System and method for replenishment by purchase with attribute based planning

Abstract

A system and method for determining whether a supply chain network resource may be replenished by purchase in order to meet demand as defined by an order. The system and method is an attribute-based system and method that may be part of an overall system and method for planning the use of supply chain network resources. The system and method may generate a planning output that may indicate the feasibility of replenishment by purchase. The system and method recognizes time events and takes into account the exact sequence of the events in order to obtain an optimal solution.

Claims

1. A method for planning the replenishment of a supply chain network resource by purchase in order to fulfill demand as defined by an order, wherein said order defining a stock keeping unit with attribute, comprising the steps:

determining time items, wherein said time items are specific points in time or time periods relating to said order and wherein at least one of said items is a purchase lead time based on said stock keeping unit with attribute, and wherein said time items further comprising a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material;

determining timing events based on said time items;

creating a timeline comprising of said timing events and time items;

selecting an applicable scenario based on said timeline from a group of predetermined scenarios; and

generating an output based on said applicable scenario.

2. The method of claim 1, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

3. The method of claim 1, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

4. The method of claim 1, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

5. The method of claim 1, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

6. The method of claim 1, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

7. The method of claim 1, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

8. The method of claim 1, wherein said time events comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

9. The method of claim 8, wherein said scenarios are based on sequencing of said timing items and time events.

10. The method of claim 9, wherein said scenarios are each associated with specific outputs.

11. The method of claim 1, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

12. The method of claim 1, further comprising the step of creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.

13. A method for planning the replenishment of one or more supply chain network resources in order to fulfill demand as defined by orders, wherein said orders having attributes, comprising the steps:

prioritizing said orders according to said order attributes;

selecting highest priority order, wherein said highest priority order defining a stock keeping unit with attribute;

selecting one of said resources for fulfilling said order;

replenishing said resources in order to completely fulfill said order, wherein said replenishment step further comprising the steps:

determining time items, wherein said time items are specific points in time or time periods relating to said order and wherein at least one of said items is a purchase lead time based on said stock keeping unit with attribute;

determining timing events based on said time items;

creating a timeline comprising of said timing events and time items;

selecting an applicable scenario based on said timeline from a group of predetermined scenarios; and

generating an output based on said applicable scenario.

14. The method of claim 13, wherein said time items further comprising a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material.

15. The method of claim 14, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

16. The method of claim 14, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

17. The method of claim 14, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

18. The method of claim 14, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

19. The method of claim 14, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

20. The method of claim 14, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

21. The method of claim 14, wherein said time events comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

22. The method of claim 21, wherein said scenarios are based on sequencing of said timing items and time events.

23. The method of claim 22, wherein said scenarios are each associated with specific outputs.

24. The method of claim 13, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

25. The method of claim 13, further comprising the step of creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.

26. A system for planning replenishment of a supply chain network resource by purchasing material in order to fulfill demand as defined by an order, said order defining a stock keeping unit with attribute, comprising:

a computer device;

a database in communication with said device;

a module for determining time items, wherein said time items are specific points in time or time periods relating to said order and at least one of said time items is a purchase lead time based on said stock keeping unit with attribute, and wherein said time items comprise a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material;

a module for determining timing events based on said time items;

a module for creating a timeline comprising of said timing events and time items; a module for selecting an applicable scenario based on said timeline from a group of predetermined scenarios;

and a module for generating an output based on said applicable scenario.

27. The system of claim 26, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

28. The system of claim 26, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

29. The system of claim 26, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

30. The system of claim 26, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

31. The system of claim 26, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

32. The system of claim 26, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

33. The system of claim 26, wherein said time events further comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

34. The system of claim 33, wherein said scenarios are based on sequencing of said timing items and time events.

35. The system of claim 34, wherein said scenarios are each associated with specific outputs.

36. The system of claim 26, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

37. The system of claim 26, further comprising a module for creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.

38. A program storage device readable by a machine, tangibly embodying a program of instructions executable by a machine to perform method steps of planning for the replenishment of supply chain network resources in order to fulfill demand as defined by one or more orders, wherein said one or more orders having attributes and each said orders defining a stock keeping unit with attribute, comprising the steps:

prioritizing said orders according to said order attributes;

selecting highest priority order;

selecting one of said resources for fulfilling said order;

replenishing said resources in order to completely fulfill said order, wherein said replenishment step further comprising the steps:

determining time items, wherein said time items are specific points in time or time periods relating to said order and wherein at least one of said time items is a purchase lead time based on said stock keeping unit with attribute;

determining timing events based on said time items;

creating a timeline comprising of said timing events and time items;

selecting an applicable scenario based on said timeline from a group of predetermined scenarios; and

generating an output based on said applicable scenario.

39. The program storage device of claim 38, wherein said time items further comprising a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material.

40. The program storage device of claim 39, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

41. The program storage device of claim 39, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

42. The program storage device of claim 39, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

43. The program storage device of claim 39, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

44. The program storage device of claim 39, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

45. The program storage device of claim 39, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

46. The program storage device of claim 39, wherein said time events comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

47. The program storage device of claim 46, wherein said scenarios are based on sequencing of said timing items and time events.

48. The program storage device of claim 47, wherein said scenarios are each associated with specific outputs.

49. The program storage device of claim 38, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

50. The program storage device of claim 38, further comprising the step of creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.

Description

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to systems and methods for planning the use of manufacturing resources, more particularly, the invention relates to attribute based planning systems and methods that can operate in highly complex and dynamic environments and generate plans for the use of multiple resources in order to fulfill demand.

2. Discussion of the Related Art

The problem of planning for the use of manufacturing resources in order to meet demand has always been a daunting task for any business having significant manufacturing capacity and/or multiple sourcing. The problem of planning the use of resources becomes even more acute in today's competitive environment because today's business climate forces many businesses to follow certain business philosophies, such as just-in-time concepts, that require highly discipline resource planning.

