Operations management

Car assembly line, a classical production system studied in operations management

Operations management is an area of management concerned with overseeing, designing, and controlling the process of production and redesigning business operations in the production of goods or services. It involves the responsibility of ensuring that business operations are efficient in terms of using as few resources as needed, and effective in terms of meeting customer requirements. It is concerned with managing the process that converts inputs (in the forms of materials, labor, and energy) into outputs (in the form of goods and/or services). The relationship of operations management to senior management in commercial contexts can be compared to the relationship of line officers to highest-level senior officers in military science. The highest-level officers shape the strategy and revise it over time, while the line officers make tactical decisions in support of carrying out the strategy. In business as in military affairs, the boundaries between levels are not always distinct; tactical information dynamically informs strategy, and individual people often move between roles over time.

Cinema queue. Operations Management studies both manufacturing and services

According to the U.S. Department of Education, operations management is the field concerned with managing and directing the physical and/or technical functions of a firm or organization, particularly those relating to development, production, and manufacturing. Operations management programs typically include instruction in principles of general management, manufacturing and production systems, plant management, equipment maintenance management, production control, industrial labor relations and skilled trades supervision, strategic manufacturing policy, systems analysis, productivity analysis and cost control, and materials planning. Management, including operations management, is like engineering in that it blends art with applied science. People skills, creativity, rational analysis, and knowledge of technology are all required for success.

History

See also: Industrial Revolution and Productivity improving technologies (historical)

Industrial Revolution

Marshall's flax mill in Holbeck

Before the First industrial revolution work was mainly done through two systems: domestic system and craft guilds. In the domestic system merchants took materials to homes where artisans performed the necessary work, craft guilds on the other hand were associations of artisans which passed work from one shop to another, for example: leather was tanned by a tanner, passed to curriers, and finally arrived at shoemakers and saddlers. The beginning of the industrial revolution is usually associated with 18th century English textile industry, with the invention of flying shuttle by John Kay in 1733, the spinning jenny by James Hargreaves in 1765, the water frame by Richard Arkwright in 1769 and the steam engine by James Watt in 1765. In 1851 at the Crystal Palace Exhibition the term American system of manufacturing was used to describe the new approach that was evolving in the United States of America which centered on two central features: interchangeable parts and extensive use of mechanization to produce them.

Henry Ford in 1919

In 1913 Henry Ford first used the concept of the assembly line in Highland Park, he characterized it as follows:

"The thing is to keep everything in motion and take the work to the man and not the man to the work. That is the real principle of our production, and conveyors are only one of many means to an end"

This became one the central ideas that led to mass production, one of the main elements of the Second Industrial Revolution, along with emergence of the electrical industry and petroleum industry.

Operations Management

Frederick Taylor

In 1911 Frederick Taylor published his "The Principles of Scientific Management", in which he characterized scientific management as:

1. The development of a true science
2. The scientific selection of the worker
3. His scientific education and development
4. Intimate friendly cooperation between management and the men

Taylor is also credited for developing stopwatch time study, this combined with Frank and Lillian Gilbreth motion study gave way to time and motion study which is centered on the concepts of standard method and standard time. Other contemporaries of Taylor worth remembering are Morris Cooke (rural electrification in 1920s) and Henry Gantt (Gantt chart). Also in 1910 Hugo Diemer published the first industrial engineering book: Factory Organization and Admnistration.

In 1913 Ford W.Harris published his "How Many parts to make at once" in which he presented the idea of the economic order quantity model. He described the problem as follows:

"Interest on capital tied up in wages, material and overhead sets a maximum limit to the quantity of parts which can be profitably manufactured at one time; "setup-up" costs on the job fix the minimum. Experience has shown one manager a way to determine the economical size of lots"

In 1931 Walter Shewhart published his Economic Control of Quality of Manufactured Product, the first systematic treatment of the subject of Statistical Process Control.

In 1943, in Japan, Taiichi Ohno arrived at Toyota Motor company. Toyota evolved a unique manufacturing system centered on two complementary notions: just in time and autonomation. In 1983 J.N Edwards published his "MRP and Kanban-American style" in which he described JIT goals in terms of seven zeros: zero defects, zero (excess) lot size, zero setups, zero breakdowns, zero handling, zero lead time and zero surging. This periods also marks the spread of Total Quality Management in Japan, ideas initially developed by American authors such as Deming, Juran and Armand V. Feigenbaum. Schnonberger identified seven fundamentals principles essential to the Japanese approach:

1.Process control

2.Easy-to-see quality

3.Insistence on compliance

4.Line stop

5.Correcting one's own errors

6.The 100% check

7.Continual improvement

In 1987 the International Organization for Standardization (ISO), recognizing the growing importance of quality, issued the ISO 9000, a family of standards related to quality management systems.

