Combination of Backward and Forward Approaches for Future Prediction by Business Intelligence Tools

Corresponding Author: Mohammad Najjartabar Bisheh Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, USA Email: Mnajjartabar@ksu.edu Abstract: Business intelligence tools now play an important role in data collection, processing and analyses of organizations. Managers may use outputs of such tools and make improved decisions. To provide an even better situation for managers, business intelligence tools may be used to predict future and display present and past statuses of the organization. Available business intelligence tools predict future based on historical data, i.e., they are said to enjoy backward approaches. In this study, it was tried to change extrapolation of historical trends according to our expectations of future events so that forward approaches could be employed for prediction purposes.

Introduction
Business intelligence is the ability to collect, process and cumulate organizational information so that all staff in every level could access what they need, hence no competitive risk may be observed (Bahrami et al., 2012). Conventional tools are well capable of analyzing every aspect of the organization; however, all they process is historical data (events happened by now) which enables the organization to more accurately review its right or wrong decisions in the past. Yet, the past cannot be changed. Under these circumstances, managers need to predict future, control upcoming events and proceed in accordance with their goals. To make this possible, most manufacturers of business intelligence tools use predictive analyses to recognize variation patterns of indices and predict customers' future (Ranjan, 2009).
Future prediction of different parameters such as demand, price, failure rate, or even weights for strategies have been used in different context such as competition and game problems (Esmaeili et al., 2015;Mohamadi and Bahrini, 2020), complex systems (Majlesinasab et al., 2019a;2019b;2019c), supply chain management (Najjartabar-Bishehet al., 2018;Najjartabar et al., 2016;Soleimanynanadegany and Tolooie, 2015;Tolooie and Soleimanynanadegany, 2005;Tolooie et al., 2020;Zahraee et al., 2020), crowd sourcing and human computer interaction (Saremi et al., 2020a;2020b;Urbaczek et al., 2020), healthcare systems (Fanoodi et al., 2019;Jahantigh et al., 2017;Maleki et al., 2014;Malmir et al., 2016;Sepehri et al., 2013), quantitative strategy planning (Khanmohammadi et al., 2019;Montazeri and Najjartabar-Bisheh, 2017) and quality control (Daryabari et al., 2019). Two general types of models have been proposed for future prediction (Van den Poel and Buckinx, 2005). The first type, based on historical data, tries to recognize data variation patterns and extend it to predict the future. The second type models enjoy a qualitative approach according to which expert ideas are used for prediction. Each model has shown to have several advantages and defects. Quantitative models reflect clearly prior patterns and trends; however, they are incapable of considering events of no historical background. On the other hand, while qualitative models focus less on prior events (since they are thoroughly based on expert judgments) they may be used to estimate new events and their impact on predictions.
It is concluded that one may combine both quantitative and qualitative models and enjoy their advantages at the same time; besides, their defects may be solved when these models are used simultaneously. The present study suggests a method to combine these approaches. Quantitative techniques were used in the first phase and then the obtained outputs were subject to review through forward approaches and expert ideas. Therefore, inaccuracy of qualitative methods could be solved and quantitative techniques are no longer in the state of without unknowingness.

Literature Review
Many applications for business intelligence have been defined so far and their tools have been used in different categories (from both theoretical and practical points of view). Bahrami et al. (2012) established a proper procedure to employ business intelligence in market management. From his viewpoint, business intelligence capabilities are: Decision support, statistical analysis, future prediction and data analysis. Tutunea and Rus (2012) suggested use of business intelligence in Small and Medium Environments (SME). They believed that making right decisions at the proper time and consequently adopting proper procedures would be used as output of a business intelligence system creating competitive advantages for SMEs. Nofal and Yusof (2013) proposed a model to combine Enterprise Resource Planning (ERP) and business intelligence. They believed ERP may be used as a strategic tool for uniform planning and management of an organization. Moreover, business intelligence tools are well capable of supporting management decisions. Through combining these two models, one may enjoy their advantages simultaneously. It was claimed that this action improved data analysis and business trends predictions. Foshay and Kuziemsky (2014) studied the feasibility of business intelligence in hygiene issues. Lasi (2013) provided a path to apply business intelligence in manufacturing environments. McBride (2014) carried out a case study on the use of business intelligence in a production chain. This study showed the importance of business intelligence in the management of the production chain. He expressed that applicability of business intelligence is not only for examination of current situation, but also for future prediction. He mentioned that by application of business intelligence in predictions through extrapolation of existing data, report making would no longer be their only use and the future may be revealed. Regression model is applied for prediction in McBride's method. Chang (2014) introduced a new concept of business intelligence as business intelligence service in cloud environment. From his point of view, financial issues of an organization are caused by the lack of sufficient and accurate information regarding upcoming risks. Using business intelligence in cloud environment, one may take advantage of cloud calculus and complex models for modelling to increase accuracy in risk analysis and predictions. Chang developed a model to help users in reviewing their actions as well as predicting the future.
It is crystal clear that many applications for business intelligence have been defined and its tools have been used in various fields. Business intelligence tools have shown an undeniable role in providing information for future prediction. It would be meaningful to conclude that an essential use of these tools is future prediction or predictive analysis.

How to Predict Future by Prevailing Business Intelligence Tools?
Some dominant tools of business intelligence and their approach for prediction of future are discussed in this section. It is worth mentioning that all tools have strategies for predictive analysis; however, the prediction procedure may differ from one tool to another. In Tableau, future prediction is carried out via historical data. This software tries to find past-concerning data and predict future by extending them. Tableau has eight equations for data fitting and automatically selects an equation which is closer to the actual data at each prediction step so that the quality is improved. Besides, the user is given the opportunity to select the prediction equation manually (Tableau Software, 2019). Alteryx is another tool providing a platform to process a great deal of data in conjunction with predictive analysis. Future prediction is performed according to the historical data by means of the following prediction models: Regression, decision tree and the forest method.
Prediction by Qlik View is performed by What-if analysis. In other words, changing effects of independent variables on dependent ones are evaluated. Historical data are not sufficient in this tool and the user may predict independent variables according to the information they have in mind and estimate their effect on dependent variables (IBM, 2011). Microsoft provides users with What-if order (similar to Qlik); in addition, users may find out what changes are needed to be made to reach a certain goal through the Goal Seeking option.
Similarly, IBM tool predicts the future according to historical information. After collection and consolidation of data, future values are predicted by model fitting and determination of data change pattern. Decision tree algorithms, linear regression, kth average, ABC analysis and balanced score table are employed in SAS tool for predictions (SAS Institute Inc., 2015). Independent variables are predicted according to dependent ones and the prediction model is selected automatically based on data trends (Thompson and Duling, 2010). TIBCO Spotfire provides an environment for predictive analysis based on statistical methods. There is no need for statistical programming and the required output may be imported from other statistical tools such as SAS or MATLAB. Information Builder employs historical data in the prediction step and predicts the future by regression or decision tree.
Most business intelligence tools provide methods for future prediction; however, there have been few scientific studies on application of prediction in this field. Some prediction methods in prevailing tools were discussed so far. All existing tools of business intelligence predict future according to historical data. Actually, they try to find trends and patterns regarding the performance of an organization in the past and extend it to future periods. In the present work, authors attempted to develop a different approach by combining backward analyses with forward analyses.

Prediction Model
Two general types of models have been proposed for future prediction. The first type employs the knowledge of past and present; data variation patterns, processes, algorithms and change paths are determined and the future is predicted accordingly. Econometric techniques, trend lines (linear, exponential, polynomial, logarithmic, power, etc.) and time series (regression models, moving average, mixed moving average, etc.) are some of the methods applied in this category. These methods are sometimes called as without unknowingness due to the fact that merely historical data concerning past events are taken into account; in fact, they are incapable of assessing effects of new events in the future on existing trends. Quantitative models are never subject to be surprised, fairly inexpensive and highly inaccurate due to assuming the past continuing within the future.
On the other hand, the second type has a qualitative approach. It is believed that a given index in any scientific field depends on many factors which are neither readily recognizable nor follow a comprehensive model. On this ground, predictions based on quantitative methods may not yield acceptable answers for corresponding scientific fields (Taghva and Alizadeh, 2005). Human mind and knowledge about his surrounding environment are the basis of all proposed techniques for the second type of models. Panel, consensus, brainstorming, Delphi and scenario are some of the known techniques of this approach. Experts in a given field consider available historical data and suggest future events based on their understandings and personal ideas. It is necessary to mention that no accurate model is used in this approach, but intuition forms its structure which is known to be the main defect of the qualitative method (Jenkins and Smith, 1994). Plural thoughts are believed to choose a rather correct path; in other words, the more people suggest their ideas, the more reliable the output would be (Taghva and Alizadeh, 2005).

