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The purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.

A mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.

This study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.

This study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.

This study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

As organizations globalize, they are facing twin challenges of (1) how to develop actionable intelligence from the vast amount of data flowing into their organization and (2) how to effectively manage the increasing risks to their supply chain. Therefore, the purpose of this paper is to bring these two issues on a single platform to understand how firms can effectively predict supply chain risk by developing and using BDA capabilities, through an automated risk alert tool.

The authors used a questionnaire-based survey methodology supported by secondary data to collect information related to managerial perceptions on how firms can develop a risk alert tool by improving BDA capabilities. A database of 213 senior and middle-level managers was developed and used to test the proposed hypothesis. Using econometric techniques, the authors identify the conditions necessary for such an automated risk management tool to be effective.

The results suggest that if organizations focus on developing an effective IT infrastructure supported by a strong BDA capability, they will be able to leverage these capabilities to develop an effective risk management tool. Moderating influences of Upstream and Downstream Supply Chain IT Infrastructure capabilities were also observed on different types of BDA capabilities within a firm. In conclusion, it was argued that the effectiveness of a risk alert tool is dependent on how well firms harness big data analytics capability.

The value of the research stems from the fact that it uses managerial surveys to identify specific BDA capabilities that can enable firms to develop risk resilience capabilities. In addition, the article is one of the few empirical studies that aims to identify how firms can use BDA capabilities within a supply chain context to develop an automated risk alert tool. The article, therefore, contributes to the literature that identifies the value of BDA capabilities within the context of supply chain risk management.

The purpose of this paper is to examine how firms can develop business risk resilience from supply chain disruption events, by developing big data analytics (BDA) capabilities within their organization. The authors test whether BDA mediates the impact of institutional response to supply chain disruption events, and information technology infrastructure capabilities (ITICs), on firm’s ability to develop risk resilience from supply chain disruption events.

The study is based on survey data collected from 225 firms, spread across several sectors in the USA and Europe. The respondents are primarily senior and middle management professionals who have experience within the information technology (IT) and supply chain domain. Validity and reliability analyses were performed using SPSS and AMOS; and covariance-based structural equation modeling was used to test the hypothesis.

The analysis reveals two significant findings. First, the authors observe that institutional experience with managing supply chain disruption events has a negative impact on firm’s ability to develop business risk resilience. However, if the organizations adopt BDA capabilities, it enables them to effectively utilize resident firm knowledge and develop supply chain risk resilience capacity. The results further suggest that BDA positively adds to an organization’s existing IT capabilities. The analysis shows that BDA mediates the impact of ITIC on the organization’s ability to develop risk resilience to supply chain disruption events.

This study is one of the few works that empirically validate the important role that BDA capabilities play in enabling firms develop business risk resilience from supply chain disruption events. The study further provides a counterpoint to the existing perspective within the supply chain risk management literature that institutional experience of managing past supply chain disruption events prepares the organization to deal with future disruption events. This paper adds to our understanding of how, by adopting BDA capabilities, firms can develop supply chain risk resilience from disruption events.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The awareness for protecting the environment has resulted in remanufacturing and recycling policies in manufacturing industries. Carbon emission is one of the most important elements affecting the environment. Carbon emission due to production and transportation creates complicated situations for the manufacturing firms by affecting the manufacturer's carbon quota. The ecological consequences posed in a reverse logistic model are the subject of this study.

The present study explores the fuzzy model of economic production for both remanufacturing and recycling with uncertain cost parameters under the cap-and-trade rule to control the carbon emission due to different modes of transportation. Due to imprecise cost parameters, the hexagonal fuzzy numbers are set to fuzzify the overall cost, which leads to correct decisions in a more confident way. The result is defuzzified by using graded mean integration.

This study offers an explicit condition to control the carbon emission of the manufacturer and reduce the optimum cost. The findings indicate that the collection of used goods that can be remanufactured must be increased. The model is validated numerically. Sensitivity analysis explores the various aspects of different parameters on net cost to accomplish the fuzzy production model.

Under fuzzy inference, the research offers a relevant contribution in the field of recycling with controlling carbon emission by using the cap-and-trade policy. This study provides a trading strategy for a manufacturer's decision to avoid losses.

The purpose of this paper is to extend Zablah et al.’s (2012) findings regarding the proper way to treat customer orientation (CO) to the study of CO among B-B salespeople in one of the most important emerging economies, India.

The authors of this study hired a professional market research firm based in Chennai, a large metropolitan city in Southern India, to manage data collection. The authors used a competing models approach to test the relationship between constructs.

CO among frontline employees operating in one of the largest emerging economies is best treated as a psychological construct that is both directly and indirectly related to performance via its ability to reduce stress and improve engagement. This finding strengthens the view of CO as a universal human work value and, more broadly, that such values operating across different cultural setting do exist. In addition, external customer mindset appears to offer a superior means to measure CO than does the widely used CO component of the SOCO scale. This conclusion is based not only upon the fact that it conceptually corresponds with the psychological nature of CO, but also that in this initial examination it exhibits a greater ability to explain employee job performance.

Managers who are able to screen and hire employees with greater CO work values should experience improved performance outcomes and also less customer ambiguity and greater satisfaction among their frontline employees. Since CO proscribes the proper way to deal with customers, greater levels of CO beliefs would counteract customer ambiguity among frontline employees operating in any environment. Accordingly, when filling frontline positions, managers should actively seek out employees who earnestly embrace the role of taking care of customers. Managers are advised to not only emphasize on salespeople whose foremost role is to take care of their customers but also to find ways to familiarize them with their products and to provide them with information regarding customer characteristics such as their background, the relationship history (especially past service and product failures), and unique preferences.

