Competitive advantage for e-business requires more accurate information and precise decision to aid international companies to analyze and predict sales forecasting trend optimizing potential profits and reduce losses. We propose an improvement model to minimize the forecast error for the time series of daily sales information. We use real international restaurant data as the basis to show the performance of our sales prediction model. Multiple time series are considered in this model to vastly improve the forecasting outcome. Those data series are combined from EPARK Company and open data like weather, into a multi-series data, our forecast model extend the previous predecessor tremendously. Various residue computations during the process are compared and discussed. We applied the model for data from different area to compare the difference respectively. Result shows a proper selection of computation method is more dynamic than a fixed method for shops in different geographic area even within the same company. In addition, analysis shows significant error reduction in forecasting achieved when open data like weather information is included in the regression process. Thus international business can be more agile and flexible for just-in-time stock inventory and better resource allocation strategy.
•Forecasted weekly sales of Walmart during holiday and non-holiday weeks across various regions to discover which factors impact sales and derive actionable insights to enhance the sales
•Analyzed predictors using Mosaic plots, scatterplots, incorporated data cleansing through outlier rectification and feature selection in SAS JMP. Generated ensemble models such as Partition Tree, Random forest, KNN Cluster to compare accuracy scores and error metrics against linear models in SAS for accurate predictions in sales. Random Forest was identified as best model with 89% accuracy
•Forecasted weekly sales of Walmart during holiday and non-holiday weeks across various regions to discover which factors impact sales and derive actionable insights to enhance the sales
•Analyzed predictors using Mosaic plots, scatterplots, incorporated data cleansing through outlier rectification and feature selection in SAS JMP. Generated ensemble models such as Partition Tree, Random forest, KNN Cluster to compare accuracy scores and error metrics against linear models in SAS for accurate predictions in sales. Random Forest was identified as best model with 89% accuracy
In this paper we attempt to review the models, process, qualitative and quantitative methods of forecasting. We also review the needs and reasons for forecasting and what methods and approaches are employed for forecasting, requirements for forecasting, what are the shortcomings and business implications of forecasting.
APPLICATION OF ECONOMETRICS
it helps u to understand why we study econometrics when im coming to know these application of econometrics my concepts are clear
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
Basic statistical & pharmaceutical statistical applicationsYogitaKolekar1
This is knowledge sharing PPT specially designed for Non-statisticians to understand basic fundamentals regarding statistics & related to pharmaceutical statistics.
How statistics involve in daily life as well as pharmaceutical industry etc., not limited.
#WhatisMeanByStatistics? #WhyStatistics? #HowStatisticsEssentialtoEverydayLife? #StatisticalApplicationsinDailyLife #Toothpaste
#IndependentDependentVariables #Tea #TypesofData #ClassificationofDiscreteVariableContinuousVariables #TypesofDataMeasurementScale
#StatisticalMethodsforAnalyzingData #ConceptofPopulationSampleandPointEstimate
#DescriptiveStatistics #InferentialStatistics
#MeasuresofCentralTendency #MeasuresofDispersion #RealLifeApplications #DataPresentation #PictorialView
#PharmaceuticalStatistics #ResearchDevelopment #Statistician
Demand Forecasting, undeniably, is the single most important component of any organizations Supply Chain. It determines the estimated demand for the future and sets the level of preparedness that is required on the supply side to match the demand. It goes without saying that if an organization doesnt get its forecasting accurate to a reasonable level, the whole supply chain gets affected. Understandably, Over/Under forecasting has deteriorating impact on any organizations Supply Chain and thereby on P and L. Having ascertained the importance of Demand Forecasting, it is only fair to discuss about the forecasting techniqueswhichareusedtopredictthefuturevaluesofdemand. The input that goes in and the modeling engine which it goes through are equally important in generating the correct forecasts and determining the Forecast Accuracy. Here, we present a very unique model that not only pre-processes the input data, but also ensembles the output of two parallel advanced forecasting engines which uses state-of-the-art Machine Learning algorithms and Time-Series algorithms to generate future forecasts. Our technique uses data-driven statistical techniques to clean the data of any potential errors or outliers and impute missing values if any. Once the forecast is generated, it is post processed with Seasonality and Trend corrections, if required.Since the final forecast is the result of statistically pre-validated ensemble of multiple models, the forecasts are stable and accuracy variation is very minimal across periods and forecast horizons. Hence it is better at estimating the future demand than the conventional techniques.
Forecasting lessons from FMCG aisles by Thinus Hermann at the 37th Annual SAPICS conference and exhibition, held at Sun City, South Africa on 1 June 2015.
Chi Square Test of Association is used to determine whether there is a statistically significant association between the two categorical variables. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type.
In this paper, feature tracking based and histogram based traffic congestion detection systems are developed. Developed all system are designed to run as real time application. In this work, ORB (Oriented FAST and Rotated BRIEF) feature extraction method have been used to develop feature tracking based traffic congestion solution. ORB is a rotation invariant, fast and resistant to noise method and contains the power of FAST and BRIEF feature extraction methods. Also, two different approaches, which are standard deviation and weighed average, have been applied to find out the congestion information by using histogram of the image to develop histogram based traffic congestion solution. Both systems have been tested on different weather conditions such as cloudy, sunny and rainy to provide various illumination at both daytime and night. For all developed systems performance results are examined to show the advantages and drawbacks of these systems.
APPLICATION OF FACEBOOK'S PROPHET ALGORITHM FOR SUCCESSFUL SALES FORECASTING ...ijnlc
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 this paper we attempt to review the models, process, qualitative and quantitative methods of forecasting. We also review the needs and reasons for forecasting and what methods and approaches are employed for forecasting, requirements for forecasting, what are the shortcomings and business implications of forecasting.
APPLICATION OF ECONOMETRICS
it helps u to understand why we study econometrics when im coming to know these application of econometrics my concepts are clear
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
Basic statistical & pharmaceutical statistical applicationsYogitaKolekar1
This is knowledge sharing PPT specially designed for Non-statisticians to understand basic fundamentals regarding statistics & related to pharmaceutical statistics.
How statistics involve in daily life as well as pharmaceutical industry etc., not limited.