Combination of Qualitative and Quantitative Approaches
Each approach has been separately discussed so far and it was mentioned that they may have series of advantages and defects. Quantitative approaches vividly reflect past trends and patterns, while they are incapable of considering events with no historical background. On the other hand, qualitative methods are less accurate in reflecting past events (because they are only based on personal judgments); however, the effects of unprecedented events on future trends may be evaluated through such methods.
Therefore, one may combine qualitative and quantitative approaches and enjoy their advantages at the same time. In other words, simultaneous application of these approaches may cover their defects and faults. A procedure is hereby introduced through which these approaches may become one. Quantitative approaches were employed in the first phase and then the outputs were reviewed based on futuristic and expert ideas. Hence, inaccuracy of qualitative methods might be solved and quantitative approaches would no longer be without unknowingness. In other words, the proposed model helps to change extrapolation of historical trends according to existing expectations on future events. These events may include any type of action in technology, politics, society, economy and other affecting changes on trends. Phases of the proposed model are explained as follows.

Phase I: Quantitative Prediction
In the first step, trend lines (linear, exponential, polynomial, power, etc.) or time series (regression models, moving average, mixed moving average, etc.) of historical data were fitted and the closest model to the historical data was selected. Model selection strongly depends on the organization business and the nature of corresponding data. It is clear that there are so many methods in hand for this purpose while testing them all could be quite costly and time-consuming. Thus, several models, expected to show better fits according to the nature of organizational data, were selected (a primarily filtration) and then the best model among the selected models with the least difference from data points was determined.

Phase II: Qualitative Prediction
As mentioned before, qualitative methods are based on expert ideas, for collection and analyses of which, taking the following steps is necessary:


First step: Appointing a supervisor to carry out the prediction process  Second step: Determination of the number of individuals contributing to the prediction process  Third step: Determination of a certain index to be predicted; there is no need for the organization to employ these models for prediction of all indices, but only important and decisive ones  Fourth step: Determination of a certain period for prediction; naturally, index value may vary within future days, months or years, then, a certain time is required to begin the prediction process  Fifth step: Collecting individuals' ideas involved in the prediction process; authors accomplished this step inspiring from a common method in maintenance and repair industry called Failure Mode and Effects Analysis (FMEA). This method has been widely used in all fields of industry since 2000

FMEA Method
FMEA tries to find factors resulting in malfunction of machineries or equipment in future. Being a preventive method, FMEA helps to remove potential failures in products or process duration before they occur. There three major factors in FMEA to consider:


Occurrence: Probability i.e., failure counts to process operation times ratio  Severity: Evaluation of failure consequences (in case of occurrence). Severity is an evolution scale reflecting a failure in case of occurrence  Detection: Probability of failure detection prior to observation of its consequences. Detection is the ability to find the reason and mechanism of failures Probable failures and problems for a given instrument were initially listed. The three indices were given a value ranging from 1 to 10 for each problem. By multiplication of these indices, Risk Probability Number (RNP) is obtained: Priority in investigation goes to problems with higher RPN values since they are more likely to happen, hence, they would bring about harsh damages. An acceptable level of RPN has to be set in every FMEA project which is defined according to the organization. RPN = 125 (Severity × Occurrence × Detection), for instance, could be an RPN level for a hypothetical FMEA project. Accordingly, RPN>125 events have to be prioritized for proper modifying procedures. Either Pareto diagram or zoned diagram may be used in order to determine cases with more priority.

Extension of FMEA Model for Prediction in Business Intelligence Tools
FMEA model was extended and employed for prediction. RPN method may be appropriate to present expert ideas on the prediction curve. As mentioned before, certain index and prediction period were given to individuals involved in the process. Each person suggested a list of probable events which in case of happening would cause some changes to quantitative predictions of that index. This step may be performed via any qualitative method such as consensus, Delphi, brainstorm, etc. According to these methods, people involved in the prediction process were to suggest a list of probable future events. However, past and present events affecting the studied index were also taken into consideration.
After preparation of the lists, they were sent to the prediction process supervisor. He integrated the ideas and removed similar ones. Then, the supervisor propounded each idea and questioned everyone regarding a certain event (which is proposed by one or more individuals). It is best to discuss each event separately so that people would not get involved in so many cases and their mutual influences. Similar to FMEA, experts were required to set a probability value for occurrence, severity and detection of a certain event. Consequences of failure and damage are always studied in FMEA (negative effects), while business intelligence (the proposed model) identifies opportunities and threats for an organization (Vinekar et al., 2009). Hence, in order to use FMEA method in business intelligence tools, one has to consider all the events (imposing both positive and negative events). The individuals filled out a form designed in this system according to their understandings (Table 1). The form header shows the given index and prediction time period. For each event, there was one form to be filled.
Severity, Occurrence and detection are given the corresponding values in FMEA method. These concepts may be redefined in business intelligence predictions as follows: 1. Severity: In case of occurrence of an event, how effectively it alters the index and quantitative prediction 2. Occurrence: How probable the event is or in case of occurrence, how probable its effectiveness is 3. Detection: To what extend it is possible to detect the consequences; in other words, how clear the consequences of probable events are  Users might assign a score ranging from 1 to 10 to the three indices (higher values imply higher chance of occurrence, more severity, or easier detection). In addition to the three indices, the effectiveness of an event is also necessary to be realized. Effectiveness illustrates negative or positive influence of an event on the studied index (it consists of two positive and negative characters). Effectiveness is not used in FMEA due to the fact that all events investigated in that area have negative consequences and cause damage, while in business intelligence, different events may lead to either positive or negative actions. By multiplication of the three indices in FMEA, Risk Priority Number (RPN) may be obtained. However, the multiplication result in the proposed model was named PN because of simultaneous consideration of positive and negative events. PN values were automatically calculated by the system which equal occurrence × severity × effect bias (the detection effect may be considered differently that is discussed in the following sections).

A Method to Achieve Consensus in Expert Ideas
It was already mentioned that contribution of more individuals in the prediction process leads to more reliable results. Hence, it is recommended that the prediction process is carried out through a team work. Under these circumstances, the displayed form in Table  1 is filled out by every individual in the team. In the meantime, another method is needed to integrate ideas i.e., all ideas have to be summarized and the outcome is illustrated in the prediction result. Importance of each individual in the team has to be manifested by a weight factor determined by the senior manager; there are two probable situations: 1. The senior manager thinks that ideas of all individuals are equally important. In this case, knowing the number of contributors, the weight factor of each individual may be obtained by the following formula: ' 1 /

Individual s weight factor
Number of contributors  2. The senior manager thinks that ideas are of different importance and some ideas are preferred to others. In this case, the senior manager is required to assign a weight factor (<1) to contributors so that the total value is 1 After determining the weight factor, the ideas have to be integrated. A PN value is calculated for each individual and event; then, all PN values have to be integrated, carried out when they are sent to the supervisor using the following formula.
It was already mentioned that contribution of more individuals in the prediction process leads to more reliable results. Hence, it is recommended that the prediction process is carried out through a team work. Under these circumstances, the displayed form in Table 1 is filled out by every individual in the team. In the meantime, another method is needed to integrate ideas i.e., all ideas have to be summarized and the outcome is illustrated in the prediction result. Importance of each individual in the team has to be manifested by a weight factor determined by the senior manager; there are two probable situations: 1. The senior manager thinks that ideas of all individuals are equally important. In this case, knowing the number of contributors, the weight factor of each individual may be obtained by the following formula: ' 1 /

Individual s weight factor
Number of contributors  2. The senior manager thinks that ideas are of different importance and some ideas are preferred to others. In this case, the senior manager is required to assign a weight factor (<1) to contributors so that the total value is 1 After determining the weight factor, the ideas have to be integrated. A PN value is calculated for each individual and event; then, all PN values have to be integrated, carried out when they are sent to the supervisor using the following formula: Prediction of effectiveness is carried out readily in case of events in the first category. Historical information about products, clients, stores, etc. is available and the effectiveness is accordingly determined. Let us take reduction in sale of a specific product (due to a governmental order, increase in costs of raw materials, change of buyers' taste, etc.) as an example. Historical sale data of this product are available. It is assumed that 10% of the total sale of the organization was due to this product. As mentioned in the previous section, individuals assign values of occurrence, severity and detection. In this example, contributors answer the following questions: Therefore, the Sale of this product, quantitatively predicted to be 1000, increased by 1048. There are many complexities regarding the prediction of second-category events due to the absence of corresponding historical data. In this situation, users are required to determine share of events. When the organization is planning to introduce a new product, for example, it has to illuminate its share in the market, thus, the effect may be shown on the prediction curve.

