occasionally be killed relatively quickly by cytotoxic challenges. The tissue can “order replacements” (e.g., via cytokine network signaling) from a deeper compartment of proliferative stem cells, but there is a delivery lag because these cells must undergo maturation, amplification via successive divisions, and terminal differentiation before they can replace the killed functional cells. Therefore, a “rational” tissue maintains an inventory of relatively mature cells (e.g., the bone marrow reserve for blood cells) for quick release when needed. This reservoir is replenished by stimulating proliferation in the stem cell compartment. Normally, stem cells have a very low risk of unrepaired carcinogenic (or other) damage, due to extensive checking and repair. But when production is rushed to meet extreme demands, error rates increase. We use a mathematical model of cell inventory management to show that decision rules that effectively manage the inventory of mature cells to maintain tissue function across a wide range of unpredictable cytotoxic challenges imply that increases in average levels of cytotoxic challenges can increase average inventory levels and reduce the average error rate in stem cell production. Thus, hormesis and related nonlinearities can emerge as a natural result of cell-inventory risk management by tissues.

Justifying RFID Investment to Enable Mobile Service Applications in Manufacturing and Supply Chain

2013 ◽  
pp. 325-348 ◽  
Author(s):  
In Lee
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Radio Frequency Identification ◽  
Business Case ◽  
Mobile Service ◽  
Wide Range ◽  
High Investment ◽  
Potential Benefits ◽  
Frequency Identification ◽  
Investment Justification

Radio Frequency Identification (RFID) technology is rapidly expanding its application area from simple inventory management to advanced location tracking and supply chain management in a wide range of industries. Because of the potential benefits gained and high investment costs incurred by RFID, firms need to carefully assess every RFID opportunity and challenge to ensure that their resources are spent judiciously. Because of the lack of analytical methods for measuring the benefits and costs, this chapter presents a mathematical model for the evaluation of RFID investment in manufacturing and supply chain. This model provides a basis for the authors’ understanding of RFID value creation and ways to build an RFID business case for an RFID investment justification.

Technological Innovation and Smart Resiliency Strategies in Supply Chain Management

2020 ◽  
Vol 11 (3) ◽  
pp. 11-22
Author(s):  
Orna T Bradley-Swanson ◽  
Darrell Norman Burrell
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Customer Service ◽  
Inventory Management ◽  
Technological Innovation ◽  
Information Technologies ◽  
New Technologies ◽  
Chain Management ◽  
Supply Chain Resilience ◽  
New Information

When it comes to supply chain management, innovations and new information technologies have the potential to improve organizational supply chain resilience, enhance customer service, cut costs, reduce risks, augment quality management, enrich reliability, and advance efficiency in smart and significant ways. Demand aggregation, inventory management, logistics preparation, and supply chain threats are just a few areas that have become managed more efficiently and effectively through the use of new technologies that improve communication and information distribution throughout a firm and its network to make it both smart, efficient, and adaptive. This article explores technological innovation as a smart resiliency strategy in supply chain management through a review of the literature.

A Colored Petri Net-based modeling method for supply chain inventory management

SIMULATION ◽  
2021 ◽  
pp. 003754972110387
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Petri Net ◽  
Simulation Modeling ◽  
Modeling Method ◽  
Operating Conditions ◽  
Supply Chain Performance ◽  
Colored Petri Net ◽  
Wide Range ◽  
The Impact

Supply chain planning and control approaches need to include a wide range of factors in order to optimize production. Supply chain simulation modeling has been identified as a potential methodology toward increasing the efficiency of current systems to this end. The purpose of this paper is to evaluate the impact of inventory management decisions on supply chain performance using a Colored Petri Net based simulation modeling method. The presented method uses hierarchical timed Colored Petri Nets to model inventory management in a multi-stage serial supply chain, under normal operating conditions, and under the presence of disruptions, for both traditional and information sharing configurations. Disruptions are introduced as canceled orders and canceled deliveries, in a time period. Supply chain performance has been evaluated, in the context of order variance amplification and stockout amplification. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results, as well as by state space analysis results.

