Managing the Seed-Corn Supply Chain at Syngenta
Each year, Syngenta Seeds, Inc. produces over 50 seed-corn hybrids and the following year markets over 100 hybrids under the NK brand name. The fact that growing seed corn is a biological process dependent upon local weather and insect conditions during the growing season complicates production plan...
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| Veröffentlicht in: | Interfaces (Providence) 2003-01, Vol.33 (1), p.80-90 |
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| creator | Jones, Philip C Kegler, Greg Lowe, Timothy J Traub, Rodney D |
| description | Each year, Syngenta Seeds, Inc. produces over 50 seed-corn hybrids and the following year markets over 100 hybrids under the NK brand name. The fact that growing seed corn is a biological process dependent upon local weather and insect conditions during the growing season complicates production planning. In addition, customers' experiences with a particular hybrid during a given year strongly influence demand for that hybrid during the next year. To help mitigate some of these yield and demand uncertainties, Syngenta (and other seed companies as well) take advantage of a second growing season for seed corn in South America, which occurs after many of the yield uncertainties and some of the demand uncertainties have been resolved or reduced. To better manage this production-planning process, Syngenta and the University of Iowa developed and implemented a second-chance production-planning model. A trial of the model showed that using it to plan 2000 production would have increased margins by approximately $5 million. Today, Syngenta uses this model to plan production for those varieties that account for 80 percent of total sales volume. |
| doi_str_mv | 10.1287/inte.33.1.80.12718 |
| format | Article |
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The fact that growing seed corn is a biological process dependent upon local weather and insect conditions during the growing season complicates production planning. In addition, customers' experiences with a particular hybrid during a given year strongly influence demand for that hybrid during the next year. To help mitigate some of these yield and demand uncertainties, Syngenta (and other seed companies as well) take advantage of a second growing season for seed corn in South America, which occurs after many of the yield uncertainties and some of the demand uncertainties have been resolved or reduced. To better manage this production-planning process, Syngenta and the University of Iowa developed and implemented a second-chance production-planning model. A trial of the model showed that using it to plan 2000 production would have increased margins by approximately $5 million. 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The fact that growing seed corn is a biological process dependent upon local weather and insect conditions during the growing season complicates production planning. In addition, customers' experiences with a particular hybrid during a given year strongly influence demand for that hybrid during the next year. To help mitigate some of these yield and demand uncertainties, Syngenta (and other seed companies as well) take advantage of a second growing season for seed corn in South America, which occurs after many of the yield uncertainties and some of the demand uncertainties have been resolved or reduced. To better manage this production-planning process, Syngenta and the University of Iowa developed and implemented a second-chance production-planning model. A trial of the model showed that using it to plan 2000 production would have increased margins by approximately $5 million. Today, Syngenta uses this model to plan production for those varieties that account for 80 percent of total sales volume.</description><subject>Agribusiness</subject><subject>Agriculture</subject><subject>Business studies</subject><subject>Capital costs</subject><subject>Case studies</subject><subject>Cereals</subject><subject>Corn</subject><subject>Females</subject><subject>food</subject><subject>Food production</subject><subject>Forecasting models</subject><subject>Genetically altered foods</subject><subject>Growing seasons</subject><subject>Hybridity</subject><subject>Inventories</subject><subject>Inventory</subject><subject>Inventory: production</subject><subject>Management</subject><subject>Management science</subject><subject>Modeling</subject><subject>Operations research</subject><subject>Planting</subject><subject>Production costs</subject><subject>Production planning</subject><subject>Random variables</subject><subject>Sales forecasting</subject><subject>Seasons</subject><subject>Seed industry</subject><subject>Seeds</subject><subject>Uncertainty</subject><subject>uncertainty. 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Industries: agriculture</topic><toplevel>online_resources</toplevel><creatorcontrib>Jones, Philip C</creatorcontrib><creatorcontrib>Kegler, Greg</creatorcontrib><creatorcontrib>Lowe, Timothy J</creatorcontrib><creatorcontrib>Traub, Rodney D</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Healthcare Administration Database</collection><collection>Science Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Interfaces (Providence)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jones, Philip C</au><au>Kegler, Greg</au><au>Lowe, Timothy J</au><au>Traub, Rodney D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Managing the Seed-Corn Supply Chain at Syngenta</atitle><jtitle>Interfaces (Providence)</jtitle><date>2003-01-01</date><risdate>2003</risdate><volume>33</volume><issue>1</issue><spage>80</spage><epage>90</epage><pages>80-90</pages><issn>0092-2102</issn><issn>2644-0865</issn><eissn>1526-551X</eissn><eissn>2644-0873</eissn><coden>INFAC4</coden><abstract>Each year, Syngenta Seeds, Inc. produces over 50 seed-corn hybrids and the following year markets over 100 hybrids under the NK brand name. The fact that growing seed corn is a biological process dependent upon local weather and insect conditions during the growing season complicates production planning. In addition, customers' experiences with a particular hybrid during a given year strongly influence demand for that hybrid during the next year. To help mitigate some of these yield and demand uncertainties, Syngenta (and other seed companies as well) take advantage of a second growing season for seed corn in South America, which occurs after many of the yield uncertainties and some of the demand uncertainties have been resolved or reduced. To better manage this production-planning process, Syngenta and the University of Iowa developed and implemented a second-chance production-planning model. A trial of the model showed that using it to plan 2000 production would have increased margins by approximately $5 million. Today, Syngenta uses this model to plan production for those varieties that account for 80 percent of total sales volume.</abstract><cop>Linthicum</cop><pub>INFORMS</pub><doi>10.1287/inte.33.1.80.12718</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0092-2102 |
| ispartof | Interfaces (Providence), 2003-01, Vol.33 (1), p.80-90 |
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| language | eng |
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| source | INFORMS PubsOnLine; Business Source Complete; JSTOR Archive Collection A-Z Listing |
| subjects | Agribusiness Agriculture Business studies Capital costs Case studies Cereals Corn Females food Food production Forecasting models Genetically altered foods Growing seasons Hybridity Inventories Inventory Inventory: production Management Management science Modeling Operations research Planting Production costs Production planning Random variables Sales forecasting Seasons Seed industry Seeds Uncertainty uncertainty. Industries: agriculture |
| title | Managing the Seed-Corn Supply Chain at Syngenta |
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