#WhatisMeanByStatistics? #WhyStatistics? #HowStatisticsEssentialtoEverydayLife? #StatisticalApplicationsinDailyLife #Toothpaste
#IndependentDependentVariables #Tea #TypesofData #ClassificationofDiscreteVariableContinuousVariables #TypesofDataMeasurementScale
#StatisticalMethodsforAnalyzingData #ConceptofPopulationSampleandPointEstimate
#DescriptiveStatistics #InferentialStatistics
#MeasuresofCentralTendency #MeasuresofDispersion #RealLifeApplications #DataPresentation #PictorialView
#PharmaceuticalStatistics #ResearchDevelopment #Statistician
Demand Forecasting, undeniably, is the single most important component of any organizations Supply Chain. It determines the estimated demand for the future and sets the level of preparedness that is required on the supply side to match the demand. It goes without saying that if an organization doesnt get its forecasting accurate to a reasonable level, the whole supply chain gets affected. Understandably, Over/Under forecasting has deteriorating impact on any organizations Supply Chain and thereby on P and L. Having ascertained the importance of Demand Forecasting, it is only fair to discuss about the forecasting techniqueswhichareusedtopredictthefuturevaluesofdemand. The input that goes in and the modeling engine which it goes through are equally important in generating the correct forecasts and determining the Forecast Accuracy. Here, we present a very unique model that not only pre-processes the input data, but also ensembles the output of two parallel advanced forecasting engines which uses state-of-the-art Machine Learning algorithms and Time-Series algorithms to generate future forecasts. Our technique uses data-driven statistical techniques to clean the data of any potential errors or outliers and impute missing values if any. Once the forecast is generated, it is post processed with Seasonality and Trend corrections, if required.Since the final forecast is the result of statistically pre-validated ensemble of multiple models, the forecasts are stable and accuracy variation is very minimal across periods and forecast horizons. Hence it is better at estimating the future demand than the conventional techniques.
Forecasting lessons from FMCG aisles by Thinus Hermann at the 37th Annual SAPICS conference and exhibition, held at Sun City, South Africa on 1 June 2015.
Chi Square Test of Association is used to determine whether there is a statistically significant association between the two categorical variables. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type.
In this paper, feature tracking based and histogram based traffic congestion detection systems are developed. Developed all system are designed to run as real time application. In this work, ORB (Oriented FAST and Rotated BRIEF) feature extraction method have been used to develop feature tracking based traffic congestion solution. ORB is a rotation invariant, fast and resistant to noise method and contains the power of FAST and BRIEF feature extraction methods. Also, two different approaches, which are standard deviation and weighed average, have been applied to find out the congestion information by using histogram of the image to develop histogram based traffic congestion solution. Both systems have been tested on different weather conditions such as cloudy, sunny and rainy to provide various illumination at both daytime and night. For all developed systems performance results are examined to show the advantages and drawbacks of these systems.
APPLICATION OF FACEBOOK'S PROPHET ALGORITHM FOR SUCCESSFUL SALES FORECASTING ...ijnlc
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
Demand Forecasting of a Perishable Dairy Drink: An ARIMA ApproachIJDKP
Any organization engaged in trading aims to maximize earnings while maintaining costs at
their bare minimum. One of the inexpensive ways to accomplish this objective is through sales forecasting.
Evidence from empirical literature has shown that sales forecasting frequently results in better customer
service, fewer returns of goods, less dead stock, and effective production scheduling. Successful sales forecasting systems are essential for the food sector because of the limited shelf life of food goods and the
significance of product quality. In this paper, we generated sales of forecasts for a perishable dairy drink
using the famous ARIMA approach. We identified the ARIMA (0, 1, 1)(0, 1, 1)12 as the proper model for
modeling the daily sales forecast of the perishable drink. After performing model diagnostics, the model
satisfied all the model assumptions, and a strong positive linear relationship (R
2 > 0.9) was observed when
the actual daily sales were regressed against the forecasted values.
Predicting future sales is intended to control the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfillment of consumer demand can be prepared in a timely and cooperation with the supplier company can be maintained properly so that the company can avoid losing sales and customers. This study aims to propose a model to predict the sales quantity (multi-products) by adopting the Recency-Frequency-Monetary (RFM) concept and Fuzzy Analytic Hierarchy Process (FAHP) method. The measurement of sales prediction accuracy in this study using a standard measurement of Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results indicate that the average MAPE value of the model was high (3.22%), so this model can be referred to as a sales prediction model.
Statistical Process ControlAgendaIntroductionNumber of.docxsusanschei
Statistical Process Control
Agenda
Introduction
Number of Ford Focus sold
Number of Ford Focus recalled
Summary and Recommendations
References
2
Introduction
Statistical Process Control
Method used for quality control
Ford Focus transmission problems
Statistical Process Control is a process that is used for quality control of a item. It monitors and control any process to ensure operations are at the full potential. In this case, Ford has been having many transmission issues with the Focus since 2012.
3
Number of Ford Focus sold
I Chart represents 1k units per month for last 40 months
Second chart: Yearly sales
Sales decreased by 89,133
Sales for the Focus have been on a rollercoaster ride since December 2015. When you observe the yearly sales you can see this vehicle has been on a rapid decline in sales.
4
I Control Chart
Sample Means1234567891011121314151617181920212223242526272829303132333435363738394011131919161719141211991010131413171516131210131171216131611997544721UCL1234567891011121314151617181920212223242526272829303132333435363738394015.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.45277698908495615.452776989084956Mean1234567891011121314151617181920212223242526272829303132333435363738394011.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.47511.475LCL123456789101112131415161718192021222324252627282930313233343536373839407.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.4972230109150447.497223010915044
Time Point
Thousand Per Month Value
Equipped cars with new shifting transmission (bad concept)
First year 3,469 reported.
More on https://highlyscalable.wordpress.com/
Data Mining Problems in Retail is an analytical report that studies how retailers can make sense of their
data by adopting advanced data analysis and optimization techniques that enable automated decision
making in the area of marketing and pricing. The report analyzes dozens of practical case studies and
research reports and presents a systematic view on the problem.
We hope that this article will be useful for data scientists, marketing specialists, and business analysts
who are looking beyond the basic statistical and data mining techniques to build comprehensive
data-driven business optimization processes and solutions.
The CorPeuM mission is to improve the execution of strategy!
The basis of all performance management is in administering an organization’s business activities (sales, marketing, production, product development, etc.) in an environment that is increasingly uncertain. As outlined in ‘What is Strategy Execution?’, a strategy execution system should support the way in which these business processes are planned and monitored. This will require a number of integrated application capabilities, including:
Business Modelling: The system should be able to model an organization’s current and proposed business processes that show how they are connected to achieve the organization’s purpose.