scholarly journals Big data analytics and anomaly prediction in the cold chain to supply chain resilience

2021 ◽  
Vol 49 (2) ◽  
pp. 315-326
Author(s):  
Augustyn Lorenc ◽  
Michał Czuba ◽  
Jakub Szarata
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Prediction Method ◽  
Real Data ◽  
Big Data Analysis ◽  
Third Party ◽  
Cold Chain ◽  
Supply Chain Resilience

The purpose of the research was to develop a prediction method to prevent disruption related to temperature anomaly in the cold chain supply. The analysed data covers the period of the entire working cycle of the thermal container. In the research, automatic Big Data analysis and mathematical modelling were used to identify the disruption. Artificial Neural Network (ANN) was used to predict possible temperature-related disruption in transport. The provided research proves that it is possible to prevent over 82% of disruptions in the cold chain. The ANN enables analyses of the temperature curve and prediction of the disruption before it occurs. The research is limited to coolbox transportation of food under -20o C, but the method could also be used for Full Transport Load (FTL) in refrigerated transport. The research is based on real data, and the developed method helps to reduce the waste in the cold chain, improve transport quality and supply chain resilience. The presented method enables not only to avoid cold chain breaks but also to reduce product damage as well as improve the transport process. It could be used by cargo forwarders, Third-Party Logistics (3PL) companies to reduce costs and waste. The literature review confirms that there is no similar method to prevent disruption in the transport chain. The use of the Internet of Things (IoT) sensors for collecting data connected with Big Data analysis and ANN enables chain resilience provision.

Inferential Approaches for Network Analysis: AMEN for Latent Factor Models

2018 ◽  
Vol 27 (2) ◽  
pp. 208-222 ◽  
Author(s):  
Shahryar Minhas ◽  
Peter D. Hoff ◽  
Michael D. Ward
Keyword(s):  
Network Analysis ◽  
Graph Model ◽  
Linear Modeling ◽  
Model Framework ◽  
Modeling Framework ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Wide Range ◽  
Out Of Sample ◽  
Latent Factor Models

We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the exponential random graph model (ERGM). Relative to these approaches, we show that the AME approach is (a) to be easy to implement; (b) interpretable in a general linear model framework; (c) computationally straightforward; (d) not prone to degeneracy; (e) captures first-, second-, and third-order network dependencies; and (f) notably outperforms ERGMs and LSMs on a variety of metrics and in an out-of-sample context. In summary, AME offers a straightforward way to undertake nuanced, principled inferential network analysis for a wide range of social science questions.

scholarly journals Trends of Research on Supply Chain Resilience: A Systematic Review Using Network Analysis

2020 ◽  
Vol 12 (11) ◽  
pp. 4343
Author(s):  
Jin Sung Rha
Keyword(s):  
Systematic Review ◽  
Supply Chain ◽  
Network Analysis ◽  
Research Trends ◽  
Research Articles ◽  
Chain Management ◽  
Supply Chain Resilience ◽  
Wide Range ◽  
Future Supply ◽  
Scopus Database

Researchers have defined resilient supply chain management in various ways and have analyzed and explained it using many managerial theories. Thus, identifying trends in existing studies could serve as a foundation for future supply chain resilience studies. However, despite the accumulation of a wide body of literature on resilient supply chains, few studies have analyzed the research trends systematically. Therefore, the present study aimed to synthesize and summarize research trends in the supply chain resilience domain using network analysis. The Scopus database and Google Scholar were used to search for research articles on supply chain resilience. We conducted an analysis by visually representing coauthorship, cocitation, PageRank, and keyword networks for 825 research articles and 1725 authors. This study identified the main topics, key articles, and major author groups of supply chain resilience research. The findings are expected to help expand the scope of research to a wide range of subfields in supply chain resilience research in the future.

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