Making Optimised Decisions about the Future
Business Analytics is critical to managing and optimising enterprise performance — whether it’s the ability to quickly spot new trends, predict the behaviour of key variables or support critical operational, tactical and strategic decisions. It is composed of three types of analytics: descriptive, predictive and prescriptive. As companies progress their use of Business Analytics, they derive exponentially more value — especially as their orientation shifts from historical to forward-looking.
The primary use is to report on what is happening. Typically supported by BI technologies that can handle large amounts of data, Descriptive Analytics supports standardised and ad-hoc reports, scorecards, alerts and basic “slice and dice” tasks. In combination with workflow and business process management, Descriptive Analytics technology can also be used to aggregate and roll-up information from multiple inputs in planning scenarios, such as budgeting.
When used for their intended purposes, BI models are excellent at helping users understand what has happened and why. Nevertheless, a BI model (descriptive or diagnostic) should not be used as the calculation engine for forward-looking scenarios, as it is sure to provide infeasible or sub optimal plans, as well as incorrect information about potential upsides and risk. Furthermore, users will likely miss important insights that would have otherwise led to additional performance improvement opportunities.
Through a variety of statistical modelling approaches, Predictive Analytics helps businesses predict the behaviour of key variables that are unknown, yet have significant impact on the performance of the business. The most obvious case is predicting demand (including volume and prices) in various forms, and additional factors such as:
Predictive models are also used for analysing information patterns to support tactical analytics, such as fraud detection or online marketing. Predictive modelling yields useful results when causal drivers are identified, and when the past behaviour of the drivers, as well as their relationship to the main variable forecasted, are stable. Predicting the future is more challenging when there are a lot of random elements (e.g., when there are multiple causal drivers, and they behave unpredictably), and/or when there are significant discrete events (e.g., a market disruption or a significant equipment failure) that happen very infrequently.
The most advanced in the spectrum of business analytics, Prescriptive Analytics is able to make the greatest impact on large scale business objectives, e.g., increasing profit, decreasing COGS, increasing service levels and improving decision-making agility. Prescriptive Analytics optimises decision making to show companies what actions to take in order to maximise profitable growth, given their business constraints and key objectives.
Prescriptive Analytics translates a forecast into a feasible plan for the business, and helps users identify the best steps to implement. There are two primary approaches – simulation and optimisation.
Simulation is best used in design situations, where it helps users identify system behaviors under different configurations, and ensures all key performance metrics are met (e.g. wait times, queue length, etc.). optimisation supports ongoing operational, tactical and strategic business planning; it leverages linear programming to identify the best outcome for a business, given constraints and objective function.
When applied to broader tactical (e.g. S&OP or Integrated Business Planning) or strategy planning scenarios, the optimisation model is used to calculate the impact of various forecasts (some from predictive analytics engines) on the business. It does so while also taking into account operational realities in the form of constraints. Constraints include:
Advanced optimisation models combine the value chain (including key constraints) with financials, to provide much higher quality information than a BI or predictive model can alone – in the process, ensuring internal data consistency and identifying infeasible outcomes. In addition, they support unique analyses, such as contribution margin, activity based costing and pro-forma financial statements, which help users find and execute optimal decisions.
|Financial Services||Cash Management|
|Financial Services||Mortgage Services Strategy & Portfolio Optimisation|
|Aerospace & Defense||Service Contract Profitability Modeling|
|Healthcare - Providers||Population Management & ACO Transition|
|Healthcare - Providers||Staff, service and resource optimisation|
|Health Plans||Health Plan Benefit Design Optimisation|
|Health Plans||Provider Network Optimisation|
|Utilities||Multiple – Strategic, Tactical & Operational in Water Utility Industry|
|Consumer Packaged Goods||Trade Promotion Optimisation (TPO)|
|Consumer Packaged Goods||Integrated Business Planning / S&OP|
|Oil & Gas||Logistics Optimisation|
|Transportation & Hospitality||Revenue Management & Logistics Optimisation|
|Retail||Price & Promotions Optimisation|
|High Tech||Integrated Business Planning (S&OP)|
|Chemicals||Integrated Business Planning (S&OP) / Capex|
|Natural Resources||Network Optimisation / Integrated Business Planning (S&OP) / Capex|
|Metals||Product Mix & Supply Planning|
|Mining||Supply Chain Planning & Blend Optimization|