Data-Driven Inventory Financing: Linking Demand Forecasting, Stock Policies, and Cash Flow Management

Abstract

Inventory is usually the largest current asset a trading or manufacturing firm must finance, yet the three disciplines that determine its size and cost are often run in isolation. Forecasters optimise accuracy metrics, operations teams set reorder rules, and treasury negotiates credit lines, with little explicit link between them. This paper develops a conceptual model that connects these layers in a single causal chain: forecast error determines safety stock, the stock policy determines average inventory, average inventory determines the financing requirement and borrowing capacity, and the cost of that financing feeds back into the holding cost that governs the stock policy. The approach is deliberately transparent. Every relationship is stated as a closed-form expression and illustrated with numerical examples that can be reproduced by hand or in a spreadsheet. Using a representative distributor, the examples show that halving forecast error releases about $14,000 of working capital per stock-keeping unit, that higher 2023 interest rates raise total inventory cost by roughly 11 percent relative to a 3 percent borrowing environment, that the financing rate shifts the cost-optimal service level, and that forecast quality materially changes peak borrowing and covenant headroom in a 13-week cash flow forecast. The paper closes with managerial implications and propositions for empirical testing.