The technology described in this patent document relates generally to inventory optimization and management.
In a typical supply chain network each location replenishes inventory from a primary supplier. In many business environments, however, locations may also source inventory from alternative suppliers when their primary supplier is out of stock or cannot deliver inventory on time. With the availability of alternative suppliers, excess inventory in the network may be distributed so that orders can be fulfilled with lower cost and a faster delivery time. This is referred to as multi-echelon inventory planning with lateral transshipment.
In accordance with the teachings described herein, systems and methods are provided for optimizing inventory in a multi-echelon inventory distribution network having at least a first echelon and a second echelon. An example method may include the steps of: receiving information identifying an inventory pool that includes at least two inventory locations within the first or second echelons; determining inventory excesses or shortages at inventory locations within the inventory pool; determining an inventory transshipment plan for transferring inventory between two or more of the inventory locations in the inventory pool based at least in part on the inventory excesses or shortages; and determining an inventory replenishment plan for replenishing inventory at one or more inventory locations in the first echelon from one or more primary supply locations in the second echelon, the inventory replenishment plan being based at least in part on the inventory excesses or shortages and accounting for any inventory transfers identified in the inventory transshipment plan.
Another example method of optimizing inventory in a multi-echelon inventory distribution network may include the steps of: determining optimum inventories for a plurality of inventory locations in a first echelon of the multi-echelon inventory distribution network based at least in part on a demand forecast; determining inventory excesses or shortages at the plurality of inventory locations in the first echelon based at least in part on a comparison between the optimum inventories for the plurality of inventory locations in the first echelon with actual inventories for the plurality of inventory locations in the first echelon; determining inventory transshipments between two or more inventory locations in the first echelon based at least in part on the inventory excesses or shortages at the plurality of inventory locations in the first echelon; and determining inventory replenishments from one or more inventory locations in a second echelon of the multi-echelon inventory distribution network to the plurality of inventory locations in the first echelon, the inventory replenishments being based at least in part on the inventory excesses or shortages at the plurality of inventory locations in the first echelon accounting for the inventory transshipments between the two or more inventory locations in the first echelon. Embodiments of the method may also include the step of: estimating delivery delays for inventory replenishments between inventory locations in two or more echelons of the multi-echelon inventory distribution network, wherein the optimum inventories for the plurality of inventory locations in the first echelon is further based on the estimated delivery delays.
A system for optimizing inventory in a multi-echelon inventory distribution network having at least a first echelon and a second echelon may include one or more processors, one or more memory devices, and central inventory optimization software stored on the one or more memory devices and executable by the one or more processors. When executed by the one or more processors, the central inventory optimization software may be configured to: receive information identifying an inventory pool that includes at least two inventory locations within the first or second echelons; determine inventory excesses or shortages at inventory locations within the inventory pool; determine an inventory transshipment plan for transferring inventory between two or more of the inventory locations in the inventory pool based at least in part on the inventory excesses or shortages; and determine an inventory replenishment plan for replenishing inventory at one or more inventory locations in the first echelon from one or more primary supply locations in the second echelon, the inventory replenishment plan being based at least in part on the inventory excesses or shortages and accounting for any inventory transfers identified in the inventory transshipment plan.
In addition, the multi-echelon supply chain network 10 also provides for lateral transshipment of inventory from alternative suppliers, as illustrated by the dotted arrows in
Multi-echelon inventory optimization has recently captured a lot of attention from executives because of its potential for expedited ordering and reduced costs. However, the problems of optimizing standard inventory replenishment (i.e., shipments from the primary supplier) and optimizing inventory transshipments (i.e., shipments from an alternative supplier) have traditionally been addressed separately. Accordingly, typical inventory transshipment plans do not account for uncertainties in the supply chain. These uncertainties, such as variations in demand and delivery, are ubiquitous and should be addressed in order to derive an optimal inventory control policy.
