MULTI ECHELON GLOBAL NETWORK ANALYSIS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] None.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] None.

FIELD

[0003] The present disclosure relates to networking, and more particularly to effectively and efficiently supplying and routing items needed to perform or complete a task. Still more particularly, one example non-limiting embodiment relates to a decision support apparatus and method that indicates which node is an optimal supply node that could have the role of a Hub for each item to be provided via a distribution network.

BACKGROUND AND SUMMARY

[0004] The world is a global network. People in Tokyo expect to receive items from New York, and people in Los Angeles wish to receive items from Sao Paulo.

[0005] To make such functionality possible in today’s highly distributed environment, many companies disperse a set of supply nodes throughout a country, region or around the world. For example, one or several networked repositories may be disposed in each of the United States, South America, Central America, Europe, Asia and Australia. The supply nodes are dispersed so those supply nodes closest to consuming node(s) are able to serve requests in a prompt and time-effective manner that minimizes distribution network overhead and latency, while still providing distributed redundancy in case a supply nodes fails. Delivery latency can be an important metric that should be minimized to provide timely delivery of items to consuming nodes. But coordinating storage and delivery of items in such distributed networks can be difficult and complicated.

[0006] Consider, by way of an example non-limiting analogy, that many companies today have a set of warehouses dispersed around a country or around the world, making stock integrated planning a hard and complicated task. It’s expensive to keep high stock volumes, and customers will probably become dissatisfied if they do not get their products in the accorded time.

[0007] To reduce complexity, it is possible to make one of the supply nodes a central hub for distributing, across a distribution network, certain types of items for a certain region. For example, it is common in an electronic network for a database node to serve as a centralized hub for distributing data requested from a centralized database. In the non-limiting analogy above, it is possible to make one of the warehouses a Hub - namely a central warehouse that distributes Parts/Products to customers and other warehouses, as a direct supplier. This is a supply chain strategy to optimize the overall costs, service level agreements, KPIs (key performance indicators), etc. Many companies have storages spread in different places and they spend a lot of money keeping a high stock to attend to their clients.

[0008] It is easy to create local distribution nodes (e.g., for stock optimization) considering only the demand of a specific site, but a local optimization may not the best choice if you want to consider global planning and reduce costs globally. The scenario is much more complex if there are many suppliers, customers and consumers (by analogy, repair stations for each Part/Product) in different places around the globe. Besides that, each country has its own importation/exportation requirements such as taxes or other barriers, and the company could have different logistics contracts for each one.

[0009] All of these factors affect the decision of choosing which supply node could be the best hub for distributing a particular item or type of item across the network (or even the decision of having no Hub but instead relying on any nodes of a distributed network capable of supplying the item).

[0010] What is desirable: a method to define which supply node (if any) is the most effective hub for each specific item to be delivered over a distribution network.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The following detailed description of exemplary non-limiting illustrative embodiments is to be read in conjunction with the drawings of which: [0012] Figure 1 shows an example non-limiting network topography model;

[0013] Figure 2 shows an example non-limiting iterative automatic computation process;

[0014] Figure 3 shows an example non-limiting computer system implementing the iterative automatic computation process;

[0015] Figure 4 shows an example non-limiting Computational Model processing arrangement;

[0016] Figure 4A shows an example non-limiting particular network topography including logistical effects in the form of different communication links;

[0017] Figure 4B shows an example non-limiting physical delivery analogy;

[0018] Figure 5 shows an example non-limiting computation by the Figure 3 system to plan during simulation;

[0019] Figure 6 shows an example non-limiting computation of supply (e.g., stock) variation during simulation;

[0020] Figure 7 shows example non-limiting accumulated network delivery costs during simulation;

[0021] Figure 8 shows an example non-limiting computer-implemented simulation process flow chart; and

[0022] Figure 9 shows an example non-limiting computer-implemented optimization process flow chart.