Metric Categories: It should be possible to view that business model in terms of a number of metric categories such as the resources it consumes, the risks being run, the workload being performed, and the outcomes that are generated. Measures from these different categories will need to be displayed in combinations. For example, to show whether an activity is worthwhile requires its costs to be shown, along with the work performed and any outcomes. In addition, these metric views should be tailored to those people responsible for particular areas of the business.
Methodology support: It should adapt to an organisation’s chosen management methodology. i.e. it should conform with the terminology used and the way in which planning activities are prescribed.
Initiative management: It should allow the creation, selection, approval and monitoring of projects/strategic initiatives that improve organizational performance and how they link to corporate goals.
Scenario planning: It should allow combinations of initiatives to be assessed and the side-by-side analysis of alternate business models, through which senior management can set future plans.
Dynamic reports and analyses: It should communicate plans and results through personalised reports, analyses, dashboards, scorecards and strategy maps but in the context of how well the plan is being executed, so that the future can be better managed.
Dynamic workflow management: The system should be able to cope with continuous planning and monitoring of execution, which intelligently involves the right people at the right time, from across the enterprise.
Most people would agree that these capabilities are essential for managing strategy and its execution. Similarly, most CPM software vendors would claim to have these, but as they say, the devil is in the detail.
Application of time series analysis in financial economicsStats Statswork
Time series analysis is widely applied to forecast the pattern/trends in the financial and market data. The main objective of a time series analysis is to develop a suitable model to describe the pattern or trend in data with more accuracy. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.
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Read Chapter 13 from Page 216-234Based on the literature answe.docxcatheryncouper
Read Chapter 13 from Page 216-234
Based on the literature answer the followings questions (each answer is worth 1point):
1. What is a Management Control System? Mention the five steps to follow.
2. What is a standard setting process? How is this process setup?
Chapter I 3
Standard Cost and
Variance Analysis
'Tonifíhí we will uncinpt tn aiuwcr ¡hive
memphvsiral qiiestitmK: I. How did ihv imiver^e come to be?
2. Wttiit is the metmins: of life? tintt.-?. Whui ihc ItcH are staruiani costs all ahoui?"
Standard costs are widely used hy manufacturing organizations.In a survey of management accounting practices conducted inthe early 1990s, 67 percent of the respondents used standard
cost systems.' Moreover, 87 percent of the respondents employed
the same cost system for internal and external reporting.^
This chapter examines standard cost systems in a real-world set-
ting. It covers the advantages and disadvantages of standard cost sys-
316
Standard Cost and Variance Analysis 117
terns, explains the standard-setting process, and shows how to
perform variance analysis. The discussion focuses on manufacturing
entities, the primary users of standard cost systems. Nevertheless, it
also contains a section on standard costs in the service sector that
explains how service organizations can use these costs.
What Are Standard Costs?
Standard costs are predetermined costs that are usually expressed
on a per unit basis.^ They constitute a carefully formulated estimate
of what future costs should be, based on a desired level of productiv-
ity and process efficiency, and a set of assumptions about the operat-
ing environment. The set of factors that can affect standard costs was
discussed in Chapter 10 (see Figures 10-5 and 10-6).
Standard costs generally consist of three major elements: labor,
materials, and overhead. How to calculate each cost element and
perform a cost roUup was discussed in Chapter 10. These procedures
do not change under a standard cost system. However, in a standard
cost system, the management team establishes the standards of per-
formance up-front, such as the amount of labor hours required, the
expected materials usage, the process yield, and normal scrap levels.
These physical standards are priced and then added together to cal-
culate the standard cost of the product or service. Once the standard
cost has been established, it is typically not changed until the next
standard-setting cycle.
Standard Costs as a Management
Control System
Management control is the process by which managers influence
other members of the organization to implement the organizational
strategies. Figure 13-1 depicts the functions of a typical management
control system.** The organization makes plans, implements these
plans, and then has a mechanism to monitor the actual results
against the plan. A standard cost system is part ofthe management
control process. Other management control systems are budgets, per-
formance evaluations, and ...
The purpose of this study is to analyze empirically by using secondary data on the possibility of corporate fraud by using various fraud theory approach. The research model in this study was tested using the ordinary least square (OLS) analysis method. A total of 310 company data were collected which consisted of financial data and other supporting data published by companies listed on the Indonesia Stock Exchange in the range of 2012 to 2017. This study provides empirical evidence that all the variant of fraud theory (fraud triangle theory, fraud diamond theory and fraud pentagon theory) can be investigated for its significant effect on corporate fraud by only using secondary data that are available and freely accessed by the public. The empirically tested research model in this study can provide a comprehensive understanding of practitioners, academics, government agencies and the general public in analyzing the topic of corporate fraud.
Traditional markets in Indonesia were created so that people from all walks of life can fulfill their needs, especially staple food products, without having to spend a lot of money. However, the prices of food products in different markets vary depending on the consumers of the particular market. The aims of this article were to compare the price difference of staple food products in several traditional markets and to find out the factors that cause the price difference. The data were collected by carrying out a survey to five traditional markets around Jakarta regarding the prices of ten staple food products. The data were analyzed quantitatively using statistical calculation ANOVA from SPSS version 22, and also qualitatively to discuss several factors underlying the price differences. Results revealed that price differences of staple food products were not only caused by market location, but other factors such as pricing strategy and consumer specification. This research implied that traditional markets were still chosen by Indonesian consumers to fulfill their needs because of the competitive price.
Airport enterprise innovation performance is a crucial issue that planners, decision makers and managers should focus in order to drive the airport enterprise performance towards sustainable development. The strategic infrastructure needs, and investments need to include improvements across all major factors that affect the innovation dimension of sustainable development.
Key objective of the paper is to highlight the challenges in airport enterprise management towards sustainable development in terms of innovation improvement. A performance evaluation towards innovation and sustainable development framework is adopted and a case study application highlights the crucial role of airport enterprise management performance innovation dimension towards sustainable development. Conventional wisdom is to stimulate the interest on topic and promote a framework addressing to evaluate airport enterprise management performance towards innovation and sustainable development.
