Exploring the parameters of automation in manufacturing enterprises.
Many factors contribute towards the automation of the manufacturing enterprises. With the increased competition and demand in the global market, automation is becoming the necessity. The purpose of this paper is to identify the parameters of automation which the manufacturing enterprise can consider for the success and competitiveness in the market. The key roles are played by technology, manpower planning and the market demand in the automation of manufacturing enterprises. The paper identifies the decision variables for the need of automation by Internal Scanning, External Scanning and Competitive Positioning of an enterprise.

Keywords: Automation, Manufacturing Enterprises, Manpower, Demand, Technological innovations, Market Developments

Article Type:
Acharya, Vikas
Sharma, Somesh
Kumar, Sunand
Pub Date:
Name: Review of Business Research Publisher: International Academy of Business and Economics Audience: Academic Format: Magazine/Journal Subject: Business, international Copyright: COPYRIGHT 2011 International Academy of Business and Economics ISSN: 1546-2609
Date: Sept, 2011 Source Volume: 11 Source Issue: 5
Geographic Scope: India Geographic Code: 9INDI India
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Full Text:

With the increased competition in global market and demand of products both in quantity and quality, automation is becoming an important tool to decrease the cost of production and increase the productivity of an industry. With the development in computers, the automation is not only applicable to control of the production processes but also to the product design, production planning and process designing. Examples are computer controlled machine tools, industrial robots, computer aided design (CAD) units and flexible manufacturing systems (FMS). Automation can be implemented into several areas of manufacturing like Engineering Design, Manufacturing Planning, Manufacturing Control, Factory Floor Automation etc.

The automation involves three logical steps- Mechanization, Automation and Super Automation. The industries are presently heading towards the super automation. Super Automation is the removal of complex mental control tasks with the machines. In general automation strengthens the economy of a country but it may eliminate the job outright, parts of several jobs and worker's control over the rate of output which results in the unemployment of the manpower.


The present work focuses on the parameter identification of automation based on the Internal Scanning, External Scanning and Competitive Positioning of the enterprise that a company must consider while automating its manufacturing system and to evaluate the performance. The main focus is on the industries which are involving complex natures of production system like steel industry.

2.1 Objectives of Research Study:

The automation is becoming a necessity to the industries to deliver better quality and variety of products at low cost to compete in the global market. The industry has to design its production procedure so as to produce its products at low cost and for this reason the industry is bound to adopt automation. Although a number of researchers have contributed to the assessment of automation needs but as due to automation the requirement of manpower decreases and initial capital investment increases a combined study is required to be done for evaluating the extent of competence provided by automation to different manufacturing enterprises. The present work focuses on identifying the various parameters of automation. The major dimensions of parameters are: -

1. What is present status & policies of the firm (Internal Scanning)?

2. How the others are doing and what is available in the market (External Scanning)?

3. How to make it more effective to earn competitiveness and profitability (Competitive Positioning)?

2.2 Literature Review:

The most of the studies on automation has begun after World War II when the globalization of the markets started and there was need of providing a better quality of the products at a reasonable cost and at increased level of demand to compete with the other organizations of same type. The recent decade's research on automation has gained headway with the development of electronics and computer industry and various methods of automatic controls like fuzzy logic, neural networks and artificial intelligence. A large number of papers had contributed to the present work. The replacement of manpower with machines and its effects like worker's training and manpower planning are also significant factors (Leon C. Megginson, 1963) and (Richard H.P. Kraft, 1970). Reeve and others had focused their study on the steel sector and interactive process model development was explained with the effects of automation on steel industry (P. J. Reeve, A. F. MacAlister and T. S. Bilkhu, 1999). Many researchers have considered that the increasing use of computers in the manufacturing industries saves a potential amount of human energy by reducing their mental task (C.A. Hudson, 1982 and Yutka Yoshitani, 1980). To apply the use of computers the material and engineering database is to be prepared and its careful analysis is required (B. Malakooti, 1986). The firm size plays an important role in the adoption of automation to a particular concern (Gian Carlo Cainarca, Massimo G. Colombo, and Sergio Mariotti, 1990). The organizational structure and its goals are also one of the deciding factors in automation project (Jack R. Meredith, 1987). The safety of the process automation at the designing stages and analysis of the possibilities of failures should also be considered (A. Toola, 1993). With the development of new technologies in control system like artificial intelligence, adaptive control systems and fuzzy logic a number of decisions are taken by the machines itself rather than by the operators. To use such technologies in process control, the research has shifted to the model development of the production processes involved in the total production cycle. Because of the initial capital investment, the firm must consider the life cycle, present method of production, available alternate methods, demand and market analysis (Richard Jackson, Andrew M. Tobias and K. Brian Haley, 1989) and (Angel Martinez Sanchez, 1991).

