Search results

1 – 10 of 441
Article
Publication date: 25 September 2023

R.S. Sreerag and Prasanna Venkatesan Shanmugam

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to…

Abstract

Purpose

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Design/methodology/approach

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

Findings

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

Research limitations/implications

The price of vegetables is not considered as the government sets the base price for the vegetables.

Originality/value

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

Details

Journal of Agribusiness in Developing and Emerging Economies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-0839

Keywords

Article
Publication date: 29 June 2023

Sapna Tyagi

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply…

Abstract

Purpose

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply chain in the “new normal” environment.

Design/methodology/approach

A systematic literature review was conducted by extracting research articles related to analytics in the healthcare supply chain from Scopus. The author used a hybrid review approach that combines bibliometric analysis with a theories, contexts, characteristics, and methodology (TCCM) framework-based review to identify various themes of analytics in the healthcare supply chain.

Findings

The hybrid review strategy yielded results that focus on prevalent theories, contexts, characteristics, and methodologies in the field of healthcare supply chain analytics. Future research should explore the resulting antecedents, decision-making processes and outcomes (ADO) framework, which integrates technological, economic, and societal concerns and outcomes. Future research agendas could also seek to apply theoretical perspectives in the field of analytics in the healthcare supply chain.

Originality/value

The result of a review of selected studies adds to the current body of work and contributes to the growth of research in the field of analytics in the healthcare supply chain. It also provides new directions to healthcare supply chain managers and academic scholars.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 14 March 2024

Sina Tarighi

The purpose of this study is to define and develop a new technological development path for latecomer firms in developing countries.

Abstract

Purpose

The purpose of this study is to define and develop a new technological development path for latecomer firms in developing countries.

Design/methodology/approach

An analytical framework for development based on the technological capability (TC) dimensions is developed and examined in the drilling sector. Since the process of TC accumulation is dynamic, the case study approach is the best method for an exploratory theory-building study. Through a comparative case study of two Iranian drilling contractors, a new path for the technological development of latecomer oil service companies is proposed.

Findings

The study of two cases indicates that despite having similar scope and levels of TC, one of them demonstrated superior technical performance. To address this difference, the concept of operational efficiency is introduced which is considered the outcome of increasing the depth of TC.

Practical implications

Although upgrading the level of technological and innovation capability is an important path for technological development, latecomers that suffer from various disadvantages can perform their routine activities with superior performance and develop through their basic operational/production capabilities. Also, specialized indicators designed for assessing the level and depth of TC in the drilling industry have important insights for evaluating the technological and competitive position of oil service companies.

Originality/value

To the best of the author’s knowledge, this study takes the first step in defining and elaborating on the concept of depth of TC as a development path for latecomers. It also introduced a novel approach to the global operational/production efficiency frontier as a target for their catch-up.

Details

Journal of Science and Technology Policy Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2053-4620

Keywords

Article
Publication date: 12 December 2023

Jeong Hoon Choi, Sangdo Choi and Nallan C. Suresh

The objective of this study is to explore the structural attributes of the pharmaceutical industry before the onset of the COVID-19 pandemic by examining the relationship between…

Abstract

Purpose

The objective of this study is to explore the structural attributes of the pharmaceutical industry before the onset of the COVID-19 pandemic by examining the relationship between inventory and firm performance and developing a taxonomy of pharmaceutical firms based on the earns-turns matrix.

Design/methodology/approach

This study examines the inventory–firm performance linkage, considering both total inventory and its discrete inventory components in pharmaceutical firms. In addition, this research develops a new taxonomy of pharmaceutical firms based on the earns-turns matrix. A large panel dataset of firms in the US pharmaceutical industry was collected for the period 2000–2019.

Findings

The results reveal that strategic groups identified based on this taxonomy show different levels of profitability and inventory turns in the earns-turns matrix. Most pharmaceutical firms moved from the low-right to the top-left section in the earns-turns matrix, indicating that these firms have generally pursued profitability rather than effective inventory management.

Research limitations/implications

This study explores the structural attributes of the pharmaceutical industry using the earns-turns matrix. This two-dimensional analysis may not, however, capture the full complexity of inventory–firm performance dynamics.

Practical implications

The mapping of strategic groups on the earns-turns matrix provides a useful tool for visual representations of the dynamics of strategic groups in terms of financial performance and inventory management performance. Practitioners can use the earns-turns matrix to benchmark their firm's position against their competitors.

Originality/value

This study broadens the scope of operations management research by introducing the earns-turns matrix as an empirical validation tool for operational and strategic management theories. This study emphasizes the effectiveness of the earns-turns matrix in analyzing strategic groups of pharmaceutical firms.

Details

International Journal of Operations & Production Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 14 July 2023

Saad Zighan, Nidal Yousef Dwaikat, Ziad Alkalha and Moheeb Abualqumboz

This study investigates the role of supply chain knowledge management in enhancing pharmaceutical supply chain resilience.

485

Abstract

Purpose

This study investigates the role of supply chain knowledge management in enhancing pharmaceutical supply chain resilience.

