Forecasting the Daily Sales of a Franchise

Historically, restaurant managers used either historical data or simple logical methods to estimate customer numbers or sales volume. These techniques usually consist of an intuitive prediction based on the experience of the manager. However, restaurant sales forecasts are a complex task because they are influenced by numerous factors that can be classified as time, weather conditions, economic factors, and random events. In this case, old techniques may give insufficient results. It is aimed to compare the estimation Simit which is one of the most consumed daily snacks in Turkey sales accuracy of the learning methods and determine the model that provides the highest accuracy and determine the factors affecting the buying behavior of one of the leading Simit chain stores in Turkey in the food sector by using popular machine learning algorithms.

Multi-Criteria Decision Making in Manufacturing Systems

In this study, an Industry 4.0-oriented electronical goods producer company's smart facility location selection problem is analyzed. The proposed problem is evaluated under environmental, economic, social, and technological criteria. The relationship between criteria are analyzed with interpretive structural modelling (ISM) and Matrice d'Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) methods. ISM method is used to assess the mutual relation of the criteria and their dependencies, whereas the MICMAC method is used to identify the importance of criteria based on their driving and dependence power. It is expected the methods used in this study which are related to evaluation of the criteria affecting the selection of the plant for a smart factory and the results of it will be useful for decision-makers and practitioners to categorize and differentiate the criteria. This study will be one of the first spearheading research to evaluate the criteria for establishing a smart factory.

Data Linkage Methods for Big Data Management in Industry 4.0

In recent years, the use of various digital devices that continuously generate massive amounts of heterogeneous, structured or unstructured data has increased. In parallel to generation, data collection, storage, and analysis technologies have developed. Big data sources have a variety of data quality. Preparing and clearing data is one of the first step of mining big data. It is often important to address the full data set found in different data sources to achieve the right result. Various techniques have been used to increase the accuracy of the data comparison. Deterministic and probabilistic linkage algorithms are the two main techniques used in literature. They have different steps to reach qualified and integrated results. To easily interpret the results of the linkage algorithm, a confusion matrix can be used. Measurements such as sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate, are considered to evaluate output quality.

Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining

In recent years, multi-criteria decision making (MCDM) is a significant part of operations research (OR) and has become an interesting topic to researcher who works in the data mining (DM) field. The aim of this chapter is to provide an in-depth presentation of the contribution of MCDM in the field of DM. In order to develop a reliable knowledge base on accumulating knowledge from previous studies, we present a review of the usage of MCDM methods in DM field. The chapter presents methodology and application. The result shows that the most usage of MCDM in DM consists of evaluating classification algorithms, weighting criteria, and ranking association rules and clusters. Finally, some future research directions are suggested at the end of chapter.

Data Management and Big Data Analytics

The internet is creating 2.5 quintillion bytes of data, and according to the statistics, the percentage of data that has been generated from last two years is 90%. This data comes from many industries like climate information, social media sites, digital images and videos, and purchase transactions. This data is big data. Big data is the data that exceeds storage and processing capacity of conventional database systems. Data in today's world (big data) is usually unstructured and qualitative in nature and can be used for various applications like sentiment analysis, increasing business, etc. About 80% of data captured today is unstructured. All this data is also big data.

A Smart Recyclable Waste Collection System Based on IoT and an Application

Waste management is the method developed to eliminate the negative effects of waste to the environment and human health. The study focuses on the subject vehicle routing. The purpose of this study is to minimize the distance of route for the vehicles which pick up recyclable items collected in containers. For this purpose, a decision support system is proposed based on the internet of things. This is to ensure that the vehicles are routed to filled containers only thanks to the data obtained from sensors. In the chapter, a municipality's recyclable waste container location information was collected and resolved. As a result of the study, the route costs developed by IoT application and the costs incurred in conventional locating results were compared. Finally, the issues that can be improved in relation to the problem have been evaluated.

Big Data Analytics

The idea that we can get value from data has been discussed, but the main challenge is to use data effectively in order to facilitate smarter and better decision making and surpass our competitors. The change leaders in organization are now dealing with big data from both within and outside the enterprise, including structured and unstructured data, machine data, online and mobile data to supplement their organizational data pool and provide and facilitate the way through which the businesses can compete and operate successfully. Companies that invest in big data can have a distinct advantage over their competitors. Therefore, in this chapter, the concepts of big data analytics along with the relevant description of different categorization, capabilities, challenges are firstly explained, and then big data analytics techniques and methods are introduced and discussed to make the readers familiar with the way big data is applied in the enterprises.

Waste Management in Thermal Power Plants

Environmental pollution and clean energy is the main challenge faced by all over world. The thermal power sector is famous as the highest creator of energy, but still it is blamed for creating Environmental pollution. So, they are trying their best to help themselves on sustainability issues. Basically, Indian powerplants are not only focusing on Sustainable Issues but also trying to develop a sustainable supply chain strategy to carry out their operations while respecting social as well as environmental issues. Sustainable supply chain management(SSCM) practices of thermal power plants mostly dependent on the practice of utilizing waste, water, energy, ash, and taking care of environment in such a manner that social, environmental, and economic factors should not be affected. So, in this chapter sustainable supply chain management practices of Indian thermal power sectors are focused, analyzed, and ranked by Maut method. Simultaneously, their interrelation and correlation are found.