• “A system comprising a set of interacting physical and digital components, which may be central-ized or distributed, that provides a combination of sensing, control, computation and networking functions, to influence outcomes in the real world through physical processes” (Boyes et al.,2018).
I-CPS is defined as a vertical industrial system based on cyber and physical systems (H. Xu et al., 2018), as depicts in Figure6. Every real physical object has at least one cyber representation, and each cyber system can be associated with a physical representation (Colombo et al., 2017). I-CPS provides productive and efficient manufacturing and automation, and enable the monitoring and control of industrial physical processes (Colombo et al., 2017; H. Xu et al., 2018). The I-IoT represents the integration of communication/network layer of theI-CPS(H. Xu et al., 2018(H. Xu et al.,2018).
Prepare Produce
Transport Utilize Recycle
Control
Computing Networking
Physical Systems Data Cyber Systems
Figure 6: I-CPSArchitecture adapted from (H. Xu et al.,2018)
deal with semi-structured problems”(Turban et al.,2011). The termIntelligent Decision Support System (IDSS)was termed at the early 1980s and aimed to embed the artificial intelligence and expert system tool intoDSS(Arnott & Pervan,2016; Kaklauskas,2015). It consisted of an interactive tool for decision making for the well-structured decision, planning situation that used expert system techniques and specific decision models (Arnott & Pervan,2016).
After a few years, in the early 1980s emerged the concept ofExecutive Information Systems (EIS)from theDSSand this new concept expanded the computerized support to top-level managers and executives through the introduction of the Online Analytical Processing (OLAP) tool that enables users to analyze multidimensional data interactively from multiple perspectives (Turban et al.,2011).
The termBusiness Intelligence (BI)was proposed by Howard Dresner in 1989 and it gained a widespread attraction until the early 2000s (Arnott & Pervan,2016; H. Chen et al.,2012; Turban et al.,2011). The change from executive information systems toBI is warranted through the introduction of dimensional modeling and data warehousing concepts (Arnott & Pervan,2016; Turban et al., 2011). TheBIconcept consists to combine architectures, tools, databases, analytical tools, applications, and methodologies (Turban et al.,2011).
Finally, the concept of BA has also risen, and Thomas H. Davenport popularized this new concept through his widely real professional article in the Harvard Business Review in 20006 (Arnott & Pervan, 2016; H. Chen et al.,2012). It emerged by the junction of theBIwith a set of new capabilities, such as optimization, forecasting, predictive modeling, and statistical analysis. Davenport and Harris definedBA as“the extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and factbased management to drive decision and actions”(Arnott & Pervan, 2016). Because the definition provides by Davenport is quite similar to the definition of BI and a wide range of practitioners do not distinguish the differences betweenBAandBI, H. Chen et al. (2012) suggested regarding the Davenport’s definition that the termBA represents the key analytical component in BI. Consequently, is proposed Business Intelligence and Analytics (BI-A)as a unified concept (H. Chen et al., 2012). All these terms, IDSS,BI,BAoriginated from the evolution of theDSSare illustrated in Figure7.
There are four levels of analytics: Descriptive, Diagnostic, Predictive and Prescriptive analytics. The value chain model of analytics developed by the Gartner Group represents a better visualization of these levels, as depicted in Figure8:
• Descriptive Analytics - consists to answer the question such as “What happened?”, this is, consists to know what is happening in the organization (Delen & Demirkan, 2013; Koch et al., 2015; Sharda et al.,2015; G. Wang et al.,2016);
• Diagnostic Analytics- is related to the question“Why did it happen?”, this is, consist to identify the cause of the problem (Koch et al.,2015; Sharda et al.,2015);
• Predictive Analytics - attempts to answer the question“What will happen?”, this is, aims to determine what is likely to happen in the future (Delen & Demirkan, 2013; Koch et al., 2015;
1960s
1970s
1980s
1990s
2000s
2010s
Computer-based
Information Systems Operations Research Management Science
Transition Processing &
Reporting Systems Optimization &
Simulation Models
Behavioral Decision Theory
PERSONAL DECISION SUPPORT SYSTEMS Artificial Intelligence
Expert Systems
INTELLIGENT DECISION SUPPORT SYSTEMS
Knowledge Management, Organization
Learning
KNOWLEDGE MANAGEMENT -BASED DSS
Data Base Threory OLAP
Dimensional Modeling
EXECUTIVE INFORMATION SYSTEMS
DATA WAREHOUSING
BUSINESS INTELLIGENCE
BUSINESS ANALYTICS Social Psychology
Group Behavior & Processes
GROUP SUPPORT SYSTEMS
NEGOTIATION SUPPORT SYSTEMS
Negotiation Threory
Optimization, Forecasting, Predictive Modeling, Statistical Analysis
Figure 7: Overview of the evolution ofDSSadapted from (Arnott & Pervan,2016)
Sharda et al., 2015). It uses mathematical algorithms and programming to find explanatory and predictive patterns within data (Delen & Demirkan,2013; G. Wang et al.,2016);
• Prescriptive Analytics - aims to answer the question“How can we made it happen?”, this is, aims to recognize the likely forecast and decision- make to achieve the best performance (Delen
& Demirkan, 2013; Koch et al., 2015; Sharda et al., 2015; G. Wang et al., 2016). Prescriptive analytics include multi-criteria decision-making, optimization, and simulation (Delen & Demirkan, 2013; G. Wang et al.,2016).