Using the Detection Measure of Occurred Events
So far, PN was realized as multiplication of effectiveness probability of an event by its effect order on the studied index. However, the detection of effects was valued by contributors in FMEA method. Occurrence and severity show the probability and effectiveness of a certain event, while the third index implies how easily these effects are recognized. The difference of indices is explained through an example: One predicts Iran-Korea game 2-0. The same person predicts Iran-Angola game 2-0 as well. The probabilities of these results as well as their effectiveness on Iran scores were equal for the predictor. However, the prediction of Iran-Korea game is more difficult because it depends on many factors. So, the probability index or easiness of detection of consequences may be used.
This index shows that whether the predictor clearly observes the event and can estimate its consequences or not. The value of this index is illustrated on the prediction curve by means of another method (1 to 10 values are given to the index; the higher the index, the easier detection of consequences). A main goal of business intelligence tools is to be simply presented so that users may realize the conditions in a short glance. Hence, the detection index is presented so that the user may readily comprehend the consequences.
In order to present the detection index, its value for each event was initially multiplied by the individuals' weight factor and then the total value was calculated. Then, the average value of the index for every event was obtained. A colored spectrum was used to present events on the prediction curve. The individuals had predicted several events according to their thoughts and determined their corresponding effectiveness. However, the prediction of consequences is not entirely simple (detection index is somehow close to 1). In such a case, the prediction curve is illustrated in a pale mode. On the other hand, in case of consensus among contributors on simplicity of estimation of consequences for people, the prediction curve is illustrated in a colorful mode.
Accordingly, when an index is predicted to decrease and the curve is colorful, one may conclude that the studied event is likely to happen and lead to significant consequences, thus, the user has to look for some compromises. But if the diagram is pale, there will be a chance for the index value decrease; though, the consequences will not be vivid.

Conclusion
In the present work, a model simultaneously applied quantitative and qualitative methods in prediction process of business intelligence tools. Quantitative methods properly reflect previous trends and patterns, yet, they are incapable of considering events of no historical background. Qualitative approaches, on the other hand, pay less attention to previous events (due to the fact that they are only based on judgments); however, they may consider new events and assess their consequences. Combining these two methods, one may enjoy their advantages at the same time.
The proposed model suggests predicting future via quantitative approaches and then, modifying the results according to expert ideas. Therefore, events of no historical background may be presented on the prediction curve and the prediction could be considered more close to the reality. Thus, the user of the business intelligence tool may observe variation trends of a certain index within time. Besides, applying quantitative approaches, they may predict future values of the index. Finally, if necessary, prediction may also be carried out through a qualitative procedure and the integrated results may be presented. Machine Learning (ML) models are taking over business prediction these days. However, ML model are still in the category of backward prediction. Combing forward prediction with ML model could be a good idea for future study of this paper.


Analysis of Performance Improvement on PT. Distribusi Air Santri (PT. DAS) Using Business Model Canvas

⎯ Since 1975, the drinking habits of the Indonesian people began to change from being merely boiling water from tap to bottled water (AMDK), thus encourage drinking water producers to continue branch out in market share. In 2013, the market share of AMDK industry was Rp 22.51T and there were around 500 competing AMDK companies. PT. Distribusi Air Santri (PT. DAS) is a company engaged in the distribution of “Air Mineral Santri” brand which located in Surabaya. This company aims to increase the market in Surabaya by 10% or sell 142,000 items / month. So to achive company goals, company need to develop business models and plan business strategies using Business Model Canvas (BMC). In the BMC analysis, an analysis of environmental conditions was carried out using the 7sMcKinsey method, External Scanning and porter strength analysis. And validation is needed so that BMCs are made accurately and precisely with the help of a SWOT analysis .

I. INTRODUCTION
HE Bottled Drinking Industry in Indonesia (AMDK) began to develop since 1973, the Indonesian people began to change the habit of consuming water from boiled water to bottled drinking water (AMDK), which encouraged the producers of bottled drinking water to develop. According to the Indonesian Bottled Drinking Water Association (ASPADIN), in 2013, AMDK's market value was IDR 22.51 trillion and this industry will continue to grow 11.1% every year. This provides an opportunity for drinking water producers to compete in the bottled water market (AMDK). Table 1 explains the assumptions of the needs and potential market of the AMDK in Surabaya. According to DISPENDUKCAPIL, in January 2019 the population of the city of Surabaya reached 3 million people. Meanwhile according to ASPADIN, 60% of Indonesian people consume bottled drinking water. If it is assumed that the price of 1 liter of water is IDR 1,000, the potential market of AMDK in Surabaya is IDR 27,000,000,000 / month. If the price per item is IDR 20,000. So the need for bottled water in Surabaya is 1,350,000 items / month. That is can be seen at Table 2. PT. Distribusi Air Mineral (PT.DAS) is the main distributor of AMDK Air Mineral Santri in Surabaya. AMDK Air Mineral Santri is mineral water that uses natural raw materials taken from Umbulan's water springs, producing a large enough water discharge so that it's guaranteed quality, clarity, freshness and stability of mineral content. Raw materials are processed with Carbon Filter, Sand Filter, then sterilized using Technology Water Treatment (TWT) which is adjusted to the Indonesian National Standard (SNI) with ultraviolet light and ozone (O3).
Sales of AMDK Air Mineral Santri in 2019 are 60,000 items/month, or about 4.5% Air Santri controls the AMDK market in Surabaya. This company plan to increase sales in 2020 by controlling AMDK's market in Surabaya by 10% or selling 142,000 items/month.
The company needs to plan business strategies to achieve company goals, one of the solution is plan a business model that consists of various elements that can describe the strategy, goals, structure, technology, processes, value for customers so that companies can compete well in the business market. The business model helps to describe all sources of competitive advantage possessed by an organization that distinguishes it from company positioning of products in the same industry. In addition, the business model is also a value chain structure , creating value by defining a series of activities ranging from raw materials to raw materials to the final customer, where the specified value is added to the overall activity. Figure 1  alongside with SWOT Analysis. The Business Model Canvas is a business model that develops a business unit and has 9 components, each component can be the first step to determine where the company develops its business model business. An effective way to evaluate company business models is to combine the classic SWOT analysis with the business model canvas. The objectives of the research to develop business models using the Business Model Canvas approach with the help of a SWOT analysis.

II. METHOD
Data collection and processing in this chapter is obtained from the results of field observations and interviews with managers and employees. The results of the company's condition analysis are managed in the form of 7's Mckinsey analysis, external scanning and five forces porter model. 7's Mckinsey is a framework for analyzing how effective an organization is to achieve organizational goals which described through 7 elements namely strategy, structure, system, shared values, skills, style and staff. In the analysis of 7's Mckinsey in this research, the most emphasized thing by the company is to increase product sales and disseminate the brand "AMDK Air Mineral Santri" so that it is better known to the public.    In the external analysis for environmental scanning there are several components contained that function to analyze the condition of the company with external parties, namely natural environment, technology, external stakeholders, socio culture. In addition for external analysis, porter's five force model method is used to analyze companies on developing business strategies in the market. According to Porter, there are 5 things that determine the level of competition and product attractiveness in the Market, namely Threat of New Entrants, Bargaining Power of Suppliers, Bargaining Power of Consumer, Threat of Substitute Products and Rivalry within the Industry.
In the Business Model Canvas analysis at PT. DAS is carried out discussions and interviews with managers and employees from various divisions who act as respondents to define the business model canvas that is currently used by companies. The Business Model Canvas consists of 9 components, there is key partners, key activities, key resources, value proposition, customer relationships, channels, customer segments, cost structure, revenue stream. SWOT analysis is shown by mapping the components of strengths, opportunities, weaknesses and threats of a company. In making a good SWOT analysis, the company must be as objective as possible to assess the company but also through consumer testimonials. SWOT analysis is performed on every element in the Business Model Canvas analysis which aims to perfect the business model owned by the company. For detail can see Figure 2 about method.

A. Analysis of 7's Mckinsey
Analysis of 7's Mckinsey can be seen at Table 3, where explain about definition of a attribute and analysis result about the attribute.

B. Analysis of external scanning
Analysis of external scanning can be seen at Table 4, where explain about definition of a attribute and analysis result about the attribute.

C. Analysis of Porter's five forces model
Analysis of Porter's five forces model can be seen at Table  5, where explain about definition of a attribute and analysis result about the attribute.

1) Analysis of Business Model Canvas
Bussiness Model Canvas can be seen in Figure 3. For explain about it can be read in below. The habit of drinking bottled mineral water, is more practical and guaranteed quality People tend to use foreign products or products that are famous for expensive prices

Bargaining Power of Suppliers
Suppliers have the power to increase and decrease product prices and quality PT. Sidogiri Mandiri Utama is a product supplier.
In maintaining good relations, PT. DAS helps suppliers in the field of marketing and product branding, as well as helping cooperation with consumers in the Surabaya area. Bargaining Power of Consumer Customers want to get quality products at affordable prices.
Customers can get lower prices by collaborating with PT. DAS, a minimum order of product 50 items / shipping, customers will get a bonus Or enter into an MoU agreement with conditions agreed by two parties Threat of Subtitute Products Threats from products that have the same function and / or have better quality and more affordable prices.
Produk lain yang memiliki fungsi yang sama dengan air mineral adalah air kelapa, teh herbal, air lemon atau infused water. Saat ini ada beberapa merk yang memproduksi dan memasarkan produk dengan bahan tersebut, karena lebih memiliki rasa. Namun kebutuhan akan air mineral tidak mudah tergantikan seutuhnya dengan produk lain sehingga penjualan air minum masih lebih unggul daripada produk minuman lain Competitive Rivalry within the Industry Competition that occurs between one company and another to become a company that controls the market.
According to ASPADIN (there are already 700 registered companies of Bottled Drinking Water in Indonesia 1. Key Partners : Other parties who work with companies to achieve company goals 2. Key Activities : The main activities undertaken by the company are key to the success of the company 3. Key Resources : Resources owned by the company or elements in the company that play a role in achieving company goals. 4. Value Proposition The proportion of the value of a product that is the reason consumers use the product (quality, price, brand, design, service, guarantee, ease of access, etc.). 5. Customer Relationships : Steps or programs owned by the company to establish good relationships with customers to retain old customers and get new customers. 6. Channels : The media used to communicate, convey values and establish relationships with customers.
7. Customer Segments : Mapping customer segments or grouping customers according to the value offered by the company. 8. Cost Structure : Details of all costs used by the company for company activities. All costs to be incurred by the company have been calculated beforehand. 9. Revenue Stream : Revenues earned by the company from the company's key activities.

