ERP Neural Network Inventory Control

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Available online at www.sciencedirect.com

Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems.

10.1016/j.procs.2017.09.039

ScienceDirect

Procedia Computer Science 114 (2017) 288–295

10.1016/j.procs.2017.09.039 1877-0509

Available online at www.sciencedirect.com

ScienceDirect

www.elsevier.com/locate/procedia

Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS

October 30 – November 1, 2017, Chicago, Illinois, USA

ERP Neural Network Inventory Control

Jad Farhat

a

_{, Michel Owayjan}

a,b

_*

a_{Department of Computer and Communications Engineering}

b_{Department of Mechatronics Engineeering}

Faculty of Engineering

American University of Science and Technology Beirut, Lebanon

Abstract

Enterprise Resource Planning (ERP) is a system of integrated applications used to have full insight over the resources of an enterprise in terms of goods, employees and customers. On the other hand, artificial neural networks are becoming a necessity in applications that require artificial intelligence. A marriage between these two concepts would yield a system capable of storing and displaying dashboards of data, and simultaneously make computed expectations that can determine the future plans of an enterprise. Many have researched different applications in which a neural network can be used in order to achieve such a system. This paper demonstrates the study and simulation of a system that can give a prediction of the goods needed for an enterprise’s inventory depending on the past history of this enterprise sale with respect to the events occurring at different time periods. The system is built using C# and using examples from a real trading cooperation history in the learning process. It was tested using fictional simulations and produced acceptable results.

Keywords: ERP; Artificial neural networks; Back-Propgation; Inventory; Decision-making, Big Data, Entity Framework

* Michel Owayjan. Tel.: +961-1-218716;

E-mail address: mowayjan@aust.edu.lb

Farhat and Owayjan/ Procedia Computer Science 00 (2017) 000–000

1. Introduction

Business data is considered by many to be the most valuable asset that an enterprise owns [1]. Many companies hire consultants and economists in order to use the data they own, (e.g. the sales from previous years, the market requirements for an upcoming period of time, or the plans of legislations, etc.) That can affect the trading market in order to decide the near and far future policies of the corporation. According to Action Learning, standard strategy consultant fees would range between $ 50,000 and $ 200,000 [2]. This highly proves that most industries and trading corporations tend to use their data resources in the best way to achieve more profit. Furthermore, these corporations use systems such as the ERP in order to handle and manipulate their data. The ERP is a descendant of previous systems that were used to store and view data used for strategy planning. As technology moves forward, just like any other business system, the ERP too witnesses added features to improve its data handling [3]. Artificial Neural Networks (ANN), on the other hand, are computational models used in machine learning based on the nervous system of the human body [4]. ANN has developed different types of learning techniques over time. In this application Back-Propagation is used. Back-Propagation is a two phase algorithm that uses error correction in order to update the weights of neurons in the learning process [5].

This paper introduces a research about one feature that can be added to the ERP in order to map stored sales data into a neural network to predict a calendar of items that may be needed in the future, depending on certain events that are occurring. The paper also discusses the applications of this research in the real world dealing with big data. After a brief literature review on the usage of Machine Learning in ERP, the paper presents the system design and explains the parameters and techniques used. The learning process is also explained as well as the simulations used to test the application and some discussion on the results.

2. Literature Review

Several researches were conducted about the use of neural networks in business solutions. The following are the most relevant studies. Automatic Inventory Control: In 2003, Nicholas Hall used a Multilayer Perceptron (MLP) neural network to design inventory control software that would predict the number of parts an inventory would need based on the demand in the last 12 months [6]. This approach is the closest to the one discussed in this paper; however, the difference is that in this research, the prediction is based on a time span with events occurring in this duration regardless of the date. In other words, the input of the system in this research is a set of events and a date-independent time span.

Neural Network ERP Evaluation: In 2007, students and researchers from Chia Nan University, National Chung Cheng University, WuFeng Institute of Technology and Miami University implemented a back-propagation neural network that compares the performance of different ERP applications in order specify which one is best for a certain corporation. Although this approach is very different and seems unrelated to the one in this research, it shows how ERP can be connected to artificial neural networks in several ways [7]. A similar system was developed in 2008 by researchers from the Department of Industrial Engineering, Sakarya University [8].