Meanwhile in 1964, a different approach was developed by Joseph Orlicky as a response to the TOYOTA Manufacturing Program: Material Requirements Planning (MRP) at IBM, latter gaining momentum in 1972 when the American Production and Inventory Control Society launched the "MRP Crusade". One of the key insights of this management system was the distinction between dependent demand and independent demand.

Recent trends in the field revolve around concepts such as Business Process Re-engineering (launched by Michael Hammer in 1993), Lean Manufacturing, Six Sigma (an approach to quality developed at Motorola between 1985-1987) and Reconfigurable Manufacturing Systems.

Topics

In a job shop machines are grouped by technological similarities regarding transformation processes, therefore a single shop can work very different products (in this picture four colors). Also notice that in this drawing each shop contains a single machine.

Flexible Manufacturing System: in the middle there are two rails for the shuttle to move pallets between machining centers (there are also FMS which use AGVs), in front of each machining center there is a buffer and in left we have a shelf for storing pallets. Usually in the back there is a similar system for managing the set of tools required for different machining operations.

A first possible distinction in production systems (technological classification) is between manufacturing systems and assembly systems. In the first category we find job shops, manufacturing cells, flexible manufacturing systems and transfer lines, in the assembly category we have fixed position systems, assembly lines and assembly shops (both manual and/or automated operations). Another possible classification is one based on Lead Time (manufacturing lead time vs delivery lead time): Engineer to Order, Purchase to Order, Make to Order, Assemble to Order and Make to Stock. According to this classification different kinds of systems will have different customer order decoupling points (CODP), meaning that Work in Progress cycle stock levels are practically nonexistent regarding operations located after the CODP (except for WIP due to queues).

Operations strategy concerns policies and plans of use of the firm productive resources with the aim of supporting long term competitive strategy. Metrics in operations management can be broadly classified into efficiency metrics and effectiveness metrics. Effectiveness metrics involve:

1.Price (actually fixed by marketing, but lower bounded by production cost): purchase price, use costs, maintenance costs, upgrade costs, disposal costs

2.Quality: specification and compliance

3.Time: productive lead time, information lead time, punctuality

4.Flexibility: mix, volume, gamma

5.Stock availability

A more recent approach, introduced by Terry Hill, involves distinguishing competitive variables in order winner and order qualifiers when defining operations strategy. Order winners are variables which permit differentiating the company from competitors, while order qualifiers are prerequisites for engaging in a transaction. This view can be seen as a unifying approach between operations management and marketing (see segmentation).

Productivity is a standard efficiency metric for evaluation of production systems, broadly speaking a ratio between outputs and inputs, and can assume many specific forms, for example: machine productivity, workforce productivity, raw material productivity, warehouse productivity (=inventory turnover). It is also useful to break up productivity in use U (productive percentage of total time) and yield η (ratio between produced volume and productive time) to better evaluate production systems performances. ABC analysis is a method for analyzing inventory based on Pareto distribution, it posits that since revenue from items on inventory will be power law distributed then it makes sense to manage items differently based on their position on a revenue-inventory level matrix, 3 classes are constructed (A,B and C) from cumulative item revenues, so in a matrix each item will have a letter (A,B or C) assigned for revenue and inventory. This method posits that items away from the diagonal should be managed differently: items in the upper part are subject to risk of obsolescence, items in the lower part are subject to risk of stockout.

Throughput is a variable which quantifies the number of parts produced in the unit of time. Although estimating throughput for a single process maybe fairly simple, doing so for an entire production system involves an additional difficulty due to the presence of queues which can come from: machine breakdowns, processing time variability, scraps, setups, maintenance time, lack of orders, lack of materials, strikes, bad coordination between resources, mix variability, plus all these inefficiencies tend to compound depending on the nature of the production system. This leads to the problem of how to define capacity measures, that is an estimation of the maximum output of a given production system, and capacity utilization.

Designing the configuration of production systems involves both technological and organizational variables. Choices in production technology involve: dimensioning capacity, fractioning capacity, capacity location, outsourcing processes, process technology, automation of operations, tradeoff between volume and variety (see Hayes-Wheelwright matrix). Choices in the organizational area involve: defining worker skills and responsibilities, team coordination, worker incentives and information flow.

Regarding the planning of production there is a basic distinction between the push approach and the pull approach, with the later including the singular approach of Just in Time. Regarding the traditional pull approach a number of techniques have been developed based on the work of Ford W.Harris (1913) which came to be know as the Economic Order Quantity Model, which formed the basis of subsequent techniques as the Wagner-Within Procedure, the News Vendor Model, Base Stock Model and the Fixed Time Period Model. These models usually involve the calculation of cycle stocks and buffer stocks, the latter usually modeled as a function of demand variability.

