Food supply chain collaboration (FSCC) is a prevalent business strategy in developed countries. Despite the widespread adoption, there is still limited literature on FSCC in developing countries, including Indonesia. Therefore, the study aimed to analyze the factors of information sharing, relationship quality, and corporate shared value on supply chain collaboration and their effect on improving the competitiveness of rice main food products in Indonesia. The conceptual framework was developed from the Relational View theory as the basis for supply chain collaboration, which could prove beneficial in increasing the competitiveness of food products. The data were collected from three rice supply chain actors, namely farmers, rice milling units, and retailers, which were analyzed using partial least squares Structural Equation Modeling. The results showed that information sharing was the variable with the greatest effect on supply chain collaboration, followed by corporate shared value and relationship quality. Information sharing carried out by supply chain actors was due to dependence, trust, and commitment to relationships. Collaboration and information sharing among actors showed the potential to positively improve the competitiveness of rice main food products. These results provided valuable insights for food supply chain actors, emphasizing the importance of collaboration by considering economic, social, and environmental aspects.

This research aims to show how decision sciences can make a significant contribution on handling the supply chain problem during Covid-19 Pandemic. The paper discusses how robust optimization handles uncertain demand in agricultural processed products supply chain problems within two scenarios during the pandemic situation, i.e., the large-scale social distancing and partial social distancing. The study assumes that demand and production capacity are uncertain during a pandemic situation. Robust counterpart methodology is employed to obtain the robust optimal solution. To this end, the uncertain data is assumed to lie within a polyhedral uncertainty set. The result shows that the robust counterpart model is a computationally tractable through linear programming problem. Numerical experiment is presented for the Bandung area with a case on sugar and cooking oil that is the most influential agricultural processed products besides the main staple food of the Indonesian people, rice.