Drivers of On-Time Delivery for Build-to-Stock Items: An Empirical Analysis of Time Series Data on Fill Rate Performance

Many firms today compete on their ability to deliver customer orders quickly and reliably. While researchers have proposed numerous factors believed to affect delivery performance, little empirical research has examined such factors using longitudinal data from real world manufacturing operations. This paper examines a list of structural, demand, and supply factors believed to impact delivery using monthly data on product assembly from a major manufacturer. We estimate econometric models that capture the impacts of these factors upon product line item fill rates. The findings provide support for several hypothesized drivers of delivery performance. Managers may benefit from the empirical approach and research findings, which identify salient variables that managers can monitor and adjust to ensure desired delivery outcomes.

[1]  Z. Drezner,et al.  Quantifying the Bullwhip E ect in a Simple Supply Chain : The Impact of Forecasting , Lead Times and Information , 2007 .

[2]  Everett E. Adam,et al.  Forecasting error evaluation in material requirements planning (MRP) production-inventory systems , 1986 .

[3]  Gérard P. Cachon,et al.  Drivers of Finished-Goods Inventory in the U.S. Automobile Industry , 2010, Manag. Sci..

[4]  Eric T. Bradlow,et al.  Structural Estimation of the Effect of Out-of-Stocks , 2010, Manag. Sci..

[5]  R. Narasimhan,et al.  Supplier integration—Finding an optimal configuration , 2006 .

[6]  Serguei Netessine,et al.  Retail Store Execution: An Empirical Study , 2006 .

[7]  K.G. Kempf,et al.  Control-oriented approaches to supply chain management in semiconductor manufacturing , 2004, Proceedings of the 2004 American Control Conference.

[8]  David J. Closs,et al.  The differential impact of product complexity, inventory level, and configuration capacity on unit and order fill rate performance , 2010 .

[9]  B. Williams,et al.  Operations management. , 2001, Optometry.

[10]  Michael Milgate,et al.  Supply chain complexity and delivery performance: an international exploratory study , 2001 .

[11]  Julia Miyaoka,et al.  How Improved Forecasts Can Degrade Decentralized Supply Chains , 2008, Manuf. Serv. Oper. Manag..

[12]  Nagesh N. Murthy,et al.  The antecedents of supply chain agility of a firm: Scale development and model testing , 2006 .

[13]  Thomas Y. Choi,et al.  An exploration of supplier selection practices across the supply chain , 1996 .

[14]  Xiande Zhao,et al.  Forecasting errors and the value of information sharing in a supply chain , 2002 .

[15]  Jr. Everette S. Gardner,et al.  Evaluating forecast performance in an inventory control system , 1990 .

[16]  Robert B. Handfield,et al.  An Empirical Study of Delivery Speed and Reliability , 1992 .

[17]  David C. Ribar,et al.  Analysis of panel data: Second Edition, Cheng Hsiao, Cambridge University Press, Cambridge, United Kingdom, 2003, ISBN 0-521-81855-9, 382 pages, [UK pound]21.95 , 2004 .

[18]  Jerry A. Hausman,et al.  Panel Data and Unobservable Individual Effects , 1981 .

[19]  Marshall L. Fisher,et al.  An Econometric Analysis of Inventory Turnover Performance in Retail Services , 2005, Manag. Sci..

[20]  M. Fisher.  What is the Right Supply Chain for Your Product , 1997 .

[21]  Hau L. Lee.  Managing supply chain inventory: Pitfalls and opportunities , 1992 .

[22]  Brian Squire,et al.  The Impact of Mass Customisation on Manufacturing Trade‐offs , 2006 .

[23]  Marshall L. Fisher,et al.  Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales , 1996, Oper. Res..

[24]  J. Hausman.  Specification tests in econometrics , 1978 .

[25]  Ramakrishnan Ramanathan,et al.  ABC inventory classification with multiple-criteria using weighted linear optimization , 2006, Comput. Oper. Res..

[26]  Lawrence M. Corbett.  A comparative study of the operations strategies of globally- and domestically-oriented New Zealand manufacturing firms , 1996 .

