The Ongoing Challenge for a Responsive Demand Supply Network: The Final Frontier—Controlling the Factory

Fordyce, Kenneth; Milne, R. John

doi:10.1007/978-0-85729-644-3_3

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…A common means of determining PLTs is to set them as a multiple of total processing time (Fordyce & Milne, 2012). Kanet (1986) finds this method superior to the other methods he considered that determine PLTs in an MRP context.…”

Section: Literature Reviewmentioning

confidence: 98%

Optimizing planned lead times for enhancing performance of MRP systems

Milne

Mahapatra

Wang

2015

International Journal of Production Economics

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Section: Literature Reviewmentioning

confidence: 98%

Optimizing planned lead times for enhancing performance of MRP systems

Milne

Mahapatra

Wang

2015

International Journal of Production Economics

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“…Kanet () defined PLT as the budgeted time between production order release date and the production order due date or completion date. A common means of determining PLTs is to set these times as a multiple of the total processing time (Fordyce and Milne, ). In the discrete remanufacturing system proposed in this paper, actual operational lead times vary because of the disassembly time, remanufacturing time and procurement time.…”

Section: Introductionmentioning

confidence: 99%

A min–max solution to optimise planned lead time in a remanufacturing system

Liu

Chen

Segerstedt

et al. 2017

Int Trans Operational Res

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Determining a rational planned lead time (PLT) is a critical and difficult problem in production planning, especially for a remanufacturing system. This paper considers an optimisation problem of the PLT in a make‐to‐order remanufacturing system to coordinate disassembly–remanufacturing–reassembly and to improve performance. This optimisation model is designed to determine the PLT that minimises the inventory holding and shortage costs. Given the unknown distribution of remanufacturing time but with known first and second moments, this model is solved by a min–max approach, which can capture distributions with the same first and second moments. How the PLT and total cost are affected by yield rate with different first and second moments, unit holding cost, unit shortage cost and purchasing lead time are also investigated through numerical examples in this paper. The results of this study are shown to be consistent with practice and can be a support to decision‐making in production planning and scheduling for remanufacturing.

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Tutorial: Illusion of capacity — Challenge of incorporating the complexity of FAB capacity (tool deployment & operating curve) into central planning for firms with substantial non-fab complexity

Fordyce

Fournier

Milne

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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The Ongoing Challenge for a Responsive Demand Supply Network: The Final Frontier—Controlling the Factory

Cited by 3 publications

References 18 publications

Optimizing planned lead times for enhancing performance of MRP systems

Optimizing planned lead times for enhancing performance of MRP systems

A min–max solution to optimise planned lead time in a remanufacturing system

Tutorial: Illusion of capacity — Challenge of incorporating the complexity of FAB capacity (tool deployment & operating curve) into central planning for firms with substantial non-fab complexity

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The Ongoing Challenge for a Responsive Demand Supply Network: The Final Frontier—Controlling the Factory

Cited by 3 publications

References 18 publications

Optimizing planned lead times for enhancing performance of MRP systems

Optimizing planned lead times for enhancing performance of MRP systems

A min–max solution to optimise planned lead time in a remanufacturing system

Tutorial: Illusion of capacity &#x2014; Challenge of incorporating the complexity of FAB capacity (tool deployment &amp; operating curve) into central planning for firms with substantial non-fab complexity

Contact Info

Product

Resources

About

Tutorial: Illusion of capacity — Challenge of incorporating the complexity of FAB capacity (tool deployment & operating curve) into central planning for firms with substantial non-fab complexity