Autonomic computing for exception management in supply chains ( part 2 )
This project investigates how continuous and discrete control theory may be applied to model, monitor and control a supply chain based on performance indicators such as rapid inventory recovery and order rate recovery. The first part of the project analyzed supply chains in the continuous time do...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/10356/40060 |
| _version_ | 1811678283701420032 |
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| author | Foo, Wei Shing. |
| author2 | Lee Siang Guan, Stephen |
| author_facet | Lee Siang Guan, Stephen Foo, Wei Shing. |
| author_sort | Foo, Wei Shing. |
| collection | NTU |
| description | This project investigates how continuous and discrete control theory may be applied to model, monitor and control a supply chain based on performance indicators such as rapid inventory recovery and order rate recovery.
The first part of the project analyzed supply chains in the continuous time domain. The behaviour of current models i.e. Automatic Pipeline Inventory and Order Based Production Control System (APIOBPCS) model and Inventory Order Based Production control system (IOBPCS) were modelled in MATLAB®. The influence of the different parameters inventory level recovery rate, Ti, demand averaging rate, Ta, and WIP adjustment rate, Tw, on performance indicators [ i.e 1) rapid inventory recovery and 2) order rate recovery ] were investigated. Ti, inventory level recovery rate, has a greater influence on both the response of inventory level recovery and order rate, ORATE, recovery. The introduction of WIP policy offers an additional option of damping and increasing the response of both the ORATE and inventory level response.
An existing literature examined the possibility of discretizing a continuous time model. the discrete model was found to most closely resemble real life situation. Inspired by this notion, the author investigated into the possibility of modelling The APIOPBCS model in the discrete time domain. The discrete time APIOBPCS model was verified by comparing the transient behaviour of the discrete models against that of the continuous model. Simulations showed Tustin and first order hold methods are able to replicate the transient behaviour of the continuous model more accurately.
While the discrete models follow the general trend of the continuous model, the Tustin model better achieved the target performance indicators while the Backward Rectangle approximation did not. The effect of different lead times on the transient behaviour of the models was also investigated. It was found that the characteristics of the models were consistent and independent of the variation of the lead times. |
| first_indexed | 2024-10-01T02:50:48Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/40060 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:50:48Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/400602023-03-04T18:45:01Z Autonomic computing for exception management in supply chains ( part 2 ) Foo, Wei Shing. Lee Siang Guan, Stephen School of Mechanical and Aerospace Engineering A*STAR Singapore Institute of Manufacturing Technology Tan Puay Siew DRNTU::Engineering::Industrial engineering::Supply chain This project investigates how continuous and discrete control theory may be applied to model, monitor and control a supply chain based on performance indicators such as rapid inventory recovery and order rate recovery. The first part of the project analyzed supply chains in the continuous time domain. The behaviour of current models i.e. Automatic Pipeline Inventory and Order Based Production Control System (APIOBPCS) model and Inventory Order Based Production control system (IOBPCS) were modelled in MATLAB®. The influence of the different parameters inventory level recovery rate, Ti, demand averaging rate, Ta, and WIP adjustment rate, Tw, on performance indicators [ i.e 1) rapid inventory recovery and 2) order rate recovery ] were investigated. Ti, inventory level recovery rate, has a greater influence on both the response of inventory level recovery and order rate, ORATE, recovery. The introduction of WIP policy offers an additional option of damping and increasing the response of both the ORATE and inventory level response. An existing literature examined the possibility of discretizing a continuous time model. the discrete model was found to most closely resemble real life situation. Inspired by this notion, the author investigated into the possibility of modelling The APIOPBCS model in the discrete time domain. The discrete time APIOBPCS model was verified by comparing the transient behaviour of the discrete models against that of the continuous model. Simulations showed Tustin and first order hold methods are able to replicate the transient behaviour of the continuous model more accurately. While the discrete models follow the general trend of the continuous model, the Tustin model better achieved the target performance indicators while the Backward Rectangle approximation did not. The effect of different lead times on the transient behaviour of the models was also investigated. It was found that the characteristics of the models were consistent and independent of the variation of the lead times. Bachelor of Engineering (Mechanical Engineering) 2010-06-10T01:17:50Z 2010-06-10T01:17:50Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40060 en Nanyang Technological University 138 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Industrial engineering::Supply chain Foo, Wei Shing. Autonomic computing for exception management in supply chains ( part 2 ) |
| title | Autonomic computing for exception management in supply chains ( part 2 ) |
| title_full | Autonomic computing for exception management in supply chains ( part 2 ) |
| title_fullStr | Autonomic computing for exception management in supply chains ( part 2 ) |
| title_full_unstemmed | Autonomic computing for exception management in supply chains ( part 2 ) |
| title_short | Autonomic computing for exception management in supply chains ( part 2 ) |
| title_sort | autonomic computing for exception management in supply chains part 2 |
| topic | DRNTU::Engineering::Industrial engineering::Supply chain |
| url | http://hdl.handle.net/10356/40060 |
| work_keys_str_mv | AT fooweishing autonomiccomputingforexceptionmanagementinsupplychainspart2 |