In the business world, companies need high performance. Performance is the result or overall success rate of a person over a period of time in carrying out tasks compared to various possibilities, such as predetermined standards of work, targets, or criteria. The purpose of the study was to analyze the influence of intellectual intelligence, emotional intelligence, and spiritual intelligence on employee performance. The population in this study were 63 employees of PT PLN (Persero). This study uses quantitative associative, with data analysis used is multiple linear regression analysis. The results showed that both intellectual intelligence, emotional intelligence, and spiritual intelligence had a positive and significant effect on employee performance. Intellectual intelligence has the greatest influence on employee performance, followed by spiritual intelligence and emotional intelligence. Intellectual intelligence, emotional intelligence and spiritual intelligence together have an effect of 52.4% on employee performance, and the remaining 47.6% is influenced by other factors not explained in this study.
The main purpose of the research study is to analyze the effect of organizational commitment, job satisfaction and work insecurity as well as their impact on the performance of Bank Aceh Syariah. The samples of the research are 209 employees which are selected with survey methods. Data was collected by using questionnaire, and then the data was analyzed with statistical methods of structural equation model (SEM). The study found that the organizational commitment and job satisfaction have a negative effect on turnover intention, but positive effect on the performance of Bank Aceh Syariah. The work insecurity has a positive effect on turnover intention, but negatif effect on the performance of the bank.
This study aims to test the effect of employee engagement and organization trust on organization citizenship behaviour and its impact on organization Effectiveness. The object of this research is the government organization of Pidie Jaya with Echelon IV Officers as a respondent. The number of sample is determined by using proportional sampling technique and Slovin equation, and it provides 171 respondents. Data is analyzed using the path analysis with the SPSS program assistance. The findings describes that employee engagement, organization trust, organization citizenship behaviour and organization Effectiveness have been going well. For the verification test of direct effect provides: employee engagement effects organization citizenship behaviour; organization trust effects organization citizenship behaviour significantly; employee engagement effects organization Effectiveness significantly; organization trust effects organization effectiveness significantly, and; organization citizenship behaviour effects organization Effectiveness significantly. These all findings prove that the previous theories are still applicable, and these also apply in Government organization of Pidie Jaya District. The originality of this research is in its novelty in term of the object, time, and statistic approach. This result contributes to academic and research area in order to develop the next model and method. For the practical, this has verified that the variables in this research need more attention from the managers especially in organization related.
This paper is an analysis on the impact machine learning, Artificial Intelligence, and robotics has on the supply chain management. The analysis covers the basis of AI in the SCM mechanisms while defining it from the ground up. Later on, to shed a true light on supply first the paper zooms in on the effects of machines in marketing. From what particular methodologies are deployed in today’s environment extending all the way to its anticipated outcomes. As the reader progresses he/she will find valuable studies on the main segments of machine learning within the supply chain itself. Certain novelties and innovations are scrutinized regarding SCM alongside these studies. These innovations are exemplified by certain cases presented in Part 3. The penultimate section briefly examines the possible drawbacks of the surge in machine application in SCM. The final section compiles the ideas presented in the paper as a whole and gives a glimpse of an estimate for the near future.
Huang (2018) decomposes the differences in quantile portfolio returns using distribution regression. The main issue of using distribution regression is that the decomposition results are path dependent. In this paper, we are able to obtain path independent decomposition results by combining the Oaxaca-Blinder decomposition and the recentered influence function regression method. We show that aggregate composition effects are all positive across quantiles and the market factor is the most significant factor which has detailed composition effect monotonically decreasing with quantiles. The main decomposition results are consistent with Huang (2018)
In Kenya, the newly promulgated constitution of 2010 (CoK, 2010), provides the basis of monitoring and evaluation as an important tool for operationalizing National and County Government projects to ensure projects success, integrity, transparency and accountability. The county governments are responsible for delivering basic services in collaboration with other agencies and partners to enhance quality of life: however, the county government projects has been marred by lack of integrity, transparency, accountability and litany of other monitoring and evaluation weakness which has undermined the impacts and success of projects including Regional Economic Blocs. Lake Region Economic Bloc (LREB) which comprised of fourteen counties bordering Lake Victoria Basin is not sparred either. The study was conducted in six LREB Counties namely, Migori, Homabay, Kisumu, Siaya, Kakamega and Vihiga chosen in a random manner. This study specifically assessed the effectiveness of Monitoring and Evaluation methods on the Performance of County Governments Projects. The study was guided by the theory of change. The research was carried out using descriptive survey design which entails both qualitative and quantitative data collection procedures. The researcher used stratified random sampling techniques to draw a sample from the study population. The qualitative method focused on group discussion and in-depth interviews. The quantitative techniques employed questionnaires to 398 purposively selected subjects from the county projects. Data collection was from two main sources; primary and secondary. Secondary sources included relevant county documents, constitution, legislations, policy documents and reports among others. The Study employed questionnaires, Focus group discussion and Interview guide as its primary data collection method. Statistical Package for Social Science (SPSS) version 18.0 was used for analysis. Data was analyzed using descriptive and inferential statistics techniques and presented in tables and figures. The study findings indicated thatM&E methods, indicated by the coefficient of effectiveness (R2) which is also evidenced by F change 109.403>p-values (0.05). This implies that this variableis significant (since the p values<0.05) and therefore should be considered as part of effectiveness of M&E systems on the performance of County Governments projects. The study concludes that there are no effective and adequate projects monitoring and evaluation methods in place for County Government Projects, which can facilitate the achievement of desired projects performance and outcomes. The study recommends that the County Government should develop a clear M&E methods for each project with clear data collection, analysis, reporting and implementation methods. This Study recommends further research to be conducted in the other Regional County Economic Blocs.
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Innovative work behavior is likely to be an important need for the increasing performance of the hospital to provide the health public services. Theoretically and empirically, the behaviors be related to employee perception on management support, information technology and employee empowerment. The study aims to determine the effect of management supports and information technology on employee empowerment as well as their impact on the innovative work behaviors of the employee of dr. Zainoel Abidin District Hospital Banda Aceh. The study conducted of 302 employees of the hospital. The data collected by questionnaire and then the data is analyzed by statistical means of structural equation model (SEM). The study found that management support and information technology have a positive and significant effect on the employee empowerment and innovative work behavior. The employee empowerment mediates the effect of management supports and information technology on the innovative work behavior.