DETAILED DESCRIPTION OF EXAMPLE NON-LIMITING EMBODIMENTS

[0023] The present disclosure uses an alternative approach by hubbing one or more Items/Parts/Products individually at different supply nodes. This decision involves several variables including proximity from client nodes to supplier nodes and logistics (which can be a range of factors depending on the application, ranging from link latency, link communication cost, link availability, taxes or other barriers for a respective region/country, local lead time, local holding costs and logistics costs). Considering the number of variables to be considered, an optimal decision can in 4 general be obtained only through usage of computational resources and iterative processing by computing arrangements such as high performance CPUs and/or GPUs or other processors executing statistical analyses such as exact solutions (exhaustive search), deterministic sampling methods and random sampling methods (i.e., genetic algorithm, particle swarm optimization), Average, Moving Weighted Average, Linear Regression, Single Exponential Smoothing, Double Exponential Smoothing, Winters Multiplicative, Intermittence Smoothing, iterative metaheuristic optimization, or other methods. While individual instances of such analyses could possibly be performed by a human, an iterative simulation as described herein typically requires such calculations to be repetitively performed a large number of times. Accordingly, performing these calculations repetitively in numerous simulations would be impractical or even impossible without a computational aid.

[0024] All of these logistical issues such as costs are known and the process and how it is impacted by hub configuration is what such processing is used to expose and optimize. The first step is to map the whole operation process (related to supply) and remove any uncertain or random values.

[0025] Example Hub Configuration

[0026] Figure 1 shows an example supply network 100 comprising network nodes N(l), N(2), ...N(N) connected to a network. There can be any number N nodes (six are shown in Figure 1 for purposes of illustration). Nodes N can be spatially distributed across regions, countries, network segments, enterprises, etc. or they can be co-located, or some can be distributed and others can be co-located.

[0027] Nodes N supply items to “consumers” such as for example clients in a client-server architecture or peers in a peer-to-peer architecture. A “consumer” is an entity in network communication with at least one (other) network node N that receives, across the network, items from the network node. The items can be data packets, messages, files, pings, or in some embodiments, physical items such as aircraft parts.

[0028] A “warehouse” (“WH”) comprises a storage repository or facility and may include a data warehouse such as a database server, a data storage facility such as 5 a server or computing peer, or a physical storage facility that stores physical items such as aircraft parts. The Figure 1 example shows that each node N is also acting as a warehouse. Different warehouses may store and provide different kinds of items. For example, one warehouse may specialize in storing archival information, whereas another warehouse may store news media information, and a third warehouse may store documentation such as user manuals. By way of analogy, in a physical distribution environment, a first warehouse could store a first collection of parts including certain wing parts, a second warehouse could store a second collection of parts including certain engine parts, and a third warehouse could store a third collection of parts including certain avionics parts.

[0029] In the example shown, any one or more network nodes N can be configured to act as a “hub”. A “hub” in this context is a network node N that supplies items to other network nodes N. A hub could but need not be the exclusive supplier of a particular item(s) on the distribution network. A hub may also supply items directly to consumers such as clients or peers, or a hub may supply items only to other network nodes which may themselves be hubs.

[0030] Thus, in one example configuration, a network node N configured as a hub supplies an item to another network node N which in turn supplies the item to a consumer. In such configuration, the hub network node N does not supply items directly to consumers but instead may supply items to other network nodes including other hubs. In another example configuration, a hub network node N supplies an item to another network node N which in turn supplies the item to a consumer, and the same hub network node supplies an item directly to a consumer.

[0031] Depending on network topology, each network node N can communicate directly with any other network node, or may communicate with other network node(s) through an intermediate network node(s) such as in a mesh or relay network.

[0032] In the particular configuration shown in Figure 1, network nodes N(3) and N(6) are currently configured to operate as hubs. Thus, hub network node N(3) supplies (at least certain kinds of) items to network nodes N(l), N(2), N(4), N(5) over the network, and hub network node N(6) supplies (at least certain kinds of other) items to network nodes N(4), N(5) over the network. Moreover, hub network nodes N(3), N(6) can communicate with one another. Meanwhile, network nodes N(l), N(2), N(4), N(5) can communicate items they receive from hubs N(3), N(6) to consumers that these network nodes receive from hubs. Furthermore, hub network nodes N(3), N(6) can in some embodiments each communicate with other hub network nodes and/or potentially directly to consumers, over the network.