Neural Network for Cost Estimation: This research was made by AURA University in 2011. It implemented a back-propagation neural network that can estimate the cost of a project or product to avoid and minimize losses. It was mainly designed for Small and Medium Enterprises. Although this system used data dedicated for the purpose of this project, the future plan of the researchers was to embed this system into an ERP, in order to manipulate its data as parameters for their network [9].

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Jad Farhat et al. / Procedia Computer Science 114 (2017) 288–295 289

ScienceDirect

www.elsevier.com/locate/procedia

Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS

October 30 – November 1, 2017, Chicago, Illinois, USA

ERP Neural Network Inventory Control

Jad Farhat

a

_{, Michel Owayjan}

a,b

_*

a_{Department of Computer and Communications Engineering}

b_{Department of Mechatronics Engineeering}

Faculty of Engineering

American University of Science and Technology Beirut, Lebanon

Abstract

Enterprise Resource Planning (ERP) is a system of integrated applications used to have full insight over the resources of an enterprise in terms of goods, employees and customers. On the other hand, artificial neural networks are becoming a necessity in applications that require artificial intelligence. A marriage between these two concepts would yield a system capable of storing and displaying dashboards of data, and simultaneously make computed expectations that can determine the future plans of an enterprise. Many have researched different applications in which a neural network can be used in order to achieve such a system. This paper demonstrates the study and simulation of a system that can give a prediction of the goods needed for an enterprise’s inventory depending on the past history of this enterprise sale with respect to the events occurring at different time periods. The system is built using C# and using examples from a real trading cooperation history in the learning process. It was tested using fictional simulations and produced acceptable results.

Keywords: ERP; Artificial neural networks; Back-Propgation; Inventory; Decision-making, Big Data, Entity Framework

* Michel Owayjan. Tel.: +961-1-218716;

E-mail address: mowayjan@aust.edu.lb

1. Introduction

Business data is considered by many to be the most valuable asset that an enterprise owns [1]. Many companies hire consultants and economists in order to use the data they own, (e.g. the sales from previous years, the market requirements for an upcoming period of time, or the plans of legislations, etc.) That can affect the trading market in order to decide the near and far future policies of the corporation. According to Action Learning, standard strategy consultant fees would range between $ 50,000 and $ 200,000 [2]. This highly proves that most industries and trading corporations tend to use their data resources in the best way to achieve more profit. Furthermore, these corporations use systems such as the ERP in order to handle and manipulate their data. The ERP is a descendant of previous systems that were used to store and view data used for strategy planning. As technology moves forward, just like any other business system, the ERP too witnesses added features to improve its data handling [3]. Artificial Neural Networks (ANN), on the other hand, are computational models used in machine learning based on the nervous system of the human body [4]. ANN has developed different types of learning techniques over time. In this application Back-Propagation is used. Back-Propagation is a two phase algorithm that uses error correction in order to update the weights of neurons in the learning process [5].

This paper introduces a research about one feature that can be added to the ERP in order to map stored sales data into a neural network to predict a calendar of items that may be needed in the future, depending on certain events that are occurring. The paper also discusses the applications of this research in the real world dealing with big data. After a brief literature review on the usage of Machine Learning in ERP, the paper presents the system design and explains the parameters and techniques used. The learning process is also explained as well as the simulations used to test the application and some discussion on the results.

2. Literature Review

Several researches were conducted about the use of neural networks in business solutions. The following are the most relevant studies. Automatic Inventory Control: In 2003, Nicholas Hall used a Multilayer Perceptron (MLP) neural network to design inventory control software that would predict the number of parts an inventory would need based on the demand in the last 12 months [6]. This approach is the closest to the one discussed in this paper; however, the difference is that in this research, the prediction is based on a time span with events occurring in this duration regardless of the date. In other words, the input of the system in this research is a set of events and a date-independent time span.