A business, such as a large manufacturer, is typically made up of a vast network of manufacturing, warehousing, marketing and other production/marketing sites. Further, each of these sites may consist of a number of resources such as assembly lines and storage facilities all interacting and working together to produce finished goods for customers. The complexity of manufacturing networks alone often makes it extremely difficult to plan and schedule the vast numbers of resources that typically make-up a manufacturing or supply chain network.

Several other factors add to the complexity of trying to plan and schedule resources in a manufacturing or supply chain network. For instance, businesses typically receive numerous orders from numerous customers, each order and each customer having different priorities and order requirements. Further, businesses will typically need to be able plan for forecasted future orders and to determine when material must be ordered and capacity reserved for expected customer orders. Automakers, for example, may receive an order from a fleet dealer, a rental car company, a private citizen or any number of both large and smaller volume customers. Each order may consist of several items. For automakers these could be different models of cars with different options. Each of these orders may also include other important information such as the date or range of dates that the customer would like to receive or be able to obtain the order goods, the location where the goods will be available such as a specific dealer site, the customer chosen option packages and possible substitute configurations. Thus, each order will typically have several parameters that define how, what, when and where that the order must be fulfilled.

Many manufacturers today adhere to one or more business philosophies such as just-in-time principles to reduce costs. Unfortunately each of these principles may be an obstacle to another principle that the manufacturer may also want to follow. Further, such philosophies add to the complexity of trying to plan the fulfillment of orders and may make it even more difficult to plan the use of resources in supply chain networks.

In order to plan effectively and accurately, planning systems will preferably be able to take into account the various constraints that may be associated with each network resource. These constraints may be based on a number of factors such as time, quantity, quality, types of goods available, and the like. Any model used to represent a resource network will also preferably be robust so that the dynamic nature of many resource networks may be accounted for. The addition of these fluid constraints in any model used with a planning system adds to the overall complexity of the planning process.

In addition, models used for planning must take into account timing issues. In order to plan network resources, realistic timing requirements must be taken into account. This means that actual time lags, for example, the advance time needed to order a component parts for an assembly line before the parts are actually needed, must be accounted for. In addition, time availability of items, such as finished goods, work in progress and raw materials, needs to be accounted for in any effective planning system.

For various reasons, some of which were described above, the cost of planning the use of resources to meet demand may be prohibitively high. In order to efficiently and effectively meet the planning needs of today's businesses, a planning system and method that is highly robust and that can recognize and accommodate the numerous constraints and the sometimes conflicting goals of businesses is highly desirable.

SUMMARY OF THE INVENTION

Accordingly, the present invention is directed to a system and method for planning the optimal use of resources of businesses to fulfill orders. More particularly, the present invention provides a system and method for generating, and maintaining constraint and attribute sensitive plans for the use of multiple resources at multiple locations to fulfill one or more orders.

Conventional planning systems for order scheduling and resource assignment are typically stock keeping unit ("SKU") driven. An alternative approach may be to use an attribute based approach. Constraints and business rules that govern supply chain planning are often driven by certain fundamental characteristics (attributes) of products, market, and the operational environment. The representation of the model should therefore be done in terms of these attributes.

According to one embodiment of the present invention, a system called an attribute based planning ("ABP") system is provided. The system may include a computing device in communication with a database. Demand, as defined by one or more orders having attributes, may be inputted into the system generating a plan for the optimal utilization of resources within a network. Orders may be either internally or externally generated orders or may even represent projected customer demand. Upon processing the orders, the ABP system may generate plans for the optimal utilization of network resources in order to fulfill demand.

According to another embodiment of the present invention, stock keeping unit attribute definition ("SAD") groups are created and defined. The ABP system, according to the present invention, may utilize SAD groups to provide various system functionalities. For instance, SAD groups may be used to prioritized orders and organize resources into groups as part of configurations and/or bill of materials ("BOMs"), and the like. In order to facilitate the various functionalities various tables may be created. For example, among the tables that may be created include a temporary table for unplanned orders, BOM and Configuration tables, SAD groups, and the like. In addition, a transportation matrix may also be created in order to track and to account for transportation issues.

According to another embodiment of the present invention, the process for planning the utilization of resources in order to meet demand may include initialization process. The initialization process may include several steps that may include a step for creating and/or loading a static model. In order to model the resource network, various items will preferably be defined. These include, available resources, locations, various types of constraints including capacity and auxiliary constraints, time buckets, business rules, user and customer preferences, configurations, transportation matrix and BOMs, and the like. The initialization steps may include the step of initializing buckets. Initialization of the buckets may be accomplished by associating one or more constraints to the buckets. The initialization process may also include the steps of placing initial assignments and allocating materials. The initialization process may also include the step of creating a temporary table or tables of unplanned orders.

According to another embodiment of the present invention, a process for processing orders may be included in the process for planning the utilization of network resources. This second process may include the steps of selecting a slice of orders based on a SAD group, loading a window with the slice of orders and their associated data, prioritizing each order in the window and processing each order in the window according to their priority. The ABP system may plan by processing orders in groups, one group at a time. Each group of orders being processed may be placed into a window. The orders in the window may be prioritized and processed for planning based on their priority. Data associated with each order may also be loaded into the window. Such data may include, for example, SKU based BOMs, configurations, subordinate stock keeping units, finished goods stock keeping units, and the like.

According to another embodiment, the process for processing orders may include the steps of processing each unplanned order in the window, one order at a time. Prior to planning for unplanned orders, the system may issue initial assignments for resources in order to freeze the resources for particular orders. Once initial assignments have been generated, planning for the unplanned orders may commence. The ABP system may review the BOMs, configurations, associated finished goods stock keeping units, subordinate stock keeping units, SAD groups, ant the like, in order to plan for each order. Each configuration (and its associated SAD groups, subordinate SKUs) associated with each order may be reviewed and checked to determine which location/resource/bucket opportunities provides the optimal opportunity.

