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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 05, May 2019, pp. 283-293, Article ID: IJMET_10_05_029
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=5
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
PREDICTIVE BUSINESS INTELLIGENCE:
CONSUMER GOODS SALES FORECASTING
USING ARTIFICIAL NEURAL NETWORK
Yulyardo and Sani M. Isa
Computer Science Department,
BINUS Graduate Program – Master in Computer Science
Bina Nusantara University, Jakarta, Indonesia 11480
ABSTRACT
Business competition between manufacturing businesses in Indonesia is getting
tighter along with the development of businesses from competing companies that have
similar businesses. One strategy that can be applied by this company is Business
Intelligence, that is by utilizing the data that is already available to help in better
decision making, such as decisions based on facts stored in the data, precisely namely
the lack of errors in the presentation of reports, and fast that is, cut down on the time
for making the usual report. The method proposed by the author is a method that can
be used to predict sales value based on existing sales data (sales forecasting). By
implementing Business Intelligence and data mining, companies can learn from the
data that has been collected, can evaluate the performance of the sales department,
can understand market trends from the products sold, and can predict future sales
levels. In addition, Business Intelligence can display detailed transaction data
recapitulation quickly.
Key words: Business Intelligence, Prediction, Sales Forecasting, Artificial Neural
Network
Cite this Article: Yulyardo and Sani M. Isa, Predictive Business Intelligence:
Consumer Goods Sales Forecasting Using Artificial Neural Network, International
Journal of Mechanical Engineering and Technology 10(5), 2019, pp. 283-293.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=5
1. INTRODUCTION
Business competition between manufacturing businesses in Indonesia is getting tighter along
with the development of businesses from competing companies that have similar businesses.
PT. X is one of the consumer goods companies that produce various kinds of daily needs
products, such as food products, beverages, and personal care products. With the existence of
tight business competition, it is necessary to have a specific strategy implemented by PT. X to
continue to exist as a consumer goods company and to be the best among similar competitors
in its class. One strategy that can be applied by this company is Business Intelligence, for
example by utilizing the data that is already available to help in better decision making,
namely decisions based on facts stored in the data, precisely namely the lack of errors in the
Yulyardo and Sani M. Isa
http://www.iaeme.com/IJMET/index.asp 284 editor@iaeme.com
presentation of reports, and fast that is, cut down on the time for making the usual report. PT.
X also has problems in presenting reports that have complex indicators that require high
accuracy in their presentation, even though these reports are very crucial for stakeholders.
In addition, there are several product sales reports that are still done manually by the Sales
Business Unit in PT. X, for example sales turnover reports per item, best-selling product
reports per period, turnover reports per branch, and others. In addition to taking time to make
the report, the report presented is minimal visualization because it is only presented in the
form of a simple table so that it will be difficult to analyze, Other problems report presented at
PT. The X is done manually by creating a query in the OLTP / Online Transaction Processing
database, then the results are processed using Excel macros. To maintain the business of PT.
X remains in the best performance, it needs a business intelligence design and a good method
of analysis. The method proposed by the author is a method that can be used to predict sales
value based on existing sales data (sales forecasting).
By implementing Business Intelligence and data mining, companies can learn from the
data that has been collected, can evaluate the performance of the sales department, can
understand market trends from the products sold, and can predict future sales levels. In
addition, Business Intelligence can display detailed transaction data recapitulation quickly.
2. RELATED WORKS
Previously, many researchers had conducted research on Business Intelligence. The following
are some of the studies that have been conducted relating to the application of Business
Intelligence. In [1] Negash proposed a Business Intelligence framework and also identified
potential research areas. The BI Framework highlights the importance of semi-structured data
to support decision making, in addition discusses matrices for BI data types (structured vs.
semi structured) and source data (internal vs. external) to guide future research. According to
him, many BI Tools exist for acquisition, integration, cleaning, searching, analyzing, and
sending structured data for analysis and decision making, but further research is needed to
integrate these bi tools and to provide actionable information.
The next three years, to be exact in 2007, according to [2] Olszak described the process of
building a Business Intelligence system and proposing a methodology of making and
implementing an organizational system. The approach involves two stages, that is the BI
Creation and BI Consumption. BI Creation is the most time consuming stage and this stage
requires the largest share of financial and labor resources in the BI cycle, this phase consists
of several stages, that is the BI business definition, such as the determination of the BI system
development strategy, identifying and preparing data sources, choose Tools BI, design and
implement BI, and find and explore new information needs and other business applications.
