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Monday, 27 Oct 2014

One of us (Sanja Petrovic) recently joined a Division of Operations Management and Information Systems in a Business School as Professor of Operational Research whilst the other (Bart MacCarthy) has been a Professor of Operations Management in the same Division for over a decade. To many of our colleagues in the Business School we occupy the same territory. We do indeed have some strong areas of overlap in our research interests but quite a number of differences in emphasis and approach also. A natural question emerges – what is   the relationship between Operational Research (OR) and Operations Management (OM)?

 Many in the OR community would say they are more or less the same. However, the reverse is not true. Many in the OM community do not perceive themselves as OR researchers or practitioners. It seems that in both the scientific literature and business communities there are no definitive answers on the scope of, and differences between OR and OM. 

This is not a new question of course. The histories of the disciplines are intertwined. Many of the classical problems in OR (e.g. the job shop scheduling problem, the travelling salesman problem, queuing and inventory models) are directly relevant to the design and management of operational systems. Many researchers across the OR and OM disciplines publish in the same academic journals but then again there are OR journals that some OM academics would not consider at all relevant and vice versa. There are quite eminent scientists who earnestly believe that OR and OM are synonymous. Others, equally eminent, resolutely maintain that OR is focussed on mathematical modelling, optimisation problems and simulation, while OM is a discipline addressing more general problems about processes and systems and involving a broader range of issues including information systems, organizational and human behaviour, ethics and other softer management disciplines. Valid observations may be that there is no clear and crisp distinction between the disciplines and there is considerable overlap. 

In text books and encyclopaedias ORis typically referred to as a discipline that deals with the application of advanced analytical methods to help make better decisions, whilst OM addresses the activities, decisions and responsibilities of managing the design, production and delivery of goods and services. There is general agreement that OR concerns the application of quantitative models and methods to understand and ‘solve’ many types of problems, including those that have a strong operational focus. This means OR involves the application of existing methods and the development of new ones in many mathematically defined operations areas. In  OR manuscripts one can find key words like optimisation, dynamic control, Markov chains, stochastic analysis, games, risk analysis, etc - but of course one can see some of these terms in some papers in OM journals also. However, there is also more and more recognition of Soft OR approaches, applying predominantly qualitative techniques with the aim to define and explore a given problem from various perspectives. 

Similarly, there is agreement that OM is concerned with the creation, design, production and delivery of products and/or services. However OM places strong emphasis on understanding ‘effectiveness’ in systems design and management, which may not be evident purely from mathematical modelling. Effective deployment and effective management and practice necessarily bring in broader human, organisational and systems issues. In OM manuscripts key words could include performance measurement, project management, supply chain management, manufacturing and production, energy/ transportation, service systems etc – but, as above, many of these terms appear in papers in OR journals also.  Additionally, there is increasing recognition in OM that appropriate mathematical modelling can lead to valuable insights into the design and management of robust operational systems, particularly in large complex systems.   

Some would argue that OR journals have prioritised manuscripts that describe methods and algorithms for solving usually well-defined theoretical problems but somewhat divorced from real-world applications. The developed models are usually a simplified representation of a real-world problem. The focus is then on rigorous evaluation of a method or algorithm and on performance comparisons with other methods and algorithms reported in the literature. The OM literature publishes both qualitative and quantitative research, perhaps with a greater emphasis on the former. The argument is that the OM literature has placed greater emphasis on real-world problems, including contemporary concepts such as lean thinking, sustainability, and globalisation. 

An interesting illustration is the domain of forecasting. The OR literature has tended to emphasise the development of algorithms and techniques for the generation of ‘accurate’ forecasts from historical data sets. The OM literature has been more concerned with forecasting as a process within organisations and its incorporation into effective planning and management regimes. Of course, one cannot live without the other. In large scale retail organisations for instance we rely on timely and accurate model-based forecasts for perhaps many hundreds of product lines across retail stores. But equally we rely on strong operational processes for the translation of such forecasts into appropriate ordering and replenishment decisions and the effective management of logistics and distribution processes to ensure on-shelf availability.            

We agree that this brief article raises more questions than answers. It has not sought to be exhaustive in discussing the foci and boundaries of the OR and OM disciplines. There is a little surprise that some have labelled OR as the more theoretical and OM the more applied discipline. Is it true? Probably not! Many counter-examples could be given but there are perhaps elements of truth in these observations. 

We conclude by noting that irrespective of the borders between the two disciplines, they should each seek to address the many challenges that arise in modern complex decision making environments and work collaboratively where appropriate. Let’s not argue too much about fuzzy discipline boundaries but instead place the emphasis on relevance and rigour in addressing real world problems.

Sanja Petrovic and Bart MacCarthy

 Nottingham University Business School

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