Abstract Paper aims This research explores proposed integrated policies and supports for university spin-offs by considering the growth level of the spin-offs. Originality According to the literature, different types of support are needed to make spin-offs become established companies. However, the literature lacks clarity in addressing the specific types of support required at each stage of spin-off growth. Research method This research employs a qualitative research method in which the data collection is based on nine interviews with the founders of the spin-offs, the inventor, the director of the technology transfer office, the head of the university incubator, the manager of the university technopark, and the director of university’s company. Main findings Each level of spin-offs’ growth has to be supported by specific policies and supports. There are different types of support expected at the pre-incubation stage, which are Intellectual Property Rights (IPRs) protection, patent incentives, royalties, matching funds between university and industry, and the Technology Transfer Office as a matchmaker of inventor and startup founders. Implications for theory and practice This study provides a theoretical contribution to the policy framework for university spin-offs and offers practical guidance for university management and incubator managers.

Abstract Paper aims This study seeks to investigate the accuracy of machine learning algorithms for estimation of the effort required for software development in the manufacturing sector to identify the most effective algorithms according to the nature and complexity of the data and the number of available attributes. Originality This work distinguishes itself from other studies in the field of effort prediction by utilizing a data repository that consists exclusively of projects from the manufacturing sector. This approach ensures that the specific characteristics of manufacturing projects are reflected in the predictions, addressing a gap in the existing literature. Another notable contribution of this study is the comparative analysis of various machine learning algorithms assessed under different dimensionality scenarios (three and five variables). Although this factor is crucial for enhancing effort estimation accuracy, it has received limited attention in the literature. Research method The investigated techniques in this work were (i) Support Vector Regression, (ii) Gradient Boosting Machines (GBM), (iii) eXtreme Gradient Boosting (XGBoost), (iv) Random Forest (RF), (v) Extreme Learning Machine (ELM); and (vi) Linear Regression (LR). Performance measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) were used to compare the results achieved by each model, considering a dataset of 230 records originating from various countries. Main findings The comparison among machine learning models revealed significant performance variations depending on the number of variables and the evaluation metrics adopted. GBM stood out for its robustness in complex scenarios, while SVR achieved the lowest mean absolute error. ELM, in turn, proved effective with fewer variables but showed sensitivity to outliers and less stability in more complex contexts. Among all the techniques evaluated, XGB yielded the worst performance across all parameters. Implications for theory and practice This study contributes by applying these models to the manufacturing sector and comparing scenarios with three and five variables. The results support a more informed selection of models based on project complexity and data dimensionality. The more research conducted in this area, the stronger the theoretical and practical conclusions can be drawn.

Abstract: Paper aims The research aims to investigating the impact of implementing Internet of Things (IoT) using Bayesian networks in the supply chain of manufacturing of Indian auto components enterprises to achieve enhanced productivity and reduced failure rates. Originality The research's originality lies in exploring IoT's impact with Bayesian Networks in Indian auto component manufacturing, showcasing Industry 4.0 applications. Research method The research utilizes Bayesian Network analysis to investigate IoT's impact in Indian auto component manufacturing supply chains, validating findings through Industry 4.0-based IoT implementation and a pilot study. Main findings Implementing IoT in Indian auto component manufacturing enhanced industry performance, productivity, and reduced failure rates with Industry 4.0 technologies. Implications for theory and practice The research offers theoretical insights into IoT and Industry 4.0's impact on the automotive industries and practical solutions for practitioners

Abstract Paper aims This study aims to identify the main risks affecting food supply chains (FSCs) in Brazil, analyze their interrelationships, and propose mitigation strategies. Originality This research presents a structured approach to modeling the interdependencies among FSC risks, offering original insights with practical applications for risk managers in the Brazilian food sector. Research method The methodology consists of three phases: (1) an Exploratory Literature Review (ELR) to identify and categorize FSC risks; (2) a survey with industry experts to collect qualitative data on the interrelationships among the identified risks; and (3) the application of Interpretive Structural Modeling (ISM) and the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis to explore the relationships among risks and propose mitigation strategies. Main findings Ten key risks were identified and ranked. Natural risks and macro-level risks emerged as the most influential, while demand, supply, and operational risks were found to be more dependent on these factors. The ISM model illustrated the interconnections among the risks, and the MICMAC analysis classified them according to their driving and dependence power. Implications for theory and practice The findings support the development of targeted mitigation strategies, strengthening risk resilience and decision-making within FSCs. Additionally, the study contributes to academic discourse by integrating structural analysis into food supply chain risk management.

Abstract Paper aims This study characterizes Purchasing and Supply Management (PSM) projects in the context of Industry 4.0 (I4.0) at a Brazilian steel company, using the reference architecture model industry 4.0 (RAMI4.0) as a basis. Originality The article adapts RAMI4.0 to the PSM context and empirically evaluates the model, promoting digitalization practices in PSM. Research method An in-depth case study was conducted using participant observation, document analysis, individual interviews, and focus groups with PSM professionals. Main findings By adapting the life cycle (dimension) to the stages of PSM deployment, this study establishes a framework for monitoring and optimizing activities throughout the PSM process, offering valuable insights. Implications for theory and practice The customization and evaluation of the RAMI4.0 model for PSM can serve as a reference framework for companies seeking to drive the digital transformation of the PSM function. This model offers a strategic perspective on how to integrate Industry 4.0 technologies across various organizational levels, with the goal of promoting digital PSM.

