Conference Agenda

Organizations may utilize a variety of tools to improve their overall forecasting accuracy in their supply chain, acknowledging the inherent uncertainty associated with historical data. The most of these tools rely on quantitative methods, using the organization's historical sales data to recognize demand patterns and forecast future demand. Yet, given that historical data may not precisely mirror future events in the demand and supply chain forecasting, it becomes essential to introduce collaborative forecasting model (CFM) in supply chain. The CFM provides a tool that allows users to input and reconcile forecast information which enables the process of collecting and reconciling forecasts in the supply chain. Our analysis of a manufacturing supply chain forecasting revealed that the forecast accuracy and decision-making process was insufficiently robust and systematic. To address this problem, we propose a novel collaborative forecasting model that provides practitioners with a robust and accurate forecasting model in supply chain.

With an emphasis on understanding practitioners’ perceptions of the concept of social inclusion and their dynamic procurement practices, this study applies social capital theory to analyze data collected through 27 semi-structured interviews with managers from diverse companies in multiple industries and regions. It is not a mystery that practitioners struggle with how to make sense of what social inclusion practices they can implement and why and how to do so. This study advocates a sense-making approach that harmonises the paradoxical goals of financial profitability and sustainability to effectively address social issues and lead to joint accomplishments. This shows the potential of this method for cultivating favourable outcomes, such as motivation, stronger connections, and improved buyer-supplier dynamics. In summary, this study contributes to the understanding of social inclusion practices in procurement by offering valuable insights into their implementation and implications and opens new avenues and directions for future research and practice.

This research is about supplier payment times. As part of encouraging timely payment to small suppliers, countries like Australia and the UK require corporations to publicly report on their payment times. Using data on approximately 500 Australian firms taken from https://paymenttimes.gov.au/, we test the determinants of payment times to small suppliers between 2021-2023. To structure our analysis, we use Oliver’s (1991) predictive framework that explains how firms respond to institutional pressures, such as paying suppliers on time and as contractually agreed. According to the framework, corporate responses to specific institutional pressures are contingent on cause, constituents, content, control and context factors. We obtain proxy measures of each factor using secondary data sources and then test them as predictors of payment performance. The paper provides new and theoretically grounded evidence on supplier payment times, an aspect of responsible purchasing that is widely discussed in practice but under-researched by academics.