Patent Application
20150100378
This disclosure relates generally to systems and methods for supply chain management, and more particularly, to systems and methods for supply chain management by inventory control and flow management.
A supply chain may include distribution centers that store inventory of products needed to be supplied to customers in response to customer demands. Managing the flow between the inventories at these distribution centers may be essential to the success of many of today's companies. Most companies may rely on supply chain management to ensure the timely delivery of products in response to customer demands, such as to ensure the smooth functioning of different aspects of production, from the ready supply of components to meet production demands to the timely transportation of finished goods from the factory to the customer.
Customer demands may fluctuate due to various reasons, such as global economy and local economy. Sometimes, customer demands for certain products, such as replacement parts for certain machines, may slowly decrease to nearly zero. The slowly decreasing demand may trap excess product inventory at an edge distribution center which is remote from a central distribution center for several years or even longer, resulting in inefficient usage of storage space at the edge distribution center.
Certain techniques have been used to manage inventories. For example, U.S. Patent Publication No. 2011/0257991, to Shukla (the '991 publication), discloses a method for managing pharmacy inventories. The method includes maintaining an online pharmacy inventory database among a plurality of participating network pharmacies, identifying over-stock products, non-moving products, slow moving products, and un-wanted products from the plurality of participating network pharmacies, and generating a redistribution list of one or more products.
Although the method of the '991 publication may be useful for reducing or eliminating the generation of expired products, the method of the '991 publication requires redistributing or transferring products between two or more entities, resulting in additional transportation cost and handling cost.
The supply chain management system of the present disclosure is directed toward solving the problem set forth above and/or other problems of the prior art.
In one aspect, the present disclosure is directed to a computer-implemented method for managing a supply chain including a central distribution center (DC) that distributes products to one or more edge DCs. The method may include determining, by one or more processors, a first rate of change of future demand for a product distributed by the edge DC over a predetermined future time horizon, and a second rate of change of historical demand for the product distributed by the edge DC over a historical time period. The method may also include updating flow of customer orders and storage space requirements for the central DC and the edge DC based on a difference between the first rate and the second rate.
In another aspect, the present disclosure is directed to a supply chain management system for managing a supply chain including a central distribution center (DC) that distributes products to one or more edge DCs. The supply chain management system may include a processor and a memory module. The memory module may be configured to store instructions, that, when executed, enable the processor to determine a first rate of change of future demand for a product distributed by the edge DC over a predetermined future time horizon, and determine a second rate of change of historical demand for the product distributed by the edge DC over a historical time period. The processor may also be enabled to update flow of customer orders and storage space requirements for the central DC and the edge DC based on a difference between the first rate and the second rate.
In yet another aspect, the present disclosure is directed to a non-transitory computer-readable storage device. The storage device may store instructions for managing a supply chain including a central distribution center (DC) that distributes products to one or more edge DCs. The instructions may include determining a first rate of change of future demand for a product distributed by the edge DC over a predetermined future time horizon, and determining a second rate of change of historical demand for the product distributed by the edge DC over a historical time period. The instructions may also include updating flow of customer orders and storage space requirements for the central DC and the edge DC based on a difference between the first rate and the second rate.
Supplier 110 may supply individual products to one or more of central DC 120, edge DCs 130 and 132, and customers 140-142. A product may represent any type of physical good that is designed, developed, manufactured, assembled, and/or delivered by supplier 110. Non-limiting examples of the product may include chemical products, mechanical products, pharmaceutical products, food, and components or replacement parts of fixed or mobile machines such as engines, tires, wheels, transmissions, pistons, rods, or shafts.
Central DC 120 may store products received from supplier 110, and may distribute the products to one or more of edge DCs 130 and 132, and customers 140-142. Edge DCs 130 and 132 may be located remotely from central DC 120, and may receive products distributed from central DC 120 and distribute the products to customers 140-142. In some embodiments, edge DC 132 may be a temporary edge DC which is temporarily established, or rented from another business entity, to accommodate for temporary surge of customer demands in a local area. In addition, in some embodiments, edge DC 130 may distribute the products to edge DC 132 which may subsequently distribute the products to customers 140-142.
