Customer Relationship Management (CRM) systems play an important role in helping companies identify and keep sales and service prospects. CRM service providers offer a range of tools and techniques that will help find, sell to and keep... more
Customer Relationship Management (CRM) systems play an important role in helping companies identify and keep sales and service prospects. CRM service providers offer a range of tools and techniques that will help find, sell to and keep customers. To be effective, CRM users usually require extensive training. Predictive CRM using machine learning expands the capabilities of traditional CRM through the provision of predictive insights for CRM users by combining internal and external data. In this paper, we will explore a novel idea of computationally learning salesmanship, its patterns and success factors to drive industry intuitions for a more predictable road to a vehicle sale. The newly discovered patterns and insights are used to act as a virtual guide or trainer for the general CRM user population.
Time series forecasting analysis has become a major tool in different applications for the Manufacturing Company. Among the most effective approaches for analyzing time series data is ARIMA (Autoregressive Integrated Moving Average). In... more
Time series forecasting analysis has become a major tool in different applications for the Manufacturing Company. Among the most effective approaches for analyzing time series data is ARIMA (Autoregressive Integrated Moving Average). In this study we used Box-Jenkins methodology to build ARIMA model for annual sales forecast for 7up Bottling Company Plc for the period from January 2010 to December 2015, given the available monthly sales data. After the model specification; the best model for production was ARIMA (1, 1, 1) and for utilization was ARIMA (0, 1, 1). A 12 months forecast have also been made to determine the expected amount of sales revenue in year 2016. The time plot reveals seasonal variation. It thus concludes that that there is increase in sales revenue of Company with time, hence these models can be adopted for sales, production, utilization and demand forecasting in Nigeria, Keywords: ARIMA, Box-Jenkins, forecasting, production and utilization model, Time series ana...
Sales forecasting is intended to control the amount of available stock so that shortages or excess stock can be minimized, the fulfillment of consumer demand can be prepared on time, and cooperation with suppliers can be maintained... more
Sales forecasting is intended to control the amount of available stock so that shortages or excess stock can be minimized, the fulfillment of consumer demand can be prepared on time, and cooperation with suppliers can be maintained properly. Therefore, this study aims to develop a new sales forecasting method that integrates the concept of RFM, the data-mining method, and the Best-Worst Method (BWM), where RFM is a strong-method in the field of database marketing and BWM is a new decision-making method. This research is a preliminary study that outlines the theory and phases of the proposed sales forecasting method. Based on the concept offered, research on the proposed sales forecasting method is very possible to be developed. This new sales forecasting method can be an alternative method for company management to optimize services to consumers, inventory efficiency, and predict the company's economic benefits in the future.
Objective: Providing that data mining has been an effective solution of improving the efficiency and the effectiveness of the retail industry, this industry has been the subject of data mining science due to the nature of its data. In... more
Objective: Providing that data mining has been an effective solution of improving the efficiency and the effectiveness of the retail industry, this industry has been the subject of data mining science due to the nature of its data. In this study, the prediction of customer behavior in the retail industry of Fast Moving Consumer Goods is aimed at increasing the quantity and quality of sales in the study of Golpakhsh Avval Co. Methods: The present study is applied in terms of purpose, using data survey to collect data. The research is based on the CRISP-DM process, which uses the RFMCL clustering model, regression classification and regression techniques as well. Eventually, a collaborative recommendation method has been applied for recommendation. Results: The result is a forecasting model recommended to the best customers goods that they have not bought on a particular date and to a certain amount, so that, the order-based sale is changed to hot sale method. The final solution involves three sub models of customer clustering, sale forecasting and a recommendation system. The five variables model –with MSE/Range accuracy of 2.24% – is solved for recommendation of sales amount. Conclusion: By implementing the developed recommender system in Golpakhsh Avval Co., the proactive production master plan would be possible to execute. In addition, the marketing approach could be transformed from visiting sales to hot sales in the future which provides considerable savings in shipping and personnel costs.
Sales Forecasting plays an important role in business. It provides appropriate and dependable information about the past and present events and likely future events. Nowadays, predicting sales has become a common method but not many known... more
Sales Forecasting plays an important role in business. It provides appropriate and dependable information about the past and present events and likely future events. Nowadays, predicting sales has become a common method but not many known approaches are applied. Sales forecasting works as a vital data input to organizational development. Machine Learning techniques are very effective tools in extracting hidden knowledge from vast dataset to boost the precision and effectiveness of estimating sales. Supervised Machine Learning is being used by many organizations to identify and solve business problems. The proposed methodology is to use regression to do a comparative study on sales forecasting. Here, three regression techniques, which are Linear Regression, Ridge Regression, and LASSO Regression are discussed in detail. The accuracy in sales prediction offers a big positive impact on business. All the regression techniques are giving excellent outcome for R-squared and RMSE value. Th...
Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of... more
Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of sales. This research precisely predicts the automotive industry sales using a bag of multiple machine learning and time series algorithms coupled with historical sales and auxiliary features. Three-year historical sales data (from 2017 till 2020) were used for the model building or training, and one-year (2020-2021) predictions were computed for 900 unique SKU's (stock-keeping units). In the present study, the SKU is a combination of sales office, core business field, and material customer group. Various data cleaning and exploratory data analysis algorithms were implemented over raw datasets before use for modeling. Mean absolute percentage error (mape) were estimated for individual predictions from time series and machine learning models. The best model was selected for unique SKU's as per the most negligible mape value.
ACI Limited is one of the top ranked Companies in household cleaning products (excluding laundry detergents) and a leading player in health and personal care. Over the years ACI has introduced several brands for operating with consistent... more
ACI Limited is one of the top ranked Companies in household cleaning products (excluding laundry detergents) and a leading player in health and personal care. Over the years ACI has introduced several brands for operating with consistent and steady penetration in their acquiring market share in the fast moving consumer goods segment with a key focus on health, hygiene & home products. In the context of this project preparation, the “in market sales volume” for Savlon brand of 96 periods spanning from January 2005 – December 2012 has been taken into account on which various forecasting techniques have been implemented to ascertain the best forecasting method to forecast the next 6-periods sales volume. Based on our analysis and results of the forecasting methods, the forecasting accuracy have also been assessed through several accuracy measures (MAD, MAPE, MSD) with due regard to data consistency within the acceptable region of tracking signal (TS & Triggs). Due to the inherent characteristics of the data set and existence of seasonal impact, the data series has exhibited error percentage beyond the acceptable region of tracking signal of ± 4. As a result, “Triggs” has been utilized to arrive at the optimum forecasting method which is found to be winter method with α, γ & δ= 0.2.
In this research, we propose a methodology for advert value calculation in CPM, CPC and CPA networks. Accurately estimating this value increases the three previous networks’ incomes by selecting the most profitable advert. By increasing... more
In this research, we propose a methodology for advert value calculation in CPM, CPC and CPA networks. Accurately estimating this value increases the three previous networks’ incomes by selecting the most profitable advert. By increasing income, publishers are better paid and improved services are afforded to advertisers. To develop this methodology, we propose a system based on traditional Machine Learning methods and Deep Learning methods. The system has two inputs and one output. The inputs are the user visit and the data about the advertiser. The output is the advert value expressed in dollars. Deep Learning predicts model behavior more precisely for many supervised problems. The three experiments carried out allow us to conclude that DL is a supervised method that is very efficient in the classification of spam adverts and in the estimation of the CTR. In the prediction of online sales, DLNN have shown, on average, worse performance than cubist and random forest methods, although better performance than model tree, model rules and linear regression methods.
This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be... more
This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.
In today's competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In... more
In today's competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series...
In today's competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this... more
In today's competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company's current system.
This study aims to develop a stochastic framework of model to forecast future sales for pharmaceutical industry. In this regard, the study focuses on Merck Pharmaceutical monthly sales data. This study examines the Sale forecasting... more
This study aims to develop a stochastic framework of model to forecast future sales for pharmaceutical industry. In this regard, the study focuses on Merck Pharmaceutical monthly sales data. This study examines the Sale forecasting models. The study includes monthly data published in the annual reports of the company from Jan. 2008 to Dec. 2012.The time series diagram shows unequal means over the time period that suggests the data is stationary. Having transformed the data, ARMA (1, 1) model is applied which shows that there will be increase in sales by $6.784m given that in the last month sales were $1bn. On the contrary, last month’s residual has an adverse effect on current month sales up to the extent of $432.942m. In this study AR (1) and MA (1) both the processes are significant at 1%.