According to another embodiment of the present invention, a determination of the best scheduling opportunity for each order may be accomplished by the following steps. First, selecting a configuration and prioritizing admissible locations. Then selecting highest priority location/finished good stock keeping unit. Initializing the finished good stock keeping unit and determining a collection of admissible resources for the highest priority location. A material feasibility may be determined for each resource. In determining material feasibility, the ABP system may first check the resource inventory to determine if inventory can supply the requested quantity of goods. If the resource inventory is unable to fully supply sufficient quantity of goods under specified parameters as defined by, for example, the user or customer, such as by need date, then the system may check other possible methods for meeting the demand through that resource. The other methods for meeting demand include, for example, replenishment by manufacturer, replenishment by purchase and/or replenishment by substitution. Once material feasibility is determined for the resource, the ABP system may make an assessment of constraint feasibility and a check of material capacity compatibility. These steps may be repeated for each acceptable location/resource/bucket opportunity.

Each location/resource/bucket opportunity may then be evaluated based on various goals and customer preferences. The best location/resource/bucket opportunity may then be selected for purposes of planning the utilization of network resources and an output may be generated.

According to another embodiment of the present invention, freeze SAD groups may be created and used to freeze one or more orders. This may be used to ensure that an order is planned even if the order is infeasible.

According to another embodiment of the present invention, the ABP system recognizes a multi-level object function to determine the optimal location/resource/bucket opportunity. The multi-level object function provides criteria for reaching desirable planning results. The ABP system preferably incorporates a multi-level objective function to determine the best placement for an order on the plan. If the ABP system finds multiple opportunities for planning an order, the ABP grades each opportunity and selects the best opportunity based on multiple objectives.

According to another embodiment of the present invention, various tables may be created to provide a number of functionalities. These tables include, for example, a pre-determined assignment table, a BOM table, a SAD groups table, and the like.

According to another embodiment of the present invention, a determination of replenishment of a resource by purchase may be accomplished by determining time items, wherein the time items are specific points in time or time periods relating to the order, determining timing events based on the time items, creating a timeline comprising of the timing events and time items, selecting an applicable scenario based on the timeline from a group of predetermined scenarios and generating an output based on the applicable scenario.

According to another embodiment of the present invention, scheduling controls may be used to derive optimal resource utilization plans. Scheduling controls are schemes used to affect the manner in which buckets are searched to find feasible opportunities to place an order. There are several types of scheduling controls available including, forward, backward, just-in-time, frozen and do not schedule.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

It is to be understood that 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.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. In the drawings:

FIG. 1 is a block diagram of an exemplary environment where a system according to one embodiment of the present invention may operate;

FIG. 2A is a flow diagram of an initialization process according to one embodiment of the present invention;

FIG. 2B is a flow diagram of a process for planning the utilization of resources in order to meet demand according to one embodiment of the present invention;

FIG. 3A is a flow diagram for a process to place initial assignments;

FIG. 3B is a flow diagram for a process to prioritize orders;

FIG. 3C is a block diagram depicting the relationship between a SAD group, orders and a freeze group table.

FIG. 4 is a flow diagram of process for resource planning in order to fulfill demand as defined by order[s];

FIGS. 5A to 5H are block diagrams depicting an exemplary process for resource planning in order to fulfill an order;

FIG. 5I is a black diagram depicting various combinations of order attribute values for exemplary orders;

FIG. 6 is a flow diagram for a process to determine material feasibility.

FIGS. 7A to 7D are timelines that depicts an exemplary inventory level and need quantity over a period of time;

FIGS. 8A to 8C are block diagrams depicting visual representations of exemplary relationships between SAD groups and SKUs in the BOM;

FIG. 9 is a block diagram depicting an exemplary relationship between a, configurations and subordinate SKUs;

FIGS. 10A and 10B is a flow diagram for a process for replenishment by manufacture;

FIGS. 10C to 10F are block diagrams depicting BOM sets;

FIG. 10G is a block diagram depicting an exemplary BOM;

FIG. 11 is a flow diagram depicting a flow process for replenishment by purchase;

FIGS. 12A to 12D are timelines that depict exemplary time events;

FIG. 13 is a flow diagram depicting a process for generating outputs based on particular scenarios.

FIG. 14A is a flow diagram depicting a process for generating outputs based on particular scenarios;

FIG. 14B is a flow diagram depicting a process for replenishment by substitution;

FIG. 15A is a flow diagram depicting a process for determining constraint feasibility;

FIG. 15B is a flow diagram for a process for determining an effective scheduling interval;

FIGS. 15C to 15F are timelines depicting various scheduling control concepts;

FIG. 16 is a block diagram depicts a visual representation of an exemplary search process for finding a satisfactory time bucket; and

FIG. 17 is a block diagram depicting an attribute base planning system according to one embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to the preferred embodiment of the present invention, examples of which are illustrated in the accompanying drawings.

The invention disclosed herein incorporates by reference the subject matter of co-pending and commonly assigned U.S. Non-provisional Patent Applications "System and Method for Order Group Planning with Attribute Based Planning," Crampton et al., Ser. No. 10/287,788 , filed on Nov. 5, 2002; "System and Method for Replenishment by Manufacture with Attribute Based Planning," Crampton et al., Ser. No. 10/287,775, filed on Nov. 5, 2002; "System and Method for Order Planning with Attribute Based Planning," Crampton et al., Ser. No. 10/287,773, filed on Nov. 5, 2002; "Strategic Shipping Planning System and Method," Weber et al., Ser. No. 09/903,855, filed on Jul. 13, 2001; and "System and Method for Supply Chain Management Including Collaboration," Zarefoss et al., Ser. No. 09/965,854, filed on Oct. 1, 2001.