Design/methodology/approach

This study focusses on the Middle East region, where semi-structured online interviews were conducted with 38 professionals from the pharmaceutical supply chain to collect empirical data.

Findings

The study reveals that supply chain knowledge management is a crucial value-adding practice that improves pharmaceutical supply chain resilience. Effective supply chain knowledge management enables organisations to develop agility, change, adaptability, problem-solving, response and innovation capabilities that support supply chain resilience. However, challenges related to supply chain management practices, people, processes and technology hinder the effective promotion of supply chain knowledge.

Practical implications

This study reminds managers that knowledge management is critical for building resilience in supply chains.

Social implications

The study highlights the importance of a resilient pharmaceutical supply chain for organisations and society. The study advocates that effective supply chain knowledge management can help ensure a sustained supply of high-quality pharmaceutical products and services during crises.

Originality/value

The study offers novel insights by examining pharmaceutical supply chain resilience from a knowledge management perspective and highlighting the potential of knowledge capabilities to enable supply chains to recover from crises and adapt to the new normal. This study also highlights the key strategic considerations for managing knowledge effectively throughout the supply chain.

Details

The International Journal of Logistics Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0957-4093

Keywords

Open Access
Article
Publication date: 28 June 2023

Siti Norida Wahab, Nusrat Ahmed and Mohamed Syazwan Ab Talib

The Indian pharmaceutical industry has contributed significantly to global healthcare by securing superior-quality, inexpensive and reachable medicines worldwide. However, supply…

5913

Abstract

Purpose

The Indian pharmaceutical industry has contributed significantly to global healthcare by securing superior-quality, inexpensive and reachable medicines worldwide. However, supply chain management (SCM) has been challenging due to constantly shifting requirements for short lifecycles of products, the convergence of industry and changeable realities on the ground. This study aims to identify, assess and prioritize the strengths, weaknesses and opportunities of the pharmaceutical SCM environment in India.

Design/methodology/approach

The paper employs a Strength, Weakness, Opportunity, Threat (SWOT) analysis and recognizes strategies to utilize the advantages of the strengths and opportunities, rectify weaknesses and resolve threats.

Findings

A variety of strategies that could have a positive effect on the Indian pharmaceutical business are presented. Findings and suggested strategies can significantly advance knowledge, enhance understanding and contribute to the growth of a successful SCM for the Indian pharmaceutical sector.

Originality/value

This paper would act as a roadmap to greater comprehension of the market leaders and market leaders' operating climate. The findings from this study will offer academic scholars and business practitioners deeper insights into the environment of SCM.

Details

Arab Gulf Journal of Scientific Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1985-9899

Keywords

Article
Publication date: 29 December 2023

Ashu Lamba, Priti Aggarwal, Sachin Gupta and Mayank Joshipura

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms…

Abstract

Purpose

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

Design/methodology/approach

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

Findings

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

Originality/value

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

Details

International Journal of Pharmaceutical and Healthcare Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-6123

Keywords

Article
Publication date: 22 February 2024

Zoubida Chorfi

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of…

Abstract

Purpose

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

Design/methodology/approach

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

Findings

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

Research limitations/implications

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

Practical implications

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

Originality/value

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 30 November 2023

Mohammad Rifat Rahman, Md. Mufidur Rahman, Athkia Subat and Tanzika Imam Tarin

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic…

Abstract

Purpose

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic product (GDP) growth, foreign direct investment (FDI) inflows, exchange rate and export growth through the long- and short-run relationship.

Design/methodology/approach

Using the time series data from 1986 to 2020, this study was developed based on the autoregressive distributed lag (ARDL) framework for co-integration. In contrast, the Toda–Yamamoto Granger Causality approach was also used for finding the direction of causality.

Findings

This study used the ARDL bounds test, which found strong co-integration among the variables, indicating a long-term relationship between them. In the long run, inflation, exchange rate and export growth significantly positively influence the pharmaceutical industry’s growth. Surprisingly, an FDI inflow has a negative impact. In the short term, the exchange rate and GDP growth were found to influence the growth of the pharmaceutical industry positively. Bidirectional causality between the growth of the pharmaceutical industry and the exchange rate was also identified using the Granger causality approach.

Research limitations/implications

This paper emphasizes developing the policy as well as making concrete decisions regarding the development of the pharmaceutical industry and economic development in Bangladesh. The results also highlight the necessity for strategic macroeconomic management to support this sector’s long-term development and global competitiveness.

Originality/value

To the best of the authors’ knowledge, this paper is conducted to identify the short- and long-run relationship of pharmaceutical industry development with the economic indicators and progress, where no study has been found on this dimension.

Details

International Journal of Pharmaceutical and Healthcare Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-6123

Keywords

Article
Publication date: 4 April 2023

Giustina Secundo, Gioconda Mele, Giuseppina Passiante and Angela Ligorio

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of…

Abstract

Purpose

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Design/methodology/approach

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Findings

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

Research limitations/implications

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

Originality/value

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

Keywords

1 – 10 of 441