The inventory plan(s) 36 is optimized based on supply and demand data, such as a demand forecast 38, the delivery lead time 40 for one or more locations, and an inventory policy 42. The inventory policy 42 may, for example, define one or more constraints on inventory replenishment and/or transshipment, such as a minimum order size, a predefined time period between inventory replenishments, etc. The demand forecast 38 may, for example, be generated from historical data (e.g., inventory and sales data 44 received from the inventory network locations) using forecasting software, such as the SAS® Demand Driven Forecasting for Retail and SAS® High-Performance Forecasting software sold by SAS Institute Inc of Cary, N.C. In addition, the central inventory optimization system 32 may receive additional inputs used in the optimization process, such as an input 45 identifying the optimization period and an input 46 identifying one or more inventory pools 48 for transshipments. It should be understood that the inputs to the central inventory optimization system 32 may be received from one or more application interfaces that are configured to receive user input or may be received from one or more other software applications.
In operation, the central inventory optimization system 32 may determine the optimum inventory for individual locations in the network 34 based on the inventory demand forecast 38 and an estimated delivery delay for inventory replenishments (i.e., inventory shipments from a primary supplier). An example method for determining the estimated delivery delay at an inventory location is described below with reference to
Using the estimated inventory excesses or shortages, the central inventory optimization system 32 may determine an optimal transshipment plan for allocating inventory within the identified inventory pools 48. Inventory pools 48 are defined (e.g., by input 46) to include locations within the network 34 that may share inventory. As shown in
Having determined the optimal inventory transshipments, the central inventory optimization system 32 may then update the on-hand and pipeline inventory with the transshipment results and use the updated inventory conditions to determine the optimal inventory replenishments for the individual network locations. That is, the optimal inventory replenishments may be determined based on the estimated inventory excesses or shortages at the individual network locations accounting for any inventory transshipments. The optimal inventory transshipment and replenishment data may, for example, then be included in one or more comprehensive inventory plans 36 for use by the network 34. One example of an inventory transshipment and replenishment plan is described below with reference to
The central inventory optimization system 32 performs the above-described inventory optimization calculations one echelon at a time, starting with the bottom echelon. In this way, the inventory optimization calculations for the higher echelons account for the optimal replenishments (and possibly transshipments) to the downstream locations. An example method that may be used by the central inventory optimization system 32 for determining an optimal transshipment and replenishment plan for individual locations in a multi-echelon network is described below with reference to
In operation, the performance simulator 62 may be used to simulate the implementation of the inventory plan 64 within a model of the multi-echelon network over a predetermined evaluation period to generate a performance report, such as a key performance indicators (KPI) report 68. For example, the performance simulator 62 may receive inventory data from the central inventory optimization system 66, such as the calculated optimal inventory, the inventory and sales data, the delivery lead time, order constraints, etc., and use the data to simulate an order replenishment process over one or more time periods at each node of the network based on a random demand generated from downstream nodes. The performance report 68 may, for example, identify the mean and variance of key performance indicators (KPIs), such as service level (e.g., fill rate, ready rate and backorder ratio), the inventory on hand, the inventory cost, the ordering quantity, and the inventory receipt.
In step 74, the method is initialized (setting k=1) so that the optimization procedure starts at the bottom echelon of inventory locations. As explained below, steps of the method are repeated for each echelon in the network so that inventory optimization calculations for the higher echelons take into account any replenishments or transshipments to the downstream locations. The echelon for which optimization calculations are currently being performed is referred to in
At step 76, the optimal inventory policies are determined for each location in echelon k based on the demand forecast, estimated delivery delays from upstream locations, and possibly other constraints such as the planned inventory receipt, holding cost, order lead-time and required service level at each location. The optimal inventory policy at each location may be determined over a protection interval having a number of periods. The protection interval may be determined as the sum of a lead-time and inventory review interval, and the optimal inventory for each period of the protection interval may be the amount needed in order to satisfy the service level requirement with minimum cost. The optimal inventory policy for each inventory location may, for example, be determined using simulation-based policy optimization. For example, the following simulation sequence may be used to determine an optimal inventory policy for each interval (t) in a planning horizon (T):
With reference again to
At step 80, the inventory excesses or shortages are determined for each location in echelon k. For example, locations with an average service level higher than a predefined target level may be considered candidates for excess inventory. The following formula may be used to calculate excess inventory at a location over the protection level:
where
Locations with an average service level lower than the predefined target level may be considered candidates for an inventory shortage. Inventory shortages for a period t in the protection interval may be calculated as follows:
InventoryShortage=max{0,Ot−It}.