This research deals with an insight and analysis of the economy projectification in a smaller country, here represented by Croatia. The study was inspired by similar research conducted in Germany, Island and Norway and it is based on similar but partly adapted methodology. The objective of this study is to measure level of economy projectification in a smaller country, and to provide relevant data related for the level of project work. The random sample of 250 companies, from both public and private sectors, was selected across nine sectors of the economy. A stratified random sampling was drawn and interviews were conducted via telephone, so as on-line survey. While analysing collected data and considering the objectives of this paper, only basic statistical analyses were applied for calculating averages and mean values. This study confirmed that projectification trends and figures in a smaller country are similar to those in larger or developed countries. During the period of last five years, the projectification level of the Croatian economy was increased from 27% (in 2013.) to33% (in 2018.). The results show significant difference in projectification among the different sectors of economy, so as changes and trends over the recent time period.
This paper is designed to show how integrated process planning and cross employee planning can be a vital part to any business operation. It will also uncover how different integrated processes and employee relations will help a business to grow. Various topics ranging from enterprise resource planning, integrated planning in supply chains, the non-linear approach, innovation and digitalization coupled with cross training and empowerment, Human resources, and Manager Employee relations complement each other and could bring an organization together. Various thought processes and intellectual reasoning skills were instrumental in all consideration of this project. Many antiquated processes were changed over the years to update operations in the business world where conventional means were not effective. Integrating product planning and employee planning optimized operations both in the product and service industry and I will accent many of these optimizations. With recent technological advances and human relations tactics, project management and organization has been streamlined and works more productively than its predecessors. Regardless of the industry, integrated process planning, and cross employee planning could possible turn a dinosaur into a competitive part of the economy.
One of the problems in big cities are transportation.They solve this problem by providing mass transportation such bus or train. People use this facility to travel between surrounding cities or within the city. Jakarta recently has a new public transportation called TransJakarta which serving people travelingfrom nearby cities and in the city.In order to move or doing business between places people in Jakarta use TransJakarta This research aims to analyse ticket price, service quality and customer value toward customer satisfaction. We conducted a research by using questionnaires given to thepassangers and developed a model using a multiple regression to process the result from questionnaires. Samples were taken from The number of sample for this reseach was 130 customers taken from one bus stop which passengers traveled from BSD City to Grogol and Slipi. The results from partial testing showed that customer value andservice quality have effect on customer satisfaction while ticket price does not have effect on customer satisfaction.
This study aims to examine the mediating effect of Trust in the relationship between Perceived Website Quality (PWQual), eWOM, and Perceived Benefits on Consumer Attitudes Toward Online Shopping in Indonesia. The sample in this research are online shopping consumers in Indonesia there 118 respondents. The design research used a survey model purposive sampling method as a sampling technique. The data analyze in this research used Structural Equation Modeling (SEM) as an analysis technique with AMOS as analysis tools. This research shows that : Perceived Website Quality has a significant effect on Perceived Benefits and Trust, Perceived Benefits and Trust has a significant effect on Consumer Attitudes Toward Online Shopping, Perceived Website Quality has a significant effect on Consumer Attitudes Toward Online Shopping through Trust
Dairying is one of the livestock productions practiced almost all over Ethiopia, involving a vast number of small, medium, or large-sized, subsistence or market-oriented farms. However, the structure and performance of dairy sectors and its products marketing both for domestic consumption and for export is generally perceived poor in Ethiopia due to different challenges. These challenges vary across different production system to another and/or from one location to another. Among other challenges seasonality of production, spoilage (lack of milk collecting facilities), poor animal health and management, inadequate supply of quality feed, low productivity and genetics ,quality problem, weak vertical integration, absence processing plant, inadequate permanent trade routes and other facilities like feeds, water, holding grounds, lack or non-provision of transport, lack of access to land, ineffectiveness and inadequate infrastructural and institutional set-ups, prevalence of diseases, lack of credit and inadequate market information are dominant in Ethiopia. Therefore, market infrastructure facilities, producers cooperative, feed quality and quantity provision system need to be strengthen for effective dairy value chain development.
This research paper examines customer intention to reorder in respect to delivery service and product satisfaction. Our research model includes delivery service, satisfaction and reorder intention. Satisfaction in this research model work as mediating variable. A survey method was adopted to collect data, collected data were analysis using SPSS to see the correlation between variables. A significant relationship was found between delivery service and reorder intention as well as moderating role was also note with satisfaction and reorder intention.
This paper examines the impact of internet use on student performance. In this cross-sectional study, one hundred twenty survey responses were collected from plus two-level students from BirendranagarSurkhet. The respondents were selected from class 11 and 12 students randomly. Frequency of internet use, location of internet use, cooperation from teachers for internet learning and peer group influence on internet use for academic purpose has been analyzed with their academic performance.one sample t test was used to analyze the data. The finding concludes all these variables have positive impact if the student use internet for learning process. Similarly, the analysis shows that the student who used internet at home for learning purpose has found highest academic achievement.
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A New Sales Forecasting Model for International Restaurants
1. www.theijbmt.com 26| Page
The International Journal of Business Management and Technology, Volume 1 Issue 1 September 2017
Research Article Open Access
A New Sales Forecasting Model for International
Restaurants
Scott Miau
Department of Management Information Systems of National Chengchi University (NCCU), Taiwan.
Abstract
Competitive advantage for e-business requires more accurate information and precise decision to aid international
companies to analyze and predict sales forecasting trend optimizing potential profits and reduce losses. We propose an
improvement model to minimize the forecast error for the time series of daily sales information. We use real
international restaurant data as the basis to show the performance of our sales prediction model. Multiple time series are
considered in this model to vastly improve the forecasting outcome. Those data series are combined from EPARK
Company and open data like weather, into a multi-series data, our forecast model extend the previous predecessor
tremendously. Various residue computations during the process are compared and discussed. We applied the model
for data from different area to compare the difference respectively. Result shows a proper selection of computation
method is more dynamic than a fixed method for shops in different geographic area even within the same company. In
addition, analysis shows significant error reduction in forecasting achieved when open data like weather information is
included in the regression process. Thus international business can be more agile and flexible for just-in-time stock
inventory and better resource allocation strategy.
Keywords: ARIMA, Regression, Sales Forecasting, Neural Network, Open Data
I. INTRODUCTION
Increasing demand for optimal quantity inventory stock and correct resources is an epidemic trend in the e-
business world. Especially important f o r retailer shop with obsolescence prone goods and restaurants with
perishables merchandise. A mismatch between products/resource availability may lead to economic losses either by
excessive stocking, or can lead to service delivery failure, customer loss and hence revenue loss. Business can learn
empirically and affect customer patterns by the products they offer, but there are other variables that are beyond the
control of a manager. Such variables could have a dramatic effect on customer behavior. One obvious factors are
promotion and holidays. Others impact factors like weather, shop size and shop location, etc. Sales forecasting
naturally be the top knowledge to acquire for a successful business.
