Large Inventory-Service Optimization in Configure-To-Order Systems

Abstract

A manufacturing process is migrated from an existing operation to a configure-to-order (CTO) system. As the CTO operation will eliminate the “machine-type model” (MTM) inventory of the existing operation, the emphasis is shifted to the components, or “building blocks”, which will still follow the build-to-stock scheme, due to their long leadtimes, and hence still require inventory. The solution involves an inventory-service trade-off of the new CTO system, resulting in performance gains, in terms of reduced inventory cost and increased service level. Other benefits include better forecast accuracy through parts commonality and risk-pooling, and increased customer demand, as orders will no longer be confined within a restricted set of pre-configured MTMs.

1 . A method of managing manufacturing logistics of multiple end products for which no finished goods inventory is kept, and while a manufacturing process migrates from an existing operation to a Web-based configure-to-order (CTO) operation, wherein a CTO operation is a hybrid of make-to-stock and make-to-order operations;
 comprising the following steps which are performed without keeping finished goods inventory for any end product; and each of which method step is performed while the manufacturing process migrates from the existing operation to the Web-based CTO operation:   maintaining an inventory of components, which components, termed “building blocks”, are built to stock, each said component having a cost wherein the cost is stored in a database;   according to the Web-based CTO operation which is the hybrid of make-to-stock and make-to-order operations, configuring-to-order multiple different end products using said components as defined by a bill of materials (BOM) structure wherein the BOM structure is stored in a database, wherein each end product involves at least one unique component; wherein the multiple different end products comprise six different end products; and wherein the end products have a demand forecast that changes weekly;   meanwhile taking customer orders from the Internet as the manufacturing process migrates from the existing operation to the Web-based CTO operation.   
     
     
         2 . The method of managing logistics of end products recited in  claim 1 , where the end products are personal computers (PCs) and the components are stock computer components. 
     
     
         3 . The method of managing manufacturing logistics of end products recited in  claim 1 , including deriving the base-stock levels from a greedy algorithm which iteratively reduces inventory budget until a budget constraint is satisfied. 
     
     
         4 - 15 . (canceled) 
     
     
         16 . The method of  claim 1 , including the processor receiving input from a database that contains optimization control parameters comprising budget limit on expected overall inventory cost and step size for a greedy algorithm, and also from a database that contains manufacturing system parameters. 
     
     
         17 . The method of  claim 16 , in which the processor accesses the databases and first performs preprocessing steps, after which the processor performs optimization processing, after which the processor enters a postprocessing phase and performs a step of computing reorder points. 
     
     
         18 . The method of  claim 17 , including after the computing of reorder points, translating the reorder points into days-of-supply and outputting to an output device or database days-of-supply for each component. 
     
     
         19 . The method of  claim 1 , in which the steps are performed where the end products are a family comprising six different end products configured from a set comprising 17 different components which are obtained from external suppliers with different supply leadtimes. 
     
     
         20 . The method of  claim 1 , comprising performing an optimization run at a week's beginning and then updating base-stock levels weekly based on outcome of the optimization. 
     
     
         21 . The method of  claim 1 , wherein the manufacturing system parameters, in the database that contains the manufacturing system parameters, comprise:
 (i) demand for end product mεM in period t=1, 2, . . . T;   (ii) inbound leadtime distribution for component iεS;   (iii) outbound leadtime distribution for end product mεM;   (iv) distribution of number of units number of units of component i used in assembly of end product mεM;   (v) unit cost of component iεS;   (vi) bill of materials for end product mεM.   
     
     
         22 . The method of  claim 1 , comprising steps, performed by the processor, of propagating a demand and computing a mean and a variance of the demand over a leadtime. 
     
     
         23 . The method of  claim 1 , comprising a step, performed by the processor, of computing reorder points. 
     
     
         24 . The method of  claim 23 , comprising translating the reorder points into days-of-supply. 
     
     
         25 . The method of  claim 21 , comprising outputting, to an output device or a database, a set of computed information comprising (a) days-of-supply for each component iεS and (b) achieved service level for each end product mεM. 
     
     
         26 . The method of  claim 1 , wherein an end product is characterized by a final assembly process of that takes no more than 2 hours, and component leadtime is days-long. 
     
     
         27 . The method of  claim 1 , wherein components comprise a memory component; a processor component; a shell component; a hard drive component; a CD ram component; a video graphics card component; an ethernet card component; and a software pre-load component. 
     
     
         28 . The method of  claim 1 , wherein components comprise:
 a memory component;   a processor component;   a shell component;   a hard drive component;   a CD rom component;   a video graphics card component;   an ethernet card component; and   a software pre-load component.