Neural Network ERP Evaluation: In 2007, students and researchers from Chia Nan University, National Chung Cheng University, WuFeng Institute of Technology and Miami University implemented a back-propagation neural network that compares the performance of different ERP applications in order specify which one is best for a certain corporation. Although this approach is very different and seems unrelated to the one in this research, it shows how ERP can be connected to artificial neural networks in several ways [7]. A similar system was developed in 2008 by researchers from the Department of Industrial Engineering, Sakarya University [8].

Neural Network for Cost Estimation: This research was made by AURA University in 2011. It implemented a back-propagation neural network that can estimate the cost of a project or product to avoid and minimize losses. It was mainly designed for Small and Medium Enterprises. Although this system used data dedicated for the purpose of this project, the future plan of the researchers was to embed this system into an ERP, in order to manipulate its data as parameters for their network [9].

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290 _{Farhat and Owayjan/ Procedia Computer Science 00 (2017) 000–000}Jad Farhat et al. / Procedia Computer Science 114 (2017) 288–295 3. System Design

The proposed system is a back-propagated neural network that retrieves the data contained in an ERP to create a calendar of expectations in terms of quantity and nature of goods needed by a certain corporation, based on samples of sales in time intervals with certain events. The system is divided into several parts that work simultaneously to achieve reliable results. The programming language and environment used were directly dependent on the application in which this system will be used. Furthermore, different considerations were taken in order to ensure the system will be relevant with big data in real world applications.

3.1. Language and Environment

When it comes to neural networks or other fields that deal with computing, developers usually prefer working with high-level interpreted languages such as Python. Python provides a variety of libraries and functions that facilitates computations in the field of matrices, which is very useful in working with neural networks [11]. However, taking other factors into consideration, such as the nature of the ERP application the neural network is used on, lead to using C#, a .NET language. An ERP might consist of hundreds of database tables that contain millions of rows of data. Microsoft developed Entity Framework (EF), an object-relational mapping (ERM) tool that makes creating database tables and querying for specific data marginally easier. EF works in the same logic object-oriented programming use. Database tables are mappings of classes written and declared in a master class extending a built-in class. Queries are fetched usbuilt-ing Language-Integrated Query (LINQ), another .NET tool that makes a developer’s life easier [12]. However, the developer must choose between enjoying the comforts of computations offered by languages like Python, or the comforts of data storage offered by .NET. In this case, since the application is an ERP, the solution was developed using C# with EF for database. This option leads to the ability of using this application in the future in a Web Application using ASP.NET Core, Microsoft’s new cross-platform framework [13].

3.2. Database

The database of the system is derived from an ERP database. The system should retrieve the amounts of items sold in a certain time interval of interest. Therefore, the database should include a Transactions table, mainly consisting of the invoices of the corporation if we suppose a trading company. This table includes columns specifying Customer and the Date of the transaction. The Transactions table is related to another table called TransactionItems listing items in a transaction with their quantities. Customers are also stored in an independent table. Figure 1 presents the tables related to the system.

Fig. 1. Database related to the ERP

Another family of tables is related to the goods sold. Items are divided into classes and sub-classes. The main branch is the ItemClass table containing the main classes (e.g. Gadgets, Stationary, Body Care Products…). This table branches into ItemSubClasses, containing braches of each class in the first table (e.g. Deodorants, Shower Gel, and Hand Soap are subclasses of Body Care Products). The Item table branches from the Sub classes and specifies items as a brand. The item table contains a column that specifies a price category of an item, represented by an integer in the range of 1-3, 1 being cheapest. The above tables are basic in any ERP. However, for this application needed another family of tables related to the Events. Events and Subevents are static tables with rows including standard events and subevents that influence the marker. These events can be made dynamic in future applications, which would greatly increase the size of the input layer neural network. As for this application, six main categorical events

were used: Holiday, Political, Climate and Weather, Economical, Season, and Health. Table 1 details the subevents chosen and their main event branch.

