Prescriptive Analytics PDF

Prescriptive Analytics? Prescriptive analytics is the third and final stage of business analytics dedicated to finding and suggesting (i.e., prescribing) the best decision options for a given situation. Prescriptive analytics encompasses the activities of (1) data collection and consolidat...

Prescriptive Analytics? Prescriptive analytics is the third and final stage of business analytics dedicated to finding and suggesting (i.e., prescribing) the best decision options for a given situation. Prescriptive analytics encompasses the activities of (1) data collection and consolidation, (2) information extraction, (3) forecasting, (4) optimization, (5) visualization, and (6) what-if analysis for first making predictions and then, based on these predictions, (a) suggesting the most appropriate time-dependent decisions (i.e., prescriptions) and (b) illustrating the implications of each decision option. Prescriptive analytics is a type of data analytics that attempts to answer the question “What do we need to do to achieve this?” It involves the use of technology to help businesses make better decisions through the analysis of raw data. Prescriptive analytics specifically factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long-term. It is the opposite of descriptive analytics, which examines decisions and outcomes after the fact. Key Takeaways  Prescriptive analytics is a form of data analytics that tries to answer “What do we need to do to achieve this?”  It uses machine learning to help businesses decide a course of action based on a computer program’s predictions.  Prescriptive analytics works with predictive analytics, which uses data to determine near-term outcomes.  When used effectively, it can help organizations make decisions based on facts and probability- weighted projections instead of conclusions based on instinct.  Prescriptive analytics isn’t foolproof—it’s only as effective as its inputs. How Prescriptive Analytics Works Prescriptive analytics tries to answer the question “How do we get to this point?” It relies on artificial intelligence (AI) techniques, such as machine learning (the ability of a computer program without additional human input), to understand and advance from the data it acquires, adapting all the while.  Machine learning makes it possible to process a tremendous amount of data available today. As new or additional data becomes available, computer programs adjust automatically to make use of it, in a process that is much faster and more comprehensive than human capabilities could manage. Prescriptive analytics works with another type of data analytics: predictive analytics, which involves the use of statistics and modeling to determine future performance, based on current and historical data. However, it goes further: Using predictive analytics’ estimation of what is likely to happen, it recommends what future course to take. Examples of Prescriptive Analytics Numerous data-intensive businesses and government agencies can benefit from using prescriptive analytics. This includes companies in the financial services and healthcare sectors, where the cost of human error is high. For instance, prescriptive analytics could be used to:  Evaluate whether a local fire department should require residents to evacuate a particular area when a wildfire is burning nearby  Predict whether an article on a particular topic will be popular with readers based on data about searches and social shares for related topics  Adjust a worker training program in real time based on how the worker is responding to each lesson  The following are examples where prescriptive analytics can be used in various settings. Prescriptive Analytics for Hospitals and Clinics Prescriptive analytics can be used by hospitals and clinics to improve the outcomes for patients. It puts healthcare data in context to evaluate the cost-effectiveness of various procedures and treatments and to evaluate official clinical methods. It can also be used to analyze which hospital patients have the highest risk of readmission so that healthcare providers can do more, via patient education and doctor follow-up to stave off constant returns to the hospital or emergency room. Prescriptive Analytics for Airlines Suppose you are the chief executive officer (CEO) of an airline, and you want to maximize your company’s profits. Prescriptive analytics can help you do this by automatically adjusting ticket prices and availability based on numerous factors, including customer demand, weather, and gasoline prices. When the algorithm identifies that this year’s pre-Christmas ticket sales from Los Angeles to New York are behind last year’s, for example, it can automatically lower prices, while making sure not to drop them too low in light of this year’s higher oil prices. At the same time, when the algorithm evaluates the higher-than-usual demand for tickets from St. Louis to Chicago because of icy road conditions, it can raise ticket prices automatically. The CEO doesn’t have to stare at a computer all day looking at what’s happening with ticket sales and market conditions and then instruct workers to log into the system and change the prices manually. Instead, a computer program can do all of this and more— and at a faster pace. Prescriptive Analytics in Banking Banking is one of the industries that can benefit from prescriptive analytics the most. That’s because companies in this sector are always trying to find ways to better serve their customers while ensuring that they remain profitable. Applying prescriptive analytical tools can help the banking sector to:  Create models for customer relationship management  Improve ways to cross-sell and upsell products and services  Recognize weaknesses that may result in losses, such as anti-money laundering (AML)  Develop key security and regulatory initiatives like compliance reporting Prescriptive Analytics in