The present invention provides a system and method for optimal planning and scheduling of resources in a manufacturing or supply chain network. The system, called an attribute based planning ("ABP") system, can model SKUs as well as attributes. The ABP system and the process implemented by the system generate an output that defines a plan for using the resources of a supply chain. The output may be in text, raw data, graphical or any other form useful for planning purposes. The ABP system may interface with various other business planning, scheduling, purchasing, collaboration, and promising type systems and applications as are known in the art, including NetWORKS Strategy™, NetWORKS Sequencing™, and NetWORKS Collaborate™, all commercially available from Manugistics, Inc. The various functionalities associated with the ABP system may be best understood by the following description together with the accompanying drawings describing the various concepts used to implement the novel aspects of the present invention.

The following description of the invention makes reference to various terms, some of which are broadly defined in the following glossary.

Glossary

ABP System: Attribute Based Planning system. A planning system for planning the utilization of resources in order to satisfy demand.

BOM: Bill of Material. A structure that describes the various ways a SKU can be manufactured. Each record typically associates a parent item with a child item and a draw quantity that tells how many child units are required for each parent unit. Additionally, attribute restrictions may be present that tell when the record may be applied.
Bucket: A time interval typically used for constraints and allocations. In a supply chain optimization system, the planning horizon is usually divided into a number of buckets. The buckets may have the same length or they may be telescoping in that bucket durations may increase. For example, buckets may be defined for a week at the day level, three weeks at the week level, and three months at the month level. This would give 13 buckets that cover 4 months. If buckets are modeled at the day level for 4 months, approximately 120 buckets would be modeled. The number of buckets is important because the fewer buckets the system models, the smaller the model and the faster the algorithm.
Configuration: A set of BOM records that completely defines a specific buildable configuration for an order for a SKU.
Constraints: Any limitation placed on any entity, such as resource, that is recognized by the ABP system. Constraints may be hard (a solution should never violate) or soft (a solution should attempt to accommodate).
Constraint Bucket: A bucket associated with a constraint that has a capacity limitation. Typically there will be a maximum. There may also be relaxation information that tells how much the capacity can be overused and the cost for doing so.
Constraint Rules: A shorthand way for defining the capacities of constraint buckets. For example, a calendar may be associated with a constraint bucketed at the weekly level where each unit of availability converts into an amount of capacity. If, for example, there is a labor constraint bucketed at the weekly level that was associated with one person and there is a calendar that defines the available shifts as two shifts available Monday to Thursday (1 person*4 days*2 shifts/day*8 hours/shift=64 hours) and one shift available on Fri (1 person*1 day*1 shift/day*8 hours=8 hours), then the capacity for a week would be 64+8=72 person hours.
Demand: The demand on a system consists of external demand and dependent demand. The external demand consists of customer orders and forecast. The dependent demand is additional demand required to meet the independent demand.
Dependant Demand: This is the set of orders that the system generates that are consequences of the way material and capacity are allocated to the independent demand.
Effective Need Date: The need date less transportation time required to deliver the goods to a specified location.
FG SKU: Finished good stock keeping unit. A SKU that is a finished good.
Flag: Flags are used to indicate the existence of a specific condition, a preference, a constraint, and the like. Flags may also be used to control the planning process. Flags are used to indicate, for example, that a scheduling opportunity is not feasible, therefore, the ABP system needs to look at other scheduling opportunities.
Freeze Group: A SAD group whereby each order associated with the group is frozen. This is used in the planning cycle ensuring that an order will be scheduled even if infeasible.
Freeze Group Table: This table identifies the SAD groups that should be treated as frozen.
Independent Demand: This is the set of orders that is given to the system as opposed to dependent demand, which is related to orders generated by the ABP system.
Initial Assignment: An assignment that is given to the system that represents previous commitments that should be honored whether feasible or not. Each time the planning process is run, initial assignments will be placed into buckets before any other orders are planned. This placement of initial assignments ensures that frozen orders are always in the same place on the plan.
Location: A location is a place in the supply chain. Every location has a name.
Need Date: The most desirable date for order delivery and/or availability.
Maximum Earliness: The time interval before the need date that the requested good will be accepted. This value is specified by a SAD Group.
Maximum Lateness: The time interval after the need date that the requested good will be accepted. This value is specified by a SAD Group.
Multi-level Objective Function: Criteria for reaching desirable planning results. The ABP system preferably incorporates a multi-level objective function to determine the best placement for an order on the plan. If the ABP system finds multiple opportunities for planning an order, the ABP grades each opportunity and selects the best opportunity based on multiple objectives. These objectives include, for example, lateness (a measure of where the order was planned versus the effective need date for the order), total use (a measure of how full the bucket on the capacity constraint in which the order is planned), and transportation (the transportation cost between the location of the resource on which the order is manufactured and the customer location on the order).
Order: Each order consists of predefined and user defined attributes. Predefined attributes may include, for example, an identifier, SKU, quantity, start date (optional), need date, customer, priority, and effective need date. Note that orders and SKUs can both have values for user defined attributes. If both have values, the value for the order always takes precedence. For example, an order could be for a blue truck with sunroof and alloy wheels for a customer by the name of Joe Dealer. Therefore, the finished good, i.e., the order, is for a truck with the following attributes, blue, sunroof, alloy wheels and Joe Dealer.
Parent Item: A BOM record relates a parent item and a subordinate item. See BOM.
Plan: An assignment of demand to resources through time used to fulfill one or more customer orders over a certain time period (i.e., planning horizon).
Period Maximums: Maximum quantity of goods that may be available for a specified bucket.
Resource: A resource may be any entity that is a source of a good including assembly lines, manufacturing machine, a warehouse, storage facility, storage tanks, suppliers, and the like. Generally each resource may be uniquely identified by a name and a location.
SAD Group: SKU attribute description group. SAD groups are associated with various modeling concepts and tell which orders are applicable. Each SAD Group can have a list of SKUs and lists of attribute values for some or all of the attributes associated with orders. For example, if SKUs for model1 @1, model1@2, and model2@2 exist, with attributes for color (red, blue), and sunroof (yes/no) then one could define a SAD Group across all model1 vehicles that are red conceptually by writing (Sku: (model1@1 or model1@2) and color (red)). SAD groups may be used to control how the ABP system schedules orders and plan the use of resources. For instance, order groups may be used to determine how orders are sorted before the orders are process, the maximum early and maximum late time for groups of orders, scheduling controls for groups of orders, metrics and objective that define the "best" placement for an order, and the like. Each SAD group may be prioritized such that orders belonging to one SAD group may be scheduled prior to orders belonging to a lower prioritized SAD group.
Scheduling Controls: Schemes used to affect the manner in which buckets are searched to find feasible place to an order. There are several types of scheduling controls available including, forward, backward, just-in-time, frozen and do not schedule.
Scheduling Opportunity: A location/resource/bucket or resource/bucket defined opportunity to schedule an order that satisfies all the constraints applicable to the order and the bucket, resource and/or location.
SKU: Each SKU associates an Item and a Location. The SKU has predefined attributes and user defined attributes. The SKU may be raw material, work-in-process ("WIP"), finished good, or both WIP and finished good. The SKU may be hard or soft constrained and if raw material will have a replenishment model that tells whether the material can be replenished, and if so, how.
Smoothing Constraint: a type of constraint that directs the system to compute the average amount of capacity per unit time for a constraint based on the load and the total amount of time available in a horizon. The maximum in each bucket is set to the amount of available time times the average amount per time unit.
Subordinate Item: A BOM record relates a parent item and a subordinate item. See BOM definition.
User Defined Attribute (UDA): An attribute that is implementation specific. For example, a customer in an automotive implementation may have a color attribute for each car. A customer in a computer implementation may not have color but may have the hard disk size.