At step 82, an inventory pool(s) is defined to include locations that are allowed to share inventory. The inventory pool(s) may be customizable to support various business requirements. For example, an inventory pool may include only peer locations in the current echelon or peer locations in upper echelons. Also, in certain embodiments, more than one inventory pool may be defined. For example, one inventory pool many include only peer locations (i.e., locations within the same echelon) and another inventory pool may include upstream locations. In addition, inventory pools may provide for a preference. For instance, if multiple pools are defined, one pool may be given transshipment preference over another pool. Once the inventory pool(s) are defined, an optimal transshipment plan may be determined to allocate inventory within the identified inventory pool(s), i.e., to identify how much inventory should be moved from locations within the pool(s) with excess inventory to locations with an inventory shortage. The inventory plan may also identify other transshipment criteria, such as a transportation mode. The optimal transshipment plan may, for example, be based on a tradeoff between inventory holding cost, transshipment cost and stock-out penalty cost subject to order constraints, such as batch size and minimum order amount.
The transshipment problem used to produce the optimal transshipment plan may, for example, be a mixed integer problem that solves the re-balance of products between locations in order to minimize inventory holding cost, transportation cost, and stock-out penalty cost. In one example, the optimal transshipment plan may be determined using an optimization model according to the following process:
Step 0: Identify excess inventory locations (step 80 of
Step 1: For each excess inventory location, determine the minimum cumulative difference, which is the amount that can be transshipped from an excess inventory location without hurting its stock-out probability. The minimum cumulative difference value is set to ei (excess inventory at i), where I is the index for the excess inventory locations.
Step 2: Eliminate locations for which ei=0 (because these locations will not be able to make any inventory transshipments even if they appear to be in an excess inventory state). If the set (i) is empty, then go to step 7.
Step 3: Identify all locations that are successors to the excess inventory locations remaining after step 2. Set index j for these locations.
Step 4: Eliminate the index j locations from step 3 that are not in deficit. If set j is empty, then go to step 7.
Step 5: Using the list of excess locations I and deficit locations j, formulate the transshipment optimization problem and solve, e.g., using the optimization algorithm set forth below.
Step 6: Update the inventory profile based on the results from step 5.
Step 7: End.
Example Optimization Algorithm for Step 5:
(i) Notation:
(ii) Decision Variables:
(iii) Optimization Model:
minimize ΣnΣtΣkcd*+nkthnk+ΣnΣtΣk−cd*−nktpnk+ΣnΣiΣjΣtΣm(cnijm+αnijmhni+(1−αnijm)hnj)Xnijtm+ΣnΣiΣjΣtΣmwnijtYnijtm (1)
subject to cd*nit=cdnit−ΣjΣtΣmXnijtm for all i and t in n. (2)
eni>=ΣjΣtΣmXnijtm for all i in n. (3)
cd*njt=cdnjt+ΣiΣ(1 to t)ΣmXnijtm, for all j and t in n. (4)
Xnijtm<=maxnijm for all i, j, t and m in n. (5)
minnijm−Xnijtm<=Q(1−Ynijtm) for all i, j, t and m in n. (6)
Xnijtm<=QYnijtm for all i, j, t and m in n. (7)
Xnijtm=βnijtmbnijtm for all i, j, t and m in n. (8)
Xnijtm>=0 only for t=1+lnijm,=0 o.w. for all i, j, t and m in n. (9)
Ynijtm={0,1} for all i, j, t and m in n. (10)
At step 84, the on-hand and pipeline inventory are updated based on the transshipment plan for echelon k, as established in step 80. Then, at step 86, the method determines if echelon k is the top echelon of the network (i.e., whether all of the echelons have been evaluated.) If the method has not reached the top echelon, then it proceeds to step 90. Otherwise, once all of the echelons have been evaluated, the method proceeds to step 88 to generate one or more inventory transshipment and replenishment plans for the multi-echelon network, for example as illustrated in
At step 90, the updated inventory conditions are used to determine the optimal inventory replenishment quantities for the individual network locations in echelon k from their primary supply nodes. The optimal inventory replenishments may, for example, be determined using an order generation process that determines order quantities based on optimal policy and current inventory position (on hand+pipeline−backorder), where order quantity=optimal inventory target−inventory position. For instance, in one example, the optimal inventory replenishments may be determined using the MIRP procedure provided by the SAS® Inventory Optimization software sold by SAS Institute Inc of Cary, N.C.
