We develop a model for scheduling the expansive product line at a company that uses a continuous chemical production process. Using the model, we generate data based on real production runs to create regression equations that can estimate both capacity usage and material waste generated by the product line complexity of a particular production run. These regression models can then be used to estimate the complexity costs imposed on the system of any particular product or customer order. Such cost estimations can be used to properly price customer orders and to most economically assign them to the production runs with the best fit. In some supply chains for products with short life, distributors often over-estimate their order quantities because of non-commitment. This results in high excess inventories at the end of product life. We propose penalty and buyback contract to remedy this problem. We analyze strategic interactions among players in the supply chain consisting of one manufacturer and two non-identical distributors and characterize the equilibria. Both contracts perfectly coordinate the system. We gained insight into setting contract parameters by using the data from a leading manufacturer in the industry. When companies invest in developing a new drug in the last phase of human clinical study, they often encounter uncertainty in patient enrollment and the unknown safety, efficacy, and tolerability of the new drug. To test new drugs, companies use clinical studies without interim analysis or clinical studies with interim analyses. Through the interim analysis, a firm tests whether the drug passes the hypothesis test of the study. It may file the new drug application, and abandon or continue the study. For a clinical study without interim, the optimal investment in the rest of the development depends on the patient enrollment up to the decision time. For a study with an interim analysis, the optimal investment in the remainder of the development also depends on the interim results. We provide conditions for accelerating, continuing the current schedule, or suspending the second part of the study. There is an optimal time for the firm to conduct the interim analyses.
","abstract_html":"<p>We develop a model for scheduling the expansive product line at a company that uses a continuous chemical production process. Using the model, we generate data based on real production runs to create regression equations that can estimate both capacity usage and material waste generated by the product line complexity of a particular production run. These regression models can then be used to estimate the complexity costs imposed on the system of any particular product or customer order. Such cost estimations can be used to properly price customer orders and to most economically assign them to the production runs with the best fit. In some supply chains for products with short life, distributors often over-estimate their order quantities because of non-commitment. This results in high excess inventories at the end of product life. We propose penalty and buyback contract to remedy this problem. We analyze strategic interactions among players in the supply chain consisting of one manufacturer and two non-identical distributors and characterize the equilibria. Both contracts perfectly coordinate the system. We gained insight into setting contract parameters by using the data from a leading manufacturer in the industry. When companies invest in developing a new drug in the last phase of human clinical study, they often encounter uncertainty in patient enrollment and the unknown safety, efficacy, and tolerability of the new drug. To test new drugs, companies use clinical studies without interim analysis or clinical studies with interim analyses. Through the interim analysis, a firm tests whether the drug passes the hypothesis test of the study. It may file the new drug application, and abandon or continue the study. For a clinical study without interim, the optimal investment in the rest of the development depends on the patient enrollment up to the decision time. For a study with an interim analysis, the optimal investment in the remainder of the development also depends on the interim results. We provide conditions for accelerating, continuing the current schedule, or suspending the second part of the study. There is an optimal time for the firm to conduct the interim analyses.</p>","abstract_has_math":false,"creators":["Tian, Zhili"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Business Administration","degree_department":null,"school":null,"contributors":["Panos Kouvelis"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-01-01T08:00:00Z","date_published":"2011-01-01T08:00:00Z","updated_at":"2026-07-24T06:13:31Z","subjects":["Management","complexity management","Coordination of decentralized supply chain","Optimal investment in Phase III clinical study","optimal time for interim analysis","Pareto improvement in supply chain","Product line complexity cost"],"languages":["English (en)"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7936/K7D50K15"],"render_values":[{"text":"https://doi.org/10.7936/K7D50K15","href":"https://doi.org/10.7936/K7D50K15","code":true}]}]},"links":{"outbound_url":"https://openscholarship.wustl.edu/etd/649","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Panos Kouvelis"]},{"key":"dc:creator","label":"Author","values":["Tian, Zhili"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2111-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Business Administration"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Management","complexity management","Coordination of decentralized