DEFINING PRESCRIPTIVE ANALYTICS
CONFUSION IN THE MARKET SPACE
To understand the progression of the prescriptive analytics category, let’s revisit Mary and Barry’s story. When Mary brought her problems to Barry, she had no idea Barry could solve them — she’d never heard of anything like prescriptive analytics! Further, while Barry knew it, he hadn’t seen any consistency in the market space on a clear way to apply it in an actual business sense. In fact, one colleague of Barry’s in the IT department told him it would be impossible for him to solve Mary’s problems in the best way possible without adding a full-time programmer — what he called an “Operations Research Ph.D.”
This problem of misinformation and lack of awareness isn’t isolated to Mary and Barry. It’s a problem that exists globally across almost every industry. To help dispel some of the false information and appropriately educate people within a business unit on prescriptive, let’s walk through the history of prescriptive analytics.
If you search Google trends today, you’ll see that interest in the topic “prescriptive analytics” has grown significantly since Google began collecting this data in 2004. The real boom began in 2013, and we’ve seen rapid growth in interest since then. It’s worth noting that there is no sign of the trend curve flattening out, as it continues to grow each year.
Figure 2. Interest in prescriptive analytics from 2004 to today
In early 2011, prescriptive analytics first appeared in Gartner (renowned global technology research firm). Since then, we’ve seen a rapid increase in interest in prescriptive analytics.
A BRIEF INTRODUCTION TO PRESCRIPTIVE ANALYTICS
To further complicate things, each discipline is made up of many sub-disciplines and variants. For example, Operations Research includes various disparate techniques like Simulation, Decision Analysis, and Optimization. It’s no wonder there’s confusion in the market space!
Our goal with this book is to simplify things — tell you what you need to know so you can make the most informed decisions about applying prescriptive analytics within your business. Therefore, we’ve outlined the concepts we feel are essential for you to know in order to begin using prescriptive analytics.
Research firms, vendors, consultants, and market leaders have trended toward dividing prescriptive analytics into two different approaches: Heuristics-based automated decision making and optimization-based decision support. We’ll dive deeper into these two approaches in the next chapter, but below are some basic definitions to get you started.
To solve operational problems, such as route optimization and logistics planning, Operations Research professionals traditionally applied optimization. With the advent of new technologies making it possible to model larger, enterprise-wide problems and provide broad support for what-if analyses, optimization now enables a new class of decision support analytics.
Advanced optimization models combine the value chain (including key constraints) with financials, providing higher quality information than what’s possible with single predictive or BI models. This also ensures internal data consistency and identifies infeasible outcomes. These models support unique analyses, such as contribution margin, activity-based costing, and Pro-forma financial statements to help users make the best possible business decisions.
Optimization is typically used to solve complex problems that involve numerous (20+) constraints, objectives, and trade-offs. Applying prescriptive analytics through optimization enables users to wade through all these factors and find the path that meets the most objectives given the defined business.
The “math” used in optimization is complex. The most important thing to note is that it uses mathematical algorithms that maximize or minimize one or more objective functions while still respecting business realities, thus always producing feasible plans.
HEURISTICS-BASED DECISION AUTOMATION
Rules-based decision automation is different. It means that when something happens, the system will decide what to do on the fly, given a set of predefined rules that have been plugged in. Mind you, these rules are typically determined by humans using gut feel and “best practices,” not by using math.
Unlike optimization, this approach cannot provide an answer outside of what has been predetermined. Further, the “math” is very different from the math used in optimization. It typically uses a form of statistics and applies algorithms to find the answer.
The most important takeaway from this section is that different methods of applying prescriptive analytics exist, and — while it’s not crucial for business leaders to dive deep into the “math” — they do need to understand the value that each approach can bring to an organization.
Earlier, we mentioned the kind of transformational value VP Mary saw from applying prescriptive analytics to help support her decision-making process. This is arguably the most important thing to know about prescriptive analytics: It truly transforms the impact one or more business units have on the entire company.
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.
PRESCRIPTIVE ANALYTICS BELONGS IN THE HANDS OF BUSINESS LEADERS
Four key factors have caused a shift from using optimization to solve operational problems to using it to solve more strategic, cross-functional problems that business executives regard as important to their success.
- We not only have more data, but it’s better and more diverse data.
- Prescriptive analytics technology is becoming significantly less black box, allowing business users to draw insights without the dependence on Data Scientists or Operations Research experts within IT Departments.
- Business leaders understand the most pressing problems they need to address.
- More and more organizations are doing it, so it’s no longer a “nice to have,” rather, it’s a must-have.
THE VALUE OF PRESCRIPTIVE ANALYTICS TO BUSINESSES
Though it may be hard to believe, our VP Mary’s story is a real example of the transformational value of prescriptive analytics, and the benefits she saw have been replicated across dozens of industries and hundreds of use cases.
The typical value realized from prescriptive analytics is 10-20X ROI. While the exact ROI depends on the specific approach to prescriptive analytics and the type of problem addressed, it’s clear that prescriptive analytics offers the most significant improvement of any of the other forms of analytics…by far!
Further, the impact can become transformational when applied end-to-end across business functions, especially when it affects the core business metrics such as operating income or return on invested capital (ROIC). Let’s look closer at the value business leaders have seen from prescriptive analytics.
ACHIEVE HIGHER CONFIDENCE IN PLANS PLUS LOWER RISK
The foundation of a solid, effective plan is having confidence in it. Optimization- based plans are, by definition, feasible. Plans based on heuristics may or may not be feasible, depending on how simple the problem is and how well the rules are set up.
With optimization based decision making, because the operational and financial flow of the business is appropriately represented, there is a higher likelihood that the results can be achieved than if the company was using rules or Excel-driven hypotheses. This includes both the ability to deliver a plan and the understanding of required actions to implement the plan. Further, it provides an understanding of the operational and financial impacts of analyzed decisions on overall objectives. A manager that produces a plan with high confidence gains respect and the ability to affect further change in the business.
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.
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.
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.
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.
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.
Table 1 shows a few of the most common examples of applying prescriptive analytics across various industries.
Table 1. Real-Life, Cross-Industry Applications of Prescriptive Analytics
|Financial Services||Cash Management Mortgage Services Strategy Portfolio Optimization|
|High Tech||Integrated Business Planning|
|Aerospace & Defense||Service Contract Profitability Modeling|
|Healthcare (Providers)||Health Plan Benefit Design Optimization Staff, Service and Resource Optimization|
|Utilities||Operational Planning (weekly
planning to 25+ year long-range planning)
|Consumer Packaged Goods||Trade Promotion Optimization
IBP/Sales and Operations Planning (S&OP)
|Oil & Gas||Logistics Optimization Commodity Trading Optimization|
|Retail||Price and Promotion Optimization|
|Natural Resources||Network Optimization IBP/S&OP CAPEX|
|Metals||Product Mix and Supply Planning|
|Mining||Supply Chain Planning Blend Optimization|
|Public Sector||Personnel Training Optimization|
|Telecomm- unications||Optimization of Channel Allocation to Spectrum|
Hopefully, after reading this chapter, you understand:
- How prescriptive analytics is different from BI and predictive analytics
- The types of approaches to prescriptive analytics and how they differ
- The importance of putting prescriptive analytics in the hands of business leaders versus Data Scientists and Operations Research PhDs
- The transformational value prescriptive analytics can drive
This is an exciting and opportune time in the prescriptive analytics market. Businesses are beginning to understand what they need to be successful — and the data is quickly becoming available (if it isn’t already). By 2020, we expect 35% market penetration in this category. Ask yourself: do you want to fall behind?