2) Analysis SWOT
For the analysis of SWOT, each variable of business model canvas is being analyze with SWOT criteria that is strength, weakness, opportunity and threat. Analysis SWOT can see in Table 6.

3) SWOT Matrix Analysis
In this step, analyze the power between internal and external component of company, strength and weakness variable for internal, while opportunity and threat variable for external. As describe at Table 7 and Table 8.

4) New Business Model Canvas
New Bussiness Model Canvas can be seen in Table 9. This section explain about new business model canvas at this case.

IV. CONCLUSION
Utilizing partners to expand the network to various new partners and collaborate with other companies. Adding other sources of income aside from selling Air Mineral Santri. Routinely provide punishment and reward to employees to increase employee motivation. The company should focus on attracting and selling products to agents and retailers. Distribute small consumer purchases to agents and retailers. Table 7. Internal SWOT Matrix Analysis Table 8. External SWOT Matrix Analysis Table 9. New business model canvas


A Novel Deceptive Jamming Approach for Hiding Actual Target and Generating False Targets

Deceptive jamming is a popular electronic countermeasure (ECM) technique that generates false targets to confuse opponent surveillance radars. This work presents a novel approach for hiding the actual target while producing multiple false targets at the same time against frequency diverse array (FDA) radar. For this purpose, the modified FDA radar is assumed to be mounted on the actual aircraft. It intercepts the opponent’s radar signals and transmits back to place nulls in the radiation pattern at the desired range and direction to exploit FDA radar’s range-dependent pattern nulling capability. The proposed deceptive jammer produces delayed versions of the intercepted signals to create false targets with multiple ranges to confuse the opponent’s radar system. The novel mathematical model is proposed whose effectiveness is verified through several simulation results for different numbers of ranges, directions, and antenna elements.

Introduction
Modern warfare is information and electronic based, which is evidently replacing the conventional platforms [1]. It requires no further confrontation between soldier to soldier, trench to trench, platoon to platoon, and platform to platform; rather, it relies on nonlinear and nonsymmetric war between system to system [2]. As the 21st century unfolds, the concept of electronic information warfare has become center of the gravity in which the radar system constitutes the key components that provide early warning capabilities [3]. Radar works as an early warning system and bestows extra time space to react against imminent threat. The principle of the radar to detect the desired target is transmitting radio waves towards the target and calculates round-trip time of the reflected waves after striking with the target [3]. To counter enemy radars, ECM techniques (also known as radar jamming techniques) were introduced [4]. These techniques are used to deny the important information about the desired aircrafts (direction of arrival, range, velocity, etc.) that any foe radar seeks [4]. In the presence of strong electronic counter measure (radar jammers) systems, it is difficult to detect the target aircraft; but there are many electronic countercountermeasure (ECCM) techniques available in literature to counter radar jammer and to locate correctly the target [5,6]. Some ECCM (which are also known as antijamming) systems steer nulls towards strong interfering signals to secure own functioning.
Mainly, there are two types of radar jamming techniques: mechanical methods (passive jamming) and electrical methods (active jamming) [3]. In mechanical methods, physical means like chaffs, decoys, corner reflectors, and stealth are used in securing the desired aircrafts' flights and to deceive enemy radars [4]. These physical means of radar jamming are traditional techniques which are not so effective. Electrical methods of radar jamming are effective and still in use [1,2]. These methods can be categorized in two types: barrage jamming (also known as noise jamming) and deceptive jamming [4]. Barrage jamming methods use radar jammers to put huge powers across the desired spectrum of the frequencies which blankets the radar's display to interrupt its normal functioning [7]. There are two main reasons of the ineffectiveness of this type which is huge power losses for longer periods of times, and even it cannot cover the entire frequency spectrum at the same time [8]. The deceptive jamming mode is used to deceive the enemy radars by showing them multiple very similar fake targets with different aircraft attitudes (range, direction, velocity, acceleration, etc.) [9].
This paper focuses only on the deception jamming, because this is the most effective way to secure the flight of the desired aircraft from the enemy radars by showing congruent false targets [10]. Therefore, implementing effective and efficient deceptive jamming (DJ) techniques has become a hotspot area of research in radar electronic countermeasures [11,12]. Many methods have been developed in the modern literature [13][14][15]. In pursuit of deceptive radar jamming, the simplest way to generate multiple false targets is to hold enemy radar signals and after doing time-modulation transmit those signals back to the enemy radar [16]. When the enemy radar receives these signals, it will perceive multiple false targets with different ranges, but with the same direction along with the actual target. A modest contribution has been made using target pose and motion information to generate false targets in [17]. To deceive the opponent radar with a number of fake targets, another effort has been made in [18] using micromotion characteristics, but both have the same issue of computation complexity.
Multiple false targets also have been achieved using electromagnetic properties (EM model modulation) and translation modulation in [19]. By exploiting the concept of sub-Nyquist sampling theorem, a series of multiple fake targets have proposed in [20,21]. Another remarkable approach has been defended to produce multiple false targets using product modulation in which an offline deceptive signal template is produced and then multiplied with enemy radar signal in [22]. Interrupted-sampling repeater jamming (ISRJ) establishes a novel approach to generate deception jamming by allowing the single radar antenna jammer to sample periodically and iterating a fraction of the intercepted enemy radar signal [23]. Inappropriately, high complexity and large computation is the main drawback of inefficiency of the above deceptive techniques in the field of electronic countermeasures. Further, these techniques are also unable to hide real target.
A recent effort has been exercised to achieve the goal by adding escort-free flight jammer drone ahead of the actual aircraft based on periodic the 0-π phase modulation which neutralizes the effectiveness of the enemy radar by displaying its multiple verisimilar false targets [24]. The escort-free flight jammer intercepts enemy radar signals and after doing phase modulation in the periodic 0-π sequence, these signals are retransmitted towards the actual target, whereby these are scattered towards the enemy radar and present multiple false targets with different ranges [24], but it considers the scenario where a separate escorting drone jammer is required which is not feasible in most practical situations.
Against to the only angle-dependent beam scanning techniques, FDA is an efficient beam scanning array used for phased array radars which has recently got tremendous attention in literature due to its greater achievement of wide angle coverage [25][26][27][28]. The radiation pattern of the phase array radar (PAR) depends only upon the direction while FDA radiation pattern depends upon the direction and range, due to its diversity in frequency across the array elements. Hence, FDA radiation pattern is capable of null steering to the particular range and direction. FDA is implemented by applying small increment in frequencies across array elements to achieve range angle-dependent beam scanning transmission [29,30]. Hence, it enables beam scanning without need of any phase shifters and physical steering [31][32][33].
A good effort in field of deceptive jamming is explored in [34] which utilizes frequency diverse array. It produces multiple fake targets at different distances across the slant range but at the same azimuthal range aligned with the actual target. The technique is unable to draw false targets at different azimuthal ranges other than azimuthal range of the actual target. A novel approach in the field of deceptive jamming has been introduced in [35] with wave scattering using FDA for space-borne synthetic aperture radar (SAR). This approach considers the scenario where we offer deceptive jamming to the opponent radar for securing our valuable targets in its own territory with the help of placing deceptive reflectors. But this technique provides no solution to tackle the opponent radar operating from its own territory without the help of any ground-based situated wave scattering reflectors. Further, this method is also unable to hide its own target from the enemy foresight.
Although a modest effort has been made in [36] to introduce the deceptive jamming approach through frequency diversity, there are a few shortcomings in the proposed technique. These include (a) that deceptive jammer should be synchronized/attached or working in collaboration with a friendly GPS satellite system, (b) deceptive jammer must have prior knowledge of the location in space of the opponent radar, (c) the method is slow because it is using FFT and IFFT to convert signals from time domain to frequency domain and then back to previous domain, which makes it performance slow, and (d) it does not give any solution if the foe radar is also an FDA radar.
Paper in the reference [37] presented a deception jamming method which generates multiple scenes (multiple false targets) using FDA radar antenna, where number of false targets depend on the number of antenna array elements. The technique in reference [38] uses the simplest way to generate nulls towards the desired direction and range in order to suppress the offered range-angle dependent interference jamming, but this technique is unable to offer deceptive jamming to confuse the opponent radar, and it also does not hide its own target from the vision of the opponent radar. Further, as we know that FDA is time, angle, and range-dependent, but in this technique, the time-dependency factor of the FDA radar is diminished by considering it as zero (t = 0), which is not an appropriate for the practical scenarios [38].
Until now, best to the authors' knowledge, available deceptive jamming techniques in the present literature are not dealing with the hiding of the actual target along the generation of false targets using FDA radars. Present literature 2 Wireless Communications and Mobile Computing also does not offer deceptive jamming for the opponent radars which work on FDA radar principle. The proposed study would investigate these limitations in depth and subsequently would present a probable solution against it. The main contribution of the work is summarized below.
(i) This research produces a novel deceptive jamming approach in the field of ECM (ii) The algorithm works against the opponent FDA radar and hides the actual target from it (iii) For this purpose, enemy radar pulse is captured by the target FDA radar, and null is placed at the radar range to hide its own target alongside after its time modulation (iv) It is equally effective to tackle ground-based opponent FDA radar in its own territory (v) The proposed technique efficiently works without help of ground-based wave-scattering reflectors or advance escort-free-drone jammers (vi) The proposed algorithm also confuses the opponent FDA radar by multiple false targets at different userdefined ranges The remaining part of the paper is organized in the following way. Sections 2 and 3 introduce mathematical background of the FDA radar and comparison with existing techniques, respectively. Section 4 depicts the proposed method to secure the flight of the actual aircraft in the enemy territory by neutralizing the dangers of enemy radars, while Section 5 shows the effectiveness and correctness of the proposed techniques via simulations in three dimensions and in two dimensions for four different cases. Finally, conclusion of the paper has been presented in Section 6.

