Technical Field

The present invention relates to a system for obtaining assignments of optimal demand point and production location pairs (demand pointproduction location) by taking into data of account sales, transportation and production planning and inputs of related capacity utilization, customer satisfaction constraint in addition to contribution margin or net profit.

Background of the Invention

Due to epidemics such as pandemics, concept drifts and uncertainties arise in the business world. In order to adapt to the said concept drifts and uncertainties, a transformation similar to Schumpeter’s creative destruction is imposed on businesses. Business planning solutions play an important role in this transformation and also enable the simulation of various what-if scenarios. Examples include proactively responding to customer demand fluctuations with liquid capital, labor, installed capacity and unmanned sustainable business models. In the state of the art, meeting the demand of customers by the most suitable production location is one of the leading problems experienced in work planning solutions. In product supply, subjective decisions of process owners regarding assignments of demand point production location play a quite dominant role and transportation costs need to be minimized in the related assignments. In addition, manual simulations performed by process owners involve data preparation procedures that require more effort and time. Therefore, today there is a need for solutions that aim to overcome the above-stated problems and then find the optimal product supply assignments in terms of demand point: production location that maximize profitability in terms of sales, transportation and production planning data and possible constraints.

The United States patent document no. US20200128027A1, an application in the state of the art, relates to a method of optimizing parameter values in a process for producing a product and this process comprises a system for controlling a set of parameters that affect a set of properties characterizing the product. The said invention is a method for optimizing the parameter values of a process, essentially controlled by a set of n parameters X that affect a set of k features Y characterizing an output of the process. The present invention includes assigning values to a set of k feature weights representing the relative importance of the said features; establishing feature behaviour mathematical relationships that yield an estimated feature for each said feature; using the said feature weights to form an objective function in terms of feature-weighted deviations between the estimated features and the corresponding specified target values for said features; and minimizing the objective function to generate a set of optimal parameter values for the said parameters, wherein a separate set of values is used for the parameter data given to each run.

Summary of the Invention

An objective of the present invention is to realize a system for finding assignments of global optimum demand point production location that maximizes the target value of total net profit or contribution margin based on planned sales volume, unit sales price, unit transportation cost, unit variable and fixed production cost.

Another objective of the present invention is to realize a system for enabling mathematical modeling to be aligned with human insights and providing a holistic view, by taking into account the traditional myopic view based on transportation cost minimization, profitability maximization and production capacities planned at different levels.

Another objective of the present invention is to realize a system for enabling businesses to focus on value-added works such as variance analysis and hypothetical scenario evaluation instead of manual, time-consuming and life- critical data operations.

Another objective of the present invention is to realize a system for enabling efficient keep/change variance analyses by using an adaptive objective function in version management, and for enabling a priori prediction of the transitions that may be caused by transportation and production cost items between different what-if scenarios by means of the related analyses.

Detailed Description of the Invention

“A Network Optimization System” realized to achieve the objectives of this invention is shown in the figure attached, in which:

Figure 1 is a schematic view of the inventive network optimization system.

The components illustrated in the figure are individually numbered, where the numbers refer to the following:

1. System

2. Electronic device

3. Application

4. Database

5. Optimization server The inventive system (1) for finding assignments of global optimum demand pointproduction location that maximize the target value of total net profit or contribution margin, comprises: at least one electronic device (2) which is configured to realize data exchange by using any remote communication protocol and to execute at least one application thereon; at least one ERP application (3) which is configured to be run on the electronic device (2) and to enable users to make requests for optimization valuation and instant reporting; at least one database (4) which is configured to store electronically the objective function data whereon the optimization process will be performed, input data related to customers, products and production locations necessary for optimization, and planning data that can be sales volume, transportation cost, variable production cost and fixed production cost; and at least one optimization server (5) which is configured to communicate with the electronic device (2) by using any remote communication protocol, to exchange data with the application (3) run on the electronic device (2) over this communication established, to process requests received from the application (3), and to manage the database (4), to process the data stored in the database (4) by means of an algorithm with an integer programming feature to optimally map each demand point to a production location, to decide on the optimum demand point and production location assignments after processing, to calculate at least the gross revenue, transportation cost, fixed and variable production cost, contribution margin and net profit values for each demand point after the decision, and to share them with the user over the application (3).