Although supply chain 100 illustrated in
System 200 may be a server, client, mainframe, desktop, laptop, network computer, workstation, personal digital assistant (PDA), tablet PC, scanner, telephony device, pager, and the like. In one embodiment, system 200 may be a computer configured to receive and process information associated with different supply chain entities involved in supply chain 100, the information including purchasing orders, inventory data, and the like. In addition, one or more constituent components of system 200 may be co-located with any one of the supply chain entities.
Processor 210 may include one or more processing devices, such as one or more microprocessors from the Pentium™ or Xeon™ family manufactured by Intel™, the Turion™ family manufactured by AMD™, or any other type of processors. As shown in
Storage 220 may include a volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, nonremovable, or other type of storage device or computer-readable medium. Storage 220 may store programs and/or other information that may be used by system 200.
Memory 230 may include one or more storage devices configured to store information used by system 200 to perform certain functions related to the disclosed embodiments. In one embodiment, memory 230 may include one or more modules (e.g., collections of one or more programs or subprograms) loaded from storage 220 or elsewhere that perform (i.e., that when executed by processor 210, enable processor 210 to perform) various procedures, operations, or processes consistent with the disclosed embodiment. For example, memory 230 may include an advanced forecasting module 231, a network modeling module 232, and a facility design and management module 233.
Advanced forecasting module 231 may generate forecast information related to one or more products at any one of the supply chain entities based on historical data associated with the product. For example, advanced forecasting module 231 may forecast a future demand for a product at each one of edge DCs 130 and 132 based on respective historical demand data for that product at edge DCs 130 and 132. In addition, advanced forecasting module 231 may forecast a rate of change of future demand for the product at each one of edge DCs 130 and 132.
Network modeling module 232 may receive the forecasted information from advanced forecasting module 231 and simulate and optimize the flow of products between the supply chain entities in order to meet certain business goals of the entire organization that includes the supply chain entities. The business goal may include at least one of response time, profit, return on net assets, inventory turns, service level, and resilience. Network modeling module 232 may simulate the flow of products based on geographical locations of each one of the supply chain entities, the transportation methods (e.g., air, ship, truck, etc.), and link capacities (e.g., quantity of materials that can be transported via a certain route). Based on the simulation results and other information such as production costs, transportation costs, and regional sales price, and the like, network modeling module 232 may generate information such as gross revenue, cost of goods sold, and profit related to one or more products or parts.
Facility design and management module 233 may receive the forecasted information from advanced forecasting module 231 and the simulation results from network modeling module 232 and may determine the physical structure and dimension of one or more of central DC 120 and edge DCs 130 and 132. For example, facility design and management module 233 may receive forecasted information representing quantity of the incoming products to be received at central DC 120 and edge DCs 130 and 132. Based on this forecasted information, facility design and management module 233 may determine dimensions and locations of shelving, racks, aisles, and the like, of central DC 120 and edge DCs 130 and 132. Facility design and management module 233 may also determine the location of incoming items within central DC 120 and edge DCs 130 and 132, based on the forecasted information. Moreover, facility design and management module 233 may simulate the movement of resources (e.g., workers, machines, transportation vehicles, etc.) throughout central DC 120 and edge DCs 130 and 132. Still further, facility design and management module 233 may modify input information in order to achieve one or more of the business goals.
I/O device 240 may include one or more components configured to communication information associated with system 200. For example, I/O device 240 may include a console with an integrated keyboard and mouse to allow a user to input parameters associated with system 200 and/or data associated with the supply chain entities in supply chain 100. I/O device 240 may include one or more displays or other peripheral devices, such as, for example, printers, cameras, microphones, speaker systems, electronic tablets, bar code readers, scanners, or any other suitable type of I/O device 240.
Network interface 250 may include one or more components configured to transmit and receive data via network 260, such as, for example, one or more modulators, demodulators, multiplexers, de-multiplexers, network communication devices, wireless devices, antennas, modems, and any other type of device configured to enable data communication via any suitable communication network. Network interface 250 may also be configured to provide remote connectivity between processor 210, storage 220, memory 230, I/O device 240, and/or database 270, to collect, analyze, and distribute data or information associated with supply chain 100 and supply chain management.
Network 260 may be any appropriate network allowing communication between or among one or more computing systems, such as, for example, the Internet, a local area network, a wide area network, a WiFi network, a workstation peer-to-peer network, a direct link network, a wireless network, or any other suitable communication network. Connection with network 260 may be wired, wireless, or any combination thereof.