Data Model for the FDA Radar
FDA radar uses small increment in frequency of each element over the antenna array. Radiation pattern of the FDA radar is a function of range, angle, and time [25][26][27][28]. FDA radar implements waveform diversity among the radiating elements which brings more functionality [29,30]. Figure 1 depicts an FDA that consists of uniform linear array (ULA) having n-isotropic radiating elements. The distance d between any two adjacent elements is taken same with the uniform current distribution. The carrier frequency of each element is incremented by a small constant frequency offset [31]. The simplest monochromatic signal is assumed to be transmitted from the nth element of the array, and it can be mathematically expressed as [32] where f n is the frequency of the nth element as f n = f 0 + ðn − 1ÞΔf for n = 1, ⋯:, N. Similarly, f 0 , Δf , and N represent carrier frequency, a small constant frequency increment, and total number of elements, respectively, in the FDA array. When signal of the nth element reaches at a far-field location after time t 0 with range R 1 (reference to the first element in the array) and azimuth direction θ, its radiating beam can be represented as [31] For t 0 = R n /c, (2) can be expressed as where c stands for the speed of light and R n = R 1 − ðn − 1Þdsin ðθÞ shows the distance from the nth element of the array to the target location. The array factor (AF) for FDA can be written as [32] After placing values of f n and R n , (4) becomes as where where ψ 0 stands for 2πf 0 ðt − R 1 /cÞ and ψ 1 stands for ψ 0 + πð N − 1Þ½Δf R 1 /c − ðdf 0 sinθÞ/c−Δf dsinθ/c, while n 2 of the fourth term (n 2 ðΔf d sin θ/cÞ) has been replaced by n in the fourth term ðnðΔf d sin θ/cÞÞ of the last expression of the AF to achieve closed form expression. So, the problem in hand is how to hide actual aircraft target from the effectiveness of the opponent FDA radar along with generation of multiple fake deceptive targets at different ranges in the direction of actual aircraft using the ULA-based FDA radar.

Comparison with Existing Techniques
The three deceptive jamming techniques [34][35][36] are selected for comparison. All of these techniques use frequency diverse arrays to generate multiple false targets. A deceptive jamming technique which is explored in [34] utilizes frequency diverse array. It generates multiple false targets at various distances across the slant range but at the same azimuthal range which aligned with the actual target as shown in the simulation ( Figure 2). A good effort [35] has been made in the field of deception jamming using FDA which generates multiple fake targets at different positions in the slant range and azimuthal range to confuse the opponent radar. Four elements array with adjacent distance of half wavelength were used in FDA with frequency offset 500 kHz, and its simulation of [35] is shown in Figure 3, which reflects actual target at slant range 7500 m and azimuthal range 0 m along with four false targets which are situated at slant ranges  Figure 4 shows the effectiveness of the technique [36] by considering the jammer at the middle of the scene (7500 m, 0 m); this algorithm generates four false targets at different locations. Algorithm assumes FDA array of eight elements with frequency offset 300 kHz. It is evident from Figures 2-4 that although all three techniques [34][35][36] are big achievement in the field of deception jamming using frequency diverse array, none is able to hide actual target alongside generating multiple false targets.

Proposed Method
The key idea behind this research is to interrupt enemy radar signals and then using these signals (after proposed modifications), we hide our object (aircraft) along with generating multiple fake targets. We assumed that the proposed (modified FDA) radar is mounted on the target aircraft, and the opponent radar is placed on the ground as shown in scenario Figure 5. In purpose of hiding its own target aircraft, the interrupted signal of the opponent radar will be transmitted back after desired changes to place null at the range and the direction of the foe radar receiver. In the current scenario, it is assumed that the opponent radar is capable of transmitting and receiving FDA radiation patterns. In order to deceive the enemy radar with multiple fake targets, timedelayed replicas of the received signal will be sent towards the enemy radar. The graphical abstract of the proposed method is shown in Figure 6 4.1. Actual Target Hiding. In this section, the mathematical model is formulated to hide the actual target. Let the radiated signal from the nth element of the enemy FDA radar which is given as where t is the time indexing within the radar pulse width T. We assume that the enemy radar is situated at distance r and at direction θ from the target. The enemy radar transmitted signal from nth element is received at mth element of the target FDA radar that can be expressed as where τ m,n = ½ r − ðn − 1Þd sin θ + dðm − 1Þ sin θ 1/c. Generally, (8) can be given as Hence the received signal can be decomposed after processing through matched filtering with exp * fj2πf n tg as processed below.
After placing values of τ m,n and f n , the expression becomes Last two terms are insignificant; so these, terms are ignored.
For simplicity, outside factor is eliminated. Alternatively, where the first factor of the above expression dictates phase shift caused by the target range, and rest factors reflect phase shifts caused by the direction and different wave paths due to physical displacement diversity of the target and the source array elements. Now, the expression y m,n ′ ′ ′ can be recomposed into these factors in this form.
where a n ðrÞ = exp f−j2πðΔf /cÞðn − 1Þrg, Now, we can transform the above received snapshot expression into vector form as Y s = y 11 , y 12 , ⋯, y 1N , y 21 , where Y s ∈ ℂ N 2 ×1 , bðθÞ ∈ ℂ N×1 , and aðr, θÞ ∈ ℂ N×1 . The superscript T and ⨂ reflect transpose and Kronecker product operators, respectively. The vector aðr, θÞ can be decomposed further in this way where a r ðrÞ ∈ ℂ N×1 and a θ ðθÞ ∈ ℂ N×1 are the range and angular steering vectors, respectively. The ⨀ is called Hadamard product operator which reflects element-wise product between vectors. After applying DOA (direction of arrival) and range algorithms, one can find direction and range of the enemy radar, but this is beyond the scope of our research. Now, in order to hide our target from the enemy radar, we have to transmit back the processed received signal towards the enemy radar carrying the desired information of range and direction of the enemy radar, but without considering the 3 rd factor, which is not part of the deceptive jamming transmission propagation. Hence, the desired signal structure will become as follows: Wireless Communications and Mobile Computing where θ 0 &R 0 are direction and range of the enemy radar, respectively. We can simplify it further in this way.
Now, first we will verify the above result using simpler way, and then we will place null in the desired radiation pattern at a certain direction and range to cover our target. The generalized phase difference between any two elements of the FDA radar is found as In (22) the 3 rd term which is insignificant, it can be ignored.
The AF at range R 1 and direction θ from the radar can be expressed as It is considered that the enemy radar is situated at range R 0 and angle θ 0 from the target. Then, we assume that the interrupted waveform with carrier frequency f 0 can be expressed as below.
Now, this is the desired expression, and it is the same equation that we have concluded before. Hence, the expression of the derived AF is same to the signal which is meant for transmission towards the enemy radar. Using this AF, a null will be placed at desired range and direction to hide its own target from the vision of the enemy radar. At the end of this section, the above AF will be factorized in order to find weights for placing null at R 0 &θ 0 . Now, we will explain how to generate null at R 0 by considering the simplest case. From the above expression, it is evident that the interelement phase difference is the same for the whole array between all adjacent elements; so, we can rewrite the above expression of the phase difference in the following way. After getting knowledge of above expressions, AF of the diverse frequency array can be modeled in the following way.
where z = exp ðjψÞ, ψ = α + β + γ, α = 2πf 0 d sin θ 0 /c, and γ = −2πΔf R 0 /c. Extra term β was added into the AF to get scanning capabilities. In standard practices, usually, β is added in the desired AF to steer the radiation patter of the desired communication.
The above expression is the simplest form of the AF for the FDA antenna arrays, which has fix the main beam direction and null directions. Steering of the beam pattern for such an array is not possible. In other words, to get control over the nulls of the radiation pattern, we need to plug in and update weights of the expression. Now, to steer the beam pattern (main beam and nulls) of the frequency diverse array, appropriate weights are necessary. Further, above AF must be put to equal to zero for the calculation of the appropriate weights to steer the beam pattern towards the desired directions.
In determining of the weights, we considered the simplest case to avoid complexity of the proposed method. To find weights of the desired radiation pattern, we will follow this procedure.
After applying these weights into the AF, we will be able to steer the null towards its desired direction θ 0 and range R 0 . By following the above mechanism, our proposed method is capable of camouflaging its own target from the lethalness of the enemy radar.