Table 1. Subevents used in this application

Subevent Event Subevent Event

Christmas and New Year Holidays Storm Weather

Easter Holidays Snow Storm Weather

Al-Fitr Holidays Sand Storm Weather

Adha Holidays Heat Wave Weather

Valentine Holidays Dollar Rate Increase Economical

Elections Political Dollar Rate Decrease Economical

Conflicts Political Winter Season

Regional Conflicts Political Summer Season

Strike Political Autumn Season

Invasion Political Disease Health

This family also consists of a table called TimeEvents, containing specific events that occurred at specific times. For example, a TimeEvent can be Christmas 2010, related to the SubEvent Christmas and New Year. The TimeEvents table is mapped onto another table called TimeLine, which serves as a digital timeline of events for an infinite time interval. Each row in this table describes a change in the status of an event, either begin or end. Each row consists of a firing event, related to the TimeEvents table, and a date. Figure 2 presents the table created for the purpose of training the neural network.

Fig. 2. Database related to the Neural Network

3.3. Neural Network System Constraints

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3. System Design

The proposed system is a back-propagated neural network that retrieves the data contained in an ERP to create a calendar of expectations in terms of quantity and nature of goods needed by a certain corporation, based on samples of sales in time intervals with certain events. The system is divided into several parts that work simultaneously to achieve reliable results. The programming language and environment used were directly dependent on the application in which this system will be used. Furthermore, different considerations were taken in order to ensure the system will be relevant with big data in real world applications.

3.1. Language and Environment

When it comes to neural networks or other fields that deal with computing, developers usually prefer working with high-level interpreted languages such as Python. Python provides a variety of libraries and functions that facilitates computations in the field of matrices, which is very useful in working with neural networks [11]. However, taking other factors into consideration, such as the nature of the ERP application the neural network is used on, lead to using C#, a .NET language. An ERP might consist of hundreds of database tables that contain millions of rows of data. Microsoft developed Entity Framework (EF), an object-relational mapping (ERM) tool that makes creating database tables and querying for specific data marginally easier. EF works in the same logic object-oriented programming use. Database tables are mappings of classes written and declared in a master class extending a built-in class. Queries are fetched usbuilt-ing Language-Integrated Query (LINQ), another .NET tool that makes a developer’s life easier [12]. However, the developer must choose between enjoying the comforts of computations offered by languages like Python, or the comforts of data storage offered by .NET. In this case, since the application is an ERP, the solution was developed using C# with EF for database. This option leads to the ability of using this application in the future in a Web Application using ASP.NET Core, Microsoft’s new cross-platform framework [13].

3.2. Database

The database of the system is derived from an ERP database. The system should retrieve the amounts of items sold in a certain time interval of interest. Therefore, the database should include a Transactions table, mainly consisting of the invoices of the corporation if we suppose a trading company. This table includes columns specifying Customer and the Date of the transaction. The Transactions table is related to another table called TransactionItems listing items in a transaction with their quantities. Customers are also stored in an independent table. Figure 1 presents the tables related to the system.

Fig. 1. Database related to the ERP

Another family of tables is related to the goods sold. Items are divided into classes and sub-classes. The main branch is the ItemClass table containing the main classes (e.g. Gadgets, Stationary, Body Care Products…). This table branches into ItemSubClasses, containing braches of each class in the first table (e.g. Deodorants, Shower Gel, and Hand Soap are subclasses of Body Care Products). The Item table branches from the Sub classes and specifies items as a brand. The item table contains a column that specifies a price category of an item, represented by an integer in the range of 1-3, 1 being cheapest. The above tables are basic in any ERP. However, for this application needed another family of tables related to the Events. Events and Subevents are static tables with rows including standard events and subevents that influence the marker. These events can be made dynamic in future applications, which would greatly increase the size of the input layer neural network. As for this application, six main categorical events

were used: Holiday, Political, Climate and Weather, Economical, Season, and Health. Table 1 details the subevents chosen and their main event branch.