Fertilizer scarcity in the Philippines can be avoided ifthe private sector has forecast the demand and stored sufficient products. Using a 5-year daily historical data on sales, weather, presence of marketing and farm-gate prices, this... more
Fertilizer scarcity in the Philippines can be avoided ifthe private sector has forecast the demand and stored sufficient products. Using a 5-year daily historical data on sales, weather, presence of marketing and farm-gate prices, this study explored the applicability of feed-forward artificial neural networks as a sales forecasting tool for inorganic fertilizers, and serve as a pioneer in using machine learning tools in increasing forecast accuracy in the fertilizer industry. The one hidden layer neural net was created using RapidminerTM and was developed in 5 stages: data-gathering, pre-processing, modeling, evaluation and selection. The results showed that, with proper data exploration, the ANN model may reach a MAPE of 6%. Furthermore, the model for each SKU must be tailored according to the product's characteristics. This study is part of a growing body of research on sales forecasting using ANN in the Philippines and will contribute to future researches on similar topics and practical application in the industry.
PENGANGGARAN DAN EVALUASI KINERJA SEKTOR PUBLIK " RAMALAN PENJUALAN " Ramalan (forecasting) adalah proses aktivitas meramalkan suatu kejadian yang mungkin akan terjadi di masa mendatang dengan mengkaji data yang ada Nafarin (2008: 96)... more
PENGANGGARAN DAN EVALUASI KINERJA SEKTOR PUBLIK " RAMALAN PENJUALAN " Ramalan (forecasting) adalah proses aktivitas meramalkan suatu kejadian yang mungkin akan terjadi di masa mendatang dengan mengkaji data yang ada Nafarin (2008: 96) sedangkan Ramalan Jualan (sales forecasting) adalah proses aktifitas memperkirakan produk yang akan dijual di masa mendatang dalam keadaan tertentu berdasarkan data yang pernah terjadi dan/atau mungkin akan terjadi. Teknik dalam membuat ramalan jualan dapat dilakukan dengan beberapa cara antara lain dengan metode kualitatif, metode kuantitatif atau gabungan keduanya. Metode Kualitatif dapat dilakukan dengan memanfaatkan pendapat para tenaga penjualan, para manajer divisi penjualan, jajaran eksekutif perusahaan, para pakar dan survei konsumen. Sedangkan Metode Kuantitatif digunakan dengan cara menggunakan metode analisis lini produk, distribusi probabilitas, analisis tren dan analisis regresi. A. Ramalan Kualitatif a. Metode Pendapat Para Tenaga Penjualan Menekankan pertimbangan dan keahlian dari para tenaga penjualan. Metode ini sering digunakan oleh perusahaan kecil dan perusahaan yang menghasilkan sedikit produk. Kelebihan dari metode pendapat para tenaga penjualan adalah: 1. Menanamkan tanggung jawab dan rasa memiliki terhadap perusahaan; 2. Ramalan dibuat oleh individu yang terdekat dengan pelanggan; Kelompok 6 1. Athabik Zuhdi M.
Untuk mempertahankan perusahaan tersebut mereka harus tetap berproduksi demi tetap berjalannya usaha mereka ditengah masalah yang dihadapi dengan cara meramalkan hasil yang harus didapat oleh perusahaan. Untuk meramalkan hasil laporan... more
Untuk mempertahankan perusahaan tersebut mereka harus tetap berproduksi demi tetap berjalannya usaha mereka ditengah masalah yang dihadapi dengan cara meramalkan hasil yang harus didapat oleh perusahaan. Untuk meramalkan hasil laporan penjualan dengan menggunakan metode peramalan kita akan dapat mengetahui apakah perusahaan mengalami kenaikan atau penurunan penjualan perusahaan. Maka dari itu, penulis mendeskripsikan beberapa hal penting mengenai Peramalan penjualan dan salah satu metode nya yaitu analisis tren.
Sales forecasting is intended to control the amount of available stock so that shortages or excess stock can be minimized, the fulfillment of consumer demand can be prepared on time, and cooperation with suppliers can be maintained... more
Sales forecasting is intended to control the amount of available stock so that shortages or excess stock can be minimized, the fulfillment of consumer demand can be prepared on time, and cooperation with suppliers can be maintained properly. Therefore, this study aims to develop a new sales forecasting method that integrates the concept of RFM, the data-mining method, and the Best-Worst Method (BWM), where RFM is a strong-method in the field of database marketing and BWM is a new decision-making method. This research is a preliminary study that outlines the theory and phases of the proposed sales forecasting method. Based on the concept offered, research on the proposed sales forecasting method is very possible to be developed. This new sales forecasting method can be an alternative method for company management to optimize services to consumers, inventory efficiency, and predict the company's economic benefits in the future.