Displaying Multiple Fake Targets.
In second part of the proposed method, we will display multiple false targets to the enemy radar. For the purpose of multiple fake deceptive targets, we will process the received signal with two steps: first, the received signal will be time modulated with appropriate delays, and then power will be maximized and transmitted towards the enemy radar. We assume that the signal transmitted from the nth element of the enemy radar and received at the mth element of the false target generator (FTG) which is located at target that is 7 Wireless Communications and Mobile Computing where τ j,m,n = τ j − τ j,n + τ j,m , We suppose that the locally generated adjustable oscillator frequency is f FTG ; the receiving and processing time delay of the enemy radar signal is τ FTG . Thus, the received signal can be down converted as follows.
As we know that our FTG is working on the principle of the frequency diverse array radar, so we can reflect that f FTG = f n . After this change, the expression can be simplified in this way.
In order to generate multiple fake targets, signal will pass through the process of time modulation. Different time delays are made as follows.
Δτ ft,i = 2Δr ft,i c , i = 1, 2, ⋯ ⋯ , k = number of false targets, where Δr ft,i = r ft,i − r j or r ft,i = r j + Δr ft,i , while Δτ ft,i , Δr ft,i , r ft,i , and r j represent new deceptive time delay increment, deceptive range increment relative to FTG, range of false targets relative to enemy radar, and reference range between enemy radar and jammer, respectively. Hence, the new updated signal will become as follows.
Before the transmission of the signal towards the enemy radar, it must undergo carrier modulation with locally generated adjustable carrier frequency. After modulation, signal will be thrown to the enemy radar.
The signal received by the enemy radar is After passing this signal through the matched filter, we get Now, place values of τ j,m,n and Δτ ft,i , and the received signal will become as As we know that f n = f 0 + ðn − 1ÞΔf , = exp j2π Terms I and II will be taken outside, while terms VI, VIII, and IX will be ignored due to their insignificance.
where A φ = exp fj2πf 0 τ FTG g × exp fj4πðf 0 r j /cÞg represents the phase change due to the enemy radar to jammer reference distance and time due to signal interception and processing time delay. For simplicity, we can ignore A φ .
Now, we can deform above result into two factors: direction and range.
y ft,i,m,n = exp −j4π For the sake of simplicity, we can ignore the 1 st factor which represents radiation pattern due to direction, and we consider only the 2 nd factor here which is our current area of discussion.
Now, we have these three terms which are range-dependent and playing important role of determining the radiation pattern of the desired signal. It is evident that first and second terms are representing fake target ranges while third term is representing actual target range. Third term is the most insignificant term as relative to other contributing terms, and due to this reason, its effect will be overcome by other terms, or it can be simply ignored.
y ft,i,m,n = exp −j4π 2f 0 r ft,i c Hence, the enemy radar will observe the fake targets rather than observing the actual target.

Simulations
Our objective in this simulation is to offer deception jamming to the enemy radars. Consider the proposed scenario as shown in Figure 5 which is based on the surface to the air signal model. The actual aircraft is situated in air far-zone field while the opponent FDA radar is located at the surface. The proposed deceptive jammer is mounted on the actual aircraft. Both rivals are working on W-band ð f 0 = 100 GHzÞ of the FDA radar having N number of isotropic antenna elements with equal interelement spacing λ/2 and uniform current distribution along the whole linear array geometry configuration. The frequency increment of the FDA radars is keptΔf = 0:3 KHz.
Our proposed deceptive jammer works in passive searching mode that means it does not transmit and receive own signals to scan opponent radar. It instead utilizes the opponent radar signals to trace the desired parameters. But as quick as the computational system at the target finds direction of arrival (DOA), range, and pulse repetition interval (PRI) of the opponent radar, our proposed method can send deceptive echoes towards the opponent radar. These deceptive-echoes carry radiation pattern with appropriate null linked to the desired range and direction. They will be transmitted back in synchronous with actual target echoes to hide the actual target and delayed versions of false echoes to generate multiple false targets. Afterwards, in result of the proposed algorithm, the opponent radar will not be able to navigate the actual aircraft. So, track mode of the opponent radar will not work here.
To avoid any waveform time-dependent periodicity and mutual interference between the array element pulse width of the echo that is kept ≤30 μs and for the sake of simplicity, the PRI is taken 0:5 ms. Four different simulation cases have been considered. In each case, one true target is assumed, and the proposed deceptive jammer is mounted on it. The technique hides the actual aircraft along with generating four false targets in each test case with the parameters given in Table 1. For 3-D simulations, ten radiating antenna elements are considered, but for the 2-D simulations, we have taken N = 10, 20, 30, 40, 50 isotropic antenna elements, in the ULA-based FDA radar for each case.

Case-I
In first case, we assume that the actual target aircraft is situated at distance 50 km and direction 50 degrees. Frequency 9 Wireless Communications and Mobile Computing of the first element in the array is taken 100 GHz, while incremental frequency is assumed 0.3 kHz. Figure 7 shows 3-D simulation of the case where null has been placed at the real target aircraft location in order to hide it from the enemy radar. We have considered different numbers of antennaelements in the array of ULA-based FDA radar for 2-D simulations.
Instead of showing results like Figures 7, 11, 15, and 19 in 3-D, we have plotted the results in more simplified way using only output power in Figures 10, 14, 18, and 22, respectively. These figures (10, 14, 18, and 22) simply further elaborate the results of Figures 7, 11, 15, and 19, respectively. These figures show two scenarios. In the first scenario, it hides the actual target by simulating equation (29), whereas eq. (29) places null in the received signal at the opponent radar's location by means when the opponent receives this signals, he will perceive min. power at the target location. In the second scenario, it generates false targets at certain ranges and angles by     also verifies our results of null position at the desired direction of the real target to cover it. The proposed method also generates four fake targets along the same direction of the actual target and at different ranges of 30 km, 40 km, 60 km, and 70 km. This has been proved in Figure 10 where the desired target is hidden along with fake targets at their respective ranges.

Case-II
For this case, we have assumed that the real-target aircraft is located at range 40 km and direction 10 degree. Carrier frequency and incremental frequency, f 0 and Δf , are considered 100 GHz and 0.3 kHz, respectively. Figure 11 represents a 3-   14 Wireless Communications and Mobile Computing D simulation where the desired null has been placed at the real target aircraft location to hide it from the opponent radar. Two-dimensional results of the null position at the desired range (40 km) of the actual-aircraft are shown in Figure 12. In another graph, we have proved that the null has been placed at the desired direction of the actual target aircraft reflected in Figure 13. Further, our technique also generates four false targets along the direction 10 degrees but at ranges of 30 km, 50 km, 60 km, and 70 km as shown in Figure 14.

Case-III
In 3 rd case, we have assumed that the real target is positioned at range of 70 km from the opponent radar along the direction 40 degrees. Our proposed technique can verify the results in Figure 15 by showing that the desired null has been place effectively at the actual target location. Its twodimensional counterpart graphs are shown in Figures 16  and 17 to validate its correctness in the desired range and direction, respectively. Figure 18 shows multiple fake targets in the direction of the real target but at distances 40 km, 50 km, 60 km, and 80 km away from the opponent radar.

Case-IV
In the last case, we have taken the actual target aircraft at distance 60 km away from the foe radar and at direction 20 degrees. The proposed model draws a null at its location to   hide the aircraft from the vision of the opponent radar, and it is evident from its 3-D simulation in Figure 19. Further exploration of the method has been disclosed in Figures 20  and 21, whereby 2-dimensional graphs have been drawn to show the placement of the null at the desired range and direc-tion, respectively. Vertical axis shows power in dB for different numbers of radiating antenna elements in the array. Lastly, to generate multiple fake targets at range 40 km, 50 km, 70 km, and 80 km along the same direction of the target aircraft, the proposed method calculates appropriate time

Conclusion and Future Directions
A novel approach in the field of deception jamming has been developed in this research which hides actual target and displays multiple fake targets at different arbitrary ranges along the same direction to the enemy radar. This method has been developed to neutralize the effectiveness and dangers of the enemy radar which ultimately guarantees the safe penetration of the actual aircraft into the enemy territory. Moreover, a number of time modulations are performed for intercepted signal to display multiple deceptive jammer false targets at different ranges but along the same direction. It is assumed that the enemy radar has the capability of range angledependent radiation pattern characteristics. FDA radar's

18
Wireless Communications and Mobile Computing radiation pattern is also time-dependent for larger pulse width. This effect has been covered by considering narrow radar pulse width. Hence, in this way, time dependency will not affect the radiation pattern of the FDA radar. One of the other emerging area of research is how to counter deception jamming (anti-jamming) using FDA-MIMO radars in the field of ECCM techniques, because in the FDA-MIMO radar, we synthesize features of both radars (FDA and MIMO), and we achieve frequency diversity due to the FDA radar as well as waveform diversity due to the MIMO radar. We will try to investigate their relation with the presented method as well. We will also explore that how we can use FDA-MIMO radars to hide the actual aircraft target from the ground-based FDA opponent radar without the help of wave-scattering reflectors or advance escort-free-drone jammer. This research can give notion towards production of airborne deceptive jammers in the future.
Finally, the effectiveness and correctness of the proposed research have been verified by doing theoretical analysis and simulations by considering different cases with a number of distinct ranges of the actual target and fake deceptive targets. To put the research in the simplest form, the FDA radar is considered with the uniform linear array configuration. Moreover, the passage of time hardware computational and accuracy capabilities increases tremendously. Hence, limitations in implementing the algorithms due to hardware will be overcome; one can implement the proposed work through hardware with the collaboration of any national/international research organization.

