تويت
Operational Research (OR) is the use of advanced analytical techniques to improve decision making. It is sometimes known as Operations Research, Management Science or Industrial Engineering. People with skills in OR hold jobs in decision support, business analytics, marketing analysis and logistics planning – as well as jobs with OR in the title.
WHY IS OR NEEDED?
Because it makes sense to make the best use of available resources. Today’s global markets and instant communications mean that customers expect high-quality products and services when they need them, where they need them. Organizations, whether public or private, need to provide these products and services as effectively and efficiently as possible. This requires careful planning and analysis – the hallmarks of good OR. This is usually based on process modelling, analysis of options or business analytics.
EXAMPLES OF OR IN ACTION
· Scheduling: of aircrews and the fleet for airlines, of vehicles in supply chains, of orders in a factory and of operating theatres in a hospital.
· Facility planning: computer simulations of airports for the rapid and safe processing of travellers, improving appointments systems for medical practice.
· Planning and forecasting: identifying possible future developments in telecommunications, deciding how much capacity is needed in a holiday business.
· Yield management: setting the prices of airline seats and hotel rooms to reflect changing demand and the risk of no shows.
· Credit scoring: deciding which customers offer the best prospects for credit companies.
· Marketing: evaluating the value of sale promotions, developing customer profiles and computing the life-time value of a customer.
· Defence and peace keeping: finding ways to deploy troops rapidly.
· Some OR methods and techniques
· Computer simulation: allowing you to try out approaches and test ideas for improvement.
· Optimization: narrowing your choices to the very best when there are so many feasible options that comparing them one by one is difficult.
· Probability and statistics: helping you measure risk, mine data to find valuable connections and insights in business analytics, test conclusions, and make reliable forecasts.
· Problem structuring: helpful when complex decisions are needed in situations with many stakeholders and competing interests.