In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this... more
In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series inv...
Business competition between manufacturing businesses in Indonesia is getting tighter along with the development of businesses from competing companies that have similar businesses. One strategy that can be applied by this company is... more
Business competition between manufacturing businesses in Indonesia is getting tighter along with the development of businesses from competing companies that have similar businesses. One strategy that can be applied by this company is Business Intelligence, that is by utilizing the data that is already available to help in better decision making, such as decisions based on facts stored in the data, precisely namely the lack of errors in the presentation of reports, and fast that is, cut down on the time for making the usual report. The method proposed by the author is a method that can be used to predict sales value based on existing sales data (sales forecasting). By implementing Business Intelligence and data mining, companies can learn from the data that has been collected, can evaluate the performance of the sales department, can understand market trends from the products sold, and can predict future sales levels. In addition, Business Intelligence can display detailed transaction data recapitulation quickly.
Analisis regresi adalah salah satu analisis yang paling populer dan luas pemakaiannya yang berguna untuk memeriksa dan memodelkan hubungan diantara variabel-variabel. Penerapannya dapat dijumpai secara luas di banyak bidang seperti... more
Analisis regresi adalah salah satu analisis yang paling populer dan luas pemakaiannya yang berguna untuk memeriksa dan memodelkan hubungan diantara variabel-variabel. Penerapannya dapat dijumpai secara luas di banyak bidang seperti teknik, ekonomi, manajemen, ilmu-ilmu biologi, ilmu-ilmu sosial, dan ilmu-ilmu pertanian. Analisis regresi dipakai secara luas untuk melakukan prediksi dan ramalan. Analisis regresi itu adalah cara atau metode yang digunakan untuk mengembangkan sebuah model atau persamaan yang menjelaskan hubungan dari beberapa variabel.
3 rd International Conference on Data Science and Applications (DSA 2022) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Science and Applications.... more
3 rd International Conference on Data Science and Applications (DSA 2022) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Science and Applications. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Data Science & Applications.
Sales Forecasting plays an important role in business. It provides appropriate and dependable information about the past and present events and likely future events. Nowadays, predicting sales has become a common method but not many known... more
Sales Forecasting plays an important role in business. It provides appropriate and dependable information about the past and present events and likely future events. Nowadays, predicting sales has become a common method but not many known approaches are applied. Sales forecasting works as a vital data input to organizational development. Machine Learning techniques are very effective tools in extracting hidden knowledge from vast dataset to boost the precision and effectiveness of estimating sales. Supervised Machine Learning is being used by many organizations to identify and solve business problems. The proposed methodology is to use regression to do a comparative study on sales forecasting. Here, three regression techniques, which are Linear Regression, Ridge Regression, and LASSO Regression are discussed in detail. The accuracy in sales prediction offers a big positive impact on business. All the regression techniques are giving excellent outcome for R-squared and RMSE value. The perfect result would be an RMSE value of zero and R-squared value of 1, but that's almost impossible in real economic datasets.
The future has always caught the attention of the human being. The thirst of exploring the future and to know the unknown has driven the human being toward inattentiveness. Companies are expanding their operations worldwide since the past... more
The future has always caught the attention of the human being. The thirst of exploring the future and to know the unknown has driven the human being toward inattentiveness. Companies are expanding their operations worldwide since the past few decades. Profit growth coupled with an effective strategy has become the primary need of global companies. Research in this area has given rise to optimization of the supply chain for higher profitability. Considering the overall strategy the company needs to plan production well in advance. The operational planning comes in picture at this moment. In order to reduce excessive inventory at each stage of the production; one should know the demand of the next stage and preferably the end customer demand. The process of sales forecasting is undertaken to predict demand at different stages. It is a complex managerial function and hence needed to be undertaken by a scientific way. The sales forecasting the function includes process of forecasting, administration, hardware, software, users and developers of forecast.
Historically sales forecasting has been considered as a side activity by most of the companies. Sales forecasting has not been considered as an important function of marketing and finance. Very few companies have seen sales forecasting by a scientific management point of view. Less research has been reported in sales forecasting in comparison to other managerial functions. Planning based on sales forecasting; may be part of a selected strategy for growth and profitability. These facts have attracted us to study sales forecasting as a managerial function.