General Concepts

The present invention relates to an attribute based system for planning orders and resources. In order to facilitate an understanding of the various novel features of the invention, examples along with an explanation of various concepts are provided below. The following example introduces concepts that may be of use in understanding the novel aspects of the present invention.

The ABP system, according to one embodiment of the present invention, is a robust planning system that can operate in a highly complex and highly dynamic environment.

Referring to FIG. 1, which is a block diagram depicting an ABP system 100 operating in an exemplary environment. The ABP system 100 may be implemented using a combination of both hardware and software. The ABP system 100 may include a database 101 or may interface with one or more databases located on a single or multiple computer devices such as personal computers, workstations, servers, and the like. FIG. 1 depicts a manufacturing network of an automaker that manufactures three basic models of cars, Sedans, SUVs, and Minivan. Since a number of options (e.g., power windows, sunroof, and the like) are offered by the automaker, the network must be able to make many variations of the three basic model types. The network comprises of three manufacturing sites 102 to 106 in Atlanta, Denver and Rockville. Each site 102 to 106 in turn comprises of multiple resources 110, 120 and 130. A resource 110 to 130 may be an assembly line, a warehouse, plant equipment, inventory or any other entity that is a source for any finished goods that may be requested by a customer. Each resource 110 to 130 may be unique in the sense that each may have differing capacity, operating time, product lines, and other constraints. Constraints may exist that are specific to a resource, to a group of resources, to a location or a group of resources. Further, the automaker may implement certain rules that place more limitations on the resources and locations. Each resource 110 to 130 may be associated with one or more materials 140. Material 140 is any component part or component material or even finished goods that may be needed by a resource to produce finished goods. One way to view the resources 110 to 130 is to view them as a well that may be replenished by, for example, manufacture, purchase or substitution. Although not shown, other materials may also support materials 140. That is, materials 140 that go into resources 110 to 130 may be made from other materials. The automaker has several customers 150 who submit orders 160 to the automaker. The orders are parsed by the ABP system 100 and a plan is generated for the optimal use of the resources for fulfilling multiple orders. Each order contains relevant information that may be used to create certain parameters when scheduling orders. These include requested delivery date and location, identification of the requested goods, quantity of requested goods, customer identification, transportation mode, and the like. The finished goods requested in the order may be viewed as a "finished good stock keeping unit" ("FG SKU"). Typically there will be multiple configurations and component materials that meet the requirements of each FG SKU ordered. For example, suppose that a customer orders a Sedan. Suppose further that to build the Sedan, all that is needed are three components, components A, B and either C or D. In this case, there are at least two configurations possible, a Sedan built with components A, B and C, and a Sedan built with components A, B and D. These differing configurations and component materials may be listed in a bill of material (herein "BOM").

In addition to the constraints and order parameters described above, ideally a robust manufacturing resource planning system will preferably be able to accommodate a number of other important factors when planning the utilization of network resources. For instance, ordering and/or manufacturing component parts and material at the right time is required if the finished goods are to be delivered on or near the desirable date. If materials are not ordered at the proper time, then there will be insufficient material to fulfill orders. Ordering of component parts prior to the time when the parts are needed is one of the keys to having efficient and timely manufacturing capabilities.

Planning and scheduling is a continuous process. Thus, any effective planning system must be able to accommodate already scheduled assignments or orders that have been scheduled before attempting to fulfill new orders using the manufacturing resources available.

Preferably a reliable planning system will be able to accommodate idiosyncrasies, business rules and goals of many types of manufacturers including those having large and complex manufacturing networks. For example, a manufacturer may follow Just-in-Time concepts and would therefore want the ordered goods to be made just before the requested delivery date. On the other hand, the manufacturer may prefer to deliver goods on the earliest date that is acceptable to the customer. These goals are specific to each manufacturer and may be specific to certain types of orders (through model, customer, priority, etc) and may help a manufacturer to realize its long-term goals. Thus, a system that recognizes and accommodates the particular needs of a manufacturer may be highly desirable.

