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This paper aims to study the growth determinants of small-sized agro-based firms in the Indian agro-industry.

The stratified random sampling method was used to collect information from sample agro-firms. A structured pretested questionnaire was designed to collect required data. Descriptive statistics and multivariate technique were used to analyze the data.

The major determinants of firm growth were firm size, managerial networking intensity, skill development of employees, product diversification and market integration. Employee skill development was found to be a significant predictor of firm growth in slow-growing firms, but held as a weak predictor of growth in fast-growing firms; whereas, use of information technology was a significant growth predictor in fast-growing firms and an insignificant growth predictor in slow-growing firms. To attain growth, small-sized agro-based firms should move from a traditional product-focus strategy to a flexible market-focus strategy.

This study engaged a small sample size and focused only on the determinants of firm growth in the agro-industry, and mapped number of firm growth predictors. The implication of this study encourages more specific investigations with large samples, i.e. how each determinant influences firm growth in the agro-industry.

The study outcome would help agro-enterprises in designing strategies and aligning their current strategy with the desired strategy for firm growth.

Policy makers especially engaged in self-employment and enterprise development can use the study outcome for policy planning.

In recent years, the electronic and computer industry in India has developed and reached to an unexpected height. The agro-industry, in contrast, is still struggling due to its inherent weakness and external threats. The need of the hour is to expedite firm-level competitiveness, managerial excellence and business strategy for growth and survival. Therefore, the present study based on survey data adds value to the firm growth strategies in the agro-industry.

Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).

The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.

The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.

The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.

The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.

This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.

Applies work study techniques to the agro‐industry, the industry which processes agricultural products for everyday consumption. Examines the peculiarities of agro‐industry production lines and labour utilisation. Describes incentives, the planning process, and manpower planning. Concludes that the agro‐industry presents a whole host of problems not normally associated with process industry.

In this chapter we examine how the small scale agro-industries located in Southern Brazil, specifically in the North of the State of Rio Grande do Sul, started to deal with changes in their production processes, how they created and adapted technologies, and devised new products. Among the main outcomes of the study we highlight the novelties observed during the field research, especially regarding the family situation and the agro-manufacturing activities, in which we observed (i) a relative raise in autonomy; (ii) improvement in both the income level and the quality of life of household members; (iii) creation of new nested markets and marketing channels; (iv) development of more environmentally sustainable products; (v) improvement of the value added to food products; and (vi) development of new interfaces between families and other social actors.

The issue of food security is one of the critical global challenges. The Government and the industries have begun apprehending the importance of green supply chain management (GSCM) implementation in their supply chains. There are various drivers or performance indicators (PIs) of GSCM in the agro-sector. This paper aims to analyse 14 PIs using an interpretive structural modelling (ISM) approach.

In this study, the PIs of GSCM were identified through a literature survey and opinions of field experts. The identified 14 PIs were modelled by applying an ISM methodology for establishing the interrelationship between the PIs and to identify the PIs having high influential power.

The result of the investigation underlined that three PIs, namely, environmental management (PI 1), regulatory pressure (PI 3) and competitive pressure (PI 2) are the significant PIs having high driving power.

The experts’ judgments were used for the development of the structural model, which could be biased influencing the reliability of the model. Also, only 14 significant PIs were considered for the analysis. This research is intended to help the policymakers, managers and supply chain designers in the food industry and in agribusiness in formulating the policies and strategies for achieving food security, conservation of the environmental resources and for improving the financial performance of the industry.

It is pioneering research focusing on the analysis of the PIs towards the implementation of GSCM in the Indian agro-industries context using an ISM approach. This research adds value to the existing knowledge base by identifying the crucial PIs, exploring their mutual relationship and highlighting their level of influence in the case sector.

This work sought to analyze targeted innovation strategies used during the pandemic to maintain companies’ competitiveness.

The methodology was a systematic literature review, analyzing how these factors can be used as leverage in decision-making and suggesting a framework tool.

As a result, nine factors were identified as drivers to stimulate competitiveness, bringing insights to structure actions in times of crisis to support agribusiness.

With this work, it is possible that other companies can base themselves and use the strategic drivers of innovation evidenced to remain competitive in the market during a period of crisis. As this is a systematic review of the literature, the application of a case study, for example, is a limitation, which could be a continuation of the work.

