Method for providing inventory optimization

• US 5,946,662 A
• Filed: 03/29/1996
• Issued: 08/31/1999
• Est. Priority Date: 03/29/1996
• Status: Expired due to Term

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Yao, David Da-Wei, Feigin, Gerald Eugene, Ettl, Markus, Lin, Grace Yuh-Jiun
• Primary Examiner(s): MacDonald, Allen R.
• Assistant Examiner(s): Jeanty, Romain
• Application Number:
  US08/625,455
• Time in Patent Office: 1,250 Days
• Field of Search: 705/8, 705/28, 705/10, 705/14, 705/29, 705/22, 364/408.1, 364/408.05, 364/488.09, 364/468.13
• US Class Current: 705/7.26
• 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 20/203 Inventory monitoring

Abstract

A method for providing inventory optimization for levels of products in a complex supply chain network for multiple internal supplier or manufacturer locations and external distributor or retailer locations. The invention constructs a representative supply chain network model to indicate the flow of products between internal and external locations, it determines inventory levels and fill rates to meet the service level requirements, calculates a total inventory cost for all products in the network, and optimizes the fill rates based on estimated gradient information of the total inventory cost.

Claims

1. A computer implemented method for optimizing inventory levels of products in a complex supply chain network including one or more internal nodes representing a supplier or a manufacturer and one or more external nodes representing a distributor or a retailer, the method comprising the steps of:

a. providing information including a supply chain network topology indicating flow of products between said internal and external nodes of said supply chain network, an external demand for each of said external nodes and service level requirements for each of said external nodes;

b. constructing a representative model of said supply chain network topology;

c. deriving an internal demand for each of said internal nodes from said external demand;

d. initializing fill rates for each of said internal nodes;

e. estimating actual lead time for said internal and external nodes before said external demand for said products is fulfilled;

f. determining said inventory levels for said internal and external nodes using said actual lead time and said internal demand;

g. determining fill rates of said external nodes to meet said service level requirements;

h. calculating a total inventory cost for all said products in said complex supply chain network;

i. estimating gradient information of said total inventory cost with respect to said initialized fill rates of said internal nodes to find a minimum total inventory cost;

j. applying an optimization routine to reset said fill rates of said internal nodes based on said gradient information; and

k. repeating steps e through j, until optimal fill rates for said internal nodes are found; and

l. modifying the inventory levels of product based on said found optimal fill rates for said internal nodes in minimizing said total inventory cost thereby optimizing inventory levels of products.

2. The method of claim 1, wherein step (b) constructs each of said nodes from a bill of materials.

3. The method of claim 2, wherein the bill of materials comprises time, location, and quantity information.

4. The method of claim 1, wherein step (a) further includes providing nominal lead time, said nominal lead time is defined for each of said internal nodes and external nodes.

5. The method of claim 4, wherein the nominal lead time is based on stochastic measurement.

6. The method of claim 4, wherein the nominal lead time is based on deterministic measurement.

7. The method of claim 1, wherein said external demand is based on stochastic measurement.

8. The method of claim 1, wherein said external demand is based on deterministic measurement.

9. The method of claim 1, wherein said service level requirements comprises a service level threshold and a fraction service percentage.

10. The method of claim 1, further comprising the step of setting minimal inventory levels by using gradient information of the total inventory cost of said representative model of the supply chain topology network with respect to a fill rate of each internal node.

11. The method of claim 1, comprising implementing the method by executing the steps on a computer.

12. The method of claim 1, wherein said step (e) said demand is performed by utilizing formula ##EQU72## where, D_i (t) is the effective demand stream, u_i is the usage and o_j is the offset.

13. The method of claim 12, wherein said actual lead time is estimated using the nominal lead time and said fill rates information of said internal and external nodes, starting from the leaf nodes and continuing to their parent nodes, ##EQU73## where L_i is i'"'"'s nominal lead time, S_>

i is the set of stores, ##EQU74## assuming ƒ

_h >

0 for all hε

S<sub>>

i</sub>, if ƒ

_j =0 for some jε

S<sub>>

i</sub>, let L_i =L_i +T_j with probability 1.

14. The method of claim 13, wherein step (c) further including a step of calculating a mean and variance of said demand over said actual lead time, said step of calculating said mean and variance is performed by utilizing formulas:

##EQU75## where, μ

_i is the mean of the cumulative demand over the lead time, and D_i^cum is the cumulative demand and σ

²_i is the variance.

15. The method of claim 14, wherein said inventory levels is determined by formulas:

space="preserve" listing-type="equation">E(I.sub.i)=σ

.sub.i H(k.sub.i), where H(k.sub.i);

=k.sub.i +G(k.sub.i)=∫

.sub.-∞

.sup.k.sbsp.i (k.sub.i -z)φ

(z)dz,

where, E (I_i) is the inventory level, φ

(Z) is the density function and σ

_i is the standard deviation.

16. The method of claim 15, wherein fill rates are determined in step (f) by formula:

##EQU76## where, α

_i and β

_i is given data, T_i is transportation time, W_i is the waiting time to receive the order and the required service level is

space="preserve" listing-type="equation">P W.sub.i ≦

β

.sub.i !=ƒ

.sub.i P T.sub.i ≦

β

.sub.i !+(1-ƒ

.sub.i)P T.sub.i +τ

.sub.i ≦

β

.sub.i !.

17. The method of claim 16, wherein step (g) calculates said total inventory cost is represented by formula ##EQU77## where, c_i is the inventory capital at store i.

18. The method of claim 1, wherein said service level requirements are met by setting minimal inventory levels at each node that has external customer demand.

19. A computer program device readable by a machine, tangibly embodying a program of instructions executable by a machine to perform method steps for optimizing inventory levels of products in a complex supply chain network including one or more internal nodes representing a supplier or a manufacturer and one or more external nodes representing a distributor or a retailer, the method comprising the steps of:

a. providing information including a supply chain network topology indicating flow of products between said internal and external nodes of said supply chain network, an external demand for each of said external nodes and service level requirements for each of said external nodes;

b. constructing a representative model of said supply chain network topology;

c. deriving an internal demand for each of said internal nodes from said external demand;

d. initializing fill rates for each of said internal nodes;

e. estimating actual lead time for said internal and external nodes before said external demand for said products is fulfilled;

f. determining said inventory levels for said internal and external nodes using said actual lead time and said internal demand;

g. determining fill rates of said external nodes to meet said service level requirements;

h. calculating a total inventory cost for all said products in said complex supply chain network;

i. estimating gradient information of said total inventory cost with respect to said initialized fill rates of said internal nodes to find a minimum total inventory cost;

j. applying an optimization routine to reset said fill rates of said internal nodes based on said gradient information; and

k. repeating steps e through j, until optimal fill rates for said internal nodes are found; and

l. modifying the inventory levels of product based on said found optimal fill rates for said internal nodes in minimizing said total inventory cost thereby optimizing inventory levels of products.