The purpose of this study is to describe and analyze the sales forecasting process, sales forecasting system, sales forecasting methods and techniques. Further proposing possibilities of improvements in existing forecasting process is also purpose of this study. We have selected three manufacturing companies for this study based on purposive sampling. Considering research interest in phenomenon study; we have selected a qualitative research strategy for this study. We have selected a case study method for our research as it is the most appropriate tool to study the relation between theory and phenomenon. For this research, we have collected the data by semistructured interviews based on a pre formed questionnaire. The questionnaire has been prepared with respect to our research purpose and open ended questions were used to gather extensive data. The data gathered during interviews, have been analyzed by the use of ‘Flow model’ suggested by Miles and Huberman (1994).
Results from this study shows that there is a need to see ‘sales forecasting’ as a management function rather than a computer activity. To achieve the best information integration throughout the supply chain, increased information visibility is needed. To achieve accuracy in both forecasting and planning; collaborative forecasting may be used. Forecasting software needs to have a suite of methods towards product specific forecasting. The need of customized softwares has also been indicated by this study. The need to measure performance of forecasting by means of accuracy, cost and customer relationship has been concluded.
Sales forecasting is intended to control the amount of available stock so that shortages or excess stock can be minimized, the fulfillment of consumer demand can be prepared on time, and cooperation with suppliers can be maintained... more
Sales forecasting is intended to control the amount of available stock so that shortages or excess stock can be minimized, the fulfillment of consumer demand can be prepared on time, and cooperation with suppliers can be maintained properly. Therefore, this study aims to develop a new sales forecasting method that integrates the concept of RFM, the data-mining method, and the Best-Worst Method (BWM), where RFM is a strong-method in the field of database marketing and BWM is a new decision-making method. This research is a preliminary study that outlines the theory and phases of the proposed sales forecasting method. Based on the concept offered, research on the proposed sales forecasting method is very possible to be developed. This new sales forecasting method can be an alternative method for company management to optimize services to consumers, inventory efficiency, and predict the company's economic benefits in the future.
We study the effect of decomposing time series into multiple components like trend, seasonal and irregular and performing the clustering on those components and generating the forecast values of each component separately. In this project... more
We study the effect of decomposing time series into multiple components like trend, seasonal and irregular and performing the clustering on those components and generating the forecast values of each component separately. In this project we are working on sales data. Multiple forecast experts are used to forecast each component series. Statistical method ARIMA, Holt winter and exponential smoothing are used to forecast these components. We performed clustering for forecasting and discovered a set of best, good and bad forecasters. Selection of best, good and bad forecasters is performed on the basis of count and rank of expert id’s generated. Since we have thousands of experts, we experiment with combining method to get better forecast. Finally absolute percentage error (APE) is used for comparing forecast.
By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation... more
By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation of Facebook's Prophet, and Amazon's DeepAR+ and CNN-QR forecasting models, algorithms have attracted a great deal of attention. The paper presents the application and comparison of the above algorithms for sales forecasting in distribution companies. A detailed comparison of the performance of algorithms over real data with different lengths of sales history was made. The results show that Prophet gives better results for items with a longer history and frequent sales, while Amazon's algorithms show superiority for items without a long history and items that are rarely sold.
When it comes to running businesses successfully, sales forecasting is a crucial component to include in the process. In this paper, the aim is to forecast the sales made by an automobile company in a particular city. The data outlines... more
When it comes to running businesses successfully, sales forecasting is a crucial component to include in the process. In this paper, the aim is to forecast the sales made by an automobile company in a particular city. The data outlines the total sales of the company manufactured cars at the end of the month from 2013 to 2014. Utilizing this data, we fit the Autoregressive Integrated Moving Average (ARIMA) time series with the regression model. Utilizing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, we identify the best fit ARIMA model and use this to obtain the sales forecast for the subsequent year. To successfully build the model we use R Programming.
Almost all state enterprises and private sector companies try to foresee future expectations. From the viewpoint of economic, productive, and efficient business management, this is highly important. By making rational decisions, all... more
Almost all state enterprises and private sector companies try to foresee future expectations. From the viewpoint of economic, productive, and efficient business management, this is highly important. By making rational decisions, all enterprises aim to rich maximum profitability by taking sales, cost, human resource needs, profits into account. For this reason, enterprises have to make reliable and reasonable forecasts to take the right decisions. Such forecasts might be used in budgeting, cost, and profit analysis. Forecasted scenarios might come true in the future with a great likelihood. The researcher utilizing time series analysis assumes that all findings that come out will be almost the same happened in the past. Analyzing the time series consist of four aims such as defining, modeling forecasting, and controlling. To define a series, it is needed to compute definitional statistics and to draw its graphic. The second purpose of analyzing the time series is to find the appropr...
