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Prescriptive Analytics

How to use prescriptive analytics and gain a competitive edge

Analytics’ best-kept secret is Decision Optimization, the prescriptive analytics solution that looks through millions of choices to help come up with the best decisions for your organisation. It is a valuable (and heavily under-utilised) tool in your corporate planning toolkit, and applicable to almost every industry:

Manufacturing

Inventory optimisation, supply chain network design, production planning, shipment planning, detailed scheduling, truck loading.

Transportation and Logistics

Warehouse location planning, fleet assignment, maintenance scheduling.

Financial Services

Portfolio optimisation and rebalancing, loan pooling, product and pricing recommendations.

Utilities, Energy & Natural Resources

Supply portfolio planning, power generation scheduling, water reservoir planning, mine operations.

Telecommunications

Network capacity planning, adaptive network configuration, antenna location planning.

General

Workforce scheduling, advertising and marketing optimisation, revenue and yield management.

Identifying an opportunity

A prescriptive analytics application needs to have a precisely defined situation. Decision Optimisation finds the best solutions by measuring value and making specific decisions to maximise that value.

Prescriptive analytics solutions are based on the following concepts:

Targets

Measurable targets such as minimising cost or delays, maximising revenue or margin, allow you to rank alternative decisions. The conflict they may have with each other result in trade-offs which Decision Optimization considers in the solution.

Limits

Quantitative limits on resources, such as physical laws, space in a warehouse, amount of raw materials, regulations, and policies distinguish acceptable from unacceptable combinations of decisions. Prescriptive analytics problems usually have multiple kinds of limits, again resulting in trade-offs.

Choices

Choosing between decisions requires trade-offs to satisfy the limits of a situation and achieve the best value of the targets.

Data

Prescriptive analytics requires data in order to deal with targets, limits and choices in a meaningful way. This data can come from a large range of sources, from GPS enabled trucks to help optimise routes to weather data to help determine flight crewing requirements in the event of airport disruptions.

When considering opportunities to apply prescriptive analytics to, think about the time frame and frequency of the decisions you’d need to make:

Weekly, daily, hourly

These are usually short-term operational decisions such as assigning employees to shifts. Prescriptive analytics solutions take out the rules-of-thumb and intuition in human decision making. This is particularly important when operations involve thousands of individual choices that must be decided within a tight timeframe.

Monthly, quarterly, annually

These tactical decisions are typically medium-term. Examples include marketing campaign planning and procuring materials. Again, the benefits of using an optimisation solution are higher than using rules-of-thumb and familiar patterns.

Annual or longer

These longer-term decisions are usually strategic, and include deciding on investments, warehouse locations, fleet sizing, and mine planning. Optimisation solutions take high-level corporate policies and puts them on a solid factual basis to provide key insights.

Why you should consider analytic decision support

Smarter decisions

From fact-based decision making to discovering non-obvious choices that maximise value or minimise cost to testing limits and assumptions can all help you achieve better performance for a lower cost.

Faster decisions

Decision Optimization allows you to automate decision processes, helping decrease the time it takes to respond to changing markets.

Effective enterprise decision support

Using specialised products within the Decision Optimization solution makes it easy to develop enterprise-wide decision management functions such as a customisable user interface, central servers, and scalable optimisation.

Ultimately, Decision Optimisation uses the investment your organisation has made in its information technology to make effective decisions.

Working with others to develop effective solutions

Creating powerful analytical decision support solutions requires a few different people in your business to work together:

Decision makers and business analysts

These are the users of the solution. They set objectives and functional requirements of the solution, such as the interface. They provide subject expertise on the business environment. And importantly, they give ongoing feedback.

Analytics and optimisation experts

These folks translate the objectives and requirements into mathematical models, implement the solution and help debug.

IT architects

These specialists create the infrastructure in which the Optimization solution operates, developing the user interface, reporting system, and deployment architecture.

You may not have all these experts within your organisation, but that’s where consultants like us come in. Let’s have a chat about where exactly Decision Optimization can help maximise value and minimise costs in your business. Book a discovery call here.

Need a bit more inspiration? Read our golden rules for making a business intelligence project successful.

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