Paper aims Developing a conceptual framework for modeling the "Deliver" business process of the Mineral Supply Chain (MiSC) upstream segment, based on a standardized perspective. Originality A novel approach is introduced that adapts the Supply Chain Operation Reference (SCOR) and the Exploration and Mining Enterprise Reference (EM) models to standardize the "Deliver" of the MiSC's upstream segment. It fills the gap in existing MiSC models, which lack a standardized framework and boundaries for their segments. Research method It followed design science research (DSR), specifically a Requirement- and development-focused research approach. A top-down modeling technique was developed to develop the "Deliver" modeling framework at three levels of abstraction, reusing, and adapting standardized models SCOR and EM. Main findings The SCOR can be adapted for "Deliver" in the upstream segment of MiSC. The study establishes the modeling basis for integration with downstream business processes. It also emphasizes the need to support the integration of MiSC with a commercialization framework. Implications for theory and practice The "Deliver" model developed offers a practical tool for MiSC practitioners to integrate upstream and downstream segments. This study encourages further research in the MiSC, especially in the upstream segment, to improve transparency and understanding for stakeholders.

Paper aims We analyze in this study the management and governance structure of technology transfer in megaprojects under an emergency context. Originality We analyze an emergency health megaproject's management and governance structure. There is a dearth of studies that explore the management and governance structure of combination megaprojects in emergencies. We focus on analyzing the literature and the management and governance structure in the technology transfer, production, and supply of the Covid-19 vaccine. We then develop a reference model for building the management and governance structure of megaprojects in emergencies. Research method We conducted a case study in Bio-Manguinhos/Fiocruz. From the case study, we can analyze real situations that contribute to the development of theories. For this, we systematically reviewed the literature to identify how the megaproject management structure has been used. We interviewed stakeholders of the Oxford/AstraZeneca/Fiocruz vaccine megaproject against Covid-19 and triangulated these data by collecting corresponding documents. Main findings We conclude that the management and governance structure of the Oxford/AstraZeneca/Fiocruz vaccine technology transfer, production, and supply megaproject was appropriate. Since this structure should not be reproduced faithfully due to its specific characteristics, we developed a reference model for structure building in emergencies. This model can support institutions and governments in structuring management and governance in critical situations. Implications for theory and practice The knowledge obtained from this study will contribute to determining management and governance structures for future megaprojects, especially in emergencies.

Paper aims: This paper aims to discuss how Remote Collaboration Tools (RCT) can support and contribute to Action Research (AR) applications in operations management research. Originality: Researchers shall capture the complexity of studied phenomena when conducting AR. Although, there is a lack of information regarding the use of RCT in AR. Hence, this paper provides original and comprehensive guidelines for the application of RCT in AR. Research method: This paper provides an ex-post analysis of two face-to-face AR cases, based on the steps proposed by Coughlan and Coughlan (2002). Based on the ex-post analysis, it explains ex-ante and how RCT can impact and contribute to AR on operations management. Main findings: The paper presents reflections, insights and guidance about the impacts of RCT in AR. RCT allow possibilities for continuity and efficient use of resources in AR. Although, when choosing RCT, special efforts should be put into team building and engagement. Implications for theory and practice: The description of how the RCT can be used in the AR cycle can contribute to theory based on empirical results as guidance to increase value considering AR quality criteria. We intend to provide material for the advance of the field of Operations, Production and Management, theoretically and practically.

Paper aims To predict monthly corn, soybean, and sugar spot prices in Brazil using hybrid forecasting techniques. Originality This study combines the Singular Spectrum Analysis with different forecasting methods. Research method This paper presents a set of hybrid forecasting approaches combining Singular Spectrum Analysis (SSA) with different univariate time series methods, ranging from complex seasonality methods to machine learning and autoregressive models to predict monthly corn, soybean, and sugar spot prices in Brazil. We carry out a range of out-of-sample forecasting experiments and use a comprehensive set of forecast evaluation metrics. We contrast the performance of the proposed approaches with that of a range of benchmark models. Main findings The results show that the proposed hybrid models present better performances, with the hybrid SSA-neural network approach providing the most competitive results in our sample. Implications for theory and practice Forecasting agricultural prices is of paramount importance to assist producers, farmers, and the industry in decision-making processes.

Paper aims The objective of this research is to propose a multicriteria negotiation model for selecting a sustainable packaging supplier in the food industry. The model aims to minimize the environmental, social and economic impacts of the production process of plastic while ensuring the operational criteria of packaging. Originality Despite the high number of scientific research using multicriteria decision-making suggested to select a supplier, the proposed negotiation model is innovative as it includes green manufacturing as a criterion for negotiation, alongside price and delivery time, to promote sustainable supply chain management. Research method The research method involves three stages: pre-negotiation, negotiation (based on FITradeoff), and post-agreement. The proposed model is illustrated using a case study of a Colombian slaughterhouse that needs to select a sustainable packaging supplier for the next two years. Main findings The main findings indicate that the proposed model that uses the FITradeoff for negotiation solves the problem of selecting sustainable packaging suppliers while promoting sustainable supply chain management in the agribusiness sector. Implications for theory and practice The model has implications for both theory and practice. It encourages interdependent companies to collaborate and enhances efficiency in greener terms for supply chains, buyer and seller firms. The proposition of an original integrative negotiation protocol for selecting sustainable suppliers in agribusiness can provide enterprises with a set of negotiation parameters to achieve successful sustainable supply chain management.