Stage BI Consumption is related to end user users, this stage can be divided into several
different steps that must be taken based on the user's wisdom and in accordance with the
needs or tasks that must be faced. The steps mainly include:
1) Logistics analysis that allows to quickly identify supply chain partners
2) Access, monitoring and analysis of facts
3) Development of alternative decisions
4) Distribution and cooperation
5) Changes in the influence of company performance
The BI system must be independent of its hardware and software platform, BI solutions
should be flexible, once business changes, organizations must adjust the BI system to new
conditions, BI solutions must be measurable, and the BI system must be based on modern
technology. In addition to the researchers mentioned above, there are also researchers who
Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network
http://www.iaeme.com/IJMET/index.asp 285 editor@iaeme.com
conducted research concerning Business Intelligence conducted in 2010, in [3] Hawking
conducted a research which aims to determine the determinants of success associated with BI
in an ERP system environment. In his research, used qualitative methods to investigate
critical success factors associated with BI adoption. Hawking et al collected samples for
analysis whose contents consisted of industry presentations sourced from 69 industries related
to SAP. And from the sample results it was concluded that several important factors in
determining the success of BI, specially Management Support, Resources, User Participation,
Team Skills, Source Systems, and Development Technology.
In 2015, [4] Joshi conducted research on data mining techniques to increase the
effectiveness of sales and marketing. The method used is K-Means Clustering & Most
Frequent Pattern. In this research proposed a system for doing sales forecasting which
consists of 2 phases. The first phase is to divide stock data in 3 different product categories,
that is Dead Stock, Slow Moving, and Fast Moving by using K-Means. The second phase uses
the Most Frequent Pattern to find frequent item attribute patterns in each product category that
has been Clusters in the first phase and provide sales trends in a concise form.
In the same year [5] Katkar conducted research in the same field, namely sales
forecasting, but with a different method, specifically using the Naïve Bayesian Classifier
approach. It is proposed a system that could classify sales of products into categories such as
Poor, Average, and Good. If the product estimate is Good, the system will provide a hint
stating that sufficient stock must be available. Then in 2016, [6] Vhatkar conducted a Sales
Forecasting study using Artificial Neural Network Models. This study explains the
forecasting method in detail. Oral Care Products sales are predicted with the help of the Back
Propagation Learning algorithm and the accuracy of the forecast is validated. Error
Calculation is also observed using Mean Absolute Deviation, Mean Squared Error, and Root
Mean Square Error.
The conclusion that can be drawn from the previous studies described above is that
Business Intelligence is growing along with technological developments, Business
Intelligence is no longer just a means of data visualization, but BI components are also able to
be used for data exploration and data mining solutions including Forecasting, Market
Basketball Analysis, Sequence Analysis, Neural Networks, and others. By utilizing a
combination of Business Intelligence and Data Mining, the solution from Business
Intelligence can be better than simply displaying data without the exploration of the data.
3. METHODOLOGY
3.1 Data warehouse
The data warehouse design method that will be used in this research is the Kimball Lifecycle
Method, [7] which is a bottom-up approach, starting with a data mart, data flowing from the
source into the data mart and then formed into a data warehouse, and implemented in stages.
There are 9 steps from the Kimball Lifecycle method, [8] which is often referred to as nine-
step methodology.
 Choose The Process: Sales Data to evaluate sales performance and achievement of
sales turnover at PT. X
 Choose The Grains: Sales Invoice
 Identify and Conform the Dimensions: Period, Site, Product, Strata Customer, Sales,
and Team Sales Dimensions
 Choose the fact: The facts chosen from this case study are sales facts, where the
measures in this table include qty and nominal turnover, targets and achievements of
each target type
Yulyardo and Sani M. Isa
http://www.iaeme.com/IJMET/index.asp 286 editor@iaeme.com
 Store Pre-calculations in fact table: At this stage, the result of the calculation is the
total = price x quantity is not displayed
 Round out the dimension table: At this stage, the dimensions that have been defined
will be made a description containing structured information about the attributes of
these dimensions
 Choose the duration of database: The data that will be entered is data for the last year,
starting from Jan 2017 to Sep 2018
 Tracking Slowly Changing Dimensions: This stage aims to overcome dimensions that
can change slowly and can become problems.
 There are 3 responses to this case, that is Overwrite, add new dimension records, and
add new fields
 Decide The Physical Design: This stage is the physical design stage of a data
warehouse. At this stage the ETL process is also carried out
Figure 1 is a star schema for designing the proposed data warehouse
Figure 1. Star Schema for Proposed Data Warehouse
3.2. Artificial Neural Network
The algorithm used for sales prediction in this study is backpropagation neural network
(BPNN), [9] below is the architecture of the BPNN algorithm
Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network
http://www.iaeme.com/IJMET/index.asp 287 editor@iaeme.com
Figure 2. BPNN Architecture
Stages of the BPNN Algorithm [10]:
 Initialization Stage
Step 0:
Initialize the connection weights between neurons using small random numbers (-0.5
to +0.5) and the rate of learning (α)
Step 1:
Do step 2 until step 9 as long as the specified stop condition is not met
Step 2:
Do step 3 through step 8 for each training pair.
 Forward Propagation Stage
Step 3:
[11]
Each input neuron (Xi) receives an input signal xi, and spreads it to all neurons in the
hidden layer.
Step 4:
Each neuron in the hidden layer adds up all incoming signals.
[12]
Each neuron in the hidden layer uses an activation function to
produce an output signal
Step 5:
Each neuron in the output layer sums up all incoming signals.
