Inventory evaluation and product slate management in large-scale continuous process industries

David L. Cooke, Thomas R. Rohleder

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Managing production and inventory for large-scale continuous processing plants is a key to success in the chemical process industry, particularly in the production of commodity polymers such as polyethylene and polypropylene. When setting up a production schedule, planners must consider the effects of off-grade production and production rate penalties, which are typically sequence-dependent as products are transitioned from one to another, as well as inventory holding costs and capacity constraints. We call this the continuous economic lot sizing and scheduling problem (CELSP) to distinguish it from closely related problems found in discrete part production industries. We present a formulation that addresses the particular aspects of the CELSP and apply the proposed mathematical modeling approach to several plants of a chemical processing company. This company was concerned with ensuring the plants were carrying proper amounts of inventory and with evaluating the number of products produced at each plant. Our results show actual overall inventory levels were close to the levels suggested by our model and therefore company plans for further inventory reductions would not be appropriate. However, the company should carefully consider the addition of new products to current plant product slates. Due to the effects of product transitions and inventory levels, even adding products with significant contribution margins may negatively affect the financial performance of the plant.

Original languageEnglish (US)
Pages (from-to)235-249
Number of pages15
JournalJournal of Operations Management
Issue number3
StatePublished - Apr 2006


  • Inventory management
  • Lot sizing
  • Process industries
  • Product decisions
  • Scheduling/sequencing

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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