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Conducting virtual audits, conducting effective virtual training, and enhancing communications with suppliers to ensure an uninterrupted supply chain are among the changes implemented to maintain operations, stay compliant, and continue manufacturing medically necessary products. The necessity for virtual audits was to allow companies and regulators to continue to evaluate the compliance stature of manufacturers while respecting stay-at-home and social distancing requirements that prevented in-person site audits. Some of these venues are free, and some require a registration fee. supply chain quality Enhanced communication with suppliers to ensure an uninterrupted supply chain has also become a priority during the pandemic.
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In this report, a nationally representative sample of kindergarten through 12th grade (K-12) public school principals were asked about their experiences with covering classrooms and hiring staff. In the spring of the 2021-2022 school year, which coincided with the coronavirus disease 2019 (COVID-19) omicron variant surge, most principals struggled to keep classrooms consistently staffed and many reported that hiring had become more challenging since the previous school year. Principals indicated that a lack of substitute teachers -- not an increase in open teaching positions -- was the main reason for classroom coverage shortages. In addition to day-to-day coverage issues, most principals reported that teacher vacancies were on the rise. Most of these principals believed that vacancies had grown more difficult to fill than in the prior school year, largely because of declining applicant counts. Principals' preferences when hiring teachers lend further insight into potential drivers of hiring challenges. A large majority of principals expressed strong preferences for like-minded teachers whose mindsets aligned with the vision and culture of the schools. Few principals prioritized the diversity of the educator workforce at their schools.
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PurposeThe purpose of this paper is to study enterprise innovation in the perspective of external supplier relationship. On this purpose, this paper examines the impact of supplier change on enterprise innovation with the moderating role of market competition.Design/methodology/approachUsing 2012–2020 empirical data of Chinese listed manufacturing enterprises, this paper investigates the relationship among supplier change, market competition and enterprise innovation through a two-way interaction model.FindingsThe results show that supplier change has a negative impact on enterprise innovation. And market competition intensifies the negative relationship between supplier change and enterprise innovation. Additional analyses indicate that the main effect and the moderating effect are more significant when the enterprise is non-state-owned or has lower ownership concentration.Originality/valueThis paper studies enterprise innovation from the perspective of external stakeholders. It focuses on supplier relationship in a dynamic variation view, instead of the traditional static ones. Moreover, this paper explores the contingency effect of market competition and gives practical implications for managers to adjust innovation strategy flexibly.
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This research aims to grasp the evolution of consumer demand and improve the resilience of the hotel industry under the public health crisis (COVID-19). Online reviews of 7,679 hotels in 10 cities were collected from Ctrip, China's major online hotel platform. Then, we applied opinion mining and time evolution to mine the change in consumer demand before, during, and after the COVID-19 pandemic. Findings show that some consumer demands (e.g. epidemic safety) will change during the outbreak period. However, during the nonoutbreak period, users were more concerned about their own check-in experience (e.g. hotel facilities, front desk services). This article provides new ideas for identifying the dynamic value of online reviews. We suggest that businesses focus on ensuring hotel safety during the crisis period. The results contribute essential theoretical and practical significance to the hotel industry's crisis management during public health crises.
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The COVID-19 outbreak has significantly impacted supply chains and has caused several supply chain disruptions in almost all industries worldwide. Moreover, increased transportation costs, labor shortages, and insufficient storage facilities have all led to food loss during the pandemic, and this disruption has affected the logistics in the food value chain. As a result, we examine the food supply chain, which is one of the key industries COVID-19 has detrimentally affected, impacting, indeed, on the entire business process from the supplier all the way to the customer. Retail businesses are thus facing supply issues, which affect consumer behavior by creating stress regarding the availability of food. This has a negative impact on the amount of food that is available as well as its quality, freshness, safety, access to markets, and affordability. This study examines the impact of COVID-19 on the United Arab Emirates food distribution systems and how consumer behavior changed in reaction to interruptions in the food supply chain and the food security problem. Hypothesis testing was used in the study's quantitative methodology to assess consumer behavior, and participants who were consumers were given a descriptive questionnaire to ascertain whether the availability and security of food had been impacted. The study used JASP 0.17.2 software to develop a model of food consumption behavior and to reveal pertinent connections between each construct. Results show that consumer food stress and consumption behavior are directly impacted by food access, food quality and safety, and food pricing. Furthermore, food stress has an impact on how consumers behave when it comes to consumption. Food stress, however, is not significantly influenced by food supply.