The electronic device (2) included in the inventive system (1) is a device such as a smartphone, tablet compputer, desktop computer or laptop configured to run at least one application (3) thereon. The said electronic device (2) has an input unit such as a key or touchscreen. The electronic device (2) is configured to establish connection with the optimization server (5) by using any remote communication protocol included in the state of art and to realize data exchange between the application (3) and the optimization server (5) over this connection established.

The application (3) included in the inventive system (1) is run on the electronic device (2) and configured to at least enable users to perform requests of optimal demand pointproduction location assignment and to provide at least one interface adapted to allow them to view the results of the assignment. In one preferred embodiment of the invention, the application (3) is configured to receive keep/change variance analysis requests from the user and share them with the optimization server (5) and share the results of the optimization with the user.

The database (4) included in the inventive system (1) is configured to communicate with the server (5) and to be managed by the optimization server (5). The said database (4) is configured to record data related to the type of objective function to be used in network optimization, such as profit margin or net profit maximization. The database (4) is configured to store the customer dimension, that contains the basic features of the current customers to be used in the optimization process, such as name, region, city, district and micro-market; the product dimension, that contains name, product type and segment features; and the production location dimension, that contains production location name and active production location indicator features. The database (4) is also configured to record planning data reflecting the variation between what-if scenarios for the related version information. The database (4) is configured to record planning data determined by sales, transportation and production functional areas.

The optimization server (5) included in the inventive system (1) is configured to establish communication with the electronic device (2) by using any remote communication protocol and to exchange data with the application (3) executed on the electronic device (2) over this communication established. In one preferred embodiment of the invention, the optimization server (5) is configured to establish communication with the electronic device (2) through a data network such as Internet. The optimization server (5) is configured to manage the database (4) by means of transactions such as making a record of new data into the database (4), deleting the data recorded in the database (4), changing the data recorded in the database (4) and updating the data recorded in the database (4). In one preferred embodiment of the invention, the optimization server (5) is configured to subject the data stored in the database (4) into transactions of pre-processing, optimization and valuation respectively. The optimization server (5) is configured to generate three different input datasets in data pre-processing: objective function coefficients, customer satisfaction and capacity constraints for all demand pointproduction location combinations, by using adaptation, master and planning data for the related version. The optimization server (5) is configured to use the objective function coefficient (equation 3 and 4), which is the sum of gross sales (equation 2), transportation cost (equation 3) and variable production cost (equation 4) for each demand point (dp): production location (pit) combination when contribution margin maximization is preferred as the objective function for the said version. The optimization server (5) is configured to additionally take into account the fixed production cost in the calculation of the objective function given in equation 5, if net profit maximization is preferred as the objective function.

The equations used depending on the choice of objective function are given below. The optimization server (5) is configured to embody the potential demand point (dp) consisting of combinations of the current customer (STP) and product (PRD) dimensions given in Equations 1 and 2, in the sales volume (vol) and price (pre) planning steps. The optimization server (5) is configured to decide, according to the simulation indicator (simulated/preset) in the sales planning step, whether to include the demand point in the network optimization or to assign it to the production location predicted in the pre-simulation version. The optimization server (5) is configured to plan the estimated unit transportation cost per customer (dpi.stp), product type (dpi.prd typ) and production location (pit) in the transportation cost (trs) planning step, as given in Equation 3. The optimization server (5) is configured to hold the estimated variable production cost per product (dpi.prd) and production location (pit) given in Equation 4, in the variable production cost (vpc) planning step. The optimization server (5) is configured to calculate the estimated fixed production cost per production location (pit) given in Equation 5, in the fixed production cost (fpc) planning step.

