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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This research aims to examine the potential tensions and management strategies for adopting artificial intelligence (AI) within Sales and Operations Planning (S&OP) environments.

We conducted in-depth interviews with eight S&OP professionals from different manufacturing firms, supplemented by interviews with AI solutions experts and secondary document analysis of various S&OP processes, to scrutinize the paradoxes associated with AI adoption in S&OP.

We revealed 12 sub-paradoxes associated with AI adoption in S&OP, culminating in 5 overarching impact pathways: (1) balancing immediate actions with long-term AI-driven strategies, (2) navigating AI adoption via centralized systems, process redesign and data unification, (3) harmonizing AI-driven S&OP identities, collaboration and technology acceptance, (4) bridging traditional human skills with innovative AI competencies and (5) managing the interrelated paradoxes of AI adoption in S&OP.

The findings provide a roadmap for firms to proactively address the possible tensions associated with adopting AI in S&OP, balancing standardization with flexibility and traditional expertise with AI capabilities.

This research offers (1) a nuanced understanding of S&OP-specific paradoxes in AI adoption, contributing to the broader literature on AI within operations management and (2) an extension to Paradox Theory by uncovering distinct manifestations at the AI–S&OP intersection.

Changes frequently made to material delivery schedules (MDSs) accumulate upstream in the supply chain (SC), causing a bullwhip effect. This article seeks to elucidate how dynamic complexity generates MDS instability at OEMs in the automotive industry.

An exploratory multiple-case study methodology involved in-depth semistructured interviews with informants at three automotive original equipment manufacturers (OEMs).

Dynamic complexity destabilizes MDSs primarily via internal horizontal interactions between product and process complexities and demand and SC complexities. A network of complexity interactions causes and moderates such instability through complexity absorption and generation and complexity importation and exportation.

The multiple-case study contributes to empirical knowledge about the dynamics of MDS instability. Deductive research to validate the identified relationships remains for Future research.

In revealing antecedents of complexity’s effect on MDS instability, the findings imply the need to develop strategies, programs, and policies dedicated to improving capacity scalability, supplier flexibility, and the flexibility of material order fulfillment.

Building on complexity literature, the authors operationalize complexity transfer and develop a framework for analyzing dynamic complexity in SCs, focusing on complexity interactions. The identification and categorization of interactions provide a granular view of the dynamic complexity that generates MDS instability. The identified and proposed importance of readiness of the SC to absorb complexity challenges the literature focus on external factors for explaining complexity outcomes. The results can be used to operationalize such dynamic interactions by introducing new variables and networks of relationships. Moreover, the work showcases how a complexity perspective could be used to discern the root causes of a complex phenomenon driven by non-linear relationships.