Predictive Cash Flow Forecasting Using Deep Learning and ERP Transaction Data in Mid-Market Manufacturing Firms

Abstract

Mid-market manufacturing firms occupy an awkward middle ground in corporate finance. They are large enough to generate transaction volumes that exceed the capacity of spreadsheet-driven treasury practices, yet too small to maintain the dedicated quantitative finance teams that large enterprises deploy for liquidity management. The result is a persistent gap between the cash-flow visibility these firms need and the forecasting tools they actually use. This paper develops a conceptual and methodological framework for closing that gap by combining deep learning models with the granular transaction data already captured inside enterprise resource planning (ERP) systems. We argue that the ERP layer—accounts receivable, accounts payable, sales orders, production schedules, inventory movements, and the general ledger—constitutes an underexploited, high-frequency data source that is naturally suited to sequence-modeling architectures such as long short-term memory (LSTM) networks and attention-based transformers. Drawing on a structured review of more than sixty sources spanning financial forecasting, machine learning, working-capital theory, ERP analytics, and SME technology adoption, we propose a forecasting pipeline that ingests ERP transaction streams, engineers liquidity-relevant features, trains a hybrid recurrent–attention model, and surfaces explainable, decision-ready forecasts through a treasury dashboard. Because no proprietary firm dataset was available for this study, the empirical section presents a clearly labeled illustrative simulation rather than results from real operating data. In that simulation, deep learning architectures reduce forecasting error relative to a seasonal ARIMA baseline by margins consistent with the comparative literature. We discuss why interpretability, data governance, and modest implementation cost are decisive for adoption in resource-constrained firms, and we close with recommendations and an explicit account of the study’s limitations. The contribution is a deployable blueprint and research agenda rather than a validated empirical claim.