A business, such as large manufacturer, typically receives a number of orders from multiple customers and/or may make forecasts for customers who have yet to submit their orders. A customer may be a third party, a division within the same business, a supply chain partner or any other buyer. Each order received by the business may be defined by its attributes such as customer name, order identification, requested SKU, need date, quantity and the like. Typically a SKU will be defined by at least two attributes, name of good[s] and location. An SKU may have values for user-defined attributes. SKUs may also be grouped into finished good ("FG") SKUs, work in process (WIP) SKUs, or raw material SKUs. The items for raw material and WIP SKUs may be subordinate in the BOM. A subordinate item is a component part or material that is needed to create another item. Note that the values for user defined attributes for orders may override the values for user-defined attributes for SKUs. For example, a SKU may have a default value of false for a sunroof user defined attribute (meaning no sunroof). A particular order can override this value and set the sunroof user defined attribute to true. These concepts may be best understood with the following example. Suppose a FG SKU can be defined by the following attributes [item=sedan, location=Atlanta, options=power steering]. Suppose further that to build the FG SKU assembly parts A, B, and C for the basic sedan model and an additional component, "steering assembly," for the power steering option are needed. In this case, there are four subordinate SKUs: assembly A in Atlanta, assembly B in Atlanta, assembly C in Atlanta and steering assembly in Atlanta

The ABP system 100 may organize SKUs, and user defined attribute values into SKU attribute description (herein "SAD") groups. A SAD group for orders will be associated with any orders having "at least" the same attributes that define the SAD group. Thus, orders that have more attributes than the attributes that define the SAD group may belong to the SAD group as long as they have "at least" the attributes that define the SAD group. For example, suppose a SAD group is defined by the following two attributes [item=Sedan and Location=Atlanta], then all orders having those two attributes, regardless of whether the orders are associated with other attributes, will belong to that SAD group. For instance, an order having the attributes [item=Sedan, Location=Atlanta and Power Windows=Yes] will belong to the SAD group. Orders that meet the conditions defined by a SAD group are considered to be part of that group. The ABP system 100 uses SAD groups to provide a number of functionalities. For instance, among other functionalities, SAD groups may be used to: define attribute-sensitive bills of materials ("BOM") for orders; define attribute sensitive inventory; define attribute-sensitive substitutions; apply constraints to orders; define maximum, early/late dates for orders; define dedicated resource groups for orders; apply metrics to orders; define scheduling controls for orders; define sort criteria to sort orders before the planning process is run; apply rate models to orders for a location; and apply violations to orders. An order violation indicates when an order violates a soft constraints or when an order could not be planned because it would have violated a hard constraint. It typically has an ID and some associated values that can be displayed to the user in a internationalized, localized format. In grouping various items into SAD groups, the planning process may be simplified providing optimal planning solutions.

A SAD group's data may be stored in a SAD group table. An example of a simple SAD group is a group that is defined by a single attribute name and value set: attribute name=Color, attribute value=Red. All orders with a value of red in the color attribute would be a part of this group. Certain fields in the table may make up the definition for each attribute name/attribute value pair in the SAD group. These include, for example, attribute type, attribute name, attribute value, index, attribute match flag, attribute present flag, and the like. Attribute type and attribute name identify which order attribute to compare against. Attribute value is the value to compare against the data from the order. An index may be used to specify how multiple name value pairs within a group may be associated (e.g., "and" or "or" connector relationships). An index of -1 means that the name/value pair is evaluated and ‘or’ed with the rest of the group. Name/value pairs with the same index are ‘and’ed together and ‘or’ed with the rest of the group. The attribute match flag specifies if the attribute value from the order should match or not match the attribute value from the name/value pair. The attribute present flag specifies the behavior if the attribute is not present on the order. For instance, if the attribute value is color, the allowable values could be "red", "blue", or no value present. This flag indicates whether no value present matches or not.

A number of attributes may be used to define a SAD group. These attributes include, for example, item identifier, planned location, requested location, start date-begin, start date-end, need date-begin, need date-end, preference date begin, preference date end, minimum quantity, maximum quantity, minimum priority, maximum priority, customer name, customer location, SKU and any other user defined attributes useful for implementing the ABP system 100. A planned location attribute may be used to group orders by the location at which they are planned. The requested location attribute may be used to group orders by each order's requested location. The start date-begin and start date-end dates attributes are used to group orders by a range of start dates. The start date-begin and start date-end attributes may be used individually or in combination to define a range of start dates. A start date can identify the soonest (i.e., earliest) bucket that an order can be planned in. This is an optional field, which may use the latest of the following dates [horizon start, the order start date, order need date-maximum earliness]. The start date identifies the soonest bucket that the order is allowed to be planned in. Note that capacity or material feasibility can make the actual start date later but no earlier than the earliest start date. The need date-begin and need date-end attributes are used to group orders by a range of need dates. Need date-begin and need date-end may be used individually or in combination to define a range of need dates. The preference date-begin and preference date-end attributes may be used to group together orders by a range of preference dates. They may be used individually or in combination to define a range of preference dates. The minimum and maximum quantity attributes may be used to group together orders based on each order's requested acceptable minimum and/or maximum quantities. The minimum priority and maximum priority attributes may be used to group orders by priority.