The Predictive and Prescriptive analytics are crucial elements in helping companies to make an ef-fective decision regarding the strategic direction of the organization. For problems such as the changes in organizational culture, sourcing decisions, supply chain configuration, and design and development of products or service, these two levels of analytics can be applied (G. Wang et al.,2016).
2.2.2 Machine Learning and Predictive Analytics
Machine Learning (ML)is considered a branch ofArtificial Intelligence (AI)technologies that focus in the design and development of algorithms that allow computers learn based on historical data (Turban et al., 2011). ML uses computers programs to automatically learn complex patterns and make intelli-gent decisions based on data (Domingos, 2012; Han et al., 2012). ML has three different categories:
Supervised learning, Unsupervised learning and Reinforcement learning (Han et al.,2012; Turban et al., 2011).
Difficulty
Value
Information
Optimization
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
What happened?
Why did it happen?
What will happen?
How can we make it happen?
Figure 8: Gartner’s Value Chain Model of Analytics adapted from (Koch et al.,2015)
• Supervised learning- this process consists to induce knowledge from a set of observations that include known outcomes, this is, algorithms learn from labeled data (Han et al., 2012; Turban et al., 2011). The supervised learning algorithms include classification and regression (Russel &
Norving,2010; Turban et al.,2011);
• Unsupervised learning - consists of discovery knowledge from a set of data without explicit outcomes, this is, the algorithms learn from unlabelled data (Turban et al., 2011). Typically, is used clustering to discover classes within the data (Han et al.,2012). The unsupervised learning algorithms include clustering segmentation and association (Turban et al.,2011);
• Reinforcement learning - in this process the algorithms learn from a series of reinforcements – rewards - good-result information or punishments - bad result information (Russel & Norving, 2010). This category differs from supervised learning because there is no historical data to learn, and from unsupervised learning because there is no natural grouping of things. This type of learning is applied to control the flight of helicopters, in autonomous search robots and other situations. The reinforcement learning algorithms include Q-Learning,Adaptive Heuristic Critic (AHR), State-Action-Reward State-Action (SARSA), Genetic Algorithms and Gradient Descent (Turban et al.,2011).
Figure9represents the categories ofMLand exemplary methods for each category.
Predictive analytics consists of diverse techniques that predict the future based on historical and current data. The core of Predictive analytics aims to uncover patterns and determine relationships in data (Gandomi & Haider,2015). It includes statistical models and other empirical methods that aim to create empirical prediction and methods for assessing the quality of those predictions in practice, this is, predictive power. Predictive power or predictive accuracy represents the ability of models to generate accurate predictions of new observation (Shmueli & Koppius,2011).