Table 1. Subevents used in this application

Subevent Event Subevent Event

Christmas and New Year Holidays Storm Weather

Easter Holidays Snow Storm Weather

Al-Fitr Holidays Sand Storm Weather

Adha Holidays Heat Wave Weather

Valentine Holidays Dollar Rate Increase Economical

Elections Political Dollar Rate Decrease Economical

Conflicts Political Winter Season

Regional Conflicts Political Summer Season

Strike Political Autumn Season

Invasion Political Disease Health

This family also consists of a table called TimeEvents, containing specific events that occurred at specific times. For example, a TimeEvent can be Christmas 2010, related to the SubEvent Christmas and New Year. The TimeEvents table is mapped onto another table called TimeLine, which serves as a digital timeline of events for an infinite time interval. Each row in this table describes a change in the status of an event, either begin or end. Each row consists of a firing event, related to the TimeEvents table, and a date. Figure 2 presents the table created for the purpose of training the neural network.

Fig. 2. Database related to the Neural Network

3.3. Neural Network System Constraints

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292 _{Farhat and Owayjan/ Procedia Computer Science 00 (2017) 000–000}Jad Farhat et al. / Procedia Computer Science 114 (2017) 288–295

Fig. 3. Example of input vector

The size of the hidden layer was changed several times during testing to achieve a better result. By trial and error, the number finally settled down to 30. As for the output, it was designed to be a rate on a scale of 0 to 5 that represents the density of an item in a certain time span with corresponding events. To evaluate the rate, the system calculates the yearly average of the item sales and derives from this value a partial average according to the length of the time span of interest. This calculation is represented by equation (1), where α is the partial average, L is the length of time span in days, and A is the yearly average.

The partial average obtained by this calculation is considered to be the theoretical value of item sales per the length time span of interest. The density we are interested in calculating is nothing but the difference between the real sales value (v) and the theoretical one. In fact, the aim of this neural network is to expect that difference. In order to map this difference on the scale from 0 to 5, three cases must be considered. Equation (2), (3), and (4) represent the cases where v > α, v < α, and v = α, respectively.

A simple calculation can be used to validate these equations. If the sold amount (v) is much greater than partial average (α), as v tends to infinity, the value of α tends to be negligible, which makes the equation (2) tend to 5. If v is greatly less than α, the value of v is negligible, and equation (3) tends to 0. If v is equal to α, the scale is in the middle, which makes it equal to 2.5. For example, if an item X sells 100 pieces a year, (A = 100), and the study is taking place on a month time, (L = 30), then the partial average calculated using equation (1) would be 8.21. This partial average is the reference taken to determine whether this item is going to have high, medium, or low sales in our span of events. Therefore if the sold items are 30, equation (2) is used to calculate the rate to be 3.63. Since the value is well above 2.5, it can be determined that item X had a high sale in our span.

3.4. Neural Network Training

For training the network, examples were generated using a real-life database from a trading company that deals with cosmetics and other daily life goods. The examples were based on transactions in specific time intervals in which the country witnessed certain events. The events were inserted in the TimeEvents data table (Fig. 4).

Fig. 4. Section of the TimeEvents database table

To generate the examples, a function was written to take each item, and generate an object of a class NNExample, containing properties that represent the factor of the item, the length of the time span, the 20 bits representing the presence or absence of each SubEvent, and a numerical value representing the output. The output was calculated based on the equations (2), (3), and (4). The examples were all stored in the database to be used for the Back-Propagation training (Fig 5). Following generating the examples, the neural network connections can finally be determined and used to compute the desired result.

Fig. 5. Section of the NNExamples table that contains the examples used for training the neural network 4. Testing and Simulations

After training the network, three simulations were made in order to test the system. Since the data of item sales was based on a real database of a small business trading corporation, similar events were used to perform the study. The result of each simulation is discussed below along with a discussion about the validity of this system with big data. Please note that the names of the diseases and future conflicts mentioned below are all fictional.