Each neuron in the output layer uses an activation function to produce an output
signal.
[13]
 Back propagation stage
Step 6:
Each neuron in the output layer calculates the error information between the signals
produced with the target pattern
Yulyardo and Sani M. Isa
http://www.iaeme.com/IJMET/index.asp 288 editor@iaeme.com
Calculate weight correction:
Calculate bias correction: [14]
Step 7:
Each neuron in the hidden layer adds up all incoming signals
Each neuron in the hidden layer calculates error information
Calculate weight correction:
Calculate bias correction: [15]
 Adjustment of weights
Step 8:
Update weights and bias on relationships between layers.
Step 9:
Check the stop condition [16]
BPNN will be applied in this study by entering input parameters obtained from the data
warehouse that has been made. There are several stages in implementing BPNN in this study,
including:
1. Prepare a dataset
In this stage, a dataset will be prepared from the data warehouse, the dataset used will be an
aggregation of sales facts and other dimensions that will be grouped by province and based on
the month of the year so that the time-series dataset is obtained. The dataset, which initially
consists of millions of rows of transactions of around 6 million rows, will be simplified to
around 800 rows at this stage.
2. Selection of attributes
Attribute selection activities are used to separate the prediction target with the attributes that
become the variables in this study. In this dataset that will be predicted is revenue in units of
Rupiah, while other attributes will be variable. Table 1 below shows the attributes in this
dataset.
Table 1. Dataset Attributes
Attribute Name Information
Province List of provinces in data warehouse
(consisting of 20 provinces)
Period The period used is in the format of the
month and year
Revenue Rp Total turnover in rupiah units, this
attribute that will be the target
prediction
Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network
http://www.iaeme.com/IJMET/index.asp 289 editor@iaeme.com
Revenue Qty The total items sold in carton units
Rupiah Return Total return in units of rupiah
Quantity Return The total items return in carton units
3. Application of Algorithms
At this stage the BPNN algorithm will be implemented in this dataset. The features will pass
through the phases of the BPNN algorithm starting from initialization, forward propagation,
back propagation, and weight adjustment, so that the predicted value is generated. All of these
phases will be built using the help of Rapid miner tools
4. RESULT AND ANALYSIS
Figure 3 shows variation in solar intensity for different days when mass flow rate of The
results of the case study included the conduct of ETL processes from the star schema that had
been designed, the results of the data warehouse were imported into BI tools, namely Qlik
Sense and Business Intelligence was designed. Business Intelligence that is built includes a
sales dashboard, this dashboard displays revenue distribution maps, the top 10 provinces
supporting revenue, the top 20 best-selling product categories per region, and a graph of
revenue growth against targets. The designed Sales Dashboard is displayed through the image
below.
Figure 3. Sales Dashboard
Analysis that can be delivered from the dashboard that has been made includes:
- Provinces that have large revenue contributions have not evenly distributed, the eastern
region of Indonesia has not contributed maximally
- Achievement of targets is still low, can be seen every month many regions do not reach the
target given
- There is one product that dominates sales, its value is far from other products
- Predictions generated using the BPNN algorithm method have movements that follow the
flow of real revenue movements. Prediction results can be used as a reference or preparation
before the sales go into the field.
The strategies that need to be taken by the company from the analysis:
- Also paying attention to the region are still not contributing maximally to revenue, by
increasing the number of sales and raising market campaigns in the region.
Yulyardo and Sani M. Isa
http://www.iaeme.com/IJMET/index.asp 290 editor@iaeme.com
- Providing a reasonable target that can be achieved by the salesmen, can be obtained for
example from the prediction results that have been made, it can also be by providing
opportunities for salesmen to get incentives that spur salesmen's desire to reach the target.
- Conduct a survey directly to the market to test whether there is one product that is only sold
in an area and is not sold in other regions, so there needs to be a review to sell products that
are in line with market segmentation.
In addition, this case study also carries the prediction of revenue made with revenue
datasets that have been grouped by province per month. The total data collected which
previously amounted to 6 million of rows was compressed to around 800 rows because it had
been aggregated. The data is time-series data that has been aggregated per province and per
month, the data consists of the code and name of the province, period per month, Revenue
Rupiah, Quantity Revenue, Rupiah Return, Quantity Return, and number of salesmen.
The dataset is then imported into rapid miner, and prediction modeling is performed.
Because the data is in the form of time series data, the windowing method is carried out,
which is to map period attributes into features. In this case study, the windowing parameter is
6, so in other words revenue prediction is the result of revenues 6 months before. After the
windowing process, sliding window validation is carried out with the following parameters:
training window width: 5, training window step: 1, test window width: 5, horizon: 1. The
meaning of the horizon here means that what will be predicted is 1 period in the future (1
month ahead).
