Demand forecasting in pharmaceutical supply chains: A case study

Merkuryeva, Galina; Valberga, Aija; Smirnov, Alexander

doi:10.1016/j.procs.2019.01.100

Cited by 75 publications

(40 citation statements)

References 3 publications

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“…Thus, gathering and managing large data in the complex and large-scale supply chain, in which entities and operations involved are distributed over various locations, undoubtedly is a big challenge. However, employing AI, machine learning, and big data in the supply chain can be solutions to address challenges of data collection, analyses, and processing [ 10 , 14 , 15 ]. Through the integration of IoT, supply chain systems are much smarter than ever, as IoT smart sensing technology and devices connectivity enable the supply chain systems to generate and collect massive data and to monitor and control the overall supply chain ecosystem, therefore leveraging great transparency, tracking, and central security features [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Shahzad

Zhang

Gherbi

2020

Sensors

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Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.

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Section: Introductionmentioning

confidence: 99%

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Shahzad

Zhang

Gherbi

2020

Sensors

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“…Merkuryeva et al [92] analyzed three prediction approaches for demand forecasting in the pharmaceutical industry: a simple moving average model, multiple linear regressions, and a symbolic regression with searches conducted through an evolutionary genetic programming. In this experiment, symbolic regression exhibited the best fit with the lowest error.…”

Section: Regression Analysismentioning

confidence: 99%

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

2020

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Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

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“…Purpose of forecasting is to obtain a fairly accurate estimation of future demand for a product or service given historical data and the current state of the environment (e.g., political, social, economic) to plan and organize businesses accordingly [8]. Demand forecasting helps inventory planning and excess inventory reduction in a company [9].…”

Section: Forecastingmentioning

confidence: 99%

Development of Cloud Based Demand Forecasting System: A Case Study in Stationery Industry

Muliady¹,

Utomo²,

Natalia³

2021

Proceedings of the First International Conference of Economics, Business &Amp; Entrepreneurship, ICEBE 2020, 1st October 2020,

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Demand forecasting in pharmaceutical supply chains: A case study

Cited by 75 publications

References 3 publications

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Development of Cloud Based Demand Forecasting System: A Case Study in Stationery Industry

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