As this is a systematic review of the literature, the application of a case study, for example, is a limitation, which could be a continuation of the work.

This work has high value because it brings insights into strategic drivers of innovation that tend to leverage or maintain the competitiveness of agribusinesses in times of crisis. With the discussion carried out on the data obtained, it is possible that agribusinesses or other types of companies can be based for decision-making in a crisis scenario from innovative actions that generate competitive advantage.

The purpose of this paper is to optimize the preventive maintenance based on fault tree (FT)–Bayesian network (BN) reliability for sugarcane harvester machine as a fundamental machine in the sugar industry that must be operated failure-free during a given period of the harvesting process.

To determine machine reliability using the algorithm developed based on mapping FTs into BNs, the common failures of 168 machines were carefully investigated over 12 years (2007–2019). This algorithm was then used to predict the harvester reliability, estimate delays by machine downtimes and their consequences on white sugar production losses that can be reduced by optimizing the preventive maintenance scheduling.

The optimization of preventive maintenance scheduling based on estimated reliability of sugarcane harvester machines using FT–BNs can reduce white sugar production losses, the operation-stopping breakdowns and the downtime costs as a crisis that the sugar industry is facing.

Machine reliability gradually decreased by 31.08% approximately, which resulted in a working time loss of 26% in the 2018–19 harvesting season. In total, the white sugar losses were estimated as 204.17 tons for burnt canes and 114.53 tons for green canes. The losses of the 2018–19 harvesting season have been 11.85 times greater than the first harvesting season. The proposed maintenance interval for critical subsystems including the hydraulic, chopper and base cutter were obtained as 1.815, 1.12 and 1.05 h, respectively.

In this study, a new approach was used to optimize preventive maintenance to reduce delays and their implications upon costs in time, inconvenience and white sugar losses. The FT–BNs algorithm was found a useful tool that was over-fitting of failure occurrence probabilities data for sugarcane harvester machine.

The purpose of this paper is to review and examine the recent investment trends of firms operating in the food, feed and biofuel production and processing sectors in Latin America. The inter-related nature of these three sub-sectors and the great expansion they have gone through in the last decade showcases a series of socioeconomic and environmental policy challenges thus making it relevant to identify their different business models through a typology.

The paper first presents an unprecedented literature review based on field observations and media coverage of agri-business strategies of the food, feed and biofuel production in the region. It then moves to an in-depth analysis of investment operations that serve to classify such firms into a business model typology considering degree of internationalization and integration. The typology is a useful mechanism to enhance public policy analysis and uncover market or government incentives behind business decisions.

By focusing on investment strategies, the paper illustrates how both market and government incentives shape and affect the performance and consolidation of different players in the food, feed and biofuel sub-sectors in Latin America. The resulting effects have strong economic as well as social and environmental implications because such economic activities have an impact on global food and energy security.

Limitations include a reliance on largely qualitative evidence and research methods due to unavailability of consistent numerical data in these specific agri-business sub-sectors.

This paper is unique in its focus on business models in a particularly relevant set of agri-business sub-sectors in Latin America and its implications to promote investment and innovation in value chain development while considering regional-specific challenges.

The purpose of this paper is to improve the productivity of a large-sized frozen chicken manufacturer in Thailand. It analyses the production process based on work study principles and identifies the bottleneck operation. It develops three models for the chicken preparation process.

First, analyse the current production system by collecting the cycle time of all operations in the production process based on work study principles. Then, design the production network and identify the bottleneck operation. After that, three methods – based on line balancing (LB), theory of constraints, and JIT concepts or ECRS (eliminate, combine, rearrange and simplify) – are proposed and implemented in the actual production line.

With the ECRS concept, the authors implement combine by combining two stations into one station, such as handling and weighing, or weighing batter and mixing it with chicken. Then, Simplify is implemented at job E, or transporting chicken using a cart instead of walking. This method can improve the cycle time and reduce the number of employees. It can increase the line efficiency by up to 94.20 per cent, reduce the number of employees by 14 persons, and reduce the labour cost by 356,160 baht/year.

Most agro-industry manufacturing processes are labour intensive. Thus, production LB can help increase productivity and reduce costs. The authors found that the case study company designed the production line without aligning it with the production network. A simple improvement can be made by adjusting the sequence of the work. In addition, the current production line was not lean. Implementation of the ECRS concept to improve production can reduce the waiting time and simplify the job.