The forecasting model is a process and a group of methods to predict the sales. Developing systematic strategy
and process in both firm level and individual store level for forecasting in needed. Goods to be bought and sold,
supply chain management and human resource management, space management main work in planning of store.
The basic information provided by the existing store is very important in the prediction of sales is the key data sets
that should be used to analyze and report on in the industry.
Towards realistic forecasting model and process, one must also consider many market factors like promotion
(price), holidays, population of the area, personal income may also have impact on sales forecasting(Lee and
Rojas,[1][2]). The final forecasting models are determined by those combined factors. However, to simplify our
research, we first select open data-weather data to our model, other market factor like promotion and holidays are
not considered here initially. According to our developed model, any further factor can be also combined to our
forecasting process by add further stage or step.
Both stated in Lee[1],Chen[3], and competition (Kaggle, sales prediction contest in 2015) result shows weather is
an important factor for forecasting sales. In the Kaggle contest also shows the best method is using a hybrid ARIMA
model, with carefully add some parameters to reduce the forecasting residue. That is, hybrid traditional method is
even better than the new deep learning methods.
Different business have different business model. More details of the sales trend can be observed if the data
is aggregated in a daily way. Daily data shows data patterns more clearly. Daily pattern is also useful for forecasting
revenue and resource management. Many predictive models were developed and compared in this way. In this paper
2. www.theijbmt.com 27| Page
A New Sales Forecasting Model for International Restaurants
we first restaurant sales data to investigate our model and build forecasting. And then weather data of restaurant
area are combined to the investigation process by different process.
Those companies have international restaurants is especially studied in this paper. We can investigate
branch/shop sales in different country/area and the impacts may vary individually. And the weather pattern in
north area is different from south area. Different weather pattern may have different degree of impact on the sales.
So we further analyze the same restaurant data but different branch located in two areas.
Like the data in Fig 1, we obtained the sales data from EPARK Company, an international business have ten plus
restaurants in Japan and eight restaurants in Taiwan. We selected two biggest restaurants, one in Taipei and one in Japan,
for the comparison in the analysis process. The tables in the database are exported from the company ERP-like system and
illustrated in Fig.1. Basically it is one consumer log table recording every sale for different restaurants in Taiwan and Japan.
Each entry in this table includes fields such as shop ID, time, guest ID, entry time, and payment of the guest, and guest
stay duration in restaurant. This data are filtered time ranges from Jan. 1, 2016 to June 31, 2017. Wefirstaggregated the entries
by sum of those payments into daily records for Japan and Taiwan, respectively. Thus basically two new tables, each contains 365 records
areusedintheanalysisprocess.
Weather data is gathered from archive web of Japan weather bureau and Taiwan Central bureau.The data parameter includes
daily average temperature, daily average atmospheric pressure, and daily average rainfall is formed in the weather table for Japan and
Taiwan,respectively(Fig.2).
Step 1 is the data cleaning process as in Livera [4], then Monthly and weekly season ality trend is first
processed before the three models applied. At the same time, weather data monthly and weekly season ality trend is
also processed. And after that, three for ecasting models are applied on this data in order to compare the irpredictive
performance.
The three models used in step 2 are:
ARIMA
Neural Network
HYBRID
The ARIMA and Neural network method is often used in time series analysis. In some cases ARIMA method
performs better but in other time series analysis case, neural network performs better. In other cases, hybrid method
mixed these two methods even performs better than these two methods. Zhang [5] pointed out that a real-world time
series generally contains both linear and nonlinear patterns. And thus we use weighted hybrid method to mix the liner
ARIMA and the non-linear Neural Network.
After the basic analysis, the three forecasting series are derived from the three models and are compared (step 3).
Residues are compared and we can choose the excellent one for further analysis. The best models are continued
investigated by correlating the residue with the weather data. If the correlation is independent enough, we conclude that
whether data has no impact in forecasting the sales. The analysis is ended at this step then the forecasting model is
Figure. 2 Weather data from
Taiwan Central weather bureau
Figure. 1 Data source from EPARK company Taiwan.
3. www.theijbmt.com 28| Page
A New Sales Forecasting Model for International Restaurants
determined from the best analysis method we select (Step 4). Otherwise if we find the correlation is dependent, it means
the residue can be more predictable by other factor like weather, so we continues the step 5,
Multivariate Auto-regression(VAR) and Granger causality test (GCA)[6]
As stated in the above, the data are sequentially computed as the process in Fig. 3
However, since the three different methods are used on the two data series (from two selected restaurants)
respectively. Each one may have different result and different forecasting. After the VAR and GCA, residue of the
VAR is computed and then approximated by the neural method. The final forecast is thus a combination of linear and
non-linear regression.
II. Methods
In our proposed model, first we use ARIMA/Neural net/Hybrid methods to know which model predict the
best result. Then after the performance comparison, we investigate if the weather factor is dependent or independent. By
using the multi-autocorrelation method for the residue and the weather, If the correlation is strong enough, we start the
vector autocorrelation regression (VAR) method with testing process(GCT) to get the better forecasting result.
Otherwise the process is stop and one of the three methods is the best forecasting method.
2.1 ARIMA
The first time series analysis method to analyze historical patterns is ARIMA. The ARIMA models,
pioneered by Box and Jenkins [7] are the most popular and effective statistical models for time series forecasting. These
are based on the fundamental principle that the future values of a time series are generated from a linear function of the
past observations and white noise terms. It is basically composed of three methods:
AR= auto-regression,I = integrated, and MA=moving average.
If a process Yt has an ARIMA (p, d, q) representation, that has an ARIMA(p,q) representation as presented by the
equation below:
t
q
p
p
pt
d
uLLLLLLY )...1()...1( 2
21
2
21
The typical process used in our caseis as follows:
Figure 3. The data analysis model we proposed
4. www.theijbmt.com 29| Page
A New Sales Forecasting Model for International Restaurants
Step1: Examine the sales data
Clean up any outliers or missing values,take a logarithm of a series to help stabilize a strong growth
trend
Modeling daily level data at least multiple seasonality levels. For the sake of simplicity, we
model the smoothed series of weekly moving average (MA).