[0033] The example non-limiting technology herein relates to techniques, methods and systems for selecting which ones of network nodes N to configure as hubs. In some embodiments, network nodes N are dedicated to operate as hubs. In other embodiments, network nodes N are dynamically reconfigurable so they can selectively be configured to act as hubs sometimes and not act as hubs other times, depending on changing needs, network conditions, item backlogs, network demand, etc.

[0034] Example Non-Limiting Method

[0035] An example non-limiting computer-performed method 200 for determining which of nodes should optimally serve as hubs as shown in Figure 2 is divided into:

• a computational (discrete event) model 202,

• a simulation forecast algorithm 204, and

• a hub optimization/selection algorithm 206.

[0036] The computational model 202 contains a mathematical formulation of a stock operation process yielded in the form of a discrete event model. This is essentially a process that determines how many stored items there are and how many need to be delivered at which times over the distribution network. This discrete event model 202 in some example non-limiting embodiments requires or uses all operational process details and parametric parameters (e.g., network latencies, link communication costs, taxes and lead times and a proposal for each component hub configuration including all possible warehouses, a subgroup of them or none). See for example Wainer, Discrete-Event Modeling and Simulation: A Practitioner's Approach (CRC Press 2009). [0037] In example non-limiting embodiments, the simulation algorithm 204 forecasts the stock operation based on discrete event simulation and automatically generates projected performance and effectiveness of current computational model 202 (including hubs configuration proposals). The simulation algorithm 204 produces and outputs performance and effectiveness indexes including (but limited to) overall Service Level Agreements (SLAs), operational expenses (i.e., communication and/or freight expenses and taxes) and stock levels represented either by quantities, storage requirements and/or financial values.

[0038] The computer-performed hub optimization/selection algorithm 206 orchestrates an iterative process of generating a proposal of hub conditions at the computational model 202, running the simulation, gathering the performance/effectiveness indexes and evaluating the overall result. Next, based on these results, a new hub condition/configuration for each network node N or other component is generated and the process repeats (iterates) until an optimal or most satisfactory solution (or in some embodiments, an acceptable solution) is found. This generation process could be based on any classical combinatorial optimization algorithm, including for example both sampling/random methods (i.e., Genetic Algorithm) and exact methods (i.e., exhaustive search). See Wang et al, Feature selection methods for big data bioinformatics: A survey from the search perspective, Volume 111, Pages 21-31 Methods (December 2016); Ye et al, Large-scale network parameter configuration using an on-line simulation framework, IEEE/ACM Transactions on Networking Volume 16 Issue 4, Pages 777-790 (August 2008).

[0039] Also, in some embodiments, the overall result is defined and evaluated based on a set of performance/effectiveness indexes fused on a multi-criteria approach, multi-objective and/or a single objective approach. See E. L. Ulungu et al, Multi-objective combinatorial optimization problems: A survey, Journal of Multi- Criteria Decision Analysis (August 1994); Marler et al, Survey of multi-objective optimization methods for engineering, Volume 26, Issue 6, pp 369—395 Structural and Multidisciplinary Optimization (April 2004); Stepanov et al, Multi-objective evacuation routing in transportation networks, Volume 198, Issue 2, Pages 435-446 European Journal of Operational Research (October 2009).

[0040] Constraints for these indexes are also, in some non-limiting embodiments, established in order to define if a determined hubs proposal is satisfactory or not.

[0041] Figure 3 shows an example non-limiting high performance computing architecture 250 that may be used to implement the Figure 2 process 200. In the example shown, one or more CPU(s) and/or GPU(s) 252 execute instructions to generate and maintain the model 202, perform the simulation 204 and perform the optimization/selection algorithm 206. The one or more CPU(s) and/or GPU(s) 252 perform these tasks by executing software instructions stored in one or more non- transitory memory device(s) 254. The one or more CPU(s) and/or GPU(s) 252 output hub selection(s) and topography configuration(s) via a network transceiver 256 in communication with the network. In one example non-limiting embodiment, such hub selection(s) and topography configuration(s) comprise commands that direct, control or otherwise cause selected network nodes N (indicated by network addresses, node ID, or other identifier(s)) to (re)configure themselves as hubs, and in some embodiments, direct, control or otherwise cause other selected network nodes N to (re)configure themselves as network nodes that do not operate as or constitute hubs.