[27]  Jinjoo Lee,et al.  Manufacturing strategy and production systems: An integrated framework , 1993 .

[28]  Darryl D. Wilson,et al.  Supply management orientation and supplier/buyer performance , 2000 .

[29]  D. P. Donk,et al.  Safety stock or safety lead time: coping with unreliability in demand and supply , 2010 .

[30]  A. Roth,et al.  A taxonomy of manufacturing strategies , 1994 .

[31]  Kenneth K. Boyer,et al.  COMPETITIVE PRIORITIES: INVESTIGATING THE NEED FOR TRADE‐OFFS IN OPERATIONS STRATEGY , 2002 .

[32]  Christopher F. Baum.  XTTEST3: Stata module to compute Modified Wald statistic for groupwise heteroskedasticity , 2000 .

[33]  J C-H Pan,et al.  Inventory models with fixed and variable lead time crash costs considerations , 2002, J. Oper. Res. Soc..

[34]  Kut C. So,et al.  Impact of supplier's lead time and forecast demand updating on retailer's order quantity variability in a two-level supply chain , 2003 .

[35]  Serguei Netessine,et al.  What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation , 2007, Manuf. Serv. Oper. Manag..

[36]  Jeffrey M. Woodbridge.  Econometric Analysis of Cross Section and Panel Data , 2002 .

[37]  Hau L. Lee,et al.  Expressions for item fill rates in periodic inventory systems , 1995 .

[38]  George Jr. Stalk Hout.  Competing Against Time : How Time Based Competition Is Reshaping Global Markets , 2002 .

[39]  Jan Fransoo,et al.  Ordering Behavior in Retail Stores and Implications for Automated Replenishment , 2010, Manag. Sci..

[40]  Cheng Hsiao,et al.  Analysis of Panel Data , 1987 .

[41]  H.L. Lee,et al.  Aligning Supply Chain Strategies with Product Uncertainties , 2002, IEEE Engineering Management Review.

[42]  R. Nagel,et al.  Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer , 1994 .

[43]  T. Davis.  Effective supply chain management , 2020, Strategic Direction.

[44]  Karen Donohue,et al.  An Empirical Study of Service Differentiation for Weapon System Service Parts , 2003, Oper. Res..

[45]  P. Diggle.  Analysis of Longitudinal Data , 1995 .

[46]  Roberto Sarmiento,et al.  Delivery reliability, manufacturing capabilities and new models of manufacturing efficiency , 2007 .

[47]  Larry P. Ritzman,et al.  The relative significance of forecast errors in multistage manufacturing , 1993 .

[48]  Feng Cheng,et al.  Product Hardware Complexity and Its Impact on Inventory and Customer On-Time Delivery , 2000 .

[49]  A. Meyer,et al.  Lasting Improvements in Manufacturing Performance: In Search of a New Theory , 1990 .

[50]  Yew Sing Lee,et al.  Pricing and Delivery-Time Performance in a Competitive Environment , 1994 .

[51]  R. Handfield.  The Role of Materials Management in Developing Time‐Based Competition , 1993 .

[52]  Albert Y. Ha,et al.  Price and Delivery Logistics Competition in a Supply Chain , 2003, Manag. Sci..

[53]  Silvanus T. Enns.  MRP performance effects due to forecast bias and demand uncertainty , 2002, Eur. J. Oper. Res..

[54]  Roger G. Schroeder,et al.  THE IMPACT OF ELECTRONIC DATA INTERCHANGE ON DELIVERY PERFORMANCE , 2001 .

[55]  Lee J. Krajewski,et al.  Kanban, MRP, and Shaping the Manufacturing Environment , 1987 .

[56]  Rajan Suri,et al.  Quick Response Manufacturing , 1998 .

[57]  David M. Drukker,et al.  Testing for Serial Correlation in Linear Panel-data Models , 2003 .

[58]  M. Fisher,et al.  CONFIGURING A SUPPLY CHAIN TO REDUCE THE COST OF DEMAND UNCERTAINTY , 1997 .

[59]  Ananth Raman,et al.  Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers? , 2010, Manag. Sci..