— Time series model is a hotspot in the research of statistics. On November 11, 2015, Tmall platform's turnover was more than $91.2 billion which caused the attention of scholars both at home and abroad. So this paper aims to forecast... more
— Time series model is a hotspot in the research of statistics. On November 11, 2015, Tmall platform's turnover was more than $91.2 billion which caused the attention of scholars both at home and abroad. So this paper aims to forecast sales of Tmall, which is helpful to the enterprises. Research methods are ARIMA model and VAR model. The first model is single-variable model and the later is multi-variable model. In the study, ARIMA model makes the sequence smooth by using two difference operation. In VAR model, five explanatory variables are transformed into one main component. By contrast, VAR model does not give detailed accurate prediction, but ARIMA model does. Therefore, single-variable time series model is more suitable for sales forecast than multi-variable model.
This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be... more
This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.
Search data can be used to forecast macroeconomic measures. The present study extends this research direction by drawing on real sales data from a household panel over two years. Specifically, the study analyzes whether search data... more
Search data can be used to forecast macroeconomic measures. The present study extends this research direction by drawing on real sales data from a household panel over two years. Specifically, the study analyzes whether search data improves forecasts for seven products groups of durable goods. The forecast model also includes the average weekly price and a dummy for the Christmas season. Forecast accuracy is indeed improved when search data is included even for product groups that have a short information and search phase. The product groups, however, need to be chosen carefully, because some durable goods show no lag between online search and purchase.
This paper examines that promotional tools such as TV advertisement, Print Media, Billboards and LCD’s create boosting impact on short-term sales. Advertisement is primarily used to attract new customers and increase purchases by existing... more
This paper examines that promotional tools such as TV advertisement, Print Media, Billboards and LCD’s create boosting impact on short-term sales. Advertisement is primarily used to attract new customers and increase purchases by existing consumers. Publicity and advertising straightforwardly have an effect on the power of loyalty a buyer has for its beloved product. Therefore, if the preferred brand puts together a strong advertisement companion, the devotion of the customer will definitely increase but on the other side if the competitor brand also goes on advertising, the loyalty may decrease. Results are positively associated and have a strong relationship of promotional tools on sales growth under occasional study of advertisement campaign by Olpers milk Pakistan in the month of Ramadan.
Machine learning has been a subject undergoing intense study across many different industries and fortunately, companies are becoming gradually more aware of the various machine learning approaches to solve their problems. However, to... more
Machine learning has been a subject undergoing intense study across many different industries and fortunately, companies are becoming gradually more aware of the various machine learning approaches to solve their problems. However, to fully harvest the potential of different machine learning models and to achieve efficient results, one needs to have a good understanding of the application of the models and the nature of data. This paper aims to investigate different approaches to obtain good results of the machine learning algorithms applied for a given forecasting task. To this end, the paper critically analyzes and investigate the applicability of machine learning algorithm in sales forecasting under dynamic conditions, develop a forecasting model based on the regression model, and evaluate the performance of four machine learning regression algorithms (Random Forest, Extreme Gradient Boosting, Support Vector Machine for Regression and Ensemble Model) using data set from Nigeria r...
Written press has been, and still in Morocco, an important mean to communicate news and knowledge. Press releases still have their advantages and devoted fanatics. Nevertheless, a general decline may be noticed worldwide due to digital... more
Written press has been, and still in Morocco, an important mean to communicate news and knowledge. Press releases still have their advantages and devoted fanatics. Nevertheless, a general decline may be noticed worldwide due to digital substitution and general reading decrease. Moroccan press undergo additional hard time as its management systems struggle to cope with a fast-paced industrial development and network modernization.This paper is an inception study. It throws reflections and insights about Moroccan press distribution systems and suggests tailored solution accordingly. Based mainly on sales analysis, we will be discussing sale points clustering and sales forecast modeling. Hopefully, it will help understand this industry's complications and puzzle out distribution and marketing possibilities.
The main objective of this project is Sales prediction, which is forecasting the future sales of a product. The attributes used as an input is weekly sales and dates of sale. This paper proposes a machine learning model to predict the... more
The main objective of this project is Sales prediction, which is forecasting the future sales of a product. The attributes used as an input is weekly sales and dates of sale. This paper proposes a machine learning model to predict the sales of a product. The machine learning technique used is linear regression. The successful prediction of sales will maximize the shopkeeper's gains.
