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May 2014 Prescriptive Analytics A business guide Contents 3 The Business Value

May 2014 Prescriptive Analytics A business guide Contents 3 The Business Value of Prescriptive Analytics 4 What is Prescriptive Analytics? 6 Prescriptive Analytics Methods 7 Integration 8 Business Applications 9 Strategy The production of this document has been sponsored by Prescriptive Analytics: a business guide Copyright butleranalytics.com 3 The Business Value of Prescriptive Analytics After fifty years of using information technology to increase the efficiency of business processes we are now firmly in the era where technology is also being used to provide us with information. Business intelligence allows us to establish what has happened and is happening in our business (often called descriptive analytics), and predictive analytics uncover patterns which can be useful in the prediction of future events. This doesn’t complete the picture however. Descriptive and predictive analytics may tell us what has happened and what may happen, but they do not tell us the best way to deploy our resources to meet the demands of the future. An example will clarify. In a retail environment our descriptive analytics will tell us sales volumes, seasonal fluctuations and so on. Predictive analytics may give us insights into which products tend to be purchased together. Armed with this knowledge we then need to know how shelf space should best be allocated and more generally how resources should be utilised to maximise revenue and/or profitability. This is where prescriptive analytics fits in - think of it as a prescription for action. The major part of prescriptive analytics is concerned with resource optimisation given a set of business rules (constraints) and predictions relating to demand, customer behaviour, the success of marketing campaigns and so on. In real business problems, optimisation may involve thousands of variables and constraints, and finding the optimal use of resources, given an objective that is to be maximised or minimised, can only be achieved using powerful computerised optimisation software. Examples abound. Airlines use prescriptive analytics to determine the allocation of seats to each particular class. Vehicle rental businesses optimise the positioning of vehicles to maximise revenue and profitability. Energy companies increasingly use prescriptive analytics and especially with the unpredictable nature of renewable energy sources. Of course this all assumes that business managers buy into the resource utilisation schedules created by prescriptive analytics techniques. As such the analytics initiative needs high level sponsorship and coordinated effort throughout the enterprise. Reporting mechanisms need to be put in place and procedures to deal with the inevitable changes of circumstances all businesses experience. To this end some businesses run some of their prescriptive analytics processes in near real-time to accommodate change, and such is the power of the optimisation algorithms and computer hardware that this has become possible for complex analytics tasks. Prescriptive analytics is clearly not a trivial undertaking. It needs close liaison between analytics teams and business management, and an integrated analytics environment capable of integrating Prescriptive Analytics: a business guide Copyright butleranalytics.com 4 business rules, predictive models and prescriptive analytics. The integration is important, and particularly in large complex businesses. Without such integration prescriptive analytics may be very difficult to achieve, if not impossible. Expect to see prescriptive analytics technologies more widely used as the user interfaces become more user friendly, and business managers become empowered to address increasingly complex optimisation problems without recourse to teams of analysts. However for large, complex prescriptive analytics tasks the analytics teams are here to stay. While most analytics technologies are concerned with what has happened or will happen, prescriptive analytics tells how to best deploy resources to optimise our operational activities - and the benefits are often substantial. What is Prescriptive Analytics? Optimisation sits at the heart of prescriptive analytics technologies, and specifically the computation of best resource usage given a set of constraints and objectives. Work planning problems represent a classic application, where work is allocated to limited human resources in a manner that meets constraints and optimises objectives. While optimisation has been used for decades in many large corporations, the compute intensive processing has traditionally been associated with very long compute times - typically days and weeks. This limited the application of the technology. However advances made in the mathematical algorithms and more powerful hardware mean that optimisation can be applied to a much broader range of problems, and in some instance execute on a near real-time basis. The three essential components in an optimisation problem are variables, constraints and objectives. In a work planning problem the variables would typically represent the number of hours work allocated to various people from a given list of tasks. The constraints would limit the way the allocation of resources could take place - no more than 20% of the personnel from any department can be engaged on a project for example. Finally the objectives state what we are trying to achieve. Often this is simply to minimise costs, or maximise profits - or both. However in the work planning problem we might be most interested in minimising the time a project takes. Each optimisation problem has its own set of variables, constraints and objectives and much of the work goes into specifying what these are. Prescriptive Analytics: a business guide Copyright butleranalytics.com 5 Prescriptive analytics can be divided into two primary activities. The first involves optimisation when the input variables are known (a stock count, or balances in accounts for example). The problem here is simply to establish the best outcome given these variables along with associated constraints and given objectives. A second set of optimisation problems comes under the heading of stochastic optimisation, a suitably off-putting name which simply indicates there is uncertainty in the input data - next month’s sales for example. This more complex category of problems will attempt to find the best solution to a business optimisation problem for the most likely future situations. Obviously there is a strong link here with statistical modelling and other forms of predictive analytics, where probabilities are assigned to variables. It is increasingly the case that prescriptive analytics is integrated with other systems. Optimisation has traditionally been an isolated activity, but today it can take inputs from business rules and predictive analytics processing, and benefits hugely from them. The business rules act as constraints (do not mail someone with an offer of a 5% discount when they have already been mailed a 10% discount - for example), and predictive analytics can provide inputs which predict variable values (the number of prospects likely to respond to a marketing campaign for example). Prescriptive analytics is still relatively new (the term was first introduced about a decade ago) and only a handful of suppliers provide the integrated environment necessary to take advantage of outputs from other processes. However prescriptive analytics does complete the analytics picture - descriptive analytics (business intelligence) and predictive analytics say what has happened or will happen, while prescriptive analytics say how things should happen. Prescriptive Analytics: a business guide Copyright butleranalytics.com 6 Prescriptive Analytics Methods Optimising complex business problems requires sophisticated technology. Recent years have witnessed major advances in the speed of optimisation algorithms and in the complexity of problem that can be addressed. The net result is the proliferating use of optimisation technologies to address everything from marketing campaign optimisation to how many business class seats should be allocated on individual flights. There are several well defined types of problem that optimisation techniques can address - and some they can’t. The earliest and often easiest form of optimisation assumed that variables and objectives were related to each other in a linear manner. If a resource usage is doubled, so is its cost. While there are some problems that are well served by this model (the use of material components in a mix for example), many are not. To cater for more complex optimisation problems, non-linear relationships have been accommodated. A good example here is a price/demand curve where demand drops off rapidly as price exceeds a certain threshold, and increases exponentially as price drops below a critical level. The solution of non-linear optimisation problems is much more complex Prescriptive Analytics: a business guide Copyright butleranalytics.com 7 than linear problems, but contemporary tools with good user interfaces help keep such problems manageable. Other problems require that variables can only take on integer values (we can’t have 2.5 airplanes for example). Another class of problem makes use of network programming, where the aim is to minimise some function of the network. A good example here is minimising the cost of transport as a given number of trucks ship goods to a network of stores. Other techniques are also finding their way into prescriptive analytics, in addition to the optimisation techniques mentioned above. Queuing problems are common in business and optimisation techniques are used to address problems from traffic flow through to minimising check-out queues in stores. Simulation is also used to model the performance of business systems and is a large domain in its own right. It is very often the case that the ‘best’ solution to various business problems simply cannot be uploads/Industriel/ prescriptive-analytics-guide.pdf