Database 270 may be one or more software and/or hardware components that store, organize, sort, filter, and/or arrange data used by system 200 and/or processor 210. Database 270 may store one or more tables, lists, or other data structures containing data associated with supply chain management. For example, database 270 may store operational data associated with each one of the supply chain entities, such as inbound and outbound orders, production schedules, production costs, and resources. The data stored in database 270 may be used by processor 210 to receive, categorize, prioritize, save, send, or otherwise manage data associated with supply chain management.
In the disclosed embodiments, it may be convenient to describe the method of supply chain management by using terms including future time horizon, time interval, historical time period, and rate of change of demand, which may be known in the art. The future time horizon is a predetermined period of time in the future during which the product demand, flow management, and inventory levels are evaluated in order to perform the supply chain management. The future time horizon may be specific to the product, and may be determined based on the cost and size of the product. For example, a future time horizon may be three months, a year, two years, or even five years from the current time. In addition, a product that is big and expensive may require constant evaluation and optimization on the product demand, flow management, and inventory levels, and therefore the future time horizon for the product may be relatively short. A time interval is a predetermined time resolution for the supply chain management. For example, a time interval may be one day, one month, or one quarter (i.e., a quarter year, or three months). A historical time period is a period of time immediately prior to the current time. A rate of change of demand for a product over a certain time period may be an average of rates of changes of demand quantities between two consecutive time intervals within the time period. Other averaging methods well known in the art, such as Auto Regressive Moving Average (ARMA), Auto Regressive Integrated Moving Average (ARIMA), and Exponential Weighted Moving Average (EWMA), etc., can also be applied where appropriate.
A conventional supply chain management method may respond to the decreasing future demand at edge DC 130 by continuing to distribute the products from central DC 120, until the demand becomes zero. A problem with this conventional method is that, until there are actual orders from customers 140 and 141, the inventory of the product may be trapped at edge DC 130 for months or even years, taking up a large volume of storage space, which is not economically efficient. However, pulling the trapped inventory from edge DC 130 back to central DC 120 may require additional shipping and handling cost, which is not economically efficient either.
In the disclosed embodiments, when a difference between the future rate of change of demand for the product over the future time horizon and the historical rate of change of demand for the product over the historical time period is beyond a tolerance range, central DC 120 may stop distributing the product to edge DC 130. In this way, the trapping of the inventory at edge DC 130 may be prevented. For example, the tolerance range may be between −2/month and 2/month. Then, the difference illustrated in
When the forecast of the customers' future orders are expected to decrease, and the difference between the future rate of change of demand and the historical rage of change of demand is outside the tolerance range, as illustrated in, for example,
After optimizing the facility designs for central DC 120 and edge DC 130, processor 210 may determine whether edge DC 130 is a temporary edge DC (step 616). If edge DC 130 is a temporary edge DC (step 616: Yes), processor 210 may determine whether the temporary edge DC is still economically viable (step 618).
For example, edge DC 130 may be a temporary edge DC 130 which is temporarily established to accommodate for the temporary surge of customer demands in a local area. When the customer demand decreases and the difference between the future rate of change of demand and the historical rate of change of demand is below a tolerance range, it is possible that it is no longer economically viable to operate temporary edge DC 130, and then temporary edge DC 130 needs to be closed. Processor 210 may determine whether temporary edge DC 130 is still economically viable by comparing the cost for maintaining temporary edge DC 130 to a total cost incurred by closing temporary edge DC 130. The total cost incurred by closing temporary edge DC 130 may include a switching cost, a transportation cost, and an inventory cost. The switching cost is the cost for closing temporary edge DC 130. The transportation cost includes the cost for transporting all of the remaining products in temporary edge DC 130 to central DC 120 or to edge DC 132, and the cost for transporting the products from central DC 120 or edge DC 132 to customers 140 and 141 that were previously receiving products from temporary edge DC 130. The inventory cost is the cost for storing the products received from temporary edge DC 130 in central DC 120 or edge DC 132.
When processor 210 determines that temporary edge DC 130 is not economically viable (step 618: No), processor 210 may transmit instructions to close temporary edge DC 130 (step 620). Then, processor 210 may update the storage space requirements for central DC 120 or edge DC 132 to store the remaining products previously stored in temporary edge DC 130 (step 622).
