Structural Signatures of Customer Value in a B2B Manufacturing System: An RFM‐QV Segmentation With LOPCOW and MARCOS

Customer prioritisation in business‐to‐business (B2B) manufacturing must be decided from production records. These records rarely contain prices, and they register demand at the level of the work order. The classical Recency–Frequency–Monetary (RFM) construct needs adaptation before it can be applied in this setting. Monetary value is unobserved, temporal resolution is coarse, and the signals that matter to operations lie outside the construct. This study extends RFM with a Quality dimension, defined as the conformity ratio weighted by volume, and a Variety dimension, defined as portfolio breadth. Criterion weights are derived with the Logarithmic Percentage Change‐Driven Objective Weighting (LOPCOW) method in its standard form. Customers are ranked with the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method and partitioned into quintile segments. The framework is applied to 5486 work orders from 120 customers of a glass processor over 2022–2024. Four structural signatures characterise the customer base and explain the behaviour of the method. Output is concentrated with a heavy tail (Gini 0.83), and the top quintile supplies 81.6% of conforming output. Setup and scrap intensity scale sublinearly with volume. The Recency boundary produces a threshold in the ranking. The compromise ordering cannot be reduced to any single dimension. An exact identity shows that a LOPCOW weight is a function of the ratio of mean to dispersion of the normalised criterion. The weight therefore measures the shape of a distribution, not managerial importance. Quality (0.58) and Recency (0.33) dominate. Removing either reassigns a third to a half of the customers, whereas removing Frequency or the volume proxy reassigns two. With volume held constant, portfolio breadth is positively associated with setup time per unit (elasticity 0.46), while burden adjusted for volume is uncorrelated with the compromise score. The segmentation is stable under moderate perturbation of the weights (mean adjusted Rand index 0.97 over 2000 draws) and moderately stable under arbitrary weights (0.52). Four objective weighting methods applied to the same matrix give four different weight vectors, so objectivity here means reproducibility rather than uniqueness. The outputs are stated as proposals for operational policy, not as demonstrated effects.