Forecast Bias Control In High-Mix, Low-Volume Data Center Rack Supply Chains For AI Infrastructure

Varun Uppalpati

doi:10.29284/mvm2ts51

Authors

Varun Uppalpati Nanyang Technological University, Singapore

DOI:

https://doi.org/10.29284/mvm2ts51

Keywords:

Forecast bias, High-mix low-volume supply chains, AI data center infrastructure, Demand forecasting, Supply chain planning.

Abstract

The intensive expansion in the artificial intelligence (AI) workload has resulted in the unprecedented need of the purpose-specific, high-performance racks of data centers, which has radically transformed the configuration of the supporting supply chains. The supply chains are of the high-mix, low-volume (HMLV) type such as high-level product customization, long-component lead time, low-density demand data, and very high capital intensity. Forecast bias - constant over estimation or under estimation of the demand has a great threat to capacity planning, inventory management and decisions on investments in such environments. Bias, unlike random forecast error, builds up over the years, and may result in continued misallocation of resources, stagnant capital, or sluggish infrastructure implementation. The review is a synthesis of the studies based on the forecasting theory, behavioral operations management, and supply chain coordination to investigate the origins of forecast bias and the mechanisms that can be used to manage the forecast bias in HMLV situations. The paper identifies bias drivers as behavioral, organizational and structural, and assesses statistical, judgmental, process-based and contractual bias management techniques. Specific consideration is drawn to the use of the methods in AI data center rack supply chains where the impact of biased forecasts is magnified by the rapid change of technologies and power and cooling limits. The review points to the fact that purely analytical solutions have their limitations and should be replaced with integrated solutions that incorporate structured judgment, cross-functional governance, and incentive alignment. Citing the best practices and uncovering the most critical knowledge gaps in the literature review, the present paper offers a basis to practitioners and researchers wanting to enhance the predictability of forecasts in complex AI infrastructure supply chains.

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