Method and system for optimized logistics planning

• US 5,450,317 A
• Filed: 11/24/1993
• Issued: 09/12/1995
• Est. Priority Date: 11/24/1993
• Status: Expired due to Term

• Current Assignee: Qwest Communications International Incorporated (Lumen Technologies, Inc.)
• Original Assignee: U.S. West Advanced Technologies, Inc.
• Inventors: Qiu, Yuping, Lu, Lu, Cox, Louis A. Jr.
• Primary Examiner(s): Hayes, Gail O.
• Assistant Examiner(s): NOT, DEFINED
• Application Number:
  US08/158,128
• Time in Patent Office: 657 Days
• Field of Search: 364/401, 364/402, 364/468, 364/403, 364/408
• US Class Current: 705/7.24
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06314 Calendaring for a resource
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

An improved logistics planning method and system for recommending optimal order quantities and timing, choice of vendor locations and storage locations, and transportation modes, for individual items and for product families. The system is designed for use in cooperation with the computer having memory and incorporates item, customer, supplier, and routing information databases. In operation, the item, customer and supplier databases are accessed in order to provide customer and warehouse demand forecasts. The routing and customer databases are similarly accessed to provide transportation cost forecasts necessary to determine optimized routing modes for selected items, customers and suppliers. The demand and transportation costs are processed in accordance with a dynamic programming model to determine stock and non-stock order/shipment solutions for the selected items and customers, including optimized supplier and routing selection, order timing and quantity.

Claims

1. A computer system for optimized logistics planning, comprising:

an item information database;

a customer information database;

a supplier information database;

a routing information database;

demand forecasting means for accessing said item, customer and supplier databases and determining warehouse and customer demand forecasts for selected items, customers and intervals;

transportation forecasting means for accessing said routing and customer databases and determining optimized routing modes for selected items, customers, and suppliers;

data processing means responsive to said demand forecasting means and said transportation forecasting means for determining stock and non-stock order/shipment solutions for said selected items and customers, including optimized supplier and routing selection, order timing and quantity;

first signal generating means for generating a first electrical signal corresponding to said determined warehouse demand forecasts, said first signal generating means in electrical communication with said demand forecasting means;

second signal generating means for generating a second electrical signal corresponding to said determined customer demand forecast, said second signal generating means in electrical communication with said demand forecasting means;

first conversion means for converting said first and second electrical signals to digital warehouse and customer demand information;

third signal generating means for generating a third electrical signal corresponding to said determined optimized routing modes, said third signal generating means in electrical communication with said transportation forecasting means;

second conversion means for converting said third electrical signal to digital routing information;

supplemental processing means in electrical communication with said first and second conversion means for processing said warehouse demand, customer demand, and routing information and determining said optimized stock and non-stock shipment/order solutions in accordance with the dynamic programming model;

##EQU6## where f(p) is the total cost function for the order quantity Q at the pth month and g(p) is the optimized order point;

where j^* is the index that minimizes the equation; and

Q=order quantity at the pth month;

M_p (Q)=material cost;

I_p (Q)=inventory cost; and

T(Q)=transportation cost; and

means for

(a) ordering and shipping said selected items from said item information database, and

(b) routing said selected items to said selected customers, in accordance with said determined .optimized stock and non-stock shipment/order solutions.

2. A computer system as in claim 1, further comprising a memory portion in electrical communication with said first and second conversion means for storing said digital warehouse information, said customer demand information and said digital routing information prior to processing.

3. A method implemented on a computer system for optimized logistics planning, comprising:

providing an item information database;

providing a customer information database;

providing a supplier information database;

providing a routing information database;

accessing said item, customer and supplier databases and determining warehouse and customer demand forecasts for selected items, customers and warehouses at selected intervals by;

(a) determining total demand of the ith warehouse (D_i) in the jth month; and

(b) determining the demand of the kth customer of the ith warehouse in the jth month in accordance with the mathematical models;

##EQU7## d_ij^k =P_i^k * d_ij, k=1, 1, . . . , C_i ;

i=1, 2, . . . , W and

j=1, 2, . . . , 12;

where,

W=the number of warehouses,

D_i =the total demand of the ith warehouse in the next 12 months,

t_i =the trend component,

C_i =the number of customers of the ith warehouse, i=1, 2, . . . , W,

P_i^k =the customer demand forecast percentage for the kth customer of the ith warehouse, and

a_ij =the demand index of the ith warehouse;

accessing said routing and customer databases and determining optimized routing modes for selected items, customers and suppliers;

determining stock and non-stock order/shipment solutions for said selected items and customers, including optimized supplier and routing selection, order timing and quantity; and

in accordance with said determined optimized stock and non-stock order/shipment solutions,

(a) ordering and shipping said selected items from said item information database, and

(b) routing said selected items to said selected customers.

4. A method implemented on a computer system for optimized logistics planning, comprising:

providing an item information database;

providing a customer information database;

providing a supplier information database;

providing a routing information database;

accessing said item, customer and supplier databases and determining warehouse and customer demand forecasts for selected items, customers and warehouses at selected intervals;

accessing said routing and customer databases and determining optimized routing modes for selected items, customers and suppliers;

determining stock and non-stock order/shipment solutions for said selected items and customers, including optimized supplier and routing selection, order timing and quantity;

generating a first electrical signal corresponding to said determined warehouse demand forecasts;

generating a second electrical signal corresponding to said determined customer demand forecast;

converting said first and second electrical signals to digital warehouse and customer demand information;

generating a third electrical signal corresponding to said determined optimized routing modes;

converting said third electrical signal to digital routing information;

processing said warehouse demand, customer demand and routing information and determining said optimized stock and non-stock shipment/order solutions in accordance with the dynamic programming model;

##EQU8## where f(p) is the total cost function for the order quantity Q at the pth month and g(p) is the optimized order point;

where j^* is the index that minimizes the equation; and

Q=order quantity at the pth month;

M_p (Q)=material cost;

I_p (Q)=inventory cost; and

T(Q)=transportation cost; and

in accordance with said determined optimized stock and non-stock shipment/order solutions,

(a) ordering and shipping said selected items from said item information database, and

(b) routing said selected items to said selected customers.

5. The method as in claim 4, further comprising providing a memory portion for storing said digital warehouse information, said customer demand information and said digital routing information prior to processing.