Shanmugam Prasanna Venkatesan

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.

The illegal harvesting of seafood and the mislabelling of dry fish products have become more prominent in recent years. Traceability in the dry fish supply chain deserves research attention, especially among small and medium enterprises (SMEs) in a developing country like India. To implement the traceability system, it is necessary to identify the variables and their inter-relationships that influence traceability in a food supply chain. This research aims to identify the awareness, benefits, drivers, and barriers that affect traceability adoption in the dry fish supply chain. Data were collected from 226 dry fish supply chain stakeholders through a cross-sectional survey (both online and offline) in Kerala and Tamil Nadu, two central maritime states of India. A conceptual model is developed, and the inter-relationship among the factors underlying the traceability implementation is analysed through correlation matrices and multiple regression modelling. The results show that the Indian dry fish stakeholders moderately understand the traceability system. Product safety and quality are identified as the primary benefits of traceability, while quality concerns and food safety regulations are observed as primary drivers. The significant barriers are the cost of a traceability system, lack of a co-ordinator and lack of government support.

The increase in demand for outpatient departments (OPDs) has contributed to overcrowded clinics and patient dissatisfaction. Computer simulation can help decision-makers meet the operational challenge of balancing the demand for outpatient services with considerations of available capacity. The paper presents a synthesis of the literature on simulation modeling in OPD using two approaches: a bibliometric analysis (employing keyword co-occurrence network) and a literature classification focusing on OPD strategy, OPD performance measures, and simulation techniques. Our review is based on 161 papers, published between 2006 and 2020, identified through a methodological search of the literature. The objective of the review is threefold: (1) to identify the major and emerging research issues in general and specialized OPD, (2) to find the commonly used performance measures in OPD and how it is associated with the strategies used to improve the performance, and (3) to identify the commonly used simulation methods for OPD modeling. A key finding from the bibliometric analysis is that most OPD research can be classified under one of the four clusters—“organization and management,”“patient satisfaction,”“overbooking,” and “performance.” We also find that patient waiting time has received much attention among the performance measures reported in the literature, followed by server idle time/overtime (server here is the OPD consultant or other healthcare resource). Our review serves as a key reference point for scholars, practitioners, students, and healthcare stakeholders, and those who use quantitative tools to aid operational decision-making.

Purpose: Reputation risk onsets in focal firm whenever any entity of its supply chain (SC) faces risk-crisis event. A framework for modeling and predicting holistic SC reputation risk is proposed by integrating operational risk (OR) drivers originating from upstream and downstream partners and focal firm. A fuzzy cognitive map (FCM) is then developed to predict and quantify Pharmaceutical SC reputation risk.Design/methodology/approach: Using event study methodology, SC reputation risk framework with 13 input OR drivers was developed. Based on pharmaceutical supply chain experts’ opinion, the correlation between reputation risk and its input drivers was estimated. The developed FCM tool was validated using nine real-life instances. A series of “what-if” scenario analyses were performed to demonstrate effectiveness of proactive and reactive mitigation strategies against reputation risk.Findings: Quality and unethical governance risks significantly impacted reputation in Pharmaceutical SC and a firm should prefer “risk avoidance” against these risks. The upstream risks significantly affect reputation in a Pharmaceutical SC as compared to the downstream risks. Proactive mitigation strategies and assertive crisis communication are suggested for upstream risks while diminishment/ bolstering/rebuilding reactive crisis communication is recommended for downstream risks.Originality/value: Reputation risk is often overlooked in SC literature. This work develops a model to quantify the reputation risk considering the indirect consequences of the ORs that originates at any point in a SC. The proposed FCM tool aids SC manager to focus on higher attribution risk events and devise an optimal combination of proactive and reactive mitigation strategies to avoid/minimize the economic loss due to reputation crisis.

Purpose: Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers.Design/methodology/approach: In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC.Findings: A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors.Originality/value: The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.