Marketing Just like banking, data analytics is very critical in the marketing sector. Marketers can use prescriptive analytics to stay ahead of consumer trends. Using past trends and past performance can give internal and external marketing departments a competitive edge. By employing prescriptive analytics, marketers can come up with effective campaigns that target specific customers at specific times like, say, advertising for a certain demographic during the Super Bowl. Corporations can also identify how to engage different customers and how to effectively price and discount their products and services. THE TRANSFORMATIONAL VALUE OF PRESCRIPTIVE & WHY BUSINESS LEADERS SHOULD CARE Prescriptive analytics has been around for a long time. However, it’s typically been used to solve highly complex, niche problems like scheduling, routing, and staffing — activities that are highly complex where the problem definition is stable, and have historically involved Data Scientists rather than people within a business unit. Now, however, we’re seeing the application of prescriptive analytics move out of the hands of Information Technology (IT) or Data Scientists and into business units. This shift has shown that prescriptive analytics is most beneficial to the organization when it’s understood and accessible to business leaders.  Address new planning challenges using the best method possible Prescriptive analytics can address questions that other forms of analytics simply cannot. Further, it often helps uncover transformational opportunities across businesses that business leaders may even think are impossible to solve.  Earn a higher return on existing assets Prescriptive analytics enable businesses to showcase how to leverage their prior investments in tools like Electronic Resource Planning (ERP) software that helps provide companies with clean, fresh data. Leaders can utilize that data for actionable insights while also guiding them on where they might be missing quality data. Lastly, because prescriptive provides the best path forward, employees can have a true impact on overarching business objectives and quickly progress their status within a company. Employees are thus motivated to continue using prescriptive analytics solutions.  Mitigate risk Risks are often quantified in either operational or financial term, but usually not in a way that truly mirrors how the business operates. Prescriptive analytics helps identify and better quantify the risk associated with both short and long-term decision-making and develop potential risk mitigation strategies.  Establish higher agility in the organization Difficult decisions take weeks or months to make, often taking up a lot of personnel time and occasionally the use of external consultants. Routine decisions that are made weekly often don’t get the same level of scrutiny or scenario analysis, as there is not enough time to manipulate and analyze so much data. Prescriptive analytics increases the organizational knowledge of how different functions impact one another and recommends a path forward, thus increasing the ability to evaluate more scenarios and delivering a faster approach to making trade-decisions.  Improve performance Prescriptive analytics uncover unique insights that can lead to better financial and operational performance, especially when deployed across functions that were previously supported through tools relying on user intuition (i.e., Excel, BI). Different types of impact include: Improving the effectiveness of the business against one or more objectives (i.e., operating income, net income) — for example, in the application of integrated planning across demand, supply, and finance. Typical impact can range from 2-5% of revenue in additional profit. Increasing the efficiency of an operation (i.e., do more with same resources, achieve the current outcome with fewer resources) — for example by improving the use and allocation of personnel and resources to best meet a set of tasks. Typical impact includes 15-20% higher throughput or 10-15% reduction in addressable cost. Maximizing the return from altering the design of a system, subject to a defined maximum risk — for example optimizing the allocation of investments. Typical impact ranges from 25-100% better NPV than Excel or heuristics-based solutions. HOW DOES PRESCRIPTIVE ANALYTICS WORK? The prescriptive analytics market consists of two categories of algorithms: Heuristic algorithms do not guarantee the best answer. If designed well, they can offer a short-cut approach to finding good answers in a reasonable amount of time. Exact algorithms guarantee the best answer. However, for difficult problems, the time to solve for the best answer can increase exponentially compared to the size of the problem. Below, we’ve listed some important criteria to consider when determining the appropriate approach to prescriptive analytics: TYPE: Some problems are naturally better for heuristics, while others are better for optimization. There are lists below citing examples of each. COMPLEXITY: How difficult is the problem? There are well known problems — e.g., traveling salesman — that can be difficult to find the best answer using optimization. In some cases, finding a good solution quickly using rules might make sense compared with optimization. PERFORMANCE: How long are you willing to wait for an answer each time you solve the problem? If an answer must be found as soon as possible, a heuristic might be a better choice. If time is not a major concern, optimization might be a better choice. FREQUENCY: How often must the problem be solved? If a new decision must be made frequently, maybe hundreds, even thousands, of times each day, then heuristics is likely to be a better choice over optimization. HEURISTICS (RULES) HOW IT WORKS Heuristics are a set of problem-dependent rules. They are best used when the problem can be narrowly defined and operational in