In this paper, vendor selection and order allocation problem is considered for a buyer dealing in multiple products to be supplied by multiple vendors. Each product has an associated lead time with stochastic demand having stochastic capacity for each vendor across entire time period. Uncertainties related to costs which are further influenced by the periodically changing incremental quantity discounts offered by various vendors. The purpose of this paper is to find an optimal trade-off of vendor selection and order allocation in the presence of uncertainties involving multiple conflicting objectives such as cost minimization, service level/quality level maximization and delivery lead time minimization concurrently.

Vendor selection problem considered here has a multi-objective optimization design subject to a set of demand, capacity and quantity discount based constraints. These constraints as well as uncertainty related to lead time have been handled using chance constraint approach. The problem is titled as “integrated dynamic vendor selection problem (IDVSP).” The proposed multi-objective IDVSP is solved using both non-pre-emptive goal programming (GP) and weighted sum aggregate objective function (AOF) technique.

Findings indicate goal achievement for different objectives from both non-pre-emptive GP and AOF procedure. While the goals are satisfactorily achieved as per the target values for cost and lead time, quality/service level was somewhat compromised in order to find an appropriate trade off.

The research work is original as it integrates dynamic as well as stochastic (uncertain) nature of supply chain simultaneously coupled with the concept of incremental quantity discounts on lot sizes.

Industrialisation has contributed to global environmental problems, especially in developed countries, but increasingly so in developing ones as well. The rising public concern for the natural environment is compelling business entities to revise their business models towards green lean (GL) management. Most manufacturing firms have realised that GL implementation is a critical factor that drives their success. Therefore, keeping in view the above said aspects, the purpose of this paper is to empirically assess the complementary impact of GL practices on environmental performance.

Data from a sample of 124 Indian manufacturing industries are analysed using a structural equation modelling technique.

Evidence suggests that GL practices such as top management commitment, government support, human resource management, health and safety of employees and public pressure and legislature have significantly positive effect on environmental performance of manufacturing industries.

The sample is limited to Indian manufacturing industries situated in northern region, with a low response rate.

Successful implementations of GL practices can lead to improved environmental performance. Manufacturing industries within emerging economies like India can improve on their GL practices by incorporating these findings into their business models, while research could be guided to focus their inquiries on this and related genres of scholarly work.

To the best of the authors’ knowledge, this study is one of the first to empirically assess the complementary impact of GL practices on environmental performance within the Indian context.

This study investigates a production process that requires N kinds of components for the production of a finished product. The producer orders the various kinds of components from different suppliers and receives the orders in lots at the beginning of each production cycle. Similar to situations often encountered in real life, the lead times are random variables with known probability distributions so that a production cycle starts whenever all N kinds of components become available. Each of the lots received at the start of a production run contains both perfect and imperfect quality components. Once all N kinds of components become available, the producer initiates a screening process to detect the imperfect components. The production of the finished product uses only perfect quality components. The imperfect components are removed from inventory whenever the screening process is completed. The percentage of components of perfect quality present in each lot is a random variable with a known probability distribution.

This production process is described and modeled mathematically and the optimal production/ordering policy is derived based on the mathematical model.

The formulated mathematical model resulted in the determination of the optimal policy consisting of the optimal number of finished items ordered to be produce during each production run, the number of components ordered from each supplier, and the reorder point. The derived closed form expression for the optimal lot size depends on the minimum of the number of perfect quality components in a lot, whereas the reorder point is determined based on the maximum lead time.

The modeling approach and results of this study provide practical implications that may be beneficial to both production and supply chain managers as well as researchers.

This modeling approach that incorporates decision-making related to the logistics of acquiring the components and accounts for the probabilistic nature of the lead times and quality of components addresses a gap in the logistics/production literature.

In today’s competitive market, product demand and its mix frequently vary due to various uncertainties, which thus imparts the overall manufacturing cost. Furthermore, uncertainties also impart the layout design in manufacturing industries in the long run. Therefore, the layout design needs to capture the possibility of uncertainties, and these uncertainties must be captured while designing the layout of a facility. Hence, an efficient facility layout design minimizes the manufacturing cost and lead time. The purpose of this paper is to propose a cellular layout design for a tower manufacturing industry.

The paper develops an embedded simulated annealing-based meta-heuristic to solve proposed cellular layout under different scenarios considering single and multi-time periods for tower manufacturing industry. A comparative study is also performed to analyze comparison among static cellular layout, a dynamic cellular layout or a robust stochastic cellular layout for the tower manufacturing industry.

The current layout of the industry is a process layout. Here, the layout for a tower manufacturing industry is proposed under SCFLP, DCFLP and RSCFLP. The proposed models and solution methodology is tested using six scenarios with different combination of time periods. Lastly, OFV value obtained for all the scenarios is compared, and it is found that RSCFLP outruns other SCFLP and DCFLP for a tower manufacturing industry. Based on the above study, it is also concluded that RSCFLP is an efficient and effective layout in tower manufacturing industry.

The paper proposes a cellular layout design for a tower manufacturing industry. The cellular layout design is found to be preferred over the traditional layout as it reduces material handling cost, manufacturing lead time and hazards. Moreover, it enhances productivity and quality.