2.2.1 Internal Scanning:

For any organization going for the automation, has to undergo a comprehensive study about its own present system. Due to large initial capital investment, the organization must consider its organizational policies, objectives, technological capabilities, operation control, performance index and human aspects.

* Organizational Policies

Firm size plays an important role because it is difficult for small scale industries to invest huge capital initially and also in case of small scale industry the research within the company has not enough space. Manufacturing enterprise sizes turn out to be positively correlated to adoption rates of complex systematic innovations (Gian Carlo Cainara, 1990). The organizational goals are another factor which affects the automation. The short range plans is the key of achieving the long range goals. The short term plans must be compatible with long range goals (Jack R. Meredith, 1987).

* Objective Clarity

The organization must be clear about what percentage it can spend on research and development of its sale and what kind of industrial standards it wants to achieve? These are two major issues especially for private enterprises which concentrate generally on their profit maximization. There is a need to develop a strong R&D base in the organization itself so that technology up gradation can take place quickly, with minimal reliance on other sources. To meet these twin objectives, investment in R&D will have to be increased (Rakesh Narain and R. C. Yadav, 1997).

* Technological Capability

Technological capability includes the study of present status of production system and the product variety the manufacturing enterprise is producing. Innovation cycle plays an important role which includes process and product innovation. Presently the concentration is shifting from product to process innovation (Angel Martinez Sanchez, 1995). A careful analysis of database and experimental data helps in finding the possible alternates of automation in the existing production system (B. Malakooti, 1986). Automation is blamed for posing risk and for increasing the chance of human error in situations involving disturbances; on the other hand, it is admitted that automation enables sophisticated process control and handles the disturbances without human interference (A. Toola, 1995). The integrated automation system can be easily developed by modeling the production complexes as a composition of components and by interrelating those components (Edgar Chacon, Isabel Besembel, Flor Narciso, Jonas Montilva and Eliezer Colina, 2002) and (S M Lee, R Harrison, A A West and M H Ong, 2007)

* Operation Control

With the advancement of control system various techniques like adaptive control, fuzzy control and artificial intelligence system helps in automation. In manufacturing enterprises the dynamic control of the activities is done by controlling the process variables. Remote operations permit an easier and more flexible configuration (S. L. Jamsa-Jounela, 2001). A prerequisite for any automatic control system is extreme accuracy during whole process (Kerim Cetinkaya, 2007). The use of computers has enabled us to integrate the different processes manufacturing and optimize the different activities and overall production procedure (M. Eugene Merchant, 1983). A model has to be developed for controlling the process parameters. The difficulties in model development are Imperfect plant data, Time varying plants, Higher order dynamics, Non-linearity, Complexity and Skills (A. A. Desrochers, 1982)

* Performance Index

Throughput and yield are two decision variables which give an idea about the machine utilization and material utilization. Throughput evaluates the number of hours for which the machine is engaged. Throughput is limited by the slowest machine in line in the production system and its control looks ahead of the production schedule. The material utilization is measured by yield and is measure of tones output to tones input. While considering saleable product prime yield is considered (P. J. Reeve, A. F. MacAlister and T. S. Bilkhu, 1997). An explicit relation between productivity, profitability and price recovery factor is given by Alder: Profitability = Productivity X Price Recovery Factor (Paul S. Alder, 1987) Turn over indicates the growth of an industry and according to Sanchez growing industries are more intensively automated than non-growing industries (Angel Martinez Sanchez, 1995).