The algorithm method used is neural network, neural network is applied to the model, and
its performance is calculated by forecasting performance elements. The process of predictive
modeling is shown in the figure below
Figure 4. Revenue Prediction Model
Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network
http://www.iaeme.com/IJMET/index.asp 291 editor@iaeme.com
The results of the modeling are as follows:
Figure 5. Revenue Prediction Model Performance
From the picture above it was concluded, the prediction results from the previous 6
months to predict the next 1 month resulted in an average accuracy of 0.718 (71.8%). The
revenue predictions that have been made will be plotted so that the trend movement can be
seen whether follow the actual data or not. The figure below is a plot between actual revenue
and revenue prediction. Actual Revenue is shown by a red line; Revenue Prediction is
indicated by a blue line.
Figure 6. Revenue Prediction vs Actual Revenue Trend line
It is expected that after the report is formed using Business Intelligence, stakeholders can
easily capture information through a friendlier visualization than using a conventional system,
Excel, with visualization based on existing data that can provide insight and be able to be
taken into consideration for decision making based on data. With the support of reporting
made with Business Intelligence reliable reports will be generated that can be analyzed by
company leaders so that decision making becomes faster and more effective. The revenue
prediction feature of the new system design can be used by companies to take preventive
actions or better campaign plans to produce better turnover for the company. With the
construction of a data warehouse system along with revenue predictions, it is proven that it
Yulyardo and Sani M. Isa
http://www.iaeme.com/IJMET/index.asp 292 editor@iaeme.com
can make decision support systems and reports faster, more precise, accurate and easier for
users.
5. CONCLUSION
From the research conducted company should do some approaches, such as data analysis,
design data warehouse, and then implement Business Intelligence to make reports become
easier. The reports can help stakeholders to know the progress, to make decision and strategy,
and to evaluate the sales performance. By using Qlik Sense BI Tools and Predictive Data
Mining using Rapidminer Tools, managers can see the revenue distribution map, sales
performance per area per period, and sales dashboard include top 20 best-selling product, top
10 best performing salesman, and etc. The report of revenue distribution map can help
managers to know which branch conform the target and to analyze which branch needs
attention to increase performance. The sales performance report shows the performance of
each sales so that managers can evaluate their job. And last, sales dashboard can help
managers to see the best performance. Overall, this reports can help company to companies to
take preventive actions or better campaign plans to produce better turnover for the company.
With the construction of a data warehouse system along with revenue predictions, it is proven
that it can make decision support systems and reports faster, more precise, accurate and easier
for users.
REFERENCES
[1] Negash, Solomon. 2004. Business Intelligence. Communications of the Association for
Information Systems, 13, 177-195.
[2] Olszak, Celina M. & Ewa Ziemba. 2007. Approach to Building and Implementing
Business Intelligence Systems. Interdisciplinary Journal of Information, Knowledge, and
Management, 2, 135-148.
[3] Hawking, Paul & Carmine Sellito. 2010. Business Intelligence (BI) Critical Success
Factors. Association for Information Systems AIS Electronic Library (AISel), 4, accessed
from: http://aisel.aisnet.org/acis2010/4
[4] Joshi, A., Pandey, N., Chawla, R., & Patil, P. (2015). Use of data mining techniques to
improve the effectiveness of sales and marketing. International Journal of Computer
Science and Mobile Computing (IJCSMC), 4, 81-87.
[5] Katkar, V., Gangopadhyay, S. P., Rathod, S., & Shetty, A. (2015, January). Sales
forecasting using data warehouse and Naïve Bayesian classifier. In Pervasive Computing
(ICPC), 2015 International Conference on (pp. 1-6). IEEE.
[6] Vhatkar, S., & Dias, J. (2016). Oral-Care Goods Sales Forecasting Using Artificial
Neural Network Model. Procedia Computer Science, 79, 238-243.
[7] Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical
Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Canada: Wiley
Publishing, Inc.
[8] Kimball, R., & Ross, M. (2010). The Kimball Group Reader: Relentlessly Practical Tools
for Data Warehousing and Business Intelligence. (First ed.). Canada: Wiley Publishing,
Inc.
[9] Parmar, A., Mistree, K., & Sompura, M. (2017). Machine Learning Techniques For
Rainfall Prediction: A Review. In Conference: 2017 International Conference on
Innovations in information Embedded and Communication Systems (ICIIECS).
Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network
http://www.iaeme.com/IJMET/index.asp 293 editor@iaeme.com
[10] Chandwani, V., Agrawal, V., & Nagar, R. (2015). Modeling slump of ready mix concrete
using genetic algorithms assisted training of Artificial Neural Networks. Expert Systems
with Applications, 42(2), 885-893.
[11] Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load
demand forecasting techniques for smart grid and buildings. Renewable and Sustainable
Energy Reviews, 50, 1352-1372
[12] Sari, N. R., Mahmudy, W. F., & Wibawa, A. P. (2016). Backpropagation on Neural
Network Method for Inflation Rate Forecasting in Indonesia. Int. J. Soft Comput. Its Appl.
[13] Chen, J. C., & Narala, N. H. R. (2017). Forecasting Currency Exchange Rates via
Feedforward Backpropagation Neural Network.