nature, rather than tactical or strategic. Additionally, they can be a good choice when the same decisions must be made hundreds, thousands, even millions of times per day. Heuristics use highly specialized techniques designed to take advantage of particular aspects of a problem. They typically require developing either a set of mathematical functions (e.g., f(x) = y); a set of instructions (e.g., “If this…then do this”); or both. Here is an analogy: Imagine you are driving in a car in a city you don’t know, trying to reach a destination that you have never been to. The only instructions you have been given are “head west until you reach a certain hill on the horizon.” You begin driving… Without GPS, a map, or a specific set of instructions, you must rely on a rules-based approach — knowledge of local traffic laws plus intuition and experience — to help guide you. You might not take the shortest route (in distance or time). You might end up driving an extra 10 kilometers and take 20 minutes longer than was necessary. You might not get to your destination at all without additional information. This approach is a good proxy for a heuristic. Since a GPS system can provide the best answer based on an exact algorithm with specified parameters (i.e., “I want the shortest distance”), it would meet the test of an optimized approach. Excel is a common tool used to make business decisions. By using features like IF statements, lookups, and functions, Excel-based rules can be defined by making a hypothesis about a potential answer. Then, when values are entered, the answer is immediately returned. Unless an optimization approach is used, there are no means to know if this is the best answer. EXAMPLES As mentioned above, certain decisions are better suited to heuristics rather than optimization. Examples of circumstances where optimization is not required and “rules of thumb” are sufficient:  RAW MATERIAL PURCHASES: e.g., purchase the cheapest source of raw material first regardless of quality  CAPACITY ALLOCATION: e.g., assign capacity to line 1 first, then line 2 second, and so on, regardless of operating efficiency or costs  MARKETING: e.g., offer customers promotional opportunities based on a website search or prior purchase  DEMAND FULFILLMENT: e.g., Tier 1 customers must always have their service level met at the expense of lower tier customers PROS  Better for decision automation, because it provides an instant output  Better for difficult problems, such as scheduling or inventory economic order quantity (EOQ)  Can be easier to learn, configure, and implement — many rules-based decisions are built into existing features in business process management (BPM), inventory management, and other software CONS  Limited benefits for holistic decision-making across functions (e.g., Integrated Business Planning) with a low ROI  Highly likely forfeiture of additional profits (or fewer costs)  “Good enough” answers are not guaranteed optimal (and often no mathematical proof)  Won’t analyze every possible scenario  Although rare, it can fail to find a good solution if instructions don’t allow it  Requires customized solutions for narrowly-defined, specific problems  May result in infeasible plans, for example, using Excel for tactical or strategic decision making  Rules are difficult to maintain or may become obsolete OPTIMIZATION HOW IT WORKS Optimization is a combination of mathematical modeling and exact algorithms used to find the optimal answer. A problem is defined by writing math equations using a model building platform. Once the model is created, it is sent to a highly specialized algorithm used to solve the problem. More information is provided later on An optimization problem consists of the following parts: DECISIONS TO BE SOLVED FOR Commonly referred to as decision variables These are the business questions to be answered. Complex problems can range from 100K to 10 million+ individual decisions. Examples  How much raw material to purchase?  How many hours to run each line?  How much product to sell to certain markets? DATA TO BE INPUT  Commonly referred to as coefficients  Depending on the problem can be costs, prices, BOMs, or yields  The shorter the planning period, the more granular the data should be Examples  How much does each ton of raw material cost?  What does it cost to run each line?  What is the recipe for SKU #123 at Plant A?  At what price is the product sold in each market? BUSINESS REALITIES/RESTRICTIONS THAT MUST BE ADHERED TO Commonly referred to as constraints or bounds These can include but are not limited to physical laws or company policies Examples   How much is raw material available to purchase?  How many hours does each line have available?  What is the maximum amount of demand for each market?  To achieve an optimal answer, an objective must be stated to either maximize or minimize a metric (e.g., profit, costs, personnel utilization, volume). The user can specify how precise they want the answer and how long they are willing to wait. The optimization algorithm then finds the best answer. SAMPLE PROBLEMS OPTIMIZATION ADDRESSES Optimization is used to solve numerous problems that are generally too complex for a heuristics-based approach. Historically, problems solved using optimization were for a specific business function:  TRANSPORTATION: shipping goods from supply to demand points at a minimal cost  EQUIPMENT REPLACEMENT: determining the optimum point in time to replace equipment  ASSIGNMENT PROBLEMS: assigning staff to equipment  GASOLINE BLENDING: for aviation fuels Over the last two decades, businesses have learned to use optimization to tackle cross-functional problems. Some of the less traditional yet significantly more valuable applications are:  Customer profitability and pricing  Asset investment planning  Product mix, blending, and substitution  Treatment path optimization  Workforce planning and training  Commodity trading