* Social Aspects

Automation is although replacing the human mental and physical effort with machines but human aspects plays an important role in the automation. Because change is not readily acceptable to the society so comprehensive planning is required to make it possible that the labor force working in the organization accept the automation process. The existing worker's are to be motivated for adopting the new automation system. Extrinsic motivation is financial incentive systems, external working conditions, management techniques and human relations while intrinsic motivation is about holistic approach, requirements, cooperation, training and autonomy (T. Martin, E. Ulich and H. J. Warnecke, 1990).

2.2.2 External Scanning:

The external scanning is the study about what is going on in the world and what is present status of the industry? The organizations have to consider the technological advancements, Govt. policies and legal aspects and product life cycle to decide the future course of action.

* Technological Advancement

The company must be aware what are technological advancements in hardware, software and system ware? Hardware means acquiring equipment that will attain the short-range goals of the firm, yet be compatible with and incorporate existing equipment as much as possible. Software is typically considered to be the most difficult technical aspect of any automation project. The probability of project completion on time and within budget is primarily a function of the amount of new, unproven software. The difficulty of compatibility between systems has made many firms sensitive to the 'systems' issue. Factory automation is essentially the task of taking standalone systems and integrating them (Jack R. Meredith, 1987). Development of control system techniques like neural network and artificial intelligence have made automation more advanced. Adaptability and opacity are two major issues considered in any automation project. Adaptability means the feasibility study and opacity means the complexity of the automatic controls which makes a worker to unable to understand the process overview (David A. Thurman, David M. Brann and Christine M. Nitchell, 1997).

* Government Policies and Legal Aspects

The government policies, environmental awareness and labor unions decide the future of automation in a particular industry. For example the economic reforms initiated by the Government since 1991 have added new dimensions to industrial growth in steel industry in particular. Certain other policy measures such as reduction in import duty of capital goods, convertibility of rupee on trade account, permission to mobilize resources from overseas financial markets and rationalization of existing tax structure for a period of time have also benefited the Indian Steel Industry. With the increase in automation the employment decreases because of the less demand of manpower. Changes in the general level of unemployment are governed by three fundamental forces: the effective growth of the labor force, the increased labor productivity and the growth of total or aggregate demand for goods and services. The general level or aggregate demand for goods and services is the prime factor in determining the general level of unemployment. Vocational education should also be taken into consideration for automation so that a proper interface between the process knowledge and the worker can be established so as to reduce the indirect labor cost (Richard H.P. Craft, 1970). The worker must be given enough space to apply their own knowledge based on their past experiences to renovate an existing production system with automated production system.

* Product Life Cycle

Product life cycle and demand decides the future of automation in any industry. For a longer product life cycle the manufacturing enterprise can rely on its fixed automation of the key processes of the system which can surely improve productivity and quality (Kun Liao and Qiang Tu, 2007). The quantity of demand of a product in the market may compel the industry to opt for automation to increase its production rate.

2.2.3 Competitive Positioning:

Competitive positioning is the study to make an organization to compete in the global market and to maximize its profit.

* Market Demand

An organization has to study the market development, market share and the competition it is getting from different organizations of the same kind in the market while adopting the automation. For sales planning and production planning market development is major factor (Yutaka Yoshitani, 1983). To enhance the market share capability, to deliver variety of products with high quality and short delivery increases the need of automation. (Reeve, MacAlister and Bilkhu, 1999). An investigation between market demand and supply and a careful integration between production and business studies is required for the enhancement of automation projects in an industry.

