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Lots of information, but no decisions. Enterprise resource planning (ERP) systems and the
Web have contributed to a pervasive information environment; decision-makers have total access to every piece of data in the organization.
The problem is that most people need a way to transform this wealth of data into actionable information that helps them make good tactical and strategic decisions.
The role of OM decision methods is to help leverage a company’s investment in information technology infrastructure by providing a way to convert data into actions.
A large nationwide bank is using OM techniques to configure complicated financial instruments for their customers. A process that previously required a human agent and took minutes or hours to perform is now executed automatically in seconds on the bank’s Intranet.
The resulting financial products are far superior to those produced by the manual process.
A major retail enterprise is using OM methodology for making decisions about customer relationship management (CRM). They are using mathematical optimization to achieve the most profitable match between a large number of customer segments, a huge variety of products and services, and an expanding number of marketing and sales channels such
• OM researchers designed a system to deal with such variables as customer schedules and requested performance times, time estimates for the required service, vehicles and personnel available, skills needed, parts and product availability and so on.
• The system was designed to automatically schedule all facets of performance in such a way as to– Provide accurate and convenient time windows for the
Sears customer– Minimize costs– Maximize certain objective measures of task
performance, including customer satisfaction. • This effort generated a one time cost reduction of $9
million as well as ongoing savings of $42 million per year.
Quantitative analysisQuantitative analysis is a scientific approach to managerial decision making whereby raw data are processed and manipulated resulting in meaningful information
Quantitative factorsQuantitative factors might be different investment alternatives, interest rates, inventory levels, demand, or labor costQualitative factorsQualitative factors such as the weather, state and federal legislation, and technology breakthroughs should also be considered– Information may be difficult to quantify but
• The best (optimal) solution to a problem is found by manipulating the model variables until a solution is found that is practical and can be implemented
• Common techniques are– SolvingSolving equations– Trial and errorTrial and error – trying various approaches
and picking the best result– Complete enumerationComplete enumeration – trying all possible
values– Using an algorithmalgorithm – a series of repeating
Profits = $1*Number Sold - $100 - $.50*Number Sold
Assume you are the new owner of Bagels R Us and you want to develop a mathematical model for your daily profits and breakeven point. Your fixed overhead is $100 per day and your variable costs are 0.50 per bagel (these are GREAT bagels). You charge $1 per bagel.
The company buys, sells, and repairs old clocks. Rebuilt springs sell for $10 per unit. Fixed cost of equipment to build springs is $1,000. Variable cost for spring material is $5 per unit.
s = 10 f = 1,000 v = 5Number of spring sets sold = X
Seeing a need for childcare in her community, Sue decided to launch her own daycare service. Her service needed to be affordable, so she decided to watch each child for $12 a day. After doing her homework, Sue came up with the following financial information: Selling Price (per child per day) $12 Operating Expenses (per month)
Insurance 400 + Rent 200 = Total OE $600Costs of goods sold $4.00 per unit
She has to have a total of 75 children in her program over the month to breakeven.If she is open only 20 days per month then she needs 75/20=3.75 children per day on the average.
Expenses per month $600 + 75*$4.00 = $900Revenue per month 75*$12 = $900
Possible Problems in the Quantitative Analysis Approach
Defining the problem– Problems are not easily identified– Conflicting viewpoints– Impact on other departments– Beginning assumptions– Solution outdated
Developing a model– Fitting the textbook models– Understanding the model