Chris Piper

• Sawhney, R.; Piper, C. J., 2002, "Value Creation Through Enriched Marketing-Operations Interfaces: An Empirical Study In The Printed Circuit Board Industry", Journal of Operations Management, January 20(3): 259 - 272.
  Abstract: The paper examines the short-term capacity interface between marketing and operations, i.e. marketing_operations interface (MOI) in relation to customer value. A field study involving 10 firms in the printed circuit board (PCB) manufacturing industry was used to develop a conceptual framework and measures of the constructs. Subsequently, a 180-plant PCB industry survey was used to test the model, finding support for the proposed relationship between MOI effectiveness and customer value.
• Piper, C. J.; Vachon, S., 2001, "Accounting for Productivity Losses in Aggregate Planning", International Journal of Production Research, January 39(17): 4001 - 4012.
  Abstract: The aggregate planning literature fails to correctly account for productivity losses incurred by employee layoffs and hires. Moreover, the literature is mostly silent on the productivity losses associated with other capacity changes such as multiple shifts and overtime. In those cases where productivity losses are considered, traditional approaches impute associated costs, but fail to account for lost capacity. It was found that when linear programming formulations have capacity constraints that account for productivity losses, the resulting production plans are superior to those obtained when productivity losses are modelled solely as costs in the objective function. Some avenues for future research are also proposed.
• Melnyk, S. A.; Piper, C. J., 1981, "Implementation of Material Requirements Planning: Safety Lead Times", International Journal of Operations and Production Management, December 2(1): 52 - 61.
  Abstract: Because previous systems of production and inventory control have produced excessive inventories, late deliveries, and poor productivity, many firms in the last 10 years have incorporated Material Requirements Planning (MRP) in their production systems. MRP eliminates uncertainty in component level demand and allows the focusing on lead time variability. Manufacturing lead time is the time required from the release of an order to production and its receipt in stock. To protect MRP from outliers' adverse effects, safety lead times can be used by the decision maker. A variety of estimation techniques can be followed, from past observed mean time to that of observing the impact of factors such as the production quantity or number of operations. Adding safety lead times reduces service levels and increases inventory. Safety lead time buffers at the parts level produced more benefits than similar buffers at the sub-assembly level. The principal benefit from MRP implementation is a reduction in element inventory.
• Piper, C. J.; Zoltners, A. A., 1976, "Some Easy Postoptimality Analysis for Zero-One Programming", Management Science, March 22(7): 759 - 765.
  Abstract: Some easy postoptimality analysis for zero-one programming is developed. First, a procedure is presented for obtaining a set K of 0-1 solutions whose objective function values are within a specified tolerance of the optimum. Then sufficiency conditions for K to contain the optimum of a revised problem are derived. Computational experience with a number of test problems indicates that K can be economically obtained and that the sufficiency tests provide a significant postoptimization capability.
• Piper, C. J.; Zoltners, A. A., 1975, "Implicit Enumeration Based Algorithms for Postoptimizing Zero-One Programs", Naval Research Logistics, December 22(4)
  Abstract: It has been shown by G. Roodman that useful postoptimization capabilities for the 0-1 integer programming problem can be obtained from an implicit enumeration algorithm modified to classify and collect all fathomed partial solutions. This paper extends the approach as follows: 1) Improved parameter ranging formulas are obtained by higher resolution classification criteria. 2) Parameters may be changed so as to tighten the original problem, in addition to relaxing it. 3) An efficient storage structure is presented to cope with difficult data collection task implicit in this approach. 4) Finally, computer implementation is facilitated by the elaboration of a unified set of algorithms.

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