[14] Wang, J., & Wang, J. (2015). Forecasting stock market indexes using principle component
analysis and stochastic time effective neural networks. Neurocomputing, 156, 68-78
[15] Wang, L., Zeng, Y., & Chen, T. (2015). Back propagation neural network with adaptive
differential evolution algorithm for time series forecasting. Expert Systems with
Applications, 42(2), 855-863.
[16] Bisoyi, N., Gupta, H., Padhy, N. P., & Chakrapani, G. J. (2018). Prediction of daily
sediment discharge using a back propagation neural network training algorithm: A case
study of the Narmada River, India. International Journal of Sediment Research.

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PREDICTIVE BUSINESS INTELLIGENCE: CONSUMER GOODS SALES FORECASTING USING ARTIFICIAL NEURAL NETWORK

  • 1. http://www.iaeme.com/IJMET/index.asp 283 editor@iaeme.com International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 05, May 2019, pp. 283-293, Article ID: IJMET_10_05_029 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=5 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication PREDICTIVE BUSINESS INTELLIGENCE: CONSUMER GOODS SALES FORECASTING USING ARTIFICIAL NEURAL NETWORK Yulyardo and Sani M. Isa Computer Science Department, BINUS Graduate Program – Master in Computer Science Bina Nusantara University, Jakarta, Indonesia 11480 ABSTRACT Business competition between manufacturing businesses in Indonesia is getting tighter along with the development of businesses from competing companies that have similar businesses. One strategy that can be applied by this company is Business Intelligence, that is by utilizing the data that is already available to help in better decision making, such as decisions based on facts stored in the data, precisely namely the lack of errors in the presentation of reports, and fast that is, cut down on the time for making the usual report. The method proposed by the author is a method that can be used to predict sales value based on existing sales data (sales forecasting). By implementing Business Intelligence and data mining, companies can learn from the data that has been collected, can evaluate the performance of the sales department, can understand market trends from the products sold, and can predict future sales levels. In addition, Business Intelligence can display detailed transaction data recapitulation quickly. Key words: Business Intelligence, Prediction, Sales Forecasting, Artificial Neural Network Cite this Article: Yulyardo and Sani M. Isa, Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network, International Journal of Mechanical Engineering and Technology 10(5), 2019, pp. 283-293. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=5 1. INTRODUCTION Business competition between manufacturing businesses in Indonesia is getting tighter along with the development of businesses from competing companies that have similar businesses. PT. X is one of the consumer goods companies that produce various kinds of daily needs products, such as food products, beverages, and personal care products. With the existence of tight business competition, it is necessary to have a specific strategy implemented by PT. X to continue to exist as a consumer goods company and to be the best among similar competitors in its class. One strategy that can be applied by this company is Business Intelligence, for example by utilizing the data that is already available to help in better decision making, namely decisions based on facts stored in the data, precisely namely the lack of errors in the
  • 2. Yulyardo and Sani M. Isa http://www.iaeme.com/IJMET/index.asp 284 editor@iaeme.com presentation of reports, and fast that is, cut down on the time for making the usual report. PT. X also has problems in presenting reports that have complex indicators that require high accuracy in their presentation, even though these reports are very crucial for stakeholders. In addition, there are several product sales reports that are still done manually by the Sales Business Unit in PT. X, for example sales turnover reports per item, best-selling product reports per period, turnover reports per branch, and others. In addition to taking time to make the report, the report presented is minimal visualization because it is only presented in the form of a simple table so that it will be difficult to analyze, Other problems report presented at PT. The X is done manually by creating a query in the OLTP / Online Transaction Processing database, then the results are processed using Excel macros. To maintain the business of PT. X remains in the best performance, it needs a business intelligence design and a good method of analysis. The method proposed by the author is a method that can be used to predict sales value based on existing sales data (sales forecasting). By implementing Business Intelligence and data mining, companies can learn from the data that has been collected, can evaluate the performance of the sales department, can understand market trends from the products sold, and can predict future sales levels. In addition, Business Intelligence can display detailed transaction data recapitulation quickly. 2. RELATED WORKS Previously, many researchers had conducted research on Business Intelligence. The following are some of the studies that have been conducted relating to the application of Business Intelligence. In [1] Negash proposed a Business Intelligence framework and also identified potential research areas. The BI Framework highlights the importance of semi-structured data to support decision making, in addition discusses matrices for BI data types (structured vs. semi structured) and source data (internal vs. external) to guide future research. According to him, many BI Tools exist for acquisition, integration, cleaning, searching, analyzing, and sending structured data for analysis and decision making, but further research is needed to integrate these bi tools and to provide actionable information. The next three years, to be exact in 2007, according to [2] Olszak described the process of building a Business Intelligence system and proposing a methodology of making and implementing an organizational system. The approach involves two stages, that is the BI Creation and BI Consumption. BI Creation is the