The ABP system 100 supports an attribute-based bill of materials (herein "BOM"). However, unlike the BOMs in traditional planning systems, the BOMs associated with the ABP system 100 allow the modeling of option-based and rule-based material requirements. For example, in the automobile industry, car models typically have a number of features and options. Each feature or option, such as power windows, drives the types of components that will be needed to provide such a feature or option. For cars with power windows, materials (i.e., component parts) such as power window assembly and a 4W motor (for driving the power window) will be needed. The presence of specific combination of features or options may also affect the materials needed for the combination. For example, if, in addition to the power window option in the above example, a second option, an automatic sunroof, is needed. This may result in the addition of a sunroof assembly and changing the 4W motor requirement (originally required only when the power window was present) to a 10 W motor requirement. BOMs are essentially recipes for SKUs or finished goods and show the required component parts or material for all possible configurations of the finished good that satisfies the attribute requirements of a SKU. For the auto industry example above, a BOM for a SKU (e.g., car model) may show the components (e.g., power window assembly, 4W motor, engine, brakes, etc.) used to build the various acceptable configurations of the SKU. Therefore, the more components that exist, the more configurations are possible and more component parts or materials may be needed. The presence or absence of a feature therefore drives the requirements for materials. Substitutions s may be used as necessary to attempt to make a configuration feasible. A substitution model may be created to facilitate substitutions. The substitution model identifies the substitutable SKUs for a given SKU, the priority, and the substitute draw quantity. If the base configuration is not feasible, then a substitute SKU may be tried. Chaining is allowed in that one may be allowed to substitute SKU2 for SKU1. If SKU2 is not feasible and has a substitute SKU3, then SKU3 may be tried. Circular substitution is detected and not allowed. Substitutes may be limited by the present of SAD Groups.

SAD groups may be used to group other items. For example, SAD groups may be used to created dedicated resource groups. Dedicated resource groups may be created and used to restrict the orders that can be planned for specified resources. Dedicated resource groups may be implemented using SAD groups. Any resource that references a dedicated resource group will only be able to manufacture orders that belong to the SAD group associated with the dedicated resource group. SAD groups may be used to group other items, some of which will be described in the following sections.

In a preferred embodiment, the ABP system 100 is a bucketed planning system. That means that the ABP system 100 attempts to plan orders into buckets. Buckets may be defined by several parameters such as time, constraints, resource, and the like. For example, an order that need to be scheduled on one of the resources depicted in FIG. 1 may be assigned to a bucket that is defined by a time interval and is specific to the resource at that location. For instance, suppose an order has been assigned to resource "A" 110 at the Atlanta location 102. The order may then be assigned to a bucket that is defined by a time interval, a resource and a location such as "September 11 to September 12," "resource A," and "Atlanta, Ga."

To support the various features provided by the present invention, a number of database tables may be created. The tables are made up of rows and columns. The value of the rows and columns will depend upon the type of tables they are. Tables may be created for a number of entities including BOM, buckets, SKU groups, SAD groups, inventory table, Items (all finished goods and subordinate materials should be listed), Locations, Resource, Resource groups, and the like.

The Overall Process

The entire process for generating a plan for utilizing resources in order to meet demand may be divided into two sub-processes. Referring to FIGS. 2A and 2B, which are two high-level flow processes 200 and 220 for creating a plan or modifying an existing plan for utilizing network resources in order to fulfill demand according to one embodiment of the present invention. For illustrative purposes, the flow process 200 will be called an initializing flow process and flow process 220 will be called an order planning flow process. For each process 200 and 220, a discussing that briefly introduces the various steps defined for each flow processes 200 and 220 is provided below followed by a more detailed discussion of specific steps and concepts introduced in the two processes.

In brief, the process 200 begins at step 202 when a static model is loaded and/or created. The static model may be modeled by defining or loading, for example, data relating to available resources, locations, calendars, metrics, constraints, business rules, SKU SAD groups, bills of materials ("BOM"), dedicated resource groups and the like. This includes SKUs that may be marked as "keep in memory" and BOM relationships that are referred by these SKUs. At step 204, initialize buckets. To initialize buckets, each bucket may be associated and/or defined by constraints. These constraints may include, for example, capacity constraints. At step 206, place initial assignments and allocate materials accordingly. At step 208, create temporary table of unplanned order or orders. A more detailed description of some of these steps is provided below. After the initialization process 200 is completed, a second process 222, for processing orders, may commence.

The second overall process 220 is for planning the utilization of network resources in order to fulfill demand as defined by, for example, orders that have not yet been planned (i.e., unplanned orders). In brief, the process 220 begins when a slice of one or more orders is selected at step 222. A slice of orders is a group of orders that are each associated with a particular SAD group based on the SAD group's attribute values. At step 224, load the next window with the selected slice of order or orders and its associated items. The items that may be associated with the order or orders include for example, configurations, BOMs, subordinate SKUs, Finished Good SKUs ("FG SKUs"), and the like. At step 226, prioritize each order in the window. Those with higher priority will generally be scheduled before those that have lower priority. At step 228 select the highest prioritized order not yet planned and plan the order. If there are any more unplanned orders in the window then the next highest prioritized order is scheduled as indicated by step 230. Otherwise at step 232, write assignments so that appropriate resources and/or materials that are needed to fulfill the orders may be reserved. Once the assignments have been written, the dynamic data in the temporary table may be unloaded at step 234. At step 236, determine whether other slices of orders need to be processed and if so, return to step 222 to begin the process 220 over again.

A more detailed description of specific steps and concepts introduced above for processes 200 and 220 follows. The initialization process 200 typically begins when the static portions of the planning model is defined at step 202. For example, the relationships between resources, dedicated resource groups and its location (e.g., manufacturing or warehouse sites) may be defined. Specific information relating to each of the resources such as capacity and the types of goods available at a particular resource may also be defined. Constraints that are generally static may be defined during initial modeling. A transportation matrix may be created to define the transportation time required for various legs of the supply chain including time required for delivery to particular customers. Items such as calendars, buckets, business rules, SKUs, BOMs may also be defined. Those skilled in the art will recognize that a number of other generally static entities may also be defined during the modeling stage of the planning process.