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This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models' prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model's performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG. [ FROM AUTHOR] Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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PurposeThe purpose of this paper was to determine the profile of dairy product consumers in the organic market.Design/methodology/approachThe study was based on a survey questionnaire developed by the author and administered to a total of 1,108 respondents. The statistical analysis (including descriptive statistics, the analysis of the discriminative function and the Chi2 test was performed with the use of Statistica 13.1 PL. The respondents' gender was the factor behind the differences in how they behaved.FindingsThe consumers indicated the channels they rely upon to find information on organic dairy products;in addition to trusting the opinions of their family members and experts, they also use web platforms. Further, they specified their preferred locations for buying favorite products during the pandemic: specialized organic food shops, large distribution chains and online stores.Practical implicationsThese outcomes will help in identifying target consumer segments and information channels for specific information and advertising messages. They also form an important resource for developing some potential strategies which the supply chain stakeholders could implement to promote organic consumption of dairy products.Originality/valueThis study identifies consumers' preferred dairy products;motives for purchasing organic dairy products;barriers that consumers believe exist in the market;sources of knowledge about products purchased by consumers;and consumers' preferred channels for purchasing organic dairy products. To the best of the author's knowledge, this is the first study of dairy product consumers in the organic market in Poland.
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Container ports operate in more challenging and volatile environments at present times. Events such as US-China trade tensions and the COVID-19 pandemic severely affect numerous container ports at various levels. Strategies pursued by container ports are key to port development and management amidst these challenges. Drawing on configuration theory, this research employs Fuzzy-set Qualitative Comparative Analysis to investigate the relation between port strategies and container throughput. The research contributes to the literature by proposing an approach to account for complexity of the port sector and offers insights into strategies adopted by major container ports. The research further identifies 10 port strategies and proposed indicators that can represent the essence of these strategies. Being able to represent strategies in a quantitative format is important for strategy analysis and performance evaluation. Results reveal that major container ports employ a combination of strategies which address both the supply and demand-side aspects of the port business. Growing digitalization and digitization coupled with advancements in information capture, diagnostics capabilities and predictive abilities means a greater role for data analytics to influence container port strategy and performance. Implications for port managers, policy makers and researchers from the perspective of port policy and management are proposed.
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This study analyzed the impact of COVID-19 outbreak and targeted required reserve ratio cut policy on stock returns of Chinese listed companies. This paper uses the data of 3,449 A-share listed companies from February 3, 2020 to December 31, 2020 for research, the empirical results showed that stock prices of private enterprises with stronger debt-paying ability and looser financing constraints, and state-owned enterprises with less supply chain credit risks performed better, in the central and western regions, enterprises with stronger solvency and looser financing constraints have better stock price performance during the early stages of pandemic. After the implementation of the targeted RRR cut policy, the stock prices of enterprises with poor solvency, private enterprises, and enterprises in central and western regions with strong financing constraints, state-owned enterprises, and enterprises in eastern region with high credit risks all showed significant reversals, and the stock prices reflected the effect of the targeted RRR cut policy in the short and medium term. Over time, the pandemic has been controlled, and the resumption of work and production has freed most enterprises from financial difficulties. However, due to sporadic outbreaks, large private enterprises and eastern enterprises with strong risk resistance and loose financing constraints enjoy better stock price performance. This study is helpful for enterprises to understand the value of financial flexibility and solvency and provides a reference for enterprises to make financial decisions: how to balance the benefits and costs of solvency. © Tian Wang, Fang Fang and Linhao Zheng, 2023.
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In supply chain management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning. © 2023 SPIE.
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Because of the Covid-19 pandemic, urgent surging demand for healthcare products such as personal protective equipment (PPE) has caused significant challenges for multi-tier supply chain management. Although a given firm may predominantly focus on an arms-length solution by targeting the first-tier supplier, the firm can still struggle with extended multi-tier suppliers it cannot choose which use unsustainable practices. One key goal is compliance across various dimensions with production, environmental and labour standards across the multi-tier supply chain, a goal that blockchain technology can be applied to manage. Based on a comprehensive literature review, this research develops a system architecture of blockchain-based multi-tier sustainable supply chain management in the PPE industry designed to identify and coordinate standards in production and social and environmental sustainability in multi-tier PPE supply chains. The architecture was validated by theoretical basis, expert opinions and technical solutions. We conclude with managerial implications for implementing blockchain technology to advance sustainable multi-tier supply chain practices.
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Supply chain managers are forced to develop crisis-induced strategies due to the complexity of crises, as opposed to the more traditional strategies that prioritize competitive priorities. The increasing frequency and severity of recent crises, such as the coronavirus outbreaks, widespread product recalls, and financial crises, highlight the need for introspective and retrospective socio-economic insights on the contexts, priorities, and themes of supply chain management in times of crisis. This article's goal is to review the literature on supply chain management during times of crisis, organizing the relevant body of scholarly work in a systematic way, outlining current research methodologies, capturing strategic priorities and themes of complexity in research studies, and highlighting opportunities for additional research. Four factors for restorative priorities are identified by the review, which is based on a systematic analysis of 250 academic publications from 1996 to 2021 and reflects operations strategy in times of crisis: Critical supplies with important services, prompt action with restoration, safety with security, and traceability with transparency are just a few examples. The analysis also reveals that network configurations and business cycle complexity, optimal choices and provisioning system complexity, complicated learning processes and demand forecast are all sources of operational complexity during crises. The build-to-cycle, organic capabilities, and operational mindfulness framings for supply chain management in emergency situations are suggested with the use of review insights. The article ends with suggestions for future research on supply chain improvements, diagnosis, solidarity, mapping, temporariness, and thresholds, as well as optimal selection issues on connecting crisis network allocations with cross-functionalities and connecting crisis systems investments with liabilities.