[0042] An Example Non-Limiting Computational Model 202

[0043] As discussed above, the discrete event computational model 202 in the example non-limiting embodiment processes as inputs various operational process details, parametric parameters and network topography. The computational model 202 in one non limiting example includes various processing modules, functions, or clusters including:

• Logistics 202a

• Supplier Cost/Lead Time or Latency 202b

• Customer or Consumer Demand 202c

• Planning Module 202d

• Stock Module 202e • Financial Module 202f

• Other.

[0044] Logistics Module 202a

[0045] In one example embodiment, the first cost to be considered is the logistic cost. The logistic costs in general depend on the type and configuration of the network. In one example non-limiting implementation, the network comprises or includes a variety of different communication links such as for example as shown in Figure 4A as one non-limiting illustrative data communications network example:

• a wireless satellite link(s) L(l);

• a wireless cellular telephone link(s) L(2);

• a wired landline link(s) L(3);

• an undersea cable link(s) L(4);

• a virtual private network/optical cable link(s) L(5);

• other.

[0046] These various links can have different costs associated with them in terms of usage, latency, reliability, and expense. For example, it may require payment to send a message over the satellite link(s) L(l), the wireless cellular telephone link(s) L(2), and/or the undersea cable link L(4). There may be other costs associated with using any of these links in terms of bandwidth, reservation, priority over other message traffic being sent over the same link, etc. Furthermore, confidential or secret messages may need to be sent over secure or encrypted links (e.g., the wired landline link L(3) or the undersea cable link(s) L(4)).

[0047] By way of a physical item distribution analogy shown in Figure 4B, the logistic cost can be separated into three types:

• the inland cost at origin,

• the air/ocean freight costs, and

• the inland costs at destination.

[0048] For example, transporting an item on an airplane probably costs more than carrying the same item on a ship, but the airplane trip has much lower latency. [0049] It is useful to map all logistic contracts and all possible combinations of origin and destination that could happen. Many contracts charge the weight of the load, so all the weight ranges may be mapped and the possibility of consolidation packages (wait some days or a minimum weight to dispatch the Parts/Products) may be considered if these factors already exist in the process. If the contracts consider different costs for dangerous goods or hazardous materials, this may appear on the logistic calculation module.

[0050] The Supplier Cost/Lead Time 202b

[0051] Important information about the supplier is the latency (or by analogy, the lead time to deliver the part/product in each warehouse) and the supplier location (that will be used by the logistic module to calculate the logistic costs). Even if the latency or lead time has some uncertainties, the supplier cost/lead time module 202b will consider the mean or the agreed lead time. Part/product supplier cost can also be considered.

[0052] The Customer or Consumer Demand 202c

[0053] The history demand is used to determinate the forecast demand and is a main input of the process. In a distribution network environment, it is possible to monitor network traffic over time and develop a history of demand. In one analogous physical delivery scenario, it is possible to obtain simulation at least a certain number of months before the start date, where the number of months is variable based on factors such as type of problem and historic data. For example, a simulator can be created to run with only one year of historic data if that period is enough to forecast a future demand. Electronic network demand, in contrast, might instead be monitored over the course of minutes, hours or days. The demand data may be separated by each client or warehouse where the demand was realized.

[0054] The demand forecast can be generated using any of various different forecast methods such as Average, Moving Weighted Average, Linear Regression, Single Exponential Smoothing, Double Exponential Smoothing, Winters Multiplicative, Intermittence Smoothing, other. See e.g., Ghobbar et al, Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model, Vol. 30, No. 4, pp. 2097-2114 Computers and Operations Research (December 2003). For example, demand forecasting, which forms the basis for the planning of inventory levels, is helpful in the repair and overhaul industry, as the one common problem facing airlines throughout the world is the need to know the short-term part demand forecast with the highest possible degree of accuracy. The high cost of modem aircraft and the expense of such repairable spares as aircraft engines and avionics constitute a large part of the total investment of many airline operators. These parts, though low in demand, are critical to aircraft operations and their unavailability can lead to excessive down time costs. A weighted moving average, Holt and Croston method may be especially useful for forecasting intermittent demand. See Ghobbar et al.