Generally, it will be preferable that some data not be inputted until the last moment. For example, highly dynamic data that tend to constantly change or data that may not be available until the last moment will preferably be inputted just before orders are planned. Thus, the dynamic portions of the model may be inputted sometime after the static portions have already been defined. Data such as new order or modified orders will generally be inputted just before the planning process begins. Even generally static data, such as data relating to constraints, resources and business rules, may have to be updated periodically and data relating to those updates may be inputted just before order planning commences. New constraints may also be added just before order planning for specific planning horizons or for specific conditions such as for a one-time customer. New or emended business rules may also be added at the last moment to reflect the constantly changing needs of businesses. Finally, the most recent orders will typically be inputted at the last moment before order planning is implemented.

Preferably the processes 200 and 220 generate plans for using resources for fulfilling demand (e.g., both independent and dependent orders). Specific buckets may be reserved for specific orders. Buckets are time intervals that may be associated with, for example, a resource and represents scheduling opportunities. At step 204, buckets are initialized by placing constraints on them. These constraints may include quantitative constraints as well as qualitative constraints. During the initialization step 204, the states (buckets) on all the constraints are created and the capacities on these states are set. Constraints may be incrementized and implemented in buckets. These constraint buckets may parallel resource buckets. Thus, for example, a specific constraint, such as a resource capacity constraint, may be defined for specified buckets. For instance, the total capacity for a particular resource may be restricted at one level during one time period while it may be restricted to another level at a different time period. Constraints may be modeled using three database tables, a constraint table, a constraint rule table, and a bucket table.

Before the ABP system 100 makes plans for fulfilling any not-yet-planned orders, it may plan for orders with pre-defined assignments during the initialization step at step 206. Initial assignments are used to "freeze" orders. That is, they may be used to reserve a location/resource/bucket[s] for fulfilling a particular order[s] before the planning process is run. This may be accomplished by initial assignments that are typically placed into buckets before any other orders are planned to ensure that they are always in the same place on the plan. Before planning order, orders with pre-determined assignments will preferably be scheduled. This may be accomplished by invoking an initial assignment placement routine.

In some situations, a pre-assignment table may be created to define pre-determined assignments. Such a table may contain information such as: the ID of the order to be assigned, the resource to which the order is assigned, the scheduled bucket for order, and the quantity of the order to be associated with this assignment (probably does not belong here). The BOM configuration information for initial assignments may be stored in a BOM table. Upon invocation, the initial assignments may be processed in the pre-assignment table. Referring to FIG. 3A, which is a flow process 300 for the placement of an initial assignment. At step 302, place the quantity of the order in the initial assignment on the designated resource in the designated bucket using the designated BOM configuration. At step 304, update material availability without assessing material feasibility for the order. Material, in this context, may be any component material or finished goods used to replenish a resource. At step 306, determine constraints that are applicable to the bucket/resource. Constraints are typically bucket/resource/location dependent. Two factors may be used to determine whether constraints are applicable. First, only constraints that apply to the designated resource on the initial assignment are considered. In other words, either the name of the resource for which the constraint is configured must exactly match the name of the designated resource or the resource group for which the constraint is configured must include the designated resource on the initial assignment. Second, constraint violations should not prevent the initial assignment from being placed in the designated bucket on the designated resource. At step 308, update the capacity in the designated bucket for all applicable constraints. At step 310, record the information relating to the assignment of the order.

At step 208 of process 200, temporary database table or tables for unplanned order or orders may be created. These unplanned orders are the targeted orders that are yet to be planned by the processes 200 and 220. The tables may identify all orders that have not been fully fulfilled through initial assignments. The tables may be used to restrict the loading of orders through the remainder of the planning process 200 to only those orders identified in the tables. In processing orders by slices or groups of orders, memory requirements of the ABP system 100 may be reduced.

In step 226 of FIG. 2B, the orders are prioritized and sorted. In general, when multiple orders are present, the orders are processed one after the other. Orders that are processed first generally get the first access to materials and capacity. Since it is preferable that certain orders be process before others, preferably the ABP system 100 uses a mechanism for prioritizing orders. This mechanism for prioritizing orders is called sorting.

The order sorting mechanism allows for modeling order groups and sorting criteria in such a way that the highest priority orders get processed first. If two orders have the same priority, they may be processed in the sequence returned by the database. Referring to FIG. 3B, which is a flow process 320 for prioritizing orders. At step 322 organize orders into order groups. At step 324, define each group by a SAD group. At step 326, prioritize each SAD group by, for example, sequential numbers. A sequence number is used to model the relative priority of these order SAD groups. Lower sequence numbers are associated with higher priority. Within each order group, orders may be sorted, ascending or descending, based on one or more attributes. At step 328, sort orders within each group by a user defined criterion. The criteria may be different for each group. For example, orders within one group may be sorted by need dates while orders for another group may be by a pre-defined priority number.

To illustrate some of the concepts described, the following example is provided. Suppose a car manufacturer makes model X cars and accepts orders for fleet, retails and stock sales. The manufacturer has three distinct groups of orders: fleet orders, retail orders and stock orders. As a result, the relative priority of these three groups may be modeled. Suppose further that the fleet orders are the most important, followed by retail orders and finally, stock orders. For each group, a separate priority criterion for sorting the various orders within that group may also be defined. For example, orders in the retail group may be sorted, ascending, by need date where as the order in the fleet group may be sorted, descending, by revenue (a user defined attribute).

The retrieval of order groups and sorting within the groups may be accomplished using an extensible interface. The ABP system 100 may support a base implementation for this interface that works as follows. SAD groups may be used to define order groups. Sequence numbers may be used to model the relative priority of these order groups. Lower sequence numbers are associated with higher priority. Within each order group, order may be sorted ascending or descending, by a single attribute. For example,