Step2: Decompose your data to remove trends or seasonality.
Step3: Autocorrelations and choosing model order,choose order by examining ACF and PACF
Step4: Evaluate and iterate the process, Check residuals, which should have no patterns and be normally
distributed, Compare model errors and fit criteria such as AIC or BIC.
Step5: Calculate forecast using the chosen model and see the accuracy.
2.2 Neural Network
Artificial neural networks are forecasting methods that are based on simple simulated mathematical models of
the brain. They allow complex nonlinear relationships between the response variable and its predictors. A neural
network can be thought of as a network of “neurons” organized in layers. The predictors (or inputs) form the bottom
layer, and the forecasts (or outputs) form the top layer. There may be intermediate layers containing “hidden neurons”.
In the hidden layer, this is then modified using a nonlinear function such as a sigmoidto give the input to the next layer.
This tends to reduce the effect of extreme input values, thus making the network somewhat robust to outliers.
The parameters b1, b2, b3 and w1,1,…,w4,3 are "learned" from the data. The values of the weights are often
restricted to prevent them becoming too large. The parameter that restricts the weights is known as the "decay
parameter" and is often set to be equal to 0.1.
With time series data like our data, lagged values of the time series can be used as inputs to a neural network.
Just as we used lagged values in a linear auto-regression model, we can use lagged values in a neural network auto-
regression.
Like the commonly used analytic tools, we use ANN(p,k) to indicate there are p lagged inputs and k nodes in
the hidden layer. For example, a ANN(9,5) model is a neural network with the last nine observations (yt−1,yt−2,…,yt−9)
used as inputs to forecast the output yt, and with five neurons in the hidden layer. A ANN(p,0) model is equivalent to
an ARIMA(p,0,0) model but without the restrictions on the parameters to ensure stationarity.
2.3 Hybrid (ARIMA,Neural network)
We propose a hybrid approach that adopts both ARIMA and Neural Network described in Section 2.2 to
adequately model the linear and nonlinear correlation structures of a time series, after a prior decomposition of the
series. This approach accumulates the unique modeling strengths of ARIMA and neural network. The proposed
method is implemented on two time series and its forecasting accuracy is compared with that of individual using
ARIMA and Neural network. Evidently, neither ARIMA nor Neural network is universally suitable for all types of time
series. This is because almost all real-world time series contains both linear and nonlinear correlation structures among
the observations. Zhang [5] has pointed out this important fact and has developed a hybrid approach that applies
ARIMA and Neural network separately for modeling linear and nonlinear components of a time series. However, we
use time saving method by using weighted average of the ARIMA forecasting and neural forecasting. i.e:
Hybrid forcast = ARIMA_forcast*ARIMA_weight+ Neural_forcast* Neural_weight
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A New Sales Forecasting Model for International Restaurants
Where the ARIMA_weight + Neural_weight is equal to 1. The two weight are defined case by case depends on
time series.
2.4 Vector Autocorrelation Regression (VAR) and Neural on residue approximation
If the correlation of the sales residue produced from three models and weather is strong enough, we use vector
autocorrelation Regression (VAR) to produce the final forecast. I.e. the pseudo code:
FORCAST_METHOD= BEST_METHOD in (ARIMA, Neural, Hybrid);
If correlation(series of FORCAST_METHOD, series of WEATHER) is significantthen
{
FORCAST_METHOD= VAR and performs GCA TEST(section 2.5);
Neural_on_VAR_residue;
}
A VAR is a linear system contains a set of m variables, each of which is expressed as a linear function of p lags
of itself and of all of the other m – 1 variables, plus an error term. (It is possible to include exogenous variables such as
seasonal dummies or time trends, but we focus on the simple case.) For simplicity, we use only two variables to denote
this, x and y, an order-p VAR would be the two equations:
βxyp represents the coefficient of y in the equation for x at lag p.
According to Zhang[6], the original data is compose of linear and non-linear component, i.e.
Where, ytis the observation at time t and Lt, Nt denote linear and nonlinear components, respectively. At first, the three
methods are fitted to the VAR linear component and the corresponding forecast ˆLt at time t is obtained. So, the residual
at time t is given by et= yt–L^
t., the residuals dataset after fitting model contains primary nonlinear component and so
can be properly modeled through an Neural network method(ANN). Using p input nodes; the ANN for residuals has
the following form:
Where f is a nonlinear function, estimated by the ANN and εtis the white noise. If ˆNtis the forecast of this ANN, then
the final forecast at time t is obtained as following:
2.5 Granger causality test
We use Granger causality analysis [6] to again verify the final result. Granger causality analysis (GCA) is a
method for investigating whether one time series can correctly forecast another one. This method is first based on
multiple regression analysis. At individual level, many studies performed F statistics on the residuals in Goebel [8]. A
recent study by Chen [9] used signed path coefficients to perform t-test at group level statistics.
If we have two time series X and Y, the paired model is as following:
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A New Sales Forecasting Model for International Restaurants
p
n
p
n
ttptnptnt
ECZYBXAY
1 1
)()(
p
n
p
n
ttptnptnt
EZCXBYAX
1 1
'''
)(
''
)(
'
Where t
X and t
Y represent the two time series at time t. )( pt
X
and )( pt
Y
represent the time series at time t-p, p
representing the number of lagged time points (order). n
A And
'
n
A are signed path coefficients. n
B And
'
n
B are
auto-regression coefficients. t
E And
'
t
E are residual. We followed Chen’s[8] extended Vector Auto-regression model
which took the co-variables t
Z at time t into account, instead of regressing them out before GCA.
III. Results
We show the steps in this section with plots and error table. For the first step in our process shown in Fig.3. The original
data of the two restaurants:
Step 1:
According to the process we stated in Fig 3. Step1 we start the ARIMA process first. We have the two de-seasonal from
the two data series.
Figure 4. Original data (TOP: Taiwan, BOTTOM: Japan)
Figure 5. Original temperature data (TOP: Taiwan, BOTTOM:Japan)
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A New Sales Forecasting Model for International Restaurants
Step 2.
Autocorrelation plots are done to see if our data is stationary. These plots can also help to choose the order
parameters for ARIMA model. If the series is correlated with its lags then, generally, there are some trend or seasonal
components and therefore its statistical properties are not constant over time.