Predictive analytics techniques can be divided into two groups (Gandomi & Haider,2015):
Machine Learning
Unsupervised Learning Reinforcement Learning
Supervised Learning Reinforcement Learning
Classification Q-Learning;
Adaptive Heuritic Critic (AHC);
State-Action-Reward State-Action (SARSA);
Genetic Algorithms;
Gradient Descent;
Decision Tree;
Neural Network;
Support Vector Machines;
Case-Based Reasoning;
Rough Sets;
Discriminant Analysis;
Logistic Regression;
Rule Induction;
Regression Trees;
Neural Network;
Support Vector Machines;
Linear Regression;
Bayesian Linear Regression;
Regression
Clustering Segmentation SOM (Neural Networks);
Adaptive Resonance Theory;
Expectation Maximization;
K-Means;
Genetic Algorithms;
Apriory;
ECLAT Algorithm;
FP-Growth;
One-Attribute Rule;
Zero-Attribute Rule;
Association
Figure 9: MLcategories and respective methods adapted from (Turban et al.,2011)
A. techniques that consist to discover the historical patterns from the outcome variables and extrap-olate them to the future, such as the moving averages;
B. and, techniques that capture the interdependency between the outcome variables and explanatory variables and exploit them to predict the future, such as the linear regression.
2.3 Business Analytics and Industry 4.0
Industry 4.0 represents the introduction of the information technologies into the industry to achieve a higher level of operational efficiency, productivity, and automatization (Drath & Horch,2014; H. Xu et al.,2018; L. Yang, 2017). TheI-IoTand I-CPSare the two key concepts that emerge with industry 4.0, transforming traditional factories into smart factories (H. Xu et al.,2018). These factories of the future or smart factories generate a massive amount of industrial data from a wide range of sources, such as theEnterprise Resource Planning (ERP)systems, distributed manufacturing environments, orders, and shipment logistics, customer buying patterns, product lifecycle operations, and technology-driven data sources, such asGlobal Positioning Systems (GPS),RFIDtracking, and others sources (Božič & Dimovski, 2019; Govindan et al., 2018; Trkman et al.,2010; Waller & Fawcett, 2013; G. Wang et al.,2016). The generated data can be converted to valuable insights for the company through data analysis and integra-tion. In this context, Business analytics and big data analytics have come up with tools and techniques to improve the decision-making process and create business value and competitive advantages to compa-nies (Božič & Dimovski,2019; Waller & Fawcett,2013; G. Wang et al., 2016). BAhas been increasingly considered to become an integral part of the organization business process and provides several benefits
to companies. Such benefits are the increasing of revenues, increase of customer satisfaction, increase of product quality, better resource planning, better insights on customer needs, optimized supply chain, better demand forecast, lower cost base (cost cutting), better compliance with regulations, and others benefits, this is, the main benefits are the improvement of the operational efficiency and the empower-ment of the organizational (Božič & Dimovski, 2019; Lueth et al., 2016; Trkman et al., 2010). BA can be used to solve generical problems from different areas of the industry, such as manufacturing, and logistics and supply chain, in order to enhance organizational performance. In manufacturing/operation, BA can be applied for predictive maintenance of equipment, machinery and assets (e.g., rescheduling the maintenance plan so that to act before the equipment failure according to historical and real-time machine performance analysis), decision-support system for industrial processes (e.g., using data from operations to automate purchase order or production scheduling decisions), manufacturing network opti-mization (e.g., correlating and optimizing performance across multiple plants), and optimizing individual machine parameters for smooth operations and optimal quality (e.g., correlating cause and effect of pa-rameters such as machine speed). In the logistics/supply chain it can be applied for monitoring of moving assets (e.g., goods in transit), cross-supplier supply chain optimization (e.g., analysing warehouse stock level and real-time supply data to forecast shortages, reduce overall inventory levels and bring efficiency to the supply chain), fleet management (e.g., analysing transportation data and fuel consumption to op-timize the distribution network), and strategic supplier management (e.g., continuously analyse quality metrics of individual suppliers) (Lueth et al.,2016). There are several examples of practical application ofBAin industry, such as the HPE – predictive maintenance of wind turbines from the Hewlett Packard Enterprise that use Predictive/ML techniques on data collected from turbines to predict when the wind turbine needs maintenance. Another example is provided by the Comma Soft AG that uses aBAtool for reducing the complexity-driven cost that consists to use optimizing available product variants through the elimination of rarely chosen product variants and very expensive product options that led to millions in saving of costs (Lueth et al.,2016). Moreover, DHL also usesBA for combining the external operational and macroeconomic data to improve the operation efficiency of their supply chain (G. Wang et al.,2016).