[0055] The frequency of demand in a certain time period (e.g., a month in the case of physical delivery) can have a direct impact on the stock flow. Such forecasted demand can be based on historical information but in the example embodiment is a projection or forecast of future demand. In example non-limiting embodiments, the generated demand will be considered to be the real demand for the simulated period.

[0056] The Planning Module 202d

[0057] Periodically or at other time intervals, planning is realized for which warehouse is to supply each type of item to each consumer. In connection with network operation, results of the planning can for example be used to designate which node N shall act as a hub for each item type that a consumer may demand. In some embodiments, the planning is not responsible for any decision. Rather, the planning receives the demands for each warehouse and calculates only the planning parameters (ROP, SS). Such planning can include contingencies such as “if node N(k) is designated as a hub for database access by a consumer and node N(k) is overloaded so that its latency is too long or node N(k) otherwise cannot supply requested items, then node N(m) shall instead act as the hub for database access to the consumer.”

[0058] By way of a physical shipment analogy, the planning would be realized for each pair: part/product and warehouse. At the planning stage, the reorder point (ROP), safety stock (SS), and stock max levels are calculated and if the part/product is below the ROP, a purchase order is created for restocking the part. The created order could be attended by the Hub or the Supplier (depending on the initial definition that indicates if the part/product has a hub and which warehouse is the hub).

[0059] Figure 5 is an example graph of planning during the simulation. In the example shown:

• the top solid graph line (“Stock_Max”) shows maximum stock over the time of a forecast,

• the dashed line below it (“ROP”) shows a reorder point,

• the line below that (“SS”) shows safety stock level, and

• the lowermost line shows an example demand forecast.

[0060] The left-hand vertical axis corresponds to number of units, and the horizontal axis corresponds to time (in this case, ye r/month). As can be seen, different forecasting algorithms can be applied to the inputs for different time periods within the forecast. For example, during some time periods, a linear regression model (·) can be applied to produce a forecast. During other time periods (► ), a moving weighted average can be applied to produce the forecast. In still other time periods ( A ), single exponential smoothing may be applied to produce the forecast. Other forecasting algorithms such as Average, Double Exponential Smoothing, Winters Multiplicative, and Intermittence Smoothing as described above can be used. See Koehler et al, Forecasting models and prediction intervals for the multiplicative Holt- Winters method, Volume 17, Issue 2, Pages 269-286 International Journal of Forecasting (April— June 2001).

[0061] The Stock Module 202e

[0062] In a physical analogy, every operation that impacts the “stock level” (i.e., the amount of items stored at a node) is handled by a stock module. The operations that may be considered are:

• Incoming order (from other warehouse or supplier);

• Received order (from other warehouse or supplier);

• Order Dispatched (to other warehouse, client or repair); • Order in backorder (two queues, one for internal orders and another for external orders);

• Reserved orders (orders waiting the consolidation packages dispatch).

[0063] Figure 6 shows an example non-limiting forecast of stock flow based on forecasted demand. The real stock accumulation of a node is shown in solid line. Forecasted incoming accumulation is shown as a dotted line. Reserved stock accumulation is shown in solid as small “blips” on the x axis. Backorders in this case (also shown) are at a very low level meaning that all current needs are being serviced from accumulated stock. It is desirable to avoid the forecasted backorders increasing beyond an acceptable level.

[0064] All the stock movements represent the real operation of the stock process.

[0065] The Financial Module 202f

[0066] Delivery of each item has a cost associated with it. For example, some network links have delivery costs as discussed above. In a physical analogy, every part/product movement has different associated costs. These costs may be managed by a financial module and should be accounted for. Below some examples of the costs considered in the stock process in a physical analogy:

• Taxes

• Importation Tax

• Income Tax

• Sales Tax

• Logistic Costs

• Origin Inland

• Destination Inland

• Air/Ocean freight

• Internal Costs (picking costs)

• Holding Costs

[0067] Figure 7 shows an example cumulative cost during the period of the simulation. The cumulative cost starts at zero and is composed of the factors listed above. In this non-limiting case, the largest component is importation tax, and the second largest is an internal picking cost (i.e., picking items from a warehouse) for customers send. Air freight is also a significant cost, as is land origin cost.

