System and Method for Packaging and Distribution Optimization

• US 20170206480A1
• Filed: 04/22/2016
• Published: 07/20/2017
• Est. Priority Date: 01/19/2016
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Naumann, Marc, Cinar, Goktug
• Granted Patent: US 10,614,395 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/083 Shipping

Abstract

A system and method for managing a supply chain for distributing products integrates inventory control with packaging optimization. Historical demand data, cost and dimension data for products, containers, and shipping vessels is obtained, and a plurality of layout configurations for packing the products into the containers is determined. Based on the historical demand data and the cost and dimension data, an optimization model is used to determine (i) a layout configuration for the packing the products in the containers from amongst the plurality of layout configurations, and (ii) a shipment frequency for shipping containers packed according to the determined layout configuration. The optimization model is configured to minimize a cost of distributing products in the supply chain.

Claims

1. A method of managing a supply chain for distributing products, comprising:

obtaining historical demand data of products in the supply chain;

obtaining cost and dimension data of the products, and containers and vessels for shipping the products;

determining a plurality of layout configurations for packing the products into the containers;

based on the historical demand data and the cost and dimension data, determining (i) a layout configuration for the packing the products in the containers from amongst the plurality of layout configurations, and (ii) a shipment frequency for shipping containers packed according to the determined layout configuration in an optimization model configured to minimize a cost of distributing the products in the supply chain; and

distributing the products according to the determined layout configuration and shipping frequency.

2. The method of claim 1, wherein the optimization model includes a first constraint requiring that a demand for products at each destination within the supply chain is satisfied by distributing the products according to the determined layout configuration and shipping frequency.

3. The method of claim 2, wherein the optimization model includes a second constraint requiring that a total number of shipments to each destination in the supply chain per year is at least equal to a number of containers packed according to the determined layout configuration that collectively include a total number of products sufficient to meet a yearly demand of that destination.

4. The method of claim 1, wherein determining the plurality of layout configurations includes:

determining a plurality of product orientations for packing the products into each layout configuration; and

for each layout configuration, selecting the product orientation enabling a maximum amount of the products to fit into the layout configuration.

5. The method of claim 1, wherein obtaining the historical demand data includes:

obtaining a plurality of periods of periodic demand data;

filtering the plurality of periods of periodic demand data to only include data corresponding to products actively distributed by the supply chain, and destinations in the supply chain that redistribute received products to other destinations in the supply chain to produce filtered demand data;

determining a historical total quantity of products demanded by each destination in the filtered demand data by adding together products distributed to that destination; and

determining a historical shipping frequency for each destination by counting a number of product distributions to that destination over the plurality of periods.

6. The method of claim 5, wherein the plurality of periods of periodic demand data includes at least 8 weeks of weekly demand data.

7. The method of claim 5, wherein the plurality of periods of periodic demand data includes precisely 8 weeks of weekly demand data.

8. The method of claim 1, wherein the determining of the layout configuration and shipping frequency in the optimization model is performed successively over time to account for changes in the historical demand data over time.

9. The method of claim 1, wherein products are sub-grouped into trade units before being packed into the containers.

10. The method of claim 9, wherein the cost of distributing the products in the supply chain is defined by a sum of picking and packaging costs Cp, inventory management costs Ci, and shipping costs Cs.

11. The method of claim 10, wherein:

the historical demand data, the cost and dimension data of the products, containers, and vessels, and the plurality of layout configurations define a plurality of parameters of the optimization model, including

L—

a the plurality of layouts configurations;

l—

a particular layout from the set L;

P—

a set of all products in the supply chain;

p—

a particular product in the set P;

W—

a set of destinations in the supply chain that redistribute received products to other destinations in the supply chain;

w—

a particular destination from the set W;

costLU_l,p—

a cost per container that includes inbound and outbound picking costs and assembly costs;

tus—

a quantity of products;

costTU_l,p—

a cost for packing each trade unit into a container; and

lus_l,p—

a quantity of products in an container for a given product p and layout configuration l;

a variable y_w determined in the optimization model represents a number of containers shipped to destination w per year; and

the picking and packing costs Cp are defined by the equation;

12. The method of claim 11, wherein:

the plurality of parameters further includes;

d_w—

a yearly demand of the destination w;

price_p—

a price of product p; and

capitalcost—

a multiplier for stock-keeping costs per year;

a variable newfreq represents the shipping frequency determined in the optimization model; and

the inventory costs Ci are defined by the equation;

13. The method of claim 12, wherein:

the plurality of parameters further includes;

costvol_w—

a cost per unit volume shipped to destination w; and

LUvol_l,p—

a volume of a container packed with product p using layout configuration l; and

the shipping costs Cs are defined by the equation;

Cs=Σ

<sub>w∈

W</sub>costvol_w·

y_w·

LUvol.

14. The method of claim 9, further comprising:

determining that a trade unit or group of trade units is too large to fit into a container; and

defining a further layout configuration as the trade unit or group of trade units not packed into the container.

15. The method of claim 9, further comprising:

determining that a trade unit or group of trade units is smaller than a smallest volume defined by the plurality of layout configurations by a predetermined factor; and

limiting a quantity of trade units or groups of trade units that can be packed into a container to a quantity of trade units or groups of trade units fitting into the smallest volume.

16. The method of to claim 1, further comprising:

determining an amount of air space remaining in a container packed according to each layout configuration, the amount of airspace indicative of a packing efficiency of the layout configuration.

17. The method of claim 14, wherein the optimization model is further configured to optimize between minimizing the cost of distributing products and maximizing the packing efficiency.

18. The method according to claim 1, wherein the optimization model is further configured to group together different products in demand at a common destination into common containers.

19. A computing device for managing distribution of products in a supply chain, comprising a processor;

and

a plurality of components executed by the processor, the plurality of components comprising;

a historical data component configured to obtain (i) historical demand data of products in the supply chain, and (ii) cost and dimension data of the products, and containers and vessels for shipping the products;

a pre-processing component configured to determine a plurality of layout configurations for packing the products into the containers;

an optimization model configured to use the historical demand data and the cost and dimension data to determine (i) a layout configuration for the packing the products in the containers from amongst the plurality of layout configurations, and (ii) a shipment frequency for shipping containers packed according to the determined layout configuration that minimizes a cost of distributing products in the supply chain; and

an output component configured to output the layout configuration and shipment frequency determined by the optimization component.

20. The computing device of claim 19, wherein the output component is further configured to operate the supply chain according to the layout configuration and shipment frequency determined by the optimization component.