most time consuming stage and this stage requires the largest share of financial and labor resources in the BI cycle, this phase consists of several stages, that is the BI business definition, such as the determination of the BI system development strategy, identifying and preparing data sources, choose Tools BI, design and implement BI, and find and explore new information needs and other business applications. Stage BI Consumption is related to end user users, this stage can be divided into several different steps that must be taken based on the user's wisdom and in accordance with the needs or tasks that must be faced. The steps mainly include: 1) Logistics analysis that allows to quickly identify supply chain partners 2) Access, monitoring and analysis of facts 3) Development of alternative decisions 4) Distribution and cooperation 5) Changes in the influence of company performance The BI system must be independent of its hardware and software platform, BI solutions should be flexible, once business changes, organizations must adjust the BI system to new conditions, BI solutions must be measurable, and the BI system must be based on modern technology. In addition to the researchers mentioned above, there are also researchers who
  • 3. Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network http://www.iaeme.com/IJMET/index.asp 285 editor@iaeme.com conducted research concerning Business Intelligence conducted in 2010, in [3] Hawking conducted a research which aims to determine the determinants of success associated with BI in an ERP system environment. In his research, used qualitative methods to investigate critical success factors associated with BI adoption. Hawking et al collected samples for analysis whose contents consisted of industry presentations sourced from 69 industries related to SAP. And from the sample results it was concluded that several important factors in determining the success of BI, specially Management Support, Resources, User Participation, Team Skills, Source Systems, and Development Technology. In 2015, [4] Joshi conducted research on data mining techniques to increase the effectiveness of sales and marketing. The method used is K-Means Clustering & Most Frequent Pattern. In this research proposed a system for doing sales forecasting which consists of 2 phases. The first phase is to divide stock data in 3 different product categories, that is Dead Stock, Slow Moving, and Fast Moving by using K-Means. The second phase uses the Most Frequent Pattern to find frequent item attribute patterns in each product category that has been Clusters in the first phase and provide sales trends in a concise form. In the same year [5] Katkar conducted research in the same field, namely sales forecasting, but with a different method, specifically using the Naïve Bayesian Classifier approach. It is proposed a system that could classify sales of products into categories such as Poor, Average, and Good. If the product estimate is Good, the system will provide a hint stating that sufficient stock must be available. Then in 2016, [6] Vhatkar conducted a Sales Forecasting study using Artificial Neural Network Models. This study explains the forecasting method in detail. Oral Care Products sales are predicted with the help of the Back Propagation Learning algorithm and the accuracy of the forecast is validated. Error Calculation is also observed using Mean Absolute Deviation, Mean Squared Error, and Root Mean Square Error. The conclusion that can be drawn from the previous studies described above is that Business Intelligence is growing along with technological developments, Business Intelligence is no longer just a means of data visualization, but BI components are also able to be used for data exploration and data mining solutions including Forecasting, Market Basketball Analysis, Sequence Analysis, Neural Networks, and others. By utilizing a combination of Business Intelligence and Data Mining, the solution from Business Intelligence can be better than simply displaying data without the exploration of the data. 3. METHODOLOGY 3.1 Data warehouse The data warehouse design method that will be used in this research is the Kimball Lifecycle Method, [7] which is a bottom-up approach, starting with a data mart, data flowing from the source into the data mart and then formed into a data warehouse, and implemented in stages. There are 9 steps from the Kimball Lifecycle method, [8] which is often referred to as nine- step methodology.  Choose The Process: Sales Data to evaluate sales performance and achievement of sales turnover at PT. X  Choose The Grains: Sales Invoice  Identify and Conform the Dimensions: Period, Site, Product, Strata Customer, Sales, and Team Sales Dimensions  Choose the fact: The facts chosen from this case study are sales facts, where the measures in this table include qty and nominal turnover, targets and achievements of each target type
  • 4. Yulyardo and Sani M. Isa http://www.iaeme.com/IJMET/index.asp 286 editor@iaeme.com  Store Pre-calculations in fact table: At this stage, the result of the calculation is the total = price x quantity is not displayed  Round out the dimension table: At this stage, the dimensions that have been defined will be made a description containing structured information about the attributes of these dimensions  Choose the duration of database: The data that will be entered is data for the last year, starting from Jan 2017 to Sep 2018  Tracking Slowly Changing Dimensions: This stage aims to overcome dimensions that can change slowly and can become problems.  There are 3 responses to this case, that is Overwrite, add new dimension records, and add new fields  Decide The Physical Design: This stage is the physical design stage of a data warehouse. At this stage the ETL process is also carried out Figure 1 is a star schema for designing the proposed data warehouse Figure 1. Star Schema for Proposed Data Warehouse 3.2. Artificial Neural Network The algorithm used for sales prediction in this study is backpropagation neural network (BPNN), [9] below is the architecture of the BPNN algorithm