Data Availability
All the data used in this work is based on simulations in MATLAB and is available for any research work.

Conflicts of Interest
All the authors declare that there is no conflict of interest.


Investigating the Impact of Social Media Marketing on Millennial's Purchase Intention of Sports Brands in China

This study mainly analyzes the importance of social media for sportswear companies, including marketing and influence, and how social media affects millennials' purchase intention of sportswear brands. This paper explores some cases of marketing activities of some sportswear companies (Nike, Hongxing Erke) on social media such as TikTok and Weibo and the influence of different social media marketing methods on customers' purchase intention through the method of case studies. The results show that social media can play a positive role in the publicity and marketing of sportswear brands. In addition, the study also showed that the use of social media has a significant impact on millennials' willingness to buy sportswear. The research has enormous implications for how sports brands, including Li Ning, can use social media to expand their marketing and influence. In the future, social media will be mainstream. Sports brands need to adapt to such changes by selling live on the primary platforms and signing famous stars to promote people's willingness to buy their sportswear.

Research background
Social media is a communicative platform that engages millions of young users with its characters of solid interaction, personalization, and information sharing [1]. This young user traffic enables social media to become a potential gathering place of value, which attracts many multinational high-end brands.
Social Media Marketing (SMM) is a new type of marketing through the Internet. It refers to companies participating in various social networking sites to achieve their business goals. Social media marketing channels comprise Forums, microblogs, blogs, SNS communities, pictures, and videos. In addition, companies can build media through self-media marketing or borrow media platforms for online marketing. Social media in marketing mainly refers to a comprehensive website with main network characteristics. Its characteristic is that most of the website content is provided directly by users (UGC), and there is no direct employment relationship between users and the website [2].
With the development of the economy, there is a tremendous in sports consumption, and people tend to have increased expenditure on sportswear for various purposes, for instance, improving sports performance, professional training, and pursuing fashion [3]. As a result, according to Statista [4], the global sportswear market has experienced exponential growth in the past few decades, which is expected to reach 191.9 in billion US dollars by 2021, achieving a 19% increase from 2016 to 2021(Statista, see figure 1).
They were benefiting from the rising awareness of healthy life; China, as the largest apparel market, has gathered more attention on sportswear with the influx of many high-end international brands into the market and the continuous expansion of consumer groups. Besides, the high growth of China's sportswear industry is mainly due to the increase in consumer awareness of sports. First, the diversification of national sports and sport types has promoted consumers purchasing sports shoes and clothing, and the per capita consumption has increased. Second, the concentration of sportswear continues to increase. The changes in clothing styles are weaker than those of ordinary men's and women's clothing [5]. At the same time, marketing and brand effects have a more significant impact on consumers' purchases, which is conducive to the leader's advantage in scale and increase market share. According to the "2020 Microblog user development report", it shows that the number of monthly individual users on Sina Weibo reached 500 million, with the age distribution of users born in the 90s accounting for 48% and users born in the 00s, occupying 30%. They are the main force for social media platform users and have also contributed to the consumption of international brands [6]. The users are called Generation Y, also knowns as Millennials, who are the first digital generation, living in a digital environment and sharing their daily lives on social media. Nam [7] pointed out that millennials' purchase intention will be mainly affected by social media platforms, and they can easily make a purchase decision by using online shopping.
China's high internet penetration rate enables the business to adopt social media marketing strategies efficiently, enabling companies to use customized information to reach target consumers directly. For example, many high-end brands publish information that uses famous mainland celebrities as their brand endorsements or focusing on social events to publish relative marketing campaigns to attract more social media users to participate in brand activities. Consequently, it is intriguing to identify what social media marketing will affect millennials' purchase intention towards sportswear.

Literature Review
As an essential digital communication platform, social media has an effective tool for band and users' communication [8]. Compared to traditional channels, social media is mainly focused on creating brand images, spreading, and consuming, which transforms the brand image into user-generated content [9]. The fundamental function of social media platforms is to encourage users to share information and communicate with each other. Sportswear brands widely use social media marketing to promote their online channel, improve brand awareness, and engage more customers. In addition, Kim and Ko [10] suggested that the effectiveness of social media marketing activities has been affected by four dimensions: entertainment, interaction, trendiness, and word of mouth, which have been considered the main factors of this study. Entertainment refers to the users obtain enjoyment and amusement from social media platforms [11]. Interaction is defined as the communication that social media platforms provide to brands and customers [12]. Besides, trendiness refers to users who can acquire the latest information, hot topics, and fashionable product information from social media platforms [13]. Subsequently, Chu and Kim [14] conclude that E-WOM is user-generated reviews of certain products with higher source credibility than online content created by marketers.
Purchase intention is the customer's decision-making process, which determines whether they will purchase the products in the future [15]. Previous research claimed that attitude and perceptions are the key indicators affecting customers' purchase intention [10]. Social media is a platform for creating and spreading brand images. To some extent, compelling social media marketing will affect customer's perception of brands [16]. Therefore, adopting the four dimensions in social media marketing will transform customer's attitudes and perceptions, thereby affecting their purchase intention. The research will be based on these four dimensions to focus on Li Ning's social media marketing activities to identify which factors contribute to millennials' purchase intention towards sportswear.
Despite many articles researching social media marketing, Kim and Ko's [10] main finding proposed that social media marketing activities directly impact customers relationships and then affect their purchase intention for luxury brands. In addition, Balakrishnan [17] found that E-WOM, online advertisement, and online communities are three practical factors that influenced customers' brand loyalty and facilitated their purchase intentions. However, few studies have researched millennial customers' perceptions and attitudes towards social media marketing campaigns for sportswear companies.

Research Objective
This study will explore what factors in social media marketing will affect customers' purchase intention toward sportswear and millennials' perceptions of these marketing activities. This study will focus on a representative sportswear brand and then discover some official data in social media marketing.

METHODS
This study will explore what factors in social media marketing will affect customers' purchase intention toward sportswear and millennials' perceptions of these marketing activities. This study will focus on a representative sportswear brand and then discover some official data in social media marketing.
This study will use the method of a case study. A case study is a study in which (a) one case (single case study) or a small number of cases (comparative case study) in their real-life context are selected, and (b) scores obtained from these cases are analysed in a qualitative manner [18].
Li Ning will be selected as the research object in this study. Li Ning sportswear company was founded in 1990, after 30 years of exploration, has gradually become a representative of China, the international leading sports brand company, and take a multi-brand business development strategy ("Li Ning Official Website"). According to the research on Li Ning released by Hong Kong Stock Company Report, the proportion of online channel business of Li Ning rose rapidly from 2015, and the company introduced the marketing concept of Internet plus. This study will select some cases of Li Ning's marketing activities in social media, including Douyin (TikTok) and Weibo. And then compared millennials' spending data on Li Ning's social media marketing with spending data on other places, such as brick-and-morta stores. In addition, this study will analyze the influence of different social media marketing methods on customers' purchase intention through these cases. For example, will consumers be more willing to buy sportswear if celebrities are invited to social media for marketing?

RESULT
The emergence and development of new media have accelerated the speed of mass media's transformation into a focused society. The brand marketing model also achieves precision marketing through big data and the use of new media platforms. For example, using new media platforms such as the WeChat service account and subscription account, Sina Weibo, Tik Tok App, Xiaohongshu, various broadcast apps, and other new media platforms to place brand advertising on a large scale. Li Ning is a typical example which adopting social media marketing to reach target customers precisely. Li Ning brought the concept of "Internet + life experience" into the company's daily business operations; exchanges between emerging channels and traditional channels have become more frequent. The consumption level of domestic customers and sports demand has steadily increased. The company focuses on improving product experience, purchase experience, and sports performance. Remodelling determined to upgrade to become a sports life scene experience provider.