4.1. Simulation I: Armed Conflict with an Enemy Nation, Summer 2018

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Fig. 3. Example of input vector

The size of the hidden layer was changed several times during testing to achieve a better result. By trial and error, the number finally settled down to 30. As for the output, it was designed to be a rate on a scale of 0 to 5 that represents the density of an item in a certain time span with corresponding events. To evaluate the rate, the system calculates the yearly average of the item sales and derives from this value a partial average according to the length of the time span of interest. This calculation is represented by equation (1), where α is the partial average, L is the length of time span in days, and A is the yearly average.

The partial average obtained by this calculation is considered to be the theoretical value of item sales per the length time span of interest. The density we are interested in calculating is nothing but the difference between the real sales value (v) and the theoretical one. In fact, the aim of this neural network is to expect that difference. In order to map this difference on the scale from 0 to 5, three cases must be considered. Equation (2), (3), and (4) represent the cases where v > α, v < α, and v = α, respectively.

A simple calculation can be used to validate these equations. If the sold amount (v) is much greater than partial average (α), as v tends to infinity, the value of α tends to be negligible, which makes the equation (2) tend to 5. If v is greatly less than α, the value of v is negligible, and equation (3) tends to 0. If v is equal to α, the scale is in the middle, which makes it equal to 2.5. For example, if an item X sells 100 pieces a year, (A = 100), and the study is taking place on a month time, (L = 30), then the partial average calculated using equation (1) would be 8.21. This partial average is the reference taken to determine whether this item is going to have high, medium, or low sales in our span of events. Therefore if the sold items are 30, equation (2) is used to calculate the rate to be 3.63. Since the value is well above 2.5, it can be determined that item X had a high sale in our span.

3.4. Neural Network Training

For training the network, examples were generated using a real-life database from a trading company that deals with cosmetics and other daily life goods. The examples were based on transactions in specific time intervals in which the country witnessed certain events. The events were inserted in the TimeEvents data table (Fig. 4).

Fig. 4. Section of the TimeEvents database table

To generate the examples, a function was written to take each item, and generate an object of a class NNExample, containing properties that represent the factor of the item, the length of the time span, the 20 bits representing the presence or absence of each SubEvent, and a numerical value representing the output. The output was calculated based on the equations (2), (3), and (4). The examples were all stored in the database to be used for the Back-Propagation training (Fig 5). Following generating the examples, the neural network connections can finally be determined and used to compute the desired result.

Fig. 5. Section of the NNExamples table that contains the examples used for training the neural network 4. Testing and Simulations

After training the network, three simulations were made in order to test the system. Since the data of item sales was based on a real database of a small business trading corporation, similar events were used to perform the study. The result of each simulation is discussed below along with a discussion about the validity of this system with big data. Please note that the names of the diseases and future conflicts mentioned below are all fictional.

4.1. Simulation I: Armed Conflict with an Enemy Nation, Summer 2018

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294 _{Farhat and Owayjan/ Procedia Computer Science 00 (2017) 000–000}Jad Farhat et al. / Procedia Computer Science 114 (2017) 288–295

4.2. Simulation II: S-Virus, Christmas 2020

The Simulation Virus (S-Virus) will hit Earth in 2020, which creates a join time span that includes the events of a disease, winter, and Christmas Season. It is a combination of the event of Swine Flu Disease spread that also spanned to winter, and any Christmas season. The expected result was that both items that were in high demand during the Swine Flu period and Christmas periods will give high rates. The result was also satisfying as high-price deodorants and other items that can be gifted in holidays gave high rates (4-4.9), while sanitizer products such as soap and hand gel also gave a very large rate (4.5-4.9). However, Stationary products also gave high ratings similar to the ones that take place in the beginning of winter season (same period the Swine Flu peaked in), while stationary sales marginally decrease during other Christmas seasons.

4.3. Simulation III: Apocalypse, Summer 2020

The apocalypse simulation is a combination of all negative events occurring at the same time in the summer of 2020. This means that the simulation span included a disease pandemic, a conflict, a regional conflict, a heat wave, a sand storm and a dollar rate decrease all at the same time. The result was that all high-price items gave low rates (0-2), while cheap items in general gave higher rates. The interesting result was that the high-price deodorants that usually have high rates in normal summers had much lower rates, which is typical in case of conflicts and dollar-rate decrease.

