Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review

Ahmed, Shehzad; Kalsoom, Tahera; Ramzan, Naeem; Pervez, Zeeshan; Azmat, Muhammad; Zeb, Bassam; Ur-Rehman, Masood

doi:10.3390/s21124158

Cited by 58 publications

(30 citation statements)

References 71 publications

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“…Around the world, even more in the COVID-19 pandemic, efforts have been made to increase the installed capacity of diagnostics [ 8 ], beds for the care of critical patients, and the inclusion of new hospital equipment and supplies, considering drug management. Note that the studies on efficient inventory management in the healthcare industry not only focus on an internal analysis, but an attempt has also been made to investigate this problem from the perspective of supply chain management [ 9 , 10 , 11 ].…”

Section: Symbology Introduction Bibliographical Review and Objectivesmentioning

confidence: 99%

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data

Rojas

Leiva

Huerta

et al. 2021

Sensors

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Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.

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Section: Symbology Introduction Bibliographical Review and Objectivesmentioning

confidence: 99%

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data

Rojas

Leiva

Huerta

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Such intelligent factories are known as smart factories [47,57]. With IoT, the factories can develop actionable insight over the data generated from production processes, maintenance schedules and surrounding environments [58].…”

Section: Industry 40mentioning

confidence: 99%

Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review

et al. 2021

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The Internet of Things (IoT) has realised the fourth industrial revolution concept; however, its applications in the manufacturing industry are relatively sparse and primarily investigated without contextual peculiarities. Our research undertakes an intricate critical review to investigate significant aspects of IoT applications in the manufacturing Industry 4.0 perspective to address this gap. We adopt a systematic literature review approach by Denyer and Tranfield (2009) to carry out critical analyses that help develop future research domains based on empirical studies. We describe key knowledge gaps in the existing literature and empirical studies by exploring the main contribution categories and finding six critical differences between traditional and manufacturing Industry 4.0 and 10 enablers and 11 challenges of IoT applications. Finally, an agenda for future research is proposed with 11 research domains to focus on the recognised gaps.

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“…One of the most important factor that reduces supply chain complexity and inaccuracies is supply chain visibility, it has wider impact on supply chain performance; application of Internet of things will be an asset in this regard (AHMED et al 2021). Furthermore, the sharing of information among suppliers and manufacturers increases the forecast accuracy that meets customer demand (GÉRARD and MARTIN 2001).…”

Section: Impact Of Information Sharing In Supply Chainmentioning

confidence: 99%

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Siddiqui

Azmat

Ahmed

et al. 2021

Supply Chain Forum: An International Journal

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In the era of modern technology, the competitive paradigm among organisations is changing at an unprecedented rate. New success measures are applied to the organisation's supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organisation has an effective and efficient supply chain and an appropriate forecasting technique. Thus, this study presents the demand-forecasting model, i.e., a good fit for the pharmaceutical sector, and shows promising results. Through this study, it is observed that combining forecasting algorithms can result in greater forecasting accuracies. Therefore, a combined forecasting technique ARIMA-HW hybrid 1 i.e. (ARHOW) combines the Autoregressive Integrated Moving Average and Holt' s-Winter model. The empirical findings confirm that ARHOW performs better than widely used forecasting techniques ARIMA, Holts Winter, ETS and Theta. The results of the study indicate that pharmaceutical companies can adopt this model for improved demand forecasting.

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Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review

Cited by 58 publications

References 71 publications

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data

Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review

A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

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