Next, processor 210 may determine the product lead time and order fulfillment location data for the selected product (step 624). The order fulfillment location data is the data related to the location of the product over the future time horizon. The product lead time is the time between the placement of an order and delivery of the product. The product lead time may include order processing time and shipping time. The product lead time may be determined by network modeling module 232 based on the order fulfillment location data.
On the other hand, if edge DC 130 is not a temporary edge DC (step 616, No), processor 210 may directly update the product lead time and order fulfillment location data for the selected product based on current information in the system (step 624). Similarly, if edge DC 130 is a temporary edge DC 130 which is still economically viable (step 618, Yes), processor 210 may also directly perform step 624.
When the customers' forecasted orders are not anticipated to change significantly, the difference between the future rate of change of demand and the historical rage of change of demand is within the tolerance range as illustrated in, for example,
When the customers' orders are forecasted to increase significantly, and the difference is outside the tolerance range as illustrated in, for example,
When processor 210 determines that edge DC 130 is capable of processing future demand over the future time horizon (step 628, Yes), processor 210 may determine whether customers with high future demand are located close to edge DC 130 (step 630). For example, processor 210 may determine whether a distance between edge DC 130 and the customers with high future demand for the product is shorter than a predetermined distance threshold, which in turn will affect the future order fulfillment time. If the customers with high future demand are located close to edge DC 130 (step 630, Yes), processor 210 may determine whether the business entity will achieve sufficient profit if edge DC 130 is expanded (step 632). For example, processor may determine whether a forecasted profit achieved by expanding the edge DC over a future time period for all of products to be evaluated is higher than a predetermined profit threshold. The future time period may be different from the future time horizon which is specific to the selected product, and may be determined based on the business operation mode of the entire business organization. If the forecasted profit is higher than the predetermined profit threshold (step 632, Yes), processor 210 may transmit instructions to expand edge DC 130 (step 634). Then, processor 210 may update storage space requirements and facility designs for all DCs (step 636). For example, processor 210 may determine the storage space requirement for central DC 120 to store the product over the future time horizon. Processor 210 may also optimize respective facility designs of central DC 120 and edge DC 130 based on the respective storage space requirements for central DC 120 and edge DC 130.
When processor 210 determines that edge DC 130 is not capable of processing future demand over the future time horizon (step 628, No), or the customers with high future demand are not located close to edge DC 130 (step 630, No), or the business entity will not achieve sufficient profit if edge DC 130 is expanded (step 632, No), processor 210 may transmit instructions to add a temporary edge DC close to the customers with the high future demand for the product (step 638). For example, processor 210 may transmit instructions to add temporary edge DC 132. Then, processor 210 may update storage space requirements and facility designs for all DCs (step 636). For example, processor 210 may determine the respective storage space requirements for central DC 120 and the newly added temporary edge DC 132 to store the product over the predetermined future time horizon. Processor 210 may also optimize the respective facility designs of central DC 120, edge DC 130, and temporary edge DC 132 based on the respective storage space requirements for central DC 120, edge DC 130, and temporary edge DC 132. After step 636, processor 210 may determine the product lead time and order fulfillment location data for the selected product (step 624).
After determining the product lead time and order fulfillment location data for the selected product, processor 210 may determine whether the selected product is the last one of the plurality of products to be evaluated (step 640). If the selected product is not the last one (step 640, No), processor 210 may select a next product (step 642). Then, process 600 may return back to step 604 where processor 210 forecasts the future demand for the next product distributed by edge DC 130 over the future time horizon. If the selected product is the last one (step 640, Yes), process 600 may be completed. Process 600 may be performed periodically (e.g., monthly, bi-monthly, quarterly, etc.), so that supply chain 100 is maintained in its optimized condition.
In the embodiment disclosed above, processor 210 may determine whether to add, or close, or expand a temporary edge DC based on the merits of a single product that is selected in step 602, if the selected product is large and costly to support such a change. Alternatively, in another embodiment, processor 210 may defer making the decision until all of the products have been evaluated, by evaluating the cumulative economic impacts and space requirement incurred by all of the products.
The disclosed supply chain management system 200 may efficiently provide optimized supply chain designs for any business organization to achieve one or more desired business goals. Based on the disclosed system and methods, trapping of the slow moving products at the edge DCs may be prevented, and unnecessary cost for redistributing the products may be reduced.
It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed supply chain management system. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed supply chain management system. It is intended that the specification and examples be considered as exemplary only, with a true scope being indicated by the following claims and their equivalents.
