Matrix methods and systems for supply chain management

Details Matrix methods and systems for supply chain management Matrix methods and systems for supply chain management

2001-03-29

2004-11-23

Voeltz, Emanuel Todd (Department: 2121)

Data processing: generic control systems or specific application

Specific application, apparatus or process

Product assembly or manufacturing

C700S097000, C700S107000, C705S007380, C705S029000

Reexamination Certificate

active

06823226

ABSTRACT:

FIELD OF THE INVENTION
This invention relates generally to methods and systems for supply chain management and, more particularly, to methods and systems for supply chain management using bit storage and calculations.
BACKGROUND OF THE INVENTION
In supply chain management, one goal is to have real time reaction up and down the supply chain. For example, many industries have moved to just-in-time models to supply parts to assembly line positions and warehouse inventories. Just-in-time supply models are cost efficient because they keep inventory low and allow quick reaction by the manufacturers to configuration changes. These same manufacturers, however, must constantly adjust the supply chain to ensure that no valuable production time is lost because an assembly line runs out of a necessary part. At the same time, customers have become more demanding and frequently demand an accurate promised delivery date of an ordered product. Often, the customer makes last minute changes to the configuration, yet still expects the same promised delivery date to be met.
Many manufacturers have complex product lines that comprise dozens of products with each product line requiring thousands of material components. For example, in automobile manufacturing most of the major producers carry a product line consisting of four to eight different models of automobiles. Design choices produce multiple combinations of possible automobiles. For instance, each automobile may come in six different exterior colors, three different interior colors, and three different model classes (such as low end, medium end, and deluxe). A typical automobile manufacturer produces very few cars with the exact same configuration. For instance, one European manufacturer has automobiles with over 300 different characteristics, each characteristic having up to five different values; thus, this manufacturer theoretically may have up to 5
300
different configurations. Manufacturers estimate that only between 2 and 8 cars have the same configuration.
In the effort to maintain customer satisfaction by accurately predicting a delivery date, manufacturers also want to be able to quickly manage the supply chain so as to be able to react quickly to market preferences and changes forced by suppliers. If the manufacturer makes changes to the configuration or enhances the product, the manufacturer would like to put the product into production as soon as possible, without leaving excess inventory in the warehouse. Also, manufacturers must be able to react quickly to changes forced by suppliers to prevent gaps in the supply chain that change capacity restrictions or require orders to be rescheduled. Examples of such changes include a shortage of raw materials by suppliers, strikes, accidents or natural disasters. When such a change occurs, manufacturers would like to be able to react quickly by rescheduling all orders to reflect the change.
Conventional systems typically store configuration data in SQL tables in relational form. Operations on the database are performed using time-consuming SQL functions. With the sheer volume of configuration and customer order information, such databases are slow and unwieldy to update. The typical automobile manufacturer may handle, for example, up to 14 million orders over a typical year, with thousands of characteristic values, such as interior and exterior color, resulting in a large variance of configurations.
Additionally, in conventional SQL systems, each order is stored using a different byte of information for each characteristic. Consequently, the order database must be large, and the writing and reading of such a large amount of information consumes a lot of run time and may require days to update. For this reason, the supply chain management systems of larger manufacturers often run updates to configurations and orders only every two weeks and limit the calculations to weekends. If updates are performed in real time, the system runs the risk of inaccurate supple levels or estimated delivery dates. In the automobile manufacturer example, for instance, hundreds of dealers are drawing from the same system at the same time. It is possible that dealers will promise a customer one delivery date based on old data, then when the database is updated see that the particular product is sold out and therefore have to disappoint the customer by changing the estimated delivery date.
SUMMARY OF A FEW ASPECTS OF THE INVENTION
In accordance with the invention, methods and system for generating an order matrix are provided that receives an order comprising one or more tokens out of a set of possible tokens, wherein a token is a combination of a characteristic and a value of the characteristic. Order are placed in an order matrix, wherein the order matrix is at least a two dimensional data structure, each row of the data structure representing a possible token and each column of the data structure representing an order.
Also, methods and systems for determining a production period for rescheduling a plurality of orders are given. The method first receives a plurality of orders having one or more tokens out of a set of possible tokens. Each token is a combination of a characteristic and a value of the characteristic. Next, an order restriction matrix is generated. The order restriction matrix is a two dimensional data structure with each row of the data structure representing a possible restriction and each column of the data structure representing an order. The method looks up the restrictions that apply to the orders and derives an order derived production restriction matrix for each order, which is evaluated to determine possible production periods for each order.
Also, methods and systems for determining potential production periods for a potential order are given. The method receives an order having one or more tokens out of a set of possible tokens. Each token is a combination of a characteristic and a value of the characteristic. For the order, one or more restrictions are determined based on the tokens. This series of restrictions is used to generate an order derived production restriction matrix for the order, which is evaluated to determine possible production periods for the order.
Also, methods and systems for generating a bill of materials for a production run are shown. The method receives a plurality of orders comprising one or more tokens out of a set of possible tokens, wherein a token is a combination of a characteristic and a value of the characteristic. It places the orders in an order matrix, wherein the order matrix is at least a two dimensional data structure, each row of the data structure representing a possible token and each column of the data structure representing an order. Next, a bill of materials matrix is generated by evaluating the order matrix, wherein the bill of materials matrix is at least a two dimensional data structure, each row of the data structure representing a possible item and each column of the data structure representing an order.
The foregoing summarizes only a few aspects of the invention and is not intended to be reflective of the full scope of the invention as claimed. Additional features and advantages of the invention are set forth in the following description, may be apparent from the description, or may be learned by practicing the invention. Moreover, both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.


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