Decision Making with Descriptive, Predictive & Prescriptive Analytics + Rule-based & Heuristics

Business insights is a key success factor influencing the performance of decision makers, specifically the quality of their decisions. Nowadays, sheer amounts of data are available for organisations to collect and analyse. Data is considered the raw material of the 21st century. The majority of raw data does not offer a lot of value in its unprocessed state. The extraction of knowledge from these datasets can help decision makers gaining valuable insights.

When making a decision, one usually needs to consider a large number of alternatives. Each of these must be evaluated using one or several criteria in order to determine the “best” decision. Mathematical optimisation is the discipline of developing realistic plans or schedules to assist highly data-intensive businesses and organisations making better decisions that provides the best possible balance between customer service and revenue goals.

“All our lives is about making decisions. So in some sense the whole world is about analytics and optimisation. Isn’t it?” — Jai Menon

According to Cambridge Advanced Learner’s Dictionary, “optimisation” is the process of making something as good or effective as possible. The ‘something’ here is a problem or situation to solve. Mathematical optimisation is always about a set of problem and solution, which is described by mathematical abstract or model. How ‘good’ a solution is by a given measure. If the goal is measurable in absolute terms, i.e. money and time, then the closer the solution is to the extreme i.e. minimum or maximum, of a goal, the better. That is, optimisation is a kind of search among the alternatives.

For over 2000 years, humans have been working with geometry, algebra and logic to find better solutions to our most challenging problems. Mathematical optimisation is a more of a general term for Operations Research (OR), the application of mathematical and scientific methods to solve complex and real-life problems. Operations Research was first developed for military operations during World War Two (WW2). British Navy and the US Navy apply the techniques to determine the best locations for radar stations and anti-submarine resources. After the war, scientists started applying mathematical optimisation to similar problems in the industry.

Optimisation Engines Make The Industry Efficient: The History

The Advanced Analytics Landscape

Advanced analytics is a grouping of analytic techniques used to predict future outcomes. Advanced analytics increases the organisation’s breakaway capability through 3 levels of analytics sophistication, which are descriptive analytics, predictive analytics and prescriptive analytics (mathematical optimisation).

0) Data Collection

In any decision making setup, the first step is to capture lots of information, the structured and unstructured data from all kind of sources i.e. numeric, text or social, of the past and present. We definitely want to collect reliable and clean data. As the principle “Garbage in, Garbage out”, the best methods will produce as good result as the data quality is.

1) Descriptive Analytics

The descriptive analytics is the simplest sophistication of analytics. It allows us to condense big data into smaller, more useful summary of information. With the enough data in hand, we start to see patterns and relate the data in a way which may shed light on connections. We can filter and drill into details to find out what happened in the past and what is happening now. For small businesses, it is still meaningful to make decisions based on these information with some industry assumptions. We can estimate that more than 80% of business analytics is still staying at this descriptive analytics level.

2) Predictive Analytics

Predictive analytics is the next step up in data reduction. It utilises a variety of statistical methods, modelling, data mining and machine learning techniques to study the patterns of the recent and historical data. These include preparing and managing the data through scoring and classification. Thereby analysts can easily build predictive models, such as demand forecast, market segmentation and product clustering, to forecast and simulate what scenarios might happen in the future.

3) Prescriptive Analytics

The emerging technology of prescriptive analytics goes beyond descriptive and predictive models by recommending (prescribing) both the action necessary to achieve the predicted outcomes and showing the likely outcomes / effects of each decision. The prescriptive analytics is an extension of predictive analytics with two additional components, i.e. actionable data and a feedback system that tracks the outcome produced by the action taken.

Uncertainty can be dealt with many ways. Businesses and organisations want to respond to the changes in their operation processes and improve their performance. Using the predicted data as input, given our objectives, various choices and influences that might affect the outcome, we can explore alternatives and understand trade-offs. As a result, we can create all possible plans or schedules that are measured by KPIs and ROI. We can fully automate expert’s knowledge by having a expert creates some models of the world and evaluate (search in) the world based on that model. Finally, we can find the optimal solution and prescribe the best possible course of action for the future.

We can make only those decisions which can be reversible (even not always possible). Depending on how fast we can react, we can make smaller decisions, synchronise with reality more frequently and re-planning or rescheduling all the time.

In short, prescriptive analytics predicts the possible consequences based on different choice of action. With that, it can recommend the best course of action for any pre-specified outcome. The more we go to the direction of prescriptive, the more sophistication is needed, the more value it can bring.

More and more businesses start looking into prescriptive analytics now. The efficient use of resources has never been more critical in terms of impact on profitability. Changes in the economy and in business process management are making smart, agile economic planning and scenario comparison a necessity. Besides that, the development in hardware and software technology had been improved exponentially. We can leverage tools that deliver a huge head start toward higher quality decisions in less time. Advances in computer hardware and optimisation software have made it possible to evaluate large planning and scheduling problems that were too difficult for computers as recently as five years ago. Advances in software technology are making optimisation accessible to nontechnical planners, schedulers and managers who make the decisions in most organisations.