The optimization server (5) is configured not to use (preset) demand points assigned to production locations as a customer satisfaction constraint, but to use the planned sales volumes for the demand points to be simulated. The optimization server (5) is configured to use additional attributes such as customer, product, product type and segment with which the related demand point is associated in the customer satisfaction constraint and to fill the total volume of demand points by a single production location. Since the baseline sales volume cannot be distributed across more than one production location in the optimization process, the optimization server (5) is configured to use a solution involving a mathematical model characterized by integer programming (IP).

The optimization server (5) is configured to use capacity constraints in the optimization process, namely production location, product type and production location, product segment and production location, and finally product and production location level. The optimization server (5) is configured to obtain the sales volumes of the preset demand points by using the related capacity figures without starting the network optimization process.

In the inventive system (1), the optimization server (5) is configured to use a linear programming algorithm to achieve profit maximization and solve the assignment problem. The optimization server (5) is configured to receive all demand pointproduction location combinations as candidate solutions to be used in the valuation and to take into account production capacity and demand satisfaction constraints. After data pre-processing, the optimization server (5) is configured to store the input data calculated in an external system in the database (4) and subject it to the optimization process. The optimization server (5) aims to maximize the value of the total objective function, which aims to maximize the contribution margin or net profit. Thus, this maximization refers to the assignment of demand point dpi" to production location pltj, customer satisfaction for demand point dpi, and a local increase in the objective function value by the unit margin gain from the dpi:pltj assignment calculated in the network optimization data preprocessing process. Moreover, in the optimization process, some of the capacity figures for demand point dpi and production location pltj are consumed due to this demand satisfaction.

The optimization server (5) is configured to use the following algorithm as an optimization model with integer programming characteristics: In this algorithm, line 1 represents the objective function. The objective function binary logical variable idpi.pitj holds the basic atomic (1/0) dpdpltj assignment decision and the variable oefd _P i, _P itj is the unit contribution margin or net profit coefficient calculated in the network optimization preprocessing step. The value of these coefficients is determined by the choice of the objective function type of the baseline version. Lines 2 to 5 represent capacity constraints determined at production location, demand point product type (dpi.prd typ), product segment (dpi.prd seg) or product code (dpi.prd) detail levels. Line 6 holds the customer satisfaction constraint for each simulated demand point. Line 7 represents the integer programming (IP) property of the basic mathematical model.

The optimization server (5) is configured to reach the optimal demand pointproduction location assignment solution using the optimization mathematical model with integer (linear) programming characteristics and to calculate the gross sales, transportation cost, fixed and variable production cost, contribution margin and net profit values for each demand point and to share them with the user in the application (3) on the electronic device (2). The optimization server (5) is configured to analyze the network optimization evaluation with the zoom in/zoom out function according to the planning dimensions such as customer, production location and product and the attributes of these planning dimensions such as city, district and product group. The optimization server (5) is configured to compare the network optimization results of different versions with the keep/change variance analysis that the user enters through the application (3) and share them with the user again through the application (3).

The optimization server (5) is configured to analyze the valuation figures for the respective version before or after each simulation, to perform keep/change variance analysis between the corresponding what-if scenarios and to generate various reports to monitor capacity consumption at different production location and product detail levels. The optimization server (5) is configured to generate additional ad hoc reports, such as sensitivity reports, depending on user requirements during the instant reporting phase. The optimization server (5) is configured to use the valuation figures obtained from the optimization run for instant reporting or to transfer them to a data visualization layer.

With the inventive system (1), although the dominant human insight in the presimulation versions of the optimization process in the known state of the art was myopically based on the minimization of transportation cost, the invention provides a holistic perspective by taking into account both transportation and production cost factors in profitability calculations. The network optimization achieved with the inventive system (1) tends to assign demand points to geographically remote but still applicable production locations with high-tech production lines. The said high-tech concept refers to machine parks with lower depreciation costs, shorter standard production cycle times and higher capacity utilization rates.

Within these basic concepts; it is possible to develop a wide variety of embodiments of the inventive “A Network Optimization System (1)”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.