ACF plots display correlation between a series and its lags. In addition to suggesting the order of differencing,
ACF plots can help in determining the order of the MA (q) model. Partial autocorrelation plots (PACF), as the name
suggests, display correlation between a variable and its lags that is not explained by previous lags. PACF plots are
useful when determining the order of the AR (p) model. In the Fig. 8 you can see that the lag is significant at various in
both Taiwan and Japan data. We will do the difference data for the further analysis.
Figure 6. The decomposition of data (TOP: Taiwan, BOTTOM: Japan)
Figure 7. The ACF and PACF of original data (TOP column: Taiwan, BOTTOM column: Japan)
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A New Sales Forecasting Model for International Restaurants
ACF plots for the differenced data is also display. In addition to suggesting the order of differencing, ACF plots
can help in determining the order of the MA (q) model. Partial autocorrelation plots (PACF), as the name suggests,
display correlation between a variable and its lags that is not explained by previous lags. PACF plots are useful when
determining the order of the AR (p) model. In the Fig. 8 you can see that the lag is significant at 7 in both Taiwan and
Japan data. It reveals the real weekly sales cycle.
Figure 8. The ACF and PACF of the differenced data (TOP column: Taiwan, BOTTOM column: Japan)
Figure 9. The residue and ACF/PACF after ARIMA process (TOP 3: Taiwan, BOTTOM 3: Japan)
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A New Sales Forecasting Model for International Restaurants
Two of the most widely used are Akaike information criteria (AIC) and Bayesian information criteria (BIC) are
used to find the best fit. These criteria are closely related and can be interpreted as an estimate of how much
information would be lost if a given model is chosen. When comparing models, one wants to minimize AIC and BIC.
We found that the best combination for Taiwan and Japan data is ARIMA (4,1,7) and ARIMA(2,1,3), after the step the
forecast model is plot in the following Fig. 10.
The neural network ANN is also applied to our two data series. And after that the hybrid method is also applied.
Figures are listed as follows. Fig. 16. Show the neural net graph and Fig.11 shows the forecast plot.
Figure 10. The ARIMA forecast (Left: Taiwan, Right: Japan)
Figure 11. The Neural network method forecast (Left: Taiwan, Right: Japan)
Figure 12. Hybrid method forecast (LEFT: Taiwan, RIGHT: Japan)
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A New Sales Forecasting Model for International Restaurants
Step 3.
The three forecast method is performed and are result comparisons are listed in Table 1. After that we choose the best
method.
Step 4.
In table1, we can see that the error value (ME/MASE) is compared for the above three methods. Not only one
method performs best for the data series. For Taiwan data, ARIMA performs the best and for Japan data. Neural
network performs the best. We conclude that even in the same company, different shop/restaurant have the different
sales forecasting model. This will lead the manager to define a better stock and resource management policy.
The next step we are trying to see if the weather impacts the sales. We first investigate the correlation of the
residue of the best method (Taiwan=ARIMA and Japan=Neural Net ANN) forecast model and the weather. Figure 13
plot the auto-correlation graph of Taiwan and Japan respectively. The correlation plot shows the Taiwan is stronger
than Japan at some lag point, i.e. lag point at 15, 16 and 18. So we continue the analysis process to identify this. The
GCT test is used for Taiwan temperature against Taiwan sales and the result is as follows:
Model 1: d.temp ~ Lags(d.temp, 1:8) + Lags(d.co2, 1:8)
Model 2: d.temp ~ Lags(d.temp, 1:8)
Res.Df Df F Pr(>F)
1 339
2 347 -8 0.9737 0.4564
This result of F PT(>f) is 0.454 shows the impact of temperature is possible have impact on sales in Taiwan. So the
improvement of the forecast is possible if we consider more factors in the regression process.
Step 5.
The final step we use VAR method to identify more about the residue improvement. Fig. 14 and Fig. 15 show the
improvement forecast and the finally result comparison are in Table 1.
Figure 13. Correlation of sales-weather (LEFT: Taiwan, RIGHT: Japan)
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A New Sales Forecasting Model for International Restaurants
Step Accuracy Taiwan Japan
ARIMA ME
RMSE
-0.436675
47.30344
-0.22
6.13
Neural Net ME
RMSE
-0.315679
59.65637
-0.01174383
2.268116
Hybrid ME
RMSE
1.228578
48.45943
0.02235358
6.118572
VARNeural ME
RMSE
-0.4283873
23.688975
No need because process
stop at step 4.
IV. Conclusions and Discussion
We have conducted experiments with four different real-world time series scenarios (restaurants and
weather) on R and R-Studio. Different analysis model has the different predictive performance and need a selecting
process in several methods to identify the best solution applicable to each individual situation. In this research
studies indicates that there is valuable information when collaborating weather data into the restaurant sales forecasts.
Proposal step-by-step combination analysis model for the sales and weather database information. Using these
predictive analysis tools allows decision maker and senior managers to gain a sharper edge response to the sensitive
market changes. Also a deeper understanding of consumer behavior for better quality of service and make more
precise strategic planning decisions.
Another advantage of our forecasting model is to more accurately prepare and allocate resource for front and
backend service staffs. Based on the employees time table shift and geographically location the manager is able to
Figure 15. LEFT: the ANN in our model, RIGHT: The Taiwan VAR series forecast (sales and weather forecast)
Figure 14. Taiwan weather sales correlation (TOP: original, BOTTOM: differenced)
Table 1. The methods we use in our process and its error listed.
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A New Sales Forecasting Model for International Restaurants
allocate from employees from other areas where service supply and demands can be matched. Anticipating the
decline in customer rate can be overcome through marketing promotions and other campaign methods to remain in
the business competitiveness. Our proposed model also shows linear methods are always computing time efficient
than non-linear methods. Use linear method in combination with the nonlinear methods can more accurately
approximate the residue and deduce the computation time in the forecast model.
From the aggregated data indicates the forecast weather condition does have an impact forecasts sales force
some restaurants. However, there are limitations for sometimes lots the weather contributes positively to the forecast
while in others it contributes negatively. For future works, we would like to add more micro studies with correlated
independent variables and interpret these bias factors to the forecast model, such as age, gender, personal income, and portal
device used. From the macro level, would also like to measure the logistics distance to the mass rapid transportation facilities,
population density to the area, and other relevant open data to this model that can identify the impact factor and improve the
residue computation of the forecasting model.
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