Improving lead time of pharmaceutical production processes using Monte Carlo simulation
•Systematic approach for improving production lead time in pharmaceutical industry.•Monte Carlo simulation allowing flexible estimation of the production lead time.•Sensitivity analysis for identifying key contributors.•What-if analysis for simulating the effect of assessed improvements.•Industrial...
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Veröffentlicht in: | Computers & chemical engineering 2014-09, Vol.68, p.255-263 |
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creator | Eberle, Lukas Gallus Sugiyama, Hirokazu Schmidt, Rainer |
description | •Systematic approach for improving production lead time in pharmaceutical industry.•Monte Carlo simulation allowing flexible estimation of the production lead time.•Sensitivity analysis for identifying key contributors.•What-if analysis for simulating the effect of assessed improvements.•Industrial case study for proving the effectiveness of the method.
Reliable product supply is one of the most critical missions of the pharmaceutical industry. The lead time, i.e. the duration between start and end of an activity, needs to be well managed in any production facility in order to make scheduling predictable, agile and flexible. We present a method for measuring and improving production lead time of pharmaceutical processes with a primary focus on Parenterals (i.e. injectables) production processes. Monte Carlo simulation is applied for quantifying the total lead time (TLT) of batch production as a probability distribution and sensitivity analysis reveals the ranking of sub-processes by impact on TLT. Based on these results, what-if analyses are performed to evaluate effects of investments, resource allocations and process improvements on TLT. An industrial case study was performed at a production site for Parenterals of F. Hoffmann-La Roche in Kaiseraugst, Switzerland, where the presented method supported analysis and decision-making of production enhancements. |
doi_str_mv | 10.1016/j.compchemeng.2014.05.017 |
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Reliable product supply is one of the most critical missions of the pharmaceutical industry. The lead time, i.e. the duration between start and end of an activity, needs to be well managed in any production facility in order to make scheduling predictable, agile and flexible. We present a method for measuring and improving production lead time of pharmaceutical processes with a primary focus on Parenterals (i.e. injectables) production processes. Monte Carlo simulation is applied for quantifying the total lead time (TLT) of batch production as a probability distribution and sensitivity analysis reveals the ranking of sub-processes by impact on TLT. Based on these results, what-if analyses are performed to evaluate effects of investments, resource allocations and process improvements on TLT. An industrial case study was performed at a production site for Parenterals of F. Hoffmann-La Roche in Kaiseraugst, Switzerland, where the presented method supported analysis and decision-making of production enhancements.</description><identifier>ISSN: 0098-1354</identifier><identifier>EISSN: 1873-4375</identifier><identifier>DOI: 10.1016/j.compchemeng.2014.05.017</identifier><identifier>CODEN: CCENDW</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Applied sciences ; Biological and medical sciences ; Chemical engineering ; Computer simulation ; Decision theory. Utility theory ; Decision-making ; Exact sciences and technology ; General pharmacology ; Impact analysis ; Industrial application ; Inventory control, production control. Distribution ; Lead time ; Logistics ; Medical sciences ; Monte Carlo methods ; Monte Carlo simulation ; Operational research and scientific management ; Operational research. Management science ; Pharmaceutical production ; Pharmaceutical technology. Pharmaceutical industry ; Pharmaceuticals ; Pharmacology. Drug treatments ; Resource allocation ; Sensitivity analysis ; Supply chain</subject><ispartof>Computers & chemical engineering, 2014-09, Vol.68, p.255-263</ispartof><rights>2014 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c487t-2e5e1fe29a9384c55e5a74e70f0f4bfb85fa4e53f3950e297689832619479da3</citedby><cites>FETCH-LOGICAL-c487t-2e5e1fe29a9384c55e5a74e70f0f4bfb85fa4e53f3950e297689832619479da3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0098135414001677$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28663646$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Eberle, Lukas Gallus</creatorcontrib><creatorcontrib>Sugiyama, Hirokazu</creatorcontrib><creatorcontrib>Schmidt, Rainer</creatorcontrib><title>Improving lead time of pharmaceutical production processes using Monte Carlo simulation</title><title>Computers & chemical engineering</title><description>•Systematic approach for improving production lead time in pharmaceutical industry.•Monte Carlo simulation allowing flexible estimation of the production lead time.•Sensitivity analysis for identifying key contributors.•What-if analysis for simulating the effect of assessed improvements.•Industrial case study for proving the effectiveness of the method.
