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Disruptions are appearing more frequently and having an ever greater impact on supply chains (SC), affecting the vulnerability and sustainability of organisations. Our study proposes an innovative approach to address contemporary challenges by introducing coopetition as a strategic capability. The aim of this study is to enable companies to adapt and thrive by applying a tool that measures and monitors different logistical scenarios to improve performance and antifragility.

With the aim of jointly planning transport activities of two competing companies, we present a linear programming model that promotes synergies which enhance resource utilisation. To demonstrate the validity of the model, a case study is conducted to measure, monitor and evaluate the results obtained after collaborating on SC activities.

Current tools to support logistics planning are not effective because they hamper information exchange, cost allocation and performance measurements. Our innovative model optimises collaborative networks (CNs) and monitors economic, environmental and social improvements. The case study shows the reduction of logistics costs (13%), carbon footprint (37%) and the improvement of social antifragility when agility and flexibility emerge.

CNs have become an effective means of enhancing resilience, but there are no empirical contributions to demonstrate how to achieve this. We provide a real case with computational experiments that provide empirical evidence of the effectiveness of the model, which measures, optimises and evaluates SC performance in coopetitive environments. This approach is a guide to researchers and practitioners when creating simulations to reduce risks and facilitate decision-making.

Grocery sellers that have entered the online business must now carry out order fulfilment activities previously done by the customer. Consequently, in a context of online sales growth, the purpose of this study is to identify and implement best practices in order to redesign the order picking process in a retailer with a store-based model.

To identify different work alternatives, an approach is developed to analyse the methods used in distinct stores of one large Spanish grocer. The methodology employed is a three-step statistical analysis that combines ANOVA and MANOVA techniques to settle on the best alternatives in each case.

Substantial improvements can be achieved by analysing the different working methods. The three-step statistical analysis identified best practices in terms of their impact on preparation time, allowing a faster working method.

To manage business processes efficiently, online grocers that operate store-based fulfilment strategies can redesign their working method using a criterion based on their own performance.

This is one of the few contributions focusing on the improvement of e-grocery fulfilment operations by disseminating best practices through decision-making criteria. This study contributes by addressing the lack of approaches studying the order picking process by considering its various features and applying best practices.