* Economical aspects

At the end organization has to consider its economical aspects like what is the sale value per employee, budgeting of the automation project and its profit to automate the production system. The demand for production increases with the increase in the sales of the products in the market and therefore the need to invest in the machinery also increases. Profits have a positive influence on expansion plans and automation of manufacturing processes (Angel Martinez Sanchez, 1991).


The literature on the automation concludes that for a manufacturing enterprise, the automation is dependent on eleven parameters (Table No.1). A manufacturing enterprise can evaluate the internal factors by studying its organizational policies, objective clarity, technological capabilities, operation control, performance index and social aspects. All these internal parameters will give an idea about the need of automation in the manufacturing enterprise. The external factors like technological advancements, government policies and legal aspects and product life cycle will decide the alternate actions which an enterprise can take to automate the production process to increase its productivity. Market demand and economical aspects are the result oriented parameters and will explain the effect of automation on the manufacturing enterprise.

For the future studies a model can be developed interrelating these parameters and a comprehensive set of deterministic variables can be explored for the evaluation of competence provided by automation to the manufacturing enterprises.


(1.) A. Toola, "The Safety of Process Automation", Automatica, Vol. 29, No. 2, pp. 541-548, 1993

(2.) Angel Martinez Sanchez, "Flexible Automation and High Technology in the U.S. Metalworking Industry", Management International Review, Vol. 31, No. 4, pp. 333-346, 1991

(3.) Angel Martinez Sanchez, "Innovation Cycles and Flexible Automation in Manufacturing Industries", Technovation, 15(6), pp. 351-362, 1995.

(4.) A. A. Desrochers, "A comparison between Adaptive Control Strategies for hot strip rolling mill", Journal of Manufacturing Systems, Volume 1, Number 2, pp. 183-194, 1982.

(5.) B. Malakooti, "A Methodology for the Automation of Medium or Small Manufacturing Companies", Computers in Industry, 7, pp. 333-341, 1986.

(6.) C. A. Hudson, "Computers in Manufacturing", Science, New Series, Vol. 215, No. 4534, pp. 818-825, 1982

(7.) David A. Thurman, David M. Brann and Christine M. Nitchell, "An Architecture to Support Incremental Automation of Complex Systems", IEEE, pp. 1174-1179, 1997.

(8.) Edgar Chacon, Isabel Besembel, Flor Narciso, Jonas Montilva and Eliezer Colina, " An Integration Architecture for Automation of Continuous Production Complex", The Instrumentation, Systems, and Automation Society, 41, pp. 95-113, 2002

(9.) Gian Carlo Cainarca, Massimo G. Colombo, and Sergio Mariotti, "Firm Size and the Adoption of Flexible Automation", Small Business Economics, Vol. 2, No. 2, pp. 129-140, 1990.

(10.) Jack R. Meredith, "Managing Factory Automation Projects", Journal of Manufacturing Systems, Volume 6, No. 2, pp.75-91, 1987.

(11.) Kerim Cetinkaya, "Design and application an integrated element selection model for press automation line", Materials and Design, 28, pp. 217-229, 2007

(12.) Kun Liao and Qiang Tu, "Leveraging automation and integration to improve manufacturing performance under uncertainty An empirical study", Journal of Manufacturing Technology Management, Vol. 19, No. 1, pp. 38-51, 2008

(13.) Leon C. Meggison, "Automation: Our Greatest Asset- Our Greatest Problem?", The Academy of Management Journal, Vol. 6, No. 3, pp. 232-244, Sep. 1963

(14.) M. Eugene Merchant, "Current status and potential for Automation in the metalworking Manufacturing Industry", Annals of the CIRP, Vol. 32/2, pp. 519-524, 1983.

(15.) P. J. Reeve, A. F. MacAlister and T. S. Bilkhu, "Control, automation and the hot rolling of steel", Philosophical Transactions: Mathematical, Physical and Engineering Science, Vol. 357, No. 1756, pp. 1549-1571, 1999.

(16.) Paul S. Alder, "A Plant productivity Measure for High Tech Manufacturing",

Interfaces, Vol. 17, No. 6, pp. 75-85, 1987.