  • 5. Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network http://www.iaeme.com/IJMET/index.asp 287 editor@iaeme.com Figure 2. BPNN Architecture Stages of the BPNN Algorithm [10]:  Initialization Stage Step 0: Initialize the connection weights between neurons using small random numbers (-0.5 to +0.5) and the rate of learning (α) Step 1: Do step 2 until step 9 as long as the specified stop condition is not met Step 2: Do step 3 through step 8 for each training pair.  Forward Propagation Stage Step 3: [11] Each input neuron (Xi) receives an input signal xi, and spreads it to all neurons in the hidden layer. Step 4: Each neuron in the hidden layer adds up all incoming signals. [12] Each neuron in the hidden layer uses an activation function to produce an output signal Step 5: Each neuron in the output layer sums up all incoming signals. Each neuron in the output layer uses an activation function to produce an output signal. [13]  Back propagation stage Step 6: Each neuron in the output layer calculates the error information between the signals produced with the target pattern
  • 6. Yulyardo and Sani M. Isa http://www.iaeme.com/IJMET/index.asp 288 editor@iaeme.com Calculate weight correction: Calculate bias correction: [14] Step 7: Each neuron in the hidden layer adds up all incoming signals Each neuron in the hidden layer calculates error information Calculate weight correction: Calculate bias correction: [15]  Adjustment of weights Step 8: Update weights and bias on relationships between layers. Step 9: Check the stop condition [16] BPNN will be applied in this study by entering input parameters obtained from the data warehouse that has been made. There are several stages in implementing BPNN in this study, including: 1. Prepare a dataset In this stage, a dataset will be prepared from the data warehouse, the dataset used will be an aggregation of sales facts and other dimensions that will be grouped by province and based on the month of the year so that the time-series dataset is obtained. The dataset, which initially consists of millions of rows of transactions of around 6 million rows, will be simplified to around 800 rows at this stage. 2. Selection of attributes Attribute selection activities are used to separate the prediction target with the attributes that become the variables in this study. In this dataset that will be predicted is revenue in units of Rupiah, while other attributes will be variable. Table 1 below shows the attributes in this dataset. Table 1. Dataset Attributes Attribute Name Information Province List of provinces in data warehouse (consisting of 20 provinces) Period The period used is in the format of the month and year Revenue Rp Total turnover in rupiah units, this attribute that will be the target prediction
  • 7. Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network http://www.iaeme.com/IJMET/index.asp 289 editor@iaeme.com Revenue Qty The total items sold in carton units Rupiah Return Total return in units of rupiah Quantity Return The total items return in carton units 3. Application of Algorithms At this stage the BPNN algorithm will be implemented in this dataset. The features will pass through the phases of the BPNN algorithm starting from initialization, forward propagation, back propagation, and weight adjustment, so that the predicted value is generated. All of these phases will be built using the help of Rapid miner tools 4. RESULT AND ANALYSIS Figure 3 shows variation in solar intensity for different days when mass flow rate of The results of the case study included the conduct of ETL processes from the star schema that had been designed, the results of the data warehouse were imported into BI tools, namely Qlik Sense and Business Intelligence was designed. Business Intelligence that is built includes a sales dashboard, this dashboard displays revenue distribution maps, the top 10 provinces supporting revenue, the top 20 best-selling product categories per region, and a graph of revenue growth against targets. The designed Sales Dashboard is displayed through the image below. Figure 3. Sales Dashboard Analysis that can be delivered from the dashboard that has been made includes: - Provinces that have large revenue contributions have not evenly distributed, the eastern region of Indonesia has not contributed maximally - Achievement of targets is still low, can be seen every month many regions do not reach the target given - There is one product that dominates sales, its value is far from other products - Predictions generated using the BPNN algorithm method have movements that follow the flow of real revenue movements. Prediction results can be used as a reference or preparation before the sales go into the field. The strategies that need to be taken by the company from the analysis: - Also paying attention to the region are still not contributing maximally to revenue, by increasing the number of sales and raising market campaigns in the region.
  • 8. Yulyardo and Sani M. Isa http://www.iaeme.com/IJMET/index.asp 290 editor@iaeme.com - Providing a reasonable target that can be achieved by the salesmen, can be obtained for example from the prediction results that have been made, it can also be by providing opportunities for salesmen to get incentives that spur salesmen's desire to reach the target. - Conduct a survey directly to the market to test whether there is one product that is only sold in an area and is not sold in other regions, so there needs to be a review to sell products that are in line with market segmentation. In addition, this case study also carries the prediction of revenue made with revenue datasets that have been grouped by province per month. The total data collected which previously amounted to 6 million of rows was compressed to around 800 rows because it had been aggregated. The data is time-series data that has been aggregated per province and per month, the data consists of the code and name of the province, period per month, Revenue Rupiah, Quantity Revenue, Rupiah Return, Quantity Return, and number of salesmen. The dataset is then imported into rapid miner, and prediction modeling is performed. Because the data is in the form of time series data, the windowing method is carried out, which is to map period attributes into features. In this case study, the windowing parameter is 6, so in other words revenue prediction is the result of revenues 6 months before. After the windowing process, sliding window validation is carried out with the following parameters: training window width: 5, training window step: 1, test window width: 5, horizon: 1. The meaning of the horizon here means that what will be predicted is 1 period in the future (1 month ahead). The algorithm method used is neural network, neural network is applied to the model, and its performance is calculated by forecasting performance elements. The process of predictive modeling is shown in the figure below Figure 4. Revenue Prediction Model