Perspective of entertainment
Entertainment is a crucial reason for users to use social media, seeking enjoyment and amusement. In detail, after the brand founder Li Ning returned to management, he created his personal Sina Weibo account and company official account, publishing some exciting and fun social media content to realize realtime communication and interaction with consumers. He created some content that is proficient in using humor and emotions, enabling followers to feel relaxed and enjoy. It is an effective method to attract more users to follow their accounts. Also, brand awareness will be increased due to the rising exposure rate.
To some extent, switching customers' perceptions and attitudes from s tedious and outdated to fun and fashion facilitates customers' purchase intention toward

Advances in Economics, Business and Management Research, volume 203
Li Ning. Li Ning's Sina blog and company official account have over 3.16 million and 1.34 million followers, respectively, which obtained exponential growth. More and more millennials tend to interact with Li Ning and publish content related to Li Ning Hashtag (Weibo, 2021). From some perspective, Li Ning's social media marketing strategy makes some progress in improving brand awareness and reputation

Perspective of interaction
Interaction is an essential factor in social media marketing activities; for example, businesses use social media to publish content and the latest information to nurture customer relationships (Godey et al., 2016). Social media platform provides a place for customers and brands to communicate effectively. In 2016, Li Ning launched the "ultra-light thirteenth-generation" running shoes. They received 45 million views with the help of innovative advertisements and social platforms such as WeChat Moments and Wei Bo. Li Ning has created an intelligent running ecosystem, using big data technology to empower content marketing, event marketing. In detail, Li Ning applies social media big data to search target customers and integrated promotional channels to send personal advertisements to them. Also, Li Ning has permanently attached importance to the integration and linkage of O2O online and offline channels. The company has been committed to constructing an integrated omnichannel inventory system to realize the exchange of goods between online and offline channels and improve customers' shopping experience. They adopt mobile Internet to penetrate innovative running shoe products into daily life and keep a close connection to cultivate customer brand loyalty.

Perspective of trendiness
In terms of trendiness, most social media users consider it a significant function because they intend to acquire the latest information and fashionable products from social media. Li Ning has captured trendiness and puts much effort into social media marketing. Li Ning has deepened the combination of professional product performance and fashion culture and put in social marketing resources to comprehensively enhance product strength. For example, Li Ning Basketball continues to integrate professional tournaments and player resources such as the CBA professional league to continue the company's competitiveness in the basketball category; debuts in New York and Paris Fashion Week with the theme of "Enlightenment" and "China Lining"; sponsors marathons such as Hangzhou and Beijing The event enhances brand influence; crossborder e-sports, join hands with e-sports club EDG and badminton champions to promote products and achieve high product exposure rate. On the other hand, Chinese people have boycotted Western apparel brands due to the Xinjiang cotton event. Some celebrities have terminated the endorsement contract with western brands, turning to cooperate with Li Ning. On the day when Li Ning's officially announced that XiaoZhan became his brand endorser, the products of his online store (Tmall) were instantly sold out, and the stock price of Li Ning immediately rose by more than 6%. It is clear that Li Ning has a precise company position, and its social media marketing strategy has combined with trendiness properly, reaching Chinese millennials accurately. Customers tend to recognize Li Ning's brand images and to make a purchase decision.

Perspective of E-WOM
E-WOM in social media marketing influences customers' perception and trust towards brands. In most scenarios, information and contents generated by users are more convincing than marketers. Li Ning has selected KOL (Key Opinion Leader) in niche market segments, mainly from native social media platforms, including TikTok, Weibo, and Xiaohong Shu. KOL has the same characteristics; they are from Generation Z, though they focus on various fields. With the help of young consumers' follow on KOL in different fields, KOL marketing is more appropriate to the Li Ning image of the fashion brand. KOL's product promotional advertisements are persuasive. Li Ning's social media marketing strategy has acquired apparent achievements. Li Ning's operating income in 2019 has reached 13.8 billion yuan, increasing 30% dramatically.

DISCUSSION
In the future, new media and social media marketing will play a more critical role in brand influence and sales, and even social media marketing will become one of the primary revenue sources for most brands. For millennials, they like to pursue something new and trendy. Besides, they are fanatical idolaters and will buy the products endorsed by the stars they like, including sportswear. Therefore, Li Ning can give full play to the influence of social media to increase the millennial generation's intention to buy Li Ning sportswear. Below are some instructions on how Li Ning could use social media in the future.
Initially, Li Ning can hire sales staff to conduct live online sales on the live platform of short video applications such as TikTok and Kuaishou, the most popular short video social platform in China or worldwide. According to TikTok Statistics -Updated June 2021, TikTok is available in over 150 courtiers and has over 1billion users and Douyin, aka TikTok in mainland China, just hit 400 million daily active users there [20]. It can be seen that short video applications are popular with the public, especially young people.

Advances in Economics, Business and Management Research, volume 203
Thus, by running ads on short videos and selling them on its live streaming platform, Li Ning can attract many millennial customers. In addition, Li Ning can do some social welfare events such as donations and promote them through social media and sales channels. For example, Hongxing Erke, a Chinese sportswear company, quickly became a trending topic on Weibo after donating 50 million yuan in supplies to Henan province's disaster relief efforts. In addition to Weibo, many netizens spontaneously entered Hongxing Erke's Taobao and Douyin studios to buy products. The total sales of Hongxing Erke Tiktok's official live broadcast reached 9.016 million yuan that night, data showed. Previously, Hongxing Erke's Taobao studio had only a few thousand viewers. On that night, 2.01 million people watched Taobao Live, and the number of Taobao Live fans also increased to 7.63 million. The numbers are still growing [21]. Li Ning can learn from Hongxing Erke's success in such a short period to make a practical contribution to society. There is a strong sense of national identity among the Chinese people currently, and they also strongly support national brands.
Second, Li Ning can focus on some stars with a large number of fans and outstanding influence. Cristiano Ronaldo, the soccer superstar, has the most followers on Instagram-340 million, more than any other account. Ronaldo has made a staggering fortune for the brand he endorsed, Nike. According to Forbes, Ronaldo posted a total of 1,515 times on social media in 2016, with 21.7% of his posts promoting Nike through a hashtag, mention, or logo. The posts generated 443.5 million interactions (likes, comments, shares, retweets, and videos watched) and were worth $474 million to Nike [23]. Like Nike, Li Ning could sign some of the top stars in all walks of life in the future, such as singers, actors, or sports superstars. These stars have the following base, so their endorsement of Li Ning is bound to bring li Ning considerable wealth. When millennials see the brands endorsed by their favourite stars, they may strongly desire to buy them, even if they have never heard of Li Ning. This is the most intuitive result of inviting top star endorsements.
Finally, Li Ning can also cooperate with popular video games such as Honor of Kings. The Honor of Kings is a famous multiplayer online battle arena (MOBA) game developed by Tencent Games subsidiary Timi Studios. The game is quite like DOTA 2 and League of Legends on PC. According to The Indian Express, Honor of Kings is the most popular and profitable mobile game globally, with more than 100 million daily active users worldwide. It is Tencent's most successful game ever, with users spending nearly $240 million [23]. The game is popular in China, especially among millennials. The Honor of Kings has teamed up with several brands to launch limited hero skins. For example, in 2018, Honor of Kings and Pizza Hut launched the skin of a famous heroine in a game. In the future, Li Ning could work with Honor of Kings to create a Li Ning logo skin for a popular hero in the game. The game's publicity is bound to let more people know about Li Ning and promote people's intention to buy it.

Conclusion of key findings
To sum up, this study aims to investigate the impact of social media marketing activities on Chinese millennials' purchase intention. To make this paper readable and understandable, this paper has selected Li Ning as a case to explore the influence by summarizing Li Ning's social media activities. Four constructs, entertainment, interaction, trendiness, EWOM, have been fully explained with the help of Li Ning. It has been found that the social media marketing activities Li Ning conducted on Weibo and Tik Tok positively influence customers' perception of brands. Consumers tend to have purchase intention when they see those promotions on social media platforms. On the other hand, Li Ning has used social media to connect customers with national fashion to increase the attention of the company's product customer base, which will help the brand power be steadily improved and the band effect established.

Research significance
Based on the results, the research has implied that Li Ning should include more entertainment, interactive, and trendiness factors in their social media marketing campaign, which will increase the brand's attention dramatically and promote customer's intention toward products. In addition, Li Ning should spend more effort on E-word of mouth, which means cooperating with more influential celebrities or KOL. For instance, Cristiano Ronaldo will improve brand image, and customers tend to interact with Li Ning and create more user-generated content on social media. Besides, Li Ning could work with E-sports by sponsoring the Esports team to reach the target niche market; Li Ning can use social media marketing to create a bond between business and customers.

Limitations
The research limitations of this study consist of different aspects. This paper adopted the case study method, which lacks primary scientific data. Thereby it may cause bias for the result because researchers' subjective perception will impact results. Also, this method may cause construct validity, and it is difficult for researchers to test the reliability of variables in the case study.

Future studies
To some extent, a case study may cause bias and the issue of various validity. Consequently, I recommend that quantitative research should be conducted in the future. The researcher can design questionnaires based on those four constructs and run reliability analysis to test those variables' internal consistency. Moreover, researchers can investigate the social media marketing influence not only in terms of the purchase intention but also in terms of brand awareness and brand perception., building new constructs to explore the relationships between variables.