What is business predictability?

Business predictability is an organization’s ability to predict forthcoming events and adapt its operations to them. It enables great market fit and customer service levels while eliminating waste and inefficiencies. It is also the foundation of the sustainable enterprise.

Business predictability platforms process large amounts of data and use artificial intelligence algorithms to help companies anticipate and adapt to changes in supply and demand.

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The history of predictability

Mankind has built on predictability. Since the beginning, people have tried to put the odds in their favor in whatever endeavor they have ventured into. In ancient times, maritime traders watched the sky trying to predict the best time to set sail in order to take their goods to the markets.

Predictability is also at the very core of businesses. A business consists in repeating a series of actions through collective effort to produce a good or service that will satisfy a market need. Without predictability in both supply and demand, the company ceases to be viable. Then, what happens when the market becomes highly changing and competitive like the current one? Companies with greater capacity to anticipate change prevail.

20th century - Processes

In the 20th century, we took process management to the maximum sophistication in order to ensure the greatest predictability in the provision of a service or product. We realized that if the organization accurately follows a series of steps, the variability of the expected result is reduced.

2000s - Data

The 2000s were the decade of data analysis. By capturing and then analyzing large volumes of data, we found we could understand the market and the customer like never before.

Today - Artificial intelligence

However, in today's rapidly transforming and competitive economy, uncertainty and change are the constant. Many remember Q1 2020, when COVID disrupted the global markets and our lives, as an inflection point. Even though we had lots of data, the situation changed so fast that we struggled to stay ahead. Unfortunately, this challenge isn’t exclusive to the pandemic. Hindsight points out this was a very tragic event, yet only one of many that change the global economy.

Analyzing data the traditional way does not permit to identify and respond to market changes fast enough. The good news is that the rise of artificial intelligence provide us with new tools to cope with such a changing environment.

How can artificial intelligence improve business predictability

Artificial intelligence can not only make accurate supply and demand predictions, but also propose actions at a breath, depth and speed that is beyond the reach of traditional analytics. This enables companies to adjust operations to the reality of the changing market in real time.

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There are four areas where algorithms particularly excel at:

  • Breath: Artificial intelligence is especially appropriate for businesses that deal with many variables, such as large product portfolios, multiple points of sale, different customer segments, etc. In traditional analytics, there is a limit to the number of variables that can be treated, which leads to making decisions with only a partial view of the business. Artificial intelligence is diametrically opposite – the more variables, the better the result.
  • Depth: Depending on the sector in which you operate, you can reach a limited depth of analysis with traditional methods, usually a few hundred combinations of variables. For example, some retailers define promotions based on a sales forecast by product, geography, and week. Artificial intelligence removes these limits and allows you to optimize to the highest level of detail.
  • Patterns: Artificial intelligence is especially capable of identifying patterns and relationships in data that are not apparently visible to humans. Why does one store underperform the others? What factors influence customer churn? Even if we have the data, quantifying the degree to which a variable affects the result is not trivial.
  • Speed: Artificial intelligence is very quick to identify trends and propose adjustments, allowing companies to anticipate and respond to market events with speed.

The five pillars of business predictability

The following components constitute the framework for a company to increase the predictability of its operations in today's volatile environment.

  • Data: Data captures both market and business dynamics and is the energy that powers the machine.
  • Algorithms: Algorithms reveal the value of the data. They identify patterns and relationships in data and can make or propose decisions based on them.
  • Processes: Processes organize the collective effort of the organization to repeatedly achieve the expected results. They capture part of a company's know-how.
  • Culture: Culture determines how people in an organization will act when there is no explicit rule or process. It allows the company to successfully face new scenarios. The balance between process and culture is key.
  • Resilience: Resilience is the organization's ability to face changes and unexpected events. It is not only about resisting and recovering, but also about predicting, anticipating and responding to events in an agile way.

Key features of business predictability platforms

A great business predictability platform has the following features:

  • It predicts the company’s business metrics, going beyond forecasting demand. Customer demand is a very nuclear metric for many businesses, and usually the first I recommend most companies to focus on. However, focusing solely on placing a product in the market is a too simplistic view of all the activities a real company has to excel at to be successful. Returns, customer calls, employee churn, inventory supply and leftovers…a great business predictability platform provides end-to-end foresight of the metrics of a company.
  • It requires minimal configuration. Business predictability tools should use machine learning models to automatically learn the data and business environment. The raison d'être of business predictability is to be fast at identifying and adapting to changes. This is not achievable if we need to spend weeks reconfiguring the software.
  • It measures and quantifies uncertainty. When predicting the future, the prediction itself is as important as the level of certainty of that prediction. Higher uncertainty means we need to build more resilience into the operations, while lower uncertainty gives us the opportunity to optimize. Great tools provide confidence intervals or a series of percentiles for the prediction. Distrust single-point forecasts.
  • It predicts at different levels of granularity and time horizons. Optimizing company operations requires working at a deep level of detail and being very agile at introducing changes – what I call making many micro decisions. However, this can make you lose control over the big numbers and long-term direction. In a competitive environment winners can’t compromise one for the other, they must get both right.
  • It combines artificial and human intelligence. I found evidence after running measurements in large companies that, while artificial intelligence can find patterns and relationships that are not apparent to humans, people in an organization hold contextual, strategic and critical thinking that can augment the AI capabilities if combined properly. A great business predictability platform captures expert inputs at certain points along the process.

Signs you need business predictability software

  • You manage a supply chain
  • You manage a large scale customer service operation
  • You have a large product portfolio
  • You face significant levels of returns and leftovers
  • You think there is room to optimize your operations
  • You operate in a market with significant competitive pressure
  • Your business deals with many variables: multiple channels or points of sale, multiple customers segments, multiple products…
  • You invested in collecting data and want to generate ROI
  • Your company is moving to a data-driven culture

Business predictability vs. demand forecasting

Demand forecasting, or demand sensing as its latest flavor, is a subset of business predictability.

Demand forecasting tools predict the market demand for the company's products and services based on a series of pre-configured variables. The scope is usually rather narrow in terms of number of use cases, industries and features they cover. They are marketed to retailers and focus on predicting short-term demand with the objective to feed data to the supply chain.

The scope of a business predictability platform is much broader and horizontal. It also excels at predicting demand and feeding a supply chain, but it goes further in three fundamental aspects. Firstly, it covers the prediction of the rest of the activities of the value chain of the company in a holistic way. Secondly, it provides agility and flexibility in the prediction process, allowing analysts to test new variables and approaches that improve results in continuous optimization cycles. Thirdly, it covers many use cases and industries.