This chapter is from the book
This chapter is from the book
This chapter is from the book
Wrap Up
Management science techniques discussed in this book have been around for decades. However, in the era of Big Data, management scientists have “rediscovered their roots” 26 and are modifying traditional techniques to better process large volumes of data, offer simpler and practical models, utilize spreadsheet modeling techniques, and offer practical solutions, which can be implemented in real time. With the availability of large amounts of data, Big Data is the major factor that boosted management science into today’s stage of prescriptive analytics.
Presently, several optimization software programs exist, which are able to model and to solve a large number of constraints and decision variables. Solver is an excellent program, licensed to Excel as an add-in from Frontline Systems. Solver can be used by practitioners to solve mathematical programming models and perform what-if analysis and optimizations to determine the best product mix, determine optimal shipping routes, maximize profit, or minimize costs. This book advocates a two-step approach when using Excel’s Solver: (a) setting up a template and (b) running Solver and analyzing the results. This approach allows the modeler to design templates that handle large amounts of input data and then reuse these templates with new data sets as these sets are continuously updated from transaction database sources.
In the era of high-volume data, ETL processes can be used to automatically capture and process input parameters. These processes query transactional records and calculate averages for technological coefficients, contribution coefficients, and right-hand side values for up-to-date available resources. Automatic capturing and processing of data allows organizations to design optimization models that are process driven. Such an approach requires analytics to be embedded within business processes 27 and continuously adjust input parameters and periodically produce optimal solutions.
Considering these challenges, the content of the book is offered with two universal principles in mind: teaching by example and explaining by intuition. Practitioners understand complex concepts by referring to examples and understand the reasoning behind these concepts by consulting intuitive explanations, not by referring to formulas and theoretical definitions. The book uses a black-box approach, which allows the practitioner to focus more upon the input-output aspects of decision making and less upon the dynamics and complexities of the model itself, which in most cases are handled by software programs.