supply chain","Optimal investment in Phase III clinical study","optimal time for interim analysis","Pareto improvement in supply chain","Product line complexity cost"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/etd/649"]},{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7936/K7D50K15"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["We develop a model for scheduling the expansive product line at a company that uses a continuous chemical production process. Using the model, we generate data based on real production runs to create regression equations that can estimate both capacity usage and material waste generated by the product line complexity of a particular production run. These regression models can then be used to estimate the complexity costs imposed on the system of any particular product or customer order. Such cost estimations can be used to properly price customer orders and to most economically assign them to the production runs with the best fit. In some supply chains for products with short life, distributors often over-estimate their order quantities because of non-commitment. This results in high excess inventories at the end of product life. We propose penalty and buyback contract to remedy this problem. We analyze strategic interactions among players in the supply chain consisting of one manufacturer and two non-identical distributors and characterize the equilibria. Both contracts perfectly coordinate the system. We gained insight into setting contract parameters by using the data from a leading manufacturer in the industry. When companies invest in developing a new drug in the last phase of human clinical study, they often encounter uncertainty in patient enrollment and the unknown safety, efficacy, and tolerability of the new drug. To test new drugs, companies use clinical studies without interim analysis or clinical studies with interim analyses. Through the interim analysis, a firm tests whether the drug passes the hypothesis test of the study. It may file the new drug application, and abandon or continue the study. For a clinical study without interim, the optimal investment in the rest of the development depends on the patient enrollment up to the decision time. For a study with an interim analysis, the optimal investment in the remainder of the development also depends on the interim results. We provide conditions for accelerating, continuing the current schedule, or suspending the second part of the study. There is an optimal time for the firm to conduct the interim analyses.
"]},{"key":"dc:title","label":"Title","values":["Study of Product Line Complexity, Supply Chain Coordination, and Phase III New Drug Development"]}]}],"canonical_facts":{"dc:contributor":["Panos Kouvelis"],"dc:creator":["Tian, Zhili"],"dc:date.available":["2111-01-01T08:00:00Z"],"dc:description.abstract":["We develop a model for scheduling the expansive product line at a company that uses a continuous chemical production process. Using the model, we generate data based on real production runs to create regression equations that can estimate both capacity usage and material waste generated by the product line complexity of a particular production run. These regression models can then be used to estimate the complexity costs imposed on the system of any particular product or customer order. Such cost estimations can be used to properly price customer orders and to most economically assign them to the production runs with the best fit. In some supply chains for products with short life, distributors often over-estimate their order quantities because of non-commitment. This results in high excess inventories at the end of product life. We propose penalty and buyback contract to remedy this problem. We analyze strategic interactions among players in the supply chain consisting of one manufacturer and two non-identical distributors and characterize the equilibria. Both contracts perfectly coordinate the system. We gained insight into setting contract parameters by using the data from a leading manufacturer in the industry. When companies invest in developing a new drug in the last phase of human clinical study, they often encounter uncertainty in patient enrollment and the unknown safety, efficacy, and tolerability of the new drug. To test new drugs, companies use clinical studies without interim analysis or clinical studies with interim analyses. Through the interim analysis, a firm tests whether the drug passes the hypothesis test of the study. It may file the new drug application, and abandon or continue the study. For a clinical study without interim, the optimal investment in the rest of the development depends on the patient enrollment up to the decision time. For a study with an interim analysis, the optimal investment in the remainder of the development also depends on the interim results. We provide conditions for accelerating, continuing the current schedule, or suspending the second part of the study. There is an optimal time for the firm to conduct the interim analyses.
"],"dc:identifier":["https://openscholarship.wustl.edu/etd/649"],"dc:identifier.doi":["https://doi.org/10.7936/K7D50K15"],"dc:language":["English (en)"],"dc:subject":["Management","complexity management","Coordination of decentralized supply chain","Optimal investment in Phase III clinical study","optimal time for interim analysis","Pareto improvement in supply chain","Product line complexity cost"],"dc:title":["Study of Product Line Complexity, Supply Chain Coordination, and Phase III New Drug Development"],"thesis:degree_discipline":["Business Administration"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T06:13:31Z"}