4.4. Simulation IV: Real Case Test, Christmas Season 2016

Christmas Season 2016 was a stable season with no major incidents. The system gave high rates for cosmetics in general and especially high and medium price deodorants. The actual results were pretty close to the expected results as deodorants sales increased during the Christmas seasons. However, the system did not match the price rates as high price deodorants did not score as high as expected. This could be due to the economic situation in the country and can be added as a new example to teach the neural network for future seasons. The figure below illustrates the results of several items of interest in two simulations, comparing the average and simulation rates, with a real similar event.

Fig. 6. (a) Summer 2018 Simulation result compared to Syrian Civil War (b) Christmas 2017 Simulation compared to the real Christmas 2017 event sales

5. Discussion and Conclusions

The simulations proved to be successful in general. However, some of the faults that can be easily noticed are due to the lack of examples. For example, Tammouz War took place in 2006, where smart phones were still not very popular. This means that people still depended on radios and lighters that work on batteries, which justifies the high

rate expected for batteries in Simulation I. More examples that include different patterns of events would result in a proportionally better result. However, the categorization of items between different price classes can be very useful in case brands changed along time. For example, if a cheap deodorant “A” was highly sold in certain events during 2006, and then removed from market afterwards, the system can be made to expect that a cheap deodorant “B” will be highly sold in the same events occurring during 2018. In case of big data, the system might run slower. This will be frustrating if the user expects live data displayed on his or her dashboard. However the system can be modified to run faster. The current system can be summarized in the flow chart displayed in Fig 7. As we can see, the system takes for each time span the case of every item by itself. However, to increase the speed, a cascaded neural network with three stages that takes in its first stage a factor representing the Item Class. The output of the first stage activates, in the positive cases, the second layer, which takes each sub class under the first class and determines its rate. This result similarly activates the third and final stage, which takes each item under the sub class.

Fig. 7. Flow chart of the used neural network References

[1] K. Smith and G. Gupta, “Preface,” in Neural Networks in Business, Techniques and Applications, Ieda Group Publishing.

[2] Action Learning. (2012, October 7). Fee Ranges For Consulting Services [Online]. Available: http://www.action-learning.com/fee-ranges-for-consulting-services

[3] Mike Newman, “ERP Systems,” ERP Systems and Competitive Advantage.

[4] Warren S. McCulloch, Walter Pitts, “Abstract”, A logical calculus of ideas immanent in nervous activity.

[5] Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (8 October 1986). "Learning representations by back-propagating errors". Nature.

[6] Nicholas Hall, “Introduction,” Automatic Inventory Control: A Neural Network Approach.

[7] I-Chiu Chang, Hsin-Ginn Hwang, Hsueh-Chih Liaw, and David Yen, “A neural network evaluation model for ERP performance from SCM perspective to enhance enterprise competitive advantage,” Expert Systems with Applications, 2008.

[8] H.R. Yazgan, S. Boran, K. Goztepe, “An ERP software selection process with using artificial neural network based on analytic network process,” Expert Systems with Applications, 2009.

[9] M. Kotb, M.Haddara. Y. Kotb, “Back Propagation Artficial Neural Network for ERP Adoption Cost Estimation,” 2011.

[10] Mary A.S., P. Ranjit Jeba Thangaiah, “Neural Networks in ERP and CRM,” International Journal of Computer Applications, 2012. [11] Damian Wolf, The 5 Best Programming Languages for AI Development, March 31, 2017.

[12] Julie Lerman, “Entity Framework – Entity Framework 6: The Ninja Edition,” MSDN Magazine Volume 28 Number 12, December 2013. [Online].

[13] Daniel Roth, Rick Anderson, Shaun Luttin, “Introduction to ASP.NET Core,” [Online] Available: https://docs.microsoft.com/en-us/aspnet/core/

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