IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0923470
(2001-08-06)
|
등록번호 |
US-7412397
(2008-08-12)
|
발명자
/ 주소 |
- Grenchus, Jr.,Edward J.
- Keene,Robert A.
- Shaikh,Asif
|
출원인 / 주소 |
- International Business Machines Corporation
|
대리인 / 주소 |
|
인용정보 |
피인용 횟수 :
5 인용 특허 :
9 |
초록
▼
Demanufacturing workload is forecast based on anticipated volumes of equipment to be disassembled and/or salvaged, as well as equipment complexity factors determined by disassembly prototyping. Staffing requirements are unique for each customer and are based on the number of pounds needed to be wor
Demanufacturing workload is forecast based on anticipated volumes of equipment to be disassembled and/or salvaged, as well as equipment complexity factors determined by disassembly prototyping. Staffing requirements are unique for each customer and are based on the number of pounds needed to be worked during each month and the associated complexity (work content multiplier) for that customer's typical or expected returns.
대표청구항
▼
We claim: 1. A method for workload planning for a demanufacturing facility characterized by a plurality of customers each having unique customer specific forecasts and processing needs including critical operations, comprising the steps of: building in computer storage a spreadsheet workload planni
We claim: 1. A method for workload planning for a demanufacturing facility characterized by a plurality of customers each having unique customer specific forecasts and processing needs including critical operations, comprising the steps of: building in computer storage a spreadsheet workload planning model for collecting and summing customer forecasts adjusted by customer unique complexity factors; determining and entering to said spreadsheet workload planning model for each of a plurality of prospective customers, a projected volume of material for processing by said demanufacturing facility; determining for each said prospective customer critical operations for processing said material, said critical operations including those operations required for removal of sensitive parts to prevent disclosure of confidential information, recovery of parts needed to satisfy a shortage requirement for build of other products, removal of parts to prevent their re-use, and removal of parts and materials as required by a vendor commodity purchaser; for each said customer, initially dismantling prototype machines in accordance with said critical operations, including identifying work content and resulting saleable, commodity, and trash items; responsive to said dismantling, determining for each customer and entering to said spreadsheet workload planning model a unique complexity factor for processing said material, said unique complexity factor representing processing time divided by said volume as defined during prototype dismantling and subsequently modified by actual experience; applying said projected volume and said unique complexity factors to said spreadsheet workload planning model for forecasting workload requirements for said processing; periodically updating said projected volume and said critical operations; responsive to updated projected volume, updated critical operations, prior customer product shipment experience and new demanufacturing product prototyping, selectively adjusting said unique complexity factors for each of said plurality of customers and entering adjusted unique complexity factors to said spreadsheet workload planning model; applying said updated projected volume and said adjusted unique complexity factors to said spreadsheet workload planning model for forecasting workload requirements for said processing; responsive to generating in said spreadsheet workload planning model a summation of said projected volume adjusted by said unique complexity factor for each of said plurality of customers, determining staffing requirements and productivity targets for a demanufacturing enterprise for processing said material for a plurality of future periods; determining said staffing requirements for each future period by summing staff requirements for all customers adjusted by expected absenteeism factor, fatigue factor, breaks requirements, and vacation patterns to create an adjusted staffing requirement for said demanufacturing enterprise; responsive to said workload requirements determining adjusted staffing requirement and resource balancing between projects; and responsive to said adjusted staffing requirement, hiring and balancing staff between projects of said demanufacturing enterprise. 2. The method of claim 1, further comprising the step of converting said volume to weight. 3. The method of claim 2, said prototyping including the step of disassembly prototyping. 4. The method of claim 3, said disassembly prototyping step being applied to new material and further comprising the step of accumulating historical data for determining said unique complexity factor for previously disassembled material. 5. The method of claim 1, said projecting step further comprising the step of determining an expected number of truckloads of said material. 6. The method of claim 3, said disassembly prototyping further including the step of determining salvageable and disposable content for said material of a given equipment type. 7. The method of claim 1, further comprising the step of periodically updating said spreadsheet workload planning model based upon actual and anticipated changes in said volume projections and said unique complexity factors. 8. The method of claim 7, further comprising the step of calculating said productivity targets for a demanufacturing enterprise using said volume projections and said unique complexity factors. 9. A method for forecasting staffing requirements for a demanufacturing enterprise characterized by a plurality of customers each having unique customer specific requirements including demanufacturing complexity and critical operations, comprising the steps of: determining for each of a plurality of prospective customers, a projected volume of material returns for processing; determining from customer specific requirements for each customer a unique complexity factor for processing said material, including identifying any critical operations; said critical operations including removal of sensitive parts to prevent disclosure of confidential information, recovery of parts needed to satisfy a shortage requirement for build of other products, removal of parts to prevent their re-use, and removal of parts and materials as required by a vendor commodity purchaser; converting projected volume of material returns for each said customer to weight, multiplying said weight by a unique complexity factor determined initially by disassembly prototyping and subsequently modified by actual experience to generate a staff requirement for each of a plurality of customers, said disassembly prototyping including dismantling prototype machines in accordance with said financial benefit and cost factors and further with respect to any said critical operations, identifying work content and resulting saleable, commodity, and trash items, said unique complexity factor initially representing time for said disassembly prototyping divided by said weight; applying said projected volume and said unique complexity factors to a workload planning model for forecasting workload requirements for said processing; periodically updating said projected volume and said critical operations; responsive to said updated projected volume and critical operations, and to customer product shipment experience and new demanufacturing product prototyping, adjusting and applying to said workload planning model said unique complexity factor for each of said plurality of customers; generating a summation of said staff requirements for all customers for a given time period and adjusting said staff requirements for all customers by an expected absenteeism factor, fatigue factor, breaks requirements, and vacation patterns to generate said staffing requirements and productivity targets for said demanufacturing enterprise; and executing said converting, generating, adjusting, and applying steps in a spreadsheet workload planning model.
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