Longitudinal Analysis of Inhibitors of Manufacturer Delivery Performance

Decision Sciences, Forthcoming

46 Pages Posted: 25 Aug 2013

See all articles by Gregory R. Heim

Gregory R. Heim

Texas A&M University - Department of Information & Operations Management

David Xiaosong Peng

University of Houston - C.T. Bauer College of Business

Shekhar Jayanthi

Rensselaer Polytechnic Institute (RPI)

Date Written: August 23, 2013

Abstract

This paper examines demand, manufacturing, and supply factors proposed to inhibit manufacturer delivery execution. Extant research proposes many factors expected to harm delivery performance. Prior cross-sectional empirical research examines such factors at the plant-level, generally finding factors arising from dynamic complexity to be significant, but factors arising from detail complexity to be insignificant. Little empirical research examines the factors using product-level operating data, which arguably makes more sense for analyzing how supply chain complexity factors inhibit delivery. For purposes of research triangulation, we use longitudinal product-level data from MRP systems to examine whether the factors inhibit internal manufacturing on-time job rates and three customer-oriented measures of delivery performance: product line item fill rates, average delivery lead times, and average tardiness. Our econometric models pool product line item data across division plants and within distinct product families, using a proprietary monthly dataset on over 100 product line items from the environmental controls manufacturing division of a Fortune 100 conglomerate. The data summarize customer ordering events of over 900 customers and supply chain activities of over 80 suppliers. The study contributes academically by finding significant detail complexity inhibitors of delivery that prior studies found insignificant. The findings demonstrate the need for empirical research using data disaggregated below the plant level unit of analysis, as they illustrate how some factors previously found insignificant indeed are significant when considered at the product-level unit of analysis. Managers can use the findings to understand better which drivers and inhibitors of delivery performance are important.

Keywords: Complexity, Delivery, Longitudinal Data, Manufacturing, Regression, Supply Chain, Uncertainty, Complexity

JEL Classification: D2, D20, M11, M, L6

Suggested Citation

Heim, Gregory R. and Peng, David Xiaosong and Jayanthi, Shekhar, Longitudinal Analysis of Inhibitors of Manufacturer Delivery Performance (August 23, 2013). Decision Sciences, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2315243

Gregory R. Heim (Contact Author)

Texas A&M University - Department of Information & Operations Management ( email )

320 Wehner Building
4217 TAMU
College Station, TX 77843-4217
United States
979-845-9218 (Phone)

HOME PAGE: http://mays.tamu.edu/info/

David Xiaosong Peng

University of Houston - C.T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

Shekhar Jayanthi

Rensselaer Polytechnic Institute (RPI) ( email )

Troy, NY 12180
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
43
Abstract Views
303
PlumX Metrics