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The purpose of this research is to identify and validate a measurement model for assessing the quality management practices among small and medium-sized enterprises, specifically for the food processing industry in developing countries such as Malaysia.

A survey was initially conducted among SMEs using a questionnaire mailed to the managing director of companies selected from the SMECorp directory. A total of 207 respondents from SMEs were used for further analysis. Two steps of analysis were undertaken to validate the measurement model of critical success factors: principal component analysis and confirmatory analysis.

Eight critical success factors of quality management practices are proposed for assessing quality management practices among SMEs in the food processing industry in Malaysia. A measurement model was then developed. PCA with Varimax rotation revealed 13 components, eight of which were retained for further analysis. First- and second-order CFAs identified the CSF measurement model along with the goodness-of-fit index. Thus, the findings also reveal the status of quality management practices among food processing SMEs in Malaysia.

This paper is to evaluate only the CSFs; therefore, further work is needed to evaluate the relationship between CSFs and organisational performance of SMEs in the food processing industry in Malaysia.

There are various papers regarding the assessment of quality management, especially on TQM practices in various industries. However, few assessments of the critical success factors of quality management practices of SMEs in the food processing industry, especially in developing countries like Malaysia, have been found to date. The findings of this paper will help the industry to identify its current quality management practice to focus on improving its performance.

The paper underpins an advanced domestic manufacturing that comes with some advanced employment specialization status of individual industries as the key determinant of foreign direct investment (FDI) and considers how FDI in the food processing industry in India relates to this focal point.

This paper investigates how inward FDI inflows relate to domestic investment and revival in the industry using Auto Regressive Distributed lags (ARDL) model over the period 2000–2017. The model allows for different specifications to study whether FDI is responsible for the revival or the prior revival induces the FDI.

The results show the lack of proper advanced specialized employment status of the food processing industry. FDI in food processing is mainly guided by exports and imports opportunities and FDI plays no role in the revival of advanced growth in the industry. This finding explains why FDI in the industry is predominantly service sector oriented.

The paper underlines (1) the proper conceptualization of human capital as an important determinant of FDI; (2) reinterpretation of Kaldor's technical progress function that uncovers how employment dynamics embedded in intermediate goods specializations play a key role in supporting a higher pace of investment (and FDI); (3) labor costs' importance should involve not only the wage rate but also the advantages that a specialized employment base and (4) FDI in manufacturing demands a greater policy focus on developing domestic bases of intermediate goods specializations.

In the age of the Fourth Industrial Revolution, Industry 4.0 can increase the productivity and competitiveness of companies in the international marketplace. The purpose of this article is to investigate the drivers for and outcomes of the adoption of Industry 4.0 technologies in the case of a food processing company located in Italy.

The present work adopted a case study approach by investigating an Italian food processing company to investigate the drivers, challenges and outcomes of Industry 4.0 adoption in the agri-food sector.

This research highlighted drivers and challenges related to the adoption of different Industry 4.0 technologies. Secondly, it underlined the impacts of Industry 4.0 in terms of firm performance, operations management, human resource management and strategy.

Industry 4.0 technologies remain underexplored from the strategic perspective in the agri-food sector. This article provides preliminary evidence on the digital transformation of food processing companies, with a focus on Industry 4.0. Practical implications for managers, CEOs and entrepreneurs are discussed.

Reports the findings of a study conducted to explain the recent poor performances of the Australian food‐processing industry, by historically analysing the structural determinants and intensity of competition that prevailed in the early 1980s. Covers the period 1979 to 1985 in depth using a research design that incorporates information from both published sources and empirical interviews of senior marketing executives. Using the Porter framework, an accepted approach to the structural analysis of industries, demonstrates that the food industry was one of the most competitive industries in the world in the early 1980s. Attempts to analyse the resultant implications of such competitive pressures on industry performance in the 1990s.

This paper aims to investigate the effect of firm-specific determinants on the entrepreneurial success (measured through the objective financial performance) of the Czech food processing firms over 2003-2014 and with the main particular focus on capital structure and productivity as the tough challenges of the firms in transition and emerging economies.

Determinants of profitability are tested econometrically, as for the estimation technique, both-way fixed effects controlling for variety over the time and across enterprises were applied. The collected micro-panel data set consists of 10,509 observations and includes 1,804 firms. Estimated regression models with fixed effects are used to quantify the determinants of the financial performance, operationalized through three key performance indicators – price–cost margin, return on assets and return on equity.

Estimated econometric models supported hypothesis assuming a positive relationship between the labour productivity and profitability. In line with the assumptions based on the development of the Czech food market, high leverage of firms led to the decrease of profitability, which can be explained by the high financial distress costs and worsened market position of firms in the competitive environment. Ageing of firms and firm size were associated with the increase of profitability indicators.

The findings of the presented research are important for investors considering agribusiness as a part of their investment portfolios and for policymakers to enhance the economic efficiency of the food industry through regulations and public support, and particularly, from the firm management viewpoint, e.g. to pay attention to the debt policy due to the negative impact of high indebtedness on firm profitability, and to the productivity factors, which proved to be important drivers of entrepreneurial success.

Although the firm-specific factors responsible for firm performance have already been studied, the food processing industry has received limited interest from the empirical analysts, and the results are not always unequivocal. This study is expected to contribute to the literature on this subject, both empirically and methodologically, as to the best of the authors’ knowledge, no study has been encountered yet where the factors determining the profitability of the Czech food processing industry have been the focus. With regards to the collected micro-data set and the estimation technique, the study can be considered as extensive not only from the perspective of the research in the Czech Republic but also from the international perspective.