4) Rule-based System

The knowledge and experience is in the head of the expert. We can create plan visually on the screen manually. There is always a possibility that human experience is enough. The best solution has been found already and it will not change significantly in the future.

The human experience and knowledge can be converted to rules and it is easily understandable by the planner. Expert’s knowledge can be automated for some conditions and actions over a set of objects which are truly in their business domain (BOM). With these rules or some propagation engines, a solution checker can check on what constraints or conditions the plan should satisfy with visual and highlighting the ‘not yet satisfied’ parts. The rules can be automatically executed without (almost) no human interaction. However, when the plan doesn’t satisfy all the constraints, the human correction is necessary. This is a Rule-based or Expert System:

IF a given set of conditions are met THEN certain actions should be taken.

5) Next best heuristic approach

Heuristics were the main tool used to solve optimisation problems in the industry. Below is an example of sequence dependent set-ups. Time shown is the time to switch from one job to the next.

Question: Assume Job 1 is running, what is the best sequence?

If we use a heuristic, we can do things that look good at the start. The sequence is 1, 5, 2, 3, 4 which the total set up is 19. It is from 3 to 4 that we get in trouble.

The optimal approach can help us find the right sequence and avoid running into pitfalls later. The optimal sequence is 1, 4, 5, 2, 3 with the total set up of 11.

There are lots of ways to potentially organise the sequence but we cannot figure it out in our head or on a piece of paper or in a simple spreadsheet, especially when the data is larger and more complex. With prescriptive analytics, we can model decisions mathematically based on a data model, creating a useful, albeit imperfect abstraction of the real world.

Integer Linear Programming with Graphical Method

Comparison of Advanced Analytics

A) Data: What had happened in the past and happening now?

• Pricing Optimisation: Past sales, price levels
• Cloud Capacity: System usage, CPUs, system capacity
• Consumer Goods: History of product sales
• Retail: History of customer purchasing data
• Manufacturing: History of machine data

B) Predict: What could happen?

• Pricing Optimisation: Predict sales level from price
• Cloud Capacity: Predict storage and memory needs
• Consumer Goods: What will be any sales forecast for the next month?
• Retail: Will you be able to predict what items will interest them?
• Manufacturing: Will you be able to predict when your machine might breakdown?

C) Optimise: What should happen?

• Pricing Optimisation: Set prices in order to maximise profit / revenue
• Cloud Capacity: Optimise how a virtual system is configured
• Consumer Goods: With the forecasted data, where should I place my inventory? How much safety stock do I need?
• Retail: What items should I place in which stores? What campaigns would be best to promote those items? How do I minimise discounts?
• Manufacturing: Using the predicted machine breakdown data, do you know the best times to schedule and conduct preventive maintenance?

Rule-based System vs Mathematical Optimization

Below is the detailed comparison between rule-based system and mathematical optimisation.

Rule-based System

• Reactive – when conditions are met, rules are triggered.
• Local – when conditions are met, rules are triggered.
• Not really abstraction of the problem/business, the rules are working on the business domain.
• Most cases, it’s faster than OR-based approach as no search at all, just actions if conditions met.
• Rules may contradict one another but for real life problem the solution is satisfactory.
• the reality/data is changing too fast, no meaning to make an optimized plan because the plan is obsolete by the time.
• Time to react is more important than the quality of the plan. It doesn’t matter much if the solutions is not perfect (maybe contradictory) or there might be better one.
• There is already accumulated human knowledge and it is valued over any machine made one.
• Changing the planning / business process is either risky or require too much change.
• The problem could be described as a control problem.
• The cost of not using better solution to be considered.

Mathematical Optimisation

• Proactive – all predefined constraints should be (usually) satisfied in a plan/solution.
• Global – it is a search guided by a goal (function).
• Abstract model is made (on which a search is conducted) to find the best values for the variables.
• If constraints are contradicting, then there is no solution for real life problem and the user will know that in advance. It is possible to make suggestions which constraints should be eliminated to produce a plan/solution which satisfies the remaining constraints.
• The aim is optimality. There is enough time to make/change the plan.
• The solution has to be bulletproof (no contradictions are acceptable). So, the quality of planning data is clean, available and cover reality.
• The problem is too complex to solve it by rules. Maintaining the rule system is too costly.
• When even small change in the plan can result in huge improvements.
• The problem can be described best by constraints. The problem is difficult to express in rules, too many combinations to express.
• The cost of building one is higher.

As a summary, mathematical optimisation helps businesses to meet aggressive goals with limited resources when there is no clear way to do it best or successfully.

Even though the mathematical optimisation (prescriptive analytics) is the most sophisticated level analytics, every technology should be used for what it does best. We need to consider the cost to make plan with or without the prescriptive analytics. With the mathematical optimisation, we may find a better solution, in which we not even knowing there is one by rule-based system. There is an opportunity cost of not using the better solution. However, the cost of building the mathematical optimisation application is relatively high. Usually the combination of both technology gives the best/optimal business value.