(17.) Richard H. P. Craft, "Manpower Planning and Its Role in the Age of Automation", Review of Educational Research, Vol. 40, No. 4, pp. 495-509, Oct. 1970

(18.) Richard Jackson, Andrew M. Tobias and K. Brian Haley, "OR at Work in Modern Manufacturing", The Journal of the Operational Research Society, Vol. 40, No. 11, pp. 993-999, 1989.

(19.) Rakesh Narain and R. C. Yadav, "Impact of Automation on Indian Manufacturing Industries", Technological Forecasting and Social Change, 55, pp. 83-98, 1997.

(20.) S M Lee, R Harrison, A A West and M H Ong, "A component-based approach to the design and Implementation of assembly automation system", J. Engineering Manufacture, Vol. 221, Part B, pp. 763-773, 2007

(21.) S. L. Jamsa-Jounela, "Current Status and Future Trends in Automation of Mineral and Metal Processing", Control Engineering Practice 9, pp. 1021-1035, 2001.

(22.) T. Martin, E. Ulich and H. J. Warnecke, "Appropriate Automation for Flexible Manufacturing", Automatica, Vol. 26, No. 3, pp. 611-616, 1990.

(23.) Yutaka Yoshitani, "The Background and Present Status of Computer Usage in the Japanese Iron and Steel Industry", Computers in Industry, 1, pp. 263-275, 1980.

Vikas Acharya, National Institute of Technology, Hamirpur, Himachal Pradesh, India

Somesh Sharma, National Institute of Technology, Hamirpur, Himachal Pradesh, India

Sunand Kumar, National Institute of Technology, Hamirpur, Himachal Pradesh, India

Vikas Acharya is pursuing his Ph.D. on "Developing Measures to Assess the Extent of Competence Provided by automation to Indian Steel Corporations" from National Institute of Technology, Hamirpur, India.

Dr. Somesh Sharma has earned his Ph.D. from Indian Institute of Science, Bangalore and presently working as Assistant Professor in National Institute of Technology, Hamirpur. His research interests are optimization, modeling, simulation and efficiency management.

Dr. Sunand Kumar is Professor and Head of Department in Mechanical Engineering Department at National Institute of Technology, Hamirpur

S.    Cardinal      Adjuvant
No.   Dimensions    dimensions          Decision Variables

1.    Internal      1. Organizational   1. Organizational Structure
      Scanning      Policies            2. Firm size
                                        3. Company's strategy and goals
                    2. Objective        4. R&D expenditure to sales
                    clarity             ratio
                                        5. Industrial standards
                    3. Technological    6. Innovation cycle 7. Safety
                    capability          8. Engineering & material
                                        9. Interrelation between
                                        production units
                    4. Operation        10. Remote calibration
                    control             11. System accuracy
                                        12. Supervision 13. Integration
                                        14. Modeling
                    5. Performance      15. Productivity
                    index               16. Price recovery factor
                                        17. Yield 18.Throughput
                                        19. Turnover
                    6. Social aspects   20. Labor force
                                        21. Labor productivity
                                        22. Motivation
2.    External      7. Technological    23. Hardware, Software and
      Scanning      advancements        System ware
                                        24. Neural Networks and
                                        Artificial Intelligence
                                        25. Adaptability 26.Opacity
                    8. Govt. Policies   27 Govt. Policies
                    & Legal             28. Environmental awareness
                    Aspects             29. Labor unions
                                        30. Unemployment 31.Vocational
                                        education 32. Direct & indirect
                                        labor cost 33. Manufacturing
                                        pro activeness
                    9. Product Life     34. Product Variety
                    Cycle               35. Customer Services
                                        36. Demand
3.    Competitive   10. Market          37. Market development
      Positioning   Demand              38. Market share
                                        39. Competition
                                        40. Integration of production
                                        & business studies
                    11. Economical      41. Sales per employee
                    aspect              42. Budgeting
                                        43. Profits
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