The purpose of this paper is to measure the Malmquist Productivity Index and its different components such as technological change, technical efficiency change and the change in scale efficiency in the Indian food industry during the period spanning 1988‐1989 to 2004‐2005. Further, it examines the variation in productivity and its components with respect to the factors internal to the firms.

The technique of data envelopment analysis has been used to measure productivity index and its different components under the assumption of variable returns to scale. Further, log‐linear regression model has been used to explain the variation in productivity and its components with respect to certain factors internal to the firms.

In spite of a strong agricultural base and being the third largest producer of food products in the world, India's food processing industry is far from tapping its full potential as a result of a low rate of technological progress/regress on the one hand and increasing inefficiencies of the firms on the other hand. It is necessary to encourage imports along with R&D to ensure faster technological progress in the Indian food industry. However, the technological possibilities depend on the mode of organization and various economic and institutional factors. Therefore, bold institutional changes are to be made side‐by‐side in order that inefficiency is substantially reduced.

The present study evaluates the contribution of technological change, technical efficiency change and scale efficiency change to total factor productivity growth in the Indian food processing industry by using the firm‐level data, collected from the Centre for Monitoring Indian Economy (CMIE). It further examines the impact of some common factors internal to the firms on their performance.

The food and beverage industry, particularly the agro‐processing and packaging industry, plays a critical role in the economies of Latin America and Caribbean countries. To remain competitive and to increase competitiveness, companies operating in this industry must achieve operational efficiency – where one contributing factor would be the efficient management of their work‐in‐progress (WIP) inventories.

Constructs from related manufacturing management research areas such as manufacturing strategy and coordination theory, are incorporated into a conceptual framework to examine the reasons WIP are needed in food plants. The framework is used to inform the development of an audit tool, which is applied in a case study.

An audit tool is proposed, which can be used by practitioners to review the structural and infrastructural decisions that impact WIP.

The audit tool is applied in one case study. Further application of the tool will test its overall comprehensiveness.

The audit tool can be used as part of a structured decision making process.

The paper brings together a number of constructs to create an audit tool that can be used in both greenfield and existing plants in the food industry.

The purpose of this study is to empirically examine the effect of marketing modification on business performance from the perspective of marketing capabilities theory.

This study employed a survey method to collect data from 225 food processing small- and medium-sized enterprises (SMEs) on the Ghana Enterprise Agency (GEA) registered list in the Eastern Region of Ghana. The effect of marketing modification on the performance of SMEs in the food processing industry was evaluated using a structural equation modeling (SEM) – path analysis.

According to the study’s findings, marketing modification has a positive and significant impact on the financial performance (FP), customer performance (CP), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs engaged in food processing. The results of this study also demonstrated the importance of marketing modification determinants, such as marketing resources (MR), cross-functional and interenterprise collaboration (CFIEC), architectural marketing capability (AMC) and marketing strategy decision implementation (MSDI), in achieving food processing SME performance in Ghana.

This current research has its limitation, even though its importance has been mentioned earlier. First, the study can be improved by expanding the sample size through the inclusion of other SMEs from other industries since it is industry specific (i.e. food processing SME). Second, this current study was conducted in Ghana. To compare results, the current study may be replicated in other emerging countries. Third, future research studies may consider how business environmental factors such as technological change (e.g. use of artificial intelligence and machine learning) moderate the relationship between marketing modification and SME performance.

The outcomes of this research study are anticipated to give profitable implications to both academicians and practitioners. For the academic aspect, this study provides an important contribution to marketing modification and performance literature by examining the impact of innovative marketing on the performance of food processing SMEs in Ghana. For practitioners, this study indicates that food processing SME owners/managers must focus on marketing modification to develop their performance. The increase in marketing modification application through marketing capabilities such as MR and CFIEC will enable owners/managers to achieve performance targets.

The application of marketing modification among food processing SMEs in Ghana will contribute greatly to their profitability, survival and growth. The growth and survival of food processing SMEs (not limited to food processing SMEs) in Ghana will help in the control of unemployment, which is a major social issue in Ghana.

The study’s findings provide solid support for the marketing capabilities theory. This study also supports the notion that food processing SMEs should perceive marketing modification and its determinants (i.e. MR, CFIEC, AMC and MSDI) as a critical strategic capacity to enhance their performance (i.e. FP, CF, IBPP and LGP). In terms of contribution, this study adds to the body of knowledge already available on marketing modification and business performance, particularly in the setting of an emerging economy.

The food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry.

The authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective.

This simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs.

The primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.

The purpose of this paper is to identify various risk drivers which affect a food processing supply chain and to create a map of how those risk drivers propagate risks through the supply chain and impact important performance measures.

This study involves experts from food processing companies to elucidate the contextual relationships among the risk drivers and between risk drivers and performance measures. This is used to quantify the relationships and to determine the indirect and overall relationships applying Fuzzy Interpretive Structural Modeling.

Three categories of risk drivers which Indian food processing companies need to pay maximum attention to minimize risks are identified. These are supplier dependency and contracting, supplier variability, visibility and traceability and manufacturing disruptions. Analysis shows that collaborating with suppliers and logistics service providers, developing mutually beneficial contracts with them while ensuring that adequate technology investments are made can significantly mitigate risks and consequently improve margins and lead to revenue growth.

This study has been carried out with experts from large food processing companies in India, and hence, the results cannot be generalized across other types of food processing companies.

The proposed methodology can help understand the interrelationships between supply chain risks and between those risks and performance measures. Thus, it can help a food processing company to create business cases for specific supply chain risk mitigation projects.

This study is one of the earliest to create a comprehensive risk propagation map for food processing companies which helps in quantifying the impact the risk drivers have on each other and on performance measures.