**3. Continued evolution of manufacturing and service applications**

As service-centric and factory-centric gains were progressively made, associated improvements in efficiency and effectiveness were obtained. As global markets have emerged, the productivity and competitiveness of companies and nations continue to be a priority. The challenge for IEs is to streamline and better integrate the product or service cycle [5]. The techniques of continuous improvement, six sigma and lean manufacturing, as described in later chapters, support this goal. For example, the concept of lean manufacturing addresses process flow and lead-time then identifies and reduces waste from the process. Six sigma creates value through consistent process output and reducing variation. At the same time, the scope of industrial engineering has expanded to also consider the consequences for safety and sustainability due to increasing public interest, regulatory pressures and corporate social responsibilities [7]. Occupational safety engineering "addresses the origins of workplace accidents, regulations and management practices towards mitigating hazard exposures, preventing harm and reducing liability" [1]. Sustainability refers to practices and efforts that balance the environmental, social and economic needs of current and future generations [7].

## **4. Total systems approach to operational decision-making**

Classical industrial engineering studied the way that people worked in the factories, and the relationship of those workers to their tools and machinery. The focus was on the individual and how to improve the effectiveness of their work. Industrial engineers continue to study how the individual works, but much greater emphasis is being placed upon studying the systems within which the work is performed in order to optimize the performance of the total system by studying the application of knowledge [3]. The need for organizations to develop and implement

**5**

*Introductory Chapter: Background and Current Trends in Industrial Engineering*

effective integrated systems has enhanced the profession of industrial engineering. The social sciences provide a source toward which IE looks for information about the behavior of human elements within a system. This is particularly true regarding decision theory and operational analysis [8]. Capturing the desires and judgments of users, stakeholders and customers requires the ability to incorporate such values and model the likelihoods and uncertainties of the alternatives. This role will increase in importance as the decision-making systems of the world continue to

Operations research has long been a specialty area within industrial engineering. It involves the development and application of mathematical models that aim to describe and/or improve real or theoretical systems [1]. This generally involves mathematical optimization to support the decision-making process. For example, the case study provided by Drs. Berhan and Kitaw presents a classic application of linear programming. Subsequent chapters in this section note the development and usage of more sophisticated mathematical models and logic, and their leveraging via computerbased platforms. As data volume, variety and speed of updating increases to support increasingly more complex problems, this linkage of these sophisticated models and computer platforms has evolved into the field of data analytics [9]. Data analytics encompasses such enhanced areas as machine learning concepts, and predictive and

The nexus of these three trends leads to the emergence of what has been termed "Industry 4.0". As noted by Amaba et al. [10], "the terms "Industry 4.0" and "Manufacturing 4.0″ describe the fourth wave of the Industrial Revolution." Each phase was driven by unique technological advances. Industry 1.0 was based on steam power to drive production machines. Industry 2.0 harnessed electricity, mass production and labor division. Industry 3.0 was driven by "computer automation and the use of electronics and IT to further automate production with robotic

The rise of Industry 4.0 is achieved by integrating digital systems with physical systems (i.e. a cyber physical integration) across the value chain to achieve intelligent manufacturing operations, otherwise known as "the smart factory."

An internet-of-things (IoT) enabled device, broadly defined, is a device connected to the internet, allowing users to access its data and to control its functions

*DOI: http://dx.doi.org/10.5772/intechopen.94606*

grow more complex.

prescriptive models.

**6. Emergence of industry 4.0**

• Internet of Things

• Cloud computing

• Big data analytics

• Additive manufacturing

machines that augmented or replaced operators" [10].

Technologies supporting Industry 4.0 include [11]:

• Artificial intelligence and machine learning

**5. Enhanced data analytics**

*Introductory Chapter: Background and Current Trends in Industrial Engineering DOI: http://dx.doi.org/10.5772/intechopen.94606*

effective integrated systems has enhanced the profession of industrial engineering. The social sciences provide a source toward which IE looks for information about the behavior of human elements within a system. This is particularly true regarding decision theory and operational analysis [8]. Capturing the desires and judgments of users, stakeholders and customers requires the ability to incorporate such values and model the likelihoods and uncertainties of the alternatives. This role will increase in importance as the decision-making systems of the world continue to grow more complex.