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After the global tourism industry has experienced the impact of the pandemic, it is critical that people gain confidence in traveling and have the impression that staying in hotels is now safe, because only in this way tourism businesses such as hotels can be fully successful in recovering. For this reason, the researchers guided by a descriptive research design and quantitative research approach, aimed to determine what people think about staying in a hotel, particularly in terms of safety and security, price, location, and service quality, in the time of COVID-19 pandemic recovery stage, focused on the local community of Calamba City, Laguna, Philippines, being one of richest cities in the country and the place where the researchers reside. Moreover, a comparative analysis of the perspective of the respondents has been performed in terms of their age, sex, and educational attainment, identifying which age, sex and educational attainment groups have more positive or negative attitude, and a higher or lower level of hotel stay intention compared with other groups. Being the first study that has assessed the tourism market particularly in terms of their perspective on hotel stay as the hospitality industry attempts to recover from the impact of the pandemic, this is expected to provide a clear picture of the need for management of hotels to continuously work on marketing efforts highlighting the information that it is now safe to practice tourism and stay in their establishments, hence, serving as a guide in coming up with promotional strategies and an action plan, as well as a motivation for researchers who wish to determine the same in their locality or country.
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PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.
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Broiler chicken eggs are one of the main and strategic foods for the people of Indonesia and contribute to regional and national inflation. Broiler egg production in Indonesia differs between regions. Areas with a surplus of eggs tend to have lower prices than areas with a deficit. This research is to measure the transmission of broiler egg prices between markets in surplus and deficit areas, using weekly price time series data for the period January 2018-December 2021. Areas of surplus broiler eggs, East Java Province (the highest broiler egg production in Indonesia) which become one of the main suppliers to the Province of East Nusa Tenggara as a deficit area. Using the Johannsen cointegration test it is found that there is no cointegration or there is no relationship between the surplus and deficit regions in the long term but not in the short term. Factors of marketing infrastructure, market information systems, and geographical conditions can be obstacles to the absence of cointegration. The VAR (Vector Auto-Regressive) Vector Error Correction model (VECM) test, found that price transmission occurred between surplus and deficit areas, meaning that between the two regions, there was market integration prior to Covid. The transmission has weakened, and due to the Covid situation, there have been restrictions on the movement of people and goods. The government and other market players need to study the response of the broiler egg market, in the short and long term so that market players can make the right policies.
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"The ongoing journey to standardization on more aspects of submission and data exchange will continue to have an impact," he notes. lan Crone, business unit director Europe-fme Life Sciences, which provides business and technology services, points out that the web-based human variations electronic application form (eAF) for centrally authorized products (CAPs) has been available for use since 4 Nov. 2022 on the European Medicines Agency's (EMA's) new product lifecycle management (PLM) portal. Renato Rjavec, Amplexor Life Sciences "Many biopharmaceutical functions have spent the last decade modernizing their base technologies, most often in a cloud/software as a service environment platform that brings foundational benefit to individual functions," states Steve Gens managing partner. Internal productivity and external regulatory requirements are both driving this data connectivity within industry, he adds, which "requires a clear cross functional digitization strategy and focus on cross-functional data governance, master data management, and ensuring all data from these various authoritative systems [are] at the same high level. " "Many biopharmaceutical functions have spent the last decade modernizing their base technologies, most often in a cloud/ software as a service environment platform that brings foundational benefit to individual functions." -
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[...]best-in-class pharma companies are focusing on reliability and resilience in the supply chain-if they can't make a product or deliver a product on time, a patient is not served, and no sale is made. People can scale to a certain degree but scaling by a factor of 100 is not possible with people in a short period of time and does not deliver on economies of scale. Pharma companies are also issuing 'green bonds' where investors can expect the contribution of capital to improve the company's sustainability.
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According to research, for example, the bio/pharmaceutical manufacturing market should witness compound annual growth in the region of 11% between 2022 and 2027 (2), thanks in part to advancing manufacturing technologies. "Most recently, biopharmaceutical manufacturing has been impacted by pressures on supply chain," specifies Antonio Crincoli, vice president of Engineering, Pharma and Consumer Health, Catalent. [...]there is a focus on quality management systems that ensure data integrity and governance, and that digitization occurs with appropriate validation, also, where necessary, that there is segregation of operating systems to eliminate risk of corruption." Antonio Crincoli, Catalent "PAT and an emphasis on process understanding have been embraced by the majority of pharmaceutical manufacturers, and there are several case studies where both artificial intelligence (Al) and machine learning (ML) have led to improved quality or increased yield, even in good manufacturing practice (GMP) facilities," adds Byrd.