  • 9. Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network http://www.iaeme.com/IJMET/index.asp 291 editor@iaeme.com The results of the modeling are as follows: Figure 5. Revenue Prediction Model Performance From the picture above it was concluded, the prediction results from the previous 6 months to predict the next 1 month resulted in an average accuracy of 0.718 (71.8%). The revenue predictions that have been made will be plotted so that the trend movement can be seen whether follow the actual data or not. The figure below is a plot between actual revenue and revenue prediction. Actual Revenue is shown by a red line; Revenue Prediction is indicated by a blue line. Figure 6. Revenue Prediction vs Actual Revenue Trend line It is expected that after the report is formed using Business Intelligence, stakeholders can easily capture information through a friendlier visualization than using a conventional system, Excel, with visualization based on existing data that can provide insight and be able to be taken into consideration for decision making based on data. With the support of reporting made with Business Intelligence reliable reports will be generated that can be analyzed by company leaders so that decision making becomes faster and more effective. The revenue prediction feature of the new system design can be used by companies to take preventive actions or better campaign plans to produce better turnover for the company. With the construction of a data warehouse system along with revenue predictions, it is proven that it
  • 10. Yulyardo and Sani M. Isa http://www.iaeme.com/IJMET/index.asp 292 editor@iaeme.com can make decision support systems and reports faster, more precise, accurate and easier for users. 5. CONCLUSION From the research conducted company should do some approaches, such as data analysis, design data warehouse, and then implement Business Intelligence to make reports become easier. The reports can help stakeholders to know the progress, to make decision and strategy, and to evaluate the sales performance. By using Qlik Sense BI Tools and Predictive Data Mining using Rapidminer Tools, managers can see the revenue distribution map, sales performance per area per period, and sales dashboard include top 20 best-selling product, top 10 best performing salesman, and etc. The report of revenue distribution map can help managers to know which branch conform the target and to analyze which branch needs attention to increase performance. The sales performance report shows the performance of each sales so that managers can evaluate their job. And last, sales dashboard can help managers to see the best performance. Overall, this reports can help company to companies to take preventive actions or better campaign plans to produce better turnover for the company. With the construction of a data warehouse system along with revenue predictions, it is proven that it can make decision support systems and reports faster, more precise, accurate and easier for users. REFERENCES [1] Negash, Solomon. 2004. Business Intelligence. Communications of the Association for Information Systems, 13, 177-195. [2] Olszak, Celina M. & Ewa Ziemba. 2007. Approach to Building and Implementing Business Intelligence Systems. Interdisciplinary Journal of Information, Knowledge, and Management, 2, 135-148. [3] Hawking, Paul & Carmine Sellito. 2010. Business Intelligence (BI) Critical Success Factors. Association for Information Systems AIS Electronic Library (AISel), 4, accessed from: http://aisel.aisnet.org/acis2010/4 [4] Joshi, A., Pandey, N., Chawla, R., & Patil, P. (2015). Use of data mining techniques to improve the effectiveness of sales and marketing. International Journal of Computer Science and Mobile Computing (IJCSMC), 4, 81-87. [5] Katkar, V., Gangopadhyay, S. P., Rathod, S., & Shetty, A. (2015, January). Sales forecasting using data warehouse and Naïve Bayesian classifier. In Pervasive Computing (ICPC), 2015 International Conference on (pp. 1-6). IEEE. [6] Vhatkar, S., & Dias, J. (2016). Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model. Procedia Computer Science, 79, 238-243. [7] Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Canada: Wiley Publishing, Inc. [8] Kimball, R., & Ross, M. (2010). The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence. (First ed.). Canada: Wiley Publishing, Inc. [9] Parmar, A., Mistree, K., & Sompura, M. (2017). Machine Learning Techniques For Rainfall Prediction: A Review. In Conference: 2017 International Conference on Innovations in information Embedded and Communication Systems (ICIIECS).
  • 11. Predictive Business Intelligence: Consumer Goods Sales Forecasting Using Artificial Neural Network http://www.iaeme.com/IJMET/index.asp 293 editor@iaeme.com [10] Chandwani, V., Agrawal, V., & Nagar, R. (2015). Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks. Expert Systems with Applications, 42(2), 885-893. [11] Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352-1372 [12] Sari, N. R., Mahmudy, W. F., & Wibawa, A. P. (2016). Backpropagation on Neural Network Method for Inflation Rate Forecasting in Indonesia. Int. J. Soft Comput. Its Appl. [13] Chen, J. C., & Narala, N. H. R. (2017). Forecasting Currency Exchange Rates via Feedforward Backpropagation Neural Network. [14] Wang, J., & Wang, J. (2015). Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing, 156, 68-78 [15] Wang, L., Zeng, Y., & Chen, T. (2015). Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Systems with Applications, 42(2), 855-863. [16] Bisoyi, N., Gupta, H., Padhy, N. P., & Chakrapani, G. J. (2018). Prediction of daily sediment discharge using a back propagation neural network training algorithm: A case study of the Narmada River, India. International Journal of Sediment Research.