**5. Enhanced data analytics**

*Concepts, Applications and Emerging Opportunities in Industrial Engineering*

order to deliver a valuable product for the market.

• The use of enhanced data analytics.

• Resulting in the emergence of Industry 4.0.

**2. Current status and trends**

industries.

analysis has now been extended to encompass materials, services and information from raw material suppliers, through distribution centers and factories, to the final customer [5]. This establishes a value chain of activities that a firm performs in

As implied in a series of articles published by the Institute of Industrial and Systems Engineers (IISE), with the beginning of this third decade of the 21st Century, the field of industrial engineering is experiencing a series of further changes, opportunities and innovations [6]. These emerging trends are built on historical precedent,

• Continued evolution of applications in the manufacturing and service

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

mitigating hazard exposures, preventing harm and reducing liability" [1].

and economic needs of current and future generations [7].

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

Sustainability refers to practices and efforts that balance the environmental, social

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

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

As service-centric and factory-centric gains were progressively made, associated

are not mutually exclusive, and overlap/interweave to a varying degree:

• The total systems approach to operational decision-making.

**4**

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 prescriptive models.
