Trading off inventory, capacity and customer service in semi-process industries: a case study at SEPPIC

This article presents a straightforward production/inventory model that can capture the trade-offs among average inventory, production capacity and customer service levels in a semi-process industry setting. The model is based on well-known approximations from queuing literature, and it supports midterm planning procedures at SEPPIC, a large specialty chemicals company. Different features are specific to a semi-process setting, such as differences in reactor yield, variations in quality requirements across products, the need for cleaning reactors when switching between product types, and the requirement to produce products in campaign sizes that are integer multiples of the reactor's batch size. This article discusses how SEPPIC's operational reality is reflected in the model and illustrates the resulting insights with real-life data from two SEPPIC product families. [Received 22 September 2011; Revised 2 March 2012, 15 June 2012; Accepted 24 June 2012]

[1]  Iftekhar A. Karimi,et al.  Integrated campaign planning and resource allocation in batch plants , 2011, Comput. Chem. Eng..

[2]  Kumar Rajaram,et al.  Buffer sizing in multi-product multi-reactor batch processes: Impact of allocation and campaign sizing policies , 2007, Eur. J. Oper. Res..

[3]  Wallace J. Hopp,et al.  Factory physics : foundations of manufacturing management , 1996 .

[4]  Eric C. Carlson,et al.  Simulation and queueing network modeling of single-product production campaigns , 1992 .

[5]  Nico Vandaele,et al.  Clips: a capacity and lead time integrated procedure for scheduling , 1998 .

[6]  Edward F. Watson An Application of Discrete-Event Simulation for Batch-Process Chemical-Plant Design , 1997 .

[7]  R. M. Felder,et al.  Simulation for the capacity planning of speciality chemicals production , 1985 .

[8]  Andreas Witt,et al.  Application of a mathematical model to an intermediate- to long-term real-world steel production planning problem based on standard software , 2011 .

[9]  Horst Zisgen,et al.  Queueing networks with batch service , 2011 .

[10]  S. Elmaghraby The Economic Lot Scheduling Problem (ELSP): Review and Extensions , 1978 .

[11]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[12]  Nico Vandaele,et al.  Advanced resource planning as a decision support module for ERP , 2011, Comput. Ind..

[13]  C ChatfieldDean The economic lot scheduling problem , 2007 .

[14]  Robert Pellerin,et al.  Engineering change order processing in ERP systems: an integrated reactive model , 2010 .

[15]  Kumar Rajaram,et al.  Campaign Planning and Scheduling for Multiproduct Batch Operations with Applications to the Food-Processing Industry , 2004, Manuf. Serv. Oper. Manag..

[16]  Edward F. Watson,et al.  Response surface analysis of a multi-product batch processing facility using a simulation metamodel , 2006 .

[17]  John A. Buzacott,et al.  Stochastic models of manufacturing systems , 1993 .

[18]  Josef Kallrath,et al.  Optimal planning in large multi-site production networks , 2000, Eur. J. Oper. Res..

[19]  B. Fleischmann The discrete lot-sizing and scheduling problem , 1990 .

[20]  Manjunath Kamath,et al.  Chapter 5 Performance evaluation of production networks , 1993, Logistics of Production and Inventory.

[21]  C. Suerie Campaign planning in time-indexed model formulations , 2003 .

[22]  A. Drexl,et al.  Proportional lotsizing and scheduling , 1995 .

[23]  Nico Vandaele,et al.  Advanced Resource Planning , 2003 .

[24]  W. Whitt,et al.  The Queueing Network Analyzer , 1983, The Bell System Technical Journal.