Engineering and construction firms operate payroll under a condition most enterprise HCM systems were never designed to represent: a single project workforce governed simultaneously by multiple collective bargaining agreements, each specifying its own wage scale, apprenticeship progression, and fringe benefit contribution schedule, often administered by a separate multiemployer benefit fund with its own reporting format. A carpenter, an electrician, and a plumber working the same project site are not variations on one classification scheme inside the payroll system; they are three independent rate structures that happen to share a paycheck cycle. This article examines the specific data integration problem that results when payroll execution, third-party benefit fund administration, and general ledger job costing must all draw from labor agreement data that changes by trade, by local union jurisdiction, and by apprenticeship level, and often changes on a schedule none of the receiving systems controls. We propose applying a four-level interoperability model, adapted from health information systems research, to this problem: technical connectivity between systems, syntactic agreement on data structure, semantic agreement on what a trade classification or apprenticeship level actually means across systems, and process agreement on which system is authoritative when a rate changes. Empirical evidence on the cost of getting this wrong is concrete: payroll fraud tied to worker misclassification in one state's construction industry alone has been estimated at hundreds of millions of dollars annually, and roughly forty percent of registered construction apprentices cancel their programs before completion, with non-union apprentices facing substantially higher cancellation risk than their unionized counterparts. We argue that treating multi-trade labor agreement data as a semantic integration problem, not merely a technical file-transfer problem, is what allows engineering and construction payroll to remain both accurate and auditable as project workforces scale across trades and jurisdictions.
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Venkata Niranjan Reddy Challa (2026) studied this question.
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