Reliable product supply is one of the most critical missions of the pharmaceutical industry. The lead time, i.e. the duration between start and end of an activity, needs to be well managed in any production facility in order to make scheduling predictable, agile and flexible. We present a method for measuring and improving production lead time of pharmaceutical processes with a primary focus on Parenterals (i.e. injectables) production processes. Monte Carlo simulation is applied for quantifying the total lead time (TLT) of batch production as a probability distribution and sensitivity analysis reveals the ranking of sub-processes by impact on TLT. Based on these results, what-if analyses are performed to evaluate effects of investments, resource allocations and process improvements on TLT. An industrial case study was performed at a production site for Parenterals of F. Hoffmann-La Roche in Kaiseraugst, Switzerland, where the presented method supported analysis and decision-making of production enhancements.</description><subject>Applied sciences</subject><subject>Biological and medical sciences</subject><subject>Chemical engineering</subject><subject>Computer simulation</subject><subject>Decision theory. Utility theory</subject><subject>Decision-making</subject><subject>Exact sciences and technology</subject><subject>General pharmacology</subject><subject>Impact analysis</subject><subject>Industrial application</subject><subject>Inventory control, production control. Distribution</subject><subject>Lead time</subject><subject>Logistics</subject><subject>Medical sciences</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Pharmaceutical production</subject><subject>Pharmaceutical technology. Pharmaceutical industry</subject><subject>Pharmaceuticals</subject><subject>Pharmacology. Drug treatments</subject><subject>Resource allocation</subject><subject>Sensitivity analysis</subject><subject>Supply chain</subject><issn>0098-1354</issn><issn>1873-4375</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkEtLxDAQgIMouK7-h3oQvLQmTdKmRym-YMXLgseQTSe7WdqmJu2C_96UXcSjh2Hm8M3rQ-iW4IxgUjzsM-26Qe-gg36b5ZiwDPMMk_IMLYgoacpoyc_RAuNKpIRydomuQthjjHMmxAJ9vnWDdwfbb5MWVJOMtoPEmWTYKd8pDdNotWqTyDSTHq3r51JDCBCSKcxt764fIamVb10SbDe1asau0YVRbYCbU16i9fPTun5NVx8vb_XjKtVMlGOaAwdiIK9URQXTnANXJYMSG2zYxmwEN4oBp4ZWHEesLEQlaF6QipVVo-gS3R_Hxqu-Jgij7GzQ0LaqBzcFSQqW5zEYjWh1RLV3IXgwcvC2U_5bEixnl3Iv_7iUs0uJuYwuY-_daY0KUYfxqtc2_A7IRVHQghWRq48cxJcPFrwM2kKvobEe9CgbZ_-x7QdDipFf</recordid><startdate>20140904</startdate><enddate>20140904</enddate><creator>Eberle, Lukas Gallus</creator><creator>Sugiyama, Hirokazu</creator><creator>Schmidt, Rainer</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140904</creationdate><title>Improving lead time of pharmaceutical production processes using Monte Carlo simulation</title><author>Eberle, Lukas Gallus ; Sugiyama, Hirokazu ; Schmidt, Rainer</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c487t-2e5e1fe29a9384c55e5a74e70f0f4bfb85fa4e53f3950e297689832619479da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Biological and medical sciences</topic><topic>Chemical engineering</topic><topic>Computer simulation</topic><topic>Decision theory. Utility theory</topic><topic>Decision-making</topic><topic>Exact sciences and technology</topic><topic>General pharmacology</topic><topic>Impact analysis</topic><topic>Industrial application</topic><topic>Inventory control, production control. Distribution</topic><topic>Lead time</topic><topic>Logistics</topic><topic>Medical sciences</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo simulation</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Pharmaceutical production</topic><topic>Pharmaceutical technology. Pharmaceutical industry</topic><topic>Pharmaceuticals</topic><topic>Pharmacology. Drug treatments</topic><topic>Resource allocation</topic><topic>Sensitivity analysis</topic><topic>Supply chain</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Eberle, Lukas Gallus</creatorcontrib><creatorcontrib>Sugiyama, Hirokazu</creatorcontrib><creatorcontrib>Schmidt, Rainer</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & chemical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eberle, Lukas Gallus</au><au>Sugiyama, Hirokazu</au><au>Schmidt, Rainer</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving lead time of pharmaceutical production processes using Monte Carlo simulation</atitle><jtitle>Computers & chemical engineering</jtitle><date>2014-09-04</date><risdate>2014</risdate><volume>68</volume><spage>255</spage><epage>263</epage><pages>255-263</pages><issn>0098-1354</issn><eissn>1873-4375</eissn><coden>CCENDW</coden><abstract>•Systematic approach for improving production lead time in pharmaceutical industry.•Monte Carlo simulation allowing flexible estimation of the production lead time.•Sensitivity analysis for identifying key contributors.•What-if analysis for simulating the effect of assessed improvements.•Industrial case study for proving the effectiveness of the method.
Reliable product supply is one of the most critical missions of the pharmaceutical industry. The lead time, i.e. the duration between start and end of an activity, needs to be well managed in any production facility in order to make scheduling predictable, agile and flexible. We present a method for measuring and improving production lead time of pharmaceutical processes with a primary focus on Parenterals (i.e. injectables) production processes. Monte Carlo simulation is applied for quantifying the total lead time (TLT) of batch production as a probability distribution and sensitivity analysis reveals the ranking of sub-processes by impact on TLT. Based on these results, what-if analyses are performed to evaluate effects of investments, resource allocations and process improvements on TLT. An industrial case study was performed at a production site for Parenterals of F. Hoffmann-La Roche in Kaiseraugst, Switzerland, where the presented method supported analysis and decision-making of production enhancements.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.compchemeng.2014.05.017</doi><tpages>9</tpages></addata></record> |
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subjects | Applied sciences Biological and medical sciences Chemical engineering Computer simulation Decision theory. Utility theory Decision-making Exact sciences and technology General pharmacology Impact analysis Industrial application Inventory control, production control. Distribution Lead time Logistics Medical sciences Monte Carlo methods Monte Carlo simulation Operational research and scientific management Operational research. Management science Pharmaceutical production Pharmaceutical technology. Pharmaceutical industry Pharmaceuticals Pharmacology. Drug treatments Resource allocation Sensitivity analysis Supply chain |
title | Improving lead time of pharmaceutical production processes using Monte Carlo simulation |
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