Margarita Hurtado-Hernandez

Bio: Margarita Hurtado-Hernandez is an academic researcher from Panamerican University. The author has contributed to research in topics: Supply chain & Skewness. The author has an hindex of 3, co-authored 4 publications receiving 37 citations.

Digital twins in supply chain management : a brief literature review

Abstract: The rapid interest in the continuous improvement of supply chain management systems has motivated the development of digital tools in the automation of business problems. Currently, companies must continually adapt to changing conditions with respect to the management of their supply chain. However, the lack of real-time data available and responsive planning systems make this adaptation difficult. The current situation of the technology of digital twins is to migrate to the digital. More and more companies will develop and introduce their own digital twins in their business processes. This manuscript presents a literature review of the current context of digital twins. A total of 4884 searches combining keywords with respect to digital twins were analyzed. The years analyzed in the databases were 2017–2019.

Studying the effects of the skewness of inter-arrival and service times on the probability distribution of waiting times

Abstract: Previous studies have shown that the mean queue length of a GI/G/1 system is significantly influenced by the skewness of inter-arrival times, but not by the skewness of service times. These results are limited because all the distributions considered in previous studies were positively skewed. To address this limitation, this paper investigates the effects of the skewness of inter-arrival and service times on the probability distribution of waiting times, when a negatively skewed distribution is used to model inter-arrival and service times. Subsequent to a series of experiments on a GI/G/1 queue using discrete-event simulation, results have shown that the lowest mean waiting time and the lowest variance of waiting times can be attained with a combination of positive inter-arrival skewness and negative service skewness. Results also show an interesting effect of the skewness of service times in the probability of no-delay in environments with a higher utilization factor.

The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control

Abstract: Personalised production allows the supply chain (SC) to exist in various dynamic fluctuations within a make-to-order (MTO) environment. An SC for personalised production has redundant inventory and...

Field-synchronized Digital Twin framework for production scheduling with uncertainty

Abstract: Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

Design and Development of Digital Twins: a Case Study in Supply Chains

Abstract: Digital twin technology consists of creating virtual replicas of objects or processes that simulate the behavior of their real counterparts. The objective is to analyze its effectiveness or behavior in certain cases to improve its effectiveness. Applied to products, machines and even complete business ecosystems, the digital twin model can reveal information from the past, optimize the present and even predict the future performance of the different areas analyzed. In the context of supply chains, digital twins are changing the way they do business, providing a range of options to facilitate collaborative environments and data-based decision making and making business processes more robust. This paper proposes the design and development of a digital twin for a case study of a pharmaceutical company. The technology used is based on simulators, solvers and data analytic tools that allow these functions to be connected in an integral interface for the company.

Combining holistic and decomposition paradigms in preference modeling with the flexibility of FITradeoff

Abstract: This paper focuses on the flexibility feature of the Flexible and Interactive Tradeoff (FITradeoff) multicriteria method for preference modeling. This method is based on the additive aggregation of criteria and using partial (incomplete; imprecise) information to be obtained from a Decision Maker (DM). The flexibility in FITradeoff for preference modeling has already considered two different perspectives: holistic evaluations and elicitation by decomposition based on the classical tradeoff procedure. This paper introduces a new feature in the flexibility of FITradeoff by combining and integrating these two paradigms: Holistic evaluations and elicitation by decomposition. This combination improves the preference modeling process, since it increases its efficiency and consistency. The use of results from behavioral studies is briefly presented. These results include those that arise from using neuroscience tools in order to modulate changes in the design of the Decision Support System and also from improving the decision process by supporting the way the analyst can interact with the DM.