Strategic Data Analytics Under Supplier Encroachment

Overview

This research examines optimal data analytics deployment strategies for retailers facing potential channel encroachment by suppliers granted access to demand forecasting systems. The study situates this problem within a vertical supply chain context where data sharing enhances collaboration but simultaneously creates competitive threats. The analysis distinguishes between two organizational governance models for data platforms and characterizes how analytics accuracy should vary under different supplier encroachment scenarios.

Methods and approach

The research employs Bayesian persuasion theory as the analytical framework, formulating the retailer's data analytics strategy as a signaling mechanism that transforms market states into demand predictions with variable informational precision. Two data governance architectures are systematically compared: a shared platform architecture that transmits identical forecasts to both parties, and a separate platform architecture that delivers high-fidelity predictions to the retailer while providing degraded information to the supplier. The comparative statics analysis examines how optimal strategies vary as supplier encroachment costs range across the cost spectrum.

Key Findings

Under shared platform governance, the retailer's optimal analytics strategy exhibits nonmonotonic behavior relative to supplier encroachment costs. When encroachment costs are sufficiently high or sufficiently low, perfectly accurate demand forecasts emerge as optimal. At intermediate encroachment cost levels, the shared platform generates informationally-relevant but systematically biased predictions that paradoxically benefit both channel members due to double marginalization effects. Under separate platform governance, informative predictions for suppliers are withheld entirely at moderate encroachment costs. Cross-structural comparison reveals that retailers derive greater preference for shared platforms when supplier encroachment costs remain relatively elevated, whereas separate platforms dominate the preference ordering at lower encroachment cost levels.

Implications

The findings establish a non-monotonic relationship between data analytics accuracy and supplier encroachment efficiency, providing quantitative guidance for algorithm design and analytics implementation in retail-supplier relationships. The analysis demonstrates that informational asymmetry in forecasting systems need not reduce channel welfare and may actually improve joint outcomes by mitigating double marginalization distortions inherent to vertical supply chains. Organizations implementing data governance systems should recognize that strategic information degradation can generate superior performance compared to uniform transparency in contexts where supplier competitive threat varies with encroachment feasibility. These results suggest that organizational choices regarding data platform architecture should be calibrated to the relative costs suppliers face in establishing competing direct-to-consumer channels or alternative distribution mechanisms. The research indicates that transparent, integrated data analytics within vertical channels produces measurable benefits when supplier encroachment costs are sufficiently high to limit competitive threats, while information partitioning becomes preferable when encroachment becomes more economically feasible.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.