Manufacturing Resource Planning (MRP II)

Joseph Orlickly and others developed Material Requirement Planning (MRP) at IBM, essentially a push approach to inventory control and production planning, which takes as input both the Master Production Schedule (MPS) and the Bill of Materials (BOM) and gives as output a schedule for the materials needed in the production process. To avoid an "explosion" of data processing in MRP (number of BOMs required in input) planning bills (such as family bills or super bills) can be useful since they allow a rationalization of input data into common codes. MRP had some notorious problems such as infinite capacity and fixed lead times, giving way later on to modifications of original software implementation in the form of MRP II and ERP. In this context problems of scheduling, loading, part type selection and applications of operations research have a significant role to play.

Value Stream Mapping, a representation of materials and information flows inside a company, mainly used in the lean manufacturing approach. The calculation of the timeline usually involves using Little's Law to derive lead time from stock levels and cycle times

Lean Manufacturing is an approach to production which arose in Toyota between the end of World War II and the seventies. It comes mainly from the ideas of Taiichi Ohno and Toyoda Sakichi which are centered on the complementary notions of Just in Time and Autonomation, all aimed at reducing waste. A series of tools have been developed mainly with the objective of replicating Toyota success: a very common implementation involves small cards known as kanbans.

There are also fields of mathematical theory which have found applications in the field of operations management such as operations research, mainly mathematical optimization problems and queue theory. Queue theory is employed in modeling queue and processing times in production systems while mathematical optimization draws heavily from multivariate calculus and linear algebra. Queue theory is based on markov chains and stochastic processes. It also worth noticing that computations of safety stocks are usually based on modeling demand as a normal distribution. When analytical models are not enough, managers may resort to using simulation.

Illustration of the Simplex method, a classical approach to solving LP optimization problems and also integer programming (ex:branch and cut). Mainly used in push approach but also in production system configuration. The interior of the green polytope geometrically represents the feasible region, while the red line indicates the sequence of pivot operations required to reach the optimal solution

A control chart: process output variable is modeled by a probability density function and for each statistic of the sample an upper control line and lower control line are fixed, when the statistic moves out of bounds, an alarm is given and possible causes are investigated. In this drawing the statistic of choice is the mean and red points represent alarm points.

Since real production processes are always affected by disturbances in both inputs and outputs, many companies implement some form of Quality management or quality control. Quality control tools include check sheets, pareto charts, Ishikawa diagrams, control charts, which are used in approaches like Total quality management and Six Sigma. Keeping quality under control is relevant to both increasing customer satisfaction and reducing processing waste.

Operations management textbooks usually cover demand forecasting, even though it is not strictly speaking an operations problem, because demand is related to some production systems variables. For example, a classic approach in dimensioning safety stocks requires calculating standard deviation of forecast errors. Demand forecasting is also a critical part of push systems, since order releases have to be planned ahead of actual clients orders. Also any serious discussion of capacity planning involves adjusting company outputs with market demands.

Other important management problems involve maintenance policies (see also reliability engineering and maintenance philosophy), safety management systems (see also safety engineering and Risk management), facility management and supply chain integration.

Organizations

The following organizations support and promote operations management:

• Association for Operations Management (APICS) which supports the Production and Inventory Management Journal
• European Operations Management Association (EurOMA) which supports the International Journal of Operations & Production Management
• Production and Operations Management Society (POMS) which supports the journal: Production and Operations Management
• Institute for Operations Research and the Management Sciences (INFORMS)
• The Manufacturing and Service Operations Management Society (MSOM) which supports the journal: Manufacturing & Service Operations Management
• Institute of Operations Management (UK)
• Association of Technology, Management, and Applied Engineering (ATMAE)

Journals

The following high-ranked academic journals are concerned with Operations Management issues:

• Management Science
• Manufacturing & Service Operations Management
• Operations Research
• Journal of Operations Management
• International Journal of Operations & Production Management
• Production and Operations Management

See also

APICS The Association for Operations Management Business process mapping Industrial engineering Industrial technology Inventory Inventory management software Lean manufacturing National Institute of Industrial Engineering Operations research

Six Sigma Project management Silver–Meal heuristic Root cause analysis Cause-and-effect analysis Benchmarking Statistical Process Control Performance metrics Control Charts Customer-driven Processes

Failure Mode and Effects Analysis Poka-Yoke Business process re-engineering Change Management TQM Malcolm Baldridge Criteria Workflow analysis Management by fact Setup reduction Work breakdown structure

Further reading

• D. Wren, The Evolution of Management Thought, 3th ed New York Wiley 1987
• W. Hopp, M. Spearman, Factory Physics, 3th ed Waveland Press, 2011 (Part 1 contains both description and critical evaluation of the historical development of the field)
• R. B. Chase, F.R. Jacobs, N.J.Aquilano, Operations Management for Competitive Advantage, 11th ed McGraw-Hill 2007
• Askin, R. G., C.R. Stanridge, Modeling & Analysis Of Manufacturing Systems, John Wiley and Sons, New York 1993
• N.A. Buzacott, G.E. Shanthikumar, Stochastic models of manufacturing systems, Prentice Hall, 1993
• D.C. Montgomery, Statistical Quality Control: A Modern Introduction, 7th edition 2012