[0068] The Simulator 204

[0069] In the example non-limiting embodiment, the simulator simulates a future operation using the defined inputs. The simulator 204 will return the approximate costs and KPIs for a normal operation considering the simulation start and end times/dates. The inputs are the projected demand for each item (e.g., part/product) and their selected hub. The simulation is based on a timeline stepwise process and evaluates the previous modules (i.e., financial, logistic, stock). Some of the functions the simulator 204 performs can be part of planning as discussed above.

[0070] The simulation results will be used to calculate a fitness function to be used and evaluated by the Optimizer 206.

[0071] Figure 8 shows an example non-limiting process flow for simulator 204. In this example, a forecast demand 306 is created based on a stored demand history 304. The forecast demand 306 is placed in an events day (or other periodic time interval) queue 308, which is queried to determine whether there are any more events in the queue (decision block 310). If no more events, the simulation ends (block 312). Otherwise, the simulation determines whether there is a planning event in the time interval being simulated (decision block 314). If so, planning based on the planning event is realized (block 316). The simulation next determines whether an external event has been received for this time interval being simulated (decision block 318). If so, the external event is realized - typically by simulating receipt of external purchased orders (block 320). The simulation next determines whether an internal event for the simulated time period has been received (decision block 322). If so, the simulation simulates receipt of internal purchased orders (block 324). The simulation then simulates attempts to attend to the orders in a backorder queue in order to prioritize external demands over internal demands (block 326). [0072] Next, the simulator simulates demands. If there is an external demand event in the simulated time period (decision block 328), the simulator realizes external demands from customers (block 330). If there is an internal demand event in the simulated time period (decision block 332), the simulator realizes internal demands from other warehouses (block 334). Other types of internal demand events (decision block 336) may cause the simulator to simulate dispatch of consolidation packages (block 338). Any new events are saved in the events day queue 308 (block 340) and simulator continues to execute until there are no more days or other time periods with events in the queue (decision block 310).

[0073] The Optimizer 206

[0074] In the example non-limiting embodiment, the selecting variable for optimization is the defined hubs. The Optimizer 206 is the part of the method whose objective is to find a best (or in some embodiments, an acceptable) solution based on a metaheuristic optimization algorithm (used because the simulation is a multimodal problem). See e.g., Blum et al, Hybrid Metaheuristics: An Emerging Approach to Optimization (Springer 2018); Blum et al, “Metaheuristics in combinatorial optimization: Overview and conceptual comparison” ACM Computing Surveys (CSUR) Volume 35 Issue 3, Pages 268-308 (September 2003). The simulator 204 main result in one example embodiment is the projected KPIs for a given scenario (i.e. global costs of the stock operations for a future period). The optimizer 206 can be implemented based on a single objective function or multi-objective depending on how many KPI variables were selected and how they were correlated to the objective function(s).

[0075] A first step of optimizer 206 to run the optimization is selecting an initial list of defined hubs for each selected parts/products (Figure 9, block 404). With this input, the initial solution will be executed and the result stored to compare in the end of optimization (Figure 9, block 406). The optimizer 206 may run in a loop creating and testing new candidate solutions until the stop criterion is satisfied (i.e., maximum number of steps) (Figure 9, block 408). Different combinatorial optimization algorithms could be invoked depending on the complexity of the problem. Examples include exact solutions (exhaustive search), deterministic sampling methods and random sampling methods (e.g., genetic algorithm, particle swarm optimization). See e.g., Eberhart et al, Comparison between genetic algorithms and particle swarm optimization, International Conference on Evolutionary Programming, Evolutionary Programming VII pp 611-616 (1998).

[0076] Example Non-Limiting Results

[0077] After the optimizer 206 runs the optimization, the system provides a suggestion of where the hub of each item (e.g., Part/Product) will be located (considering only the available warehouses) based on the lowest operational costs and other KPIs (as defined in the fitness function). These suggestions can be further implemented on the real planning process. In other embodiments, the Figure 3 system generates a signal(s) based on the optimizer 206 that commands network nodes to selectively configure themselves as hubs or not as hubs.

[0078] All publications cited above are incorporated herein by reference as if expressly set forth.

[0079] While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.