At step 92, the internal demand at locations in the next echelon (echelon k+1) is determined based on the inventory replenishment quantities determined at step 90. The method then increments to the next echelon (k=k+1) at step 94, and the method returns to step 76.
It should be understood that similar to the other processing flows described herein, one or more of the steps and the order in the flowchart may be altered, deleted, modified and/or augmented and still achieve the desired outcome.
(1) If SLtype=ReadyRate, find a minimum integer St that satisfies:
prob(Zt≦St)≧α;
(2) Else if SLtype=FillRate, find a minimum integer St that satisfies the following expressions:
(3) Else, find a minimum integer St that satisfies the following expressions:
WHERE,
With reference again to
(X−a)=(X−a)+−(a−X)+;
(X−a)2=[(X−a)+]+[(a−X)+]2;
WHERE,
The delivery delays may then be calculated at step 116, for example using the following algorithms:
WHERE,
With reference now to
At step 136, the method determines if all of the echelons have been considered. If not, then the method returns to step 126 to calculate demand for the nodes in the next echelon (set at step 134). Otherwise, if all of the echelons have been considered, then the method ends at step 138.
In certain examples, the inventory pools may also define delivery modes. For instance, the inventory pool depicted in
A simulation may then be run to estimate the average service level during the protection interval with the current on-hand and pipeline inventory, as illustrated in
Having determined the inventory excesses and shortages, the optimal transshipment plan may be determined, for example as described above with reference to step 82 of
The optimal transshipment plan may then be used to update the planned inventory receipts for each network node, as illustrated in
The above steps may then be repeated to determine the optimal transshipment plan for the middle echelon (W1 and W2), as shown in
Using the updated inventory receipt shown in
The illustrated inventory plan 250 presents the inventory location (e.g., the buyer) with suggested optimal orders, projected delivery, projected service level, and other inventory replenishment information. The example plan 250 includes a primary source orders field 252 that displays the suggested order from the primary supplier and an alternative source order field 254 that displays the suggested order from an alternative source (i.e., transshipment orders). Also included are a planned order receipts field 256 that displays inventory data projections for the current period and for a number of periods into the future and a replenishment plan metrics field 258 that displays order information, such as the projected service level, the order amounts and the projected costs.
Residing within computer 312 is a main processor 324 which is comprised of a host central processing unit 326 (CPU). Software applications 327, such as the method of the present invention, may be loaded from, for example, disk 328 (or other device), into main memory 329 from which the software application 327 may be run on the host CPU 326. The main processor 324 operates in conjunction with a memory subsystem 330. The memory subsystem 330 is comprised of the main memory 329, which may be comprised of a number of memory components, and a memory and bus controller 332 which operates to control access to the main memory 329. The main memory 329 and controller 332 may be in communication with a graphics system 334 through a bus 336. Other buses may exist, such as a PCI bus 337, which interfaces to I/O devices or storage devices, such as disk 328 or a CDROM, or to provide network access.
This written description uses examples to disclose the invention, including the best mode, and also to enable a person skilled in the art to make and use the invention. The patentable scope of the invention may include other examples that occur to those skilled in the art.
It is further noted that the systems and methods described herein may be implemented on various types of computer architectures, such as for example on a single general purpose computer or workstation, or on a networked system, or in a client-server configuration, or in an application service provider configuration.
Additionally, the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem. The software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform methods described herein. Other implementations may also be used, however, such as firmware or even appropriately designed hardware configured to carry out the methods and systems described herein.
The systems' and methods' data (e.g., associations, mappings, etc.) may be stored and implemented in one or more different types of computer-implemented ways, such as different types of storage devices and programming constructs (e.g., data stores, RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.
The systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) that contain instructions for use in execution by a processor to perform the methods' operations and implement the systems described herein.
The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes but is not limited to a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.
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Number | Date | Country | |
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20120054076 A1 | Mar 2012 | US |