It is a great time for business forecasting and planning and those who do it! We have technology and data analytics tools that could not have been imagined just 30 years ago. We have able to shift from manual, time-consuming data collection and analysis to enabling better business decisions quicker and easier, adding more value to our businesses.

It has been a time of great expansion of data, and of great advancement in forecasting tools to exploit it. We can better understand the purchasing behavior of customers and consumers. And we have better forecasting methods and models to generate demand forecast and revenue projections.

Demand Forecasters and Planners are now business partners in decision making across the entire enterprise – and that should be our goal.

Your Role Is Only Getting More Important

Operational and financial plans can are only as good as the underlying demand forecasts and plans. This places Demand Planners in uniquely important position, and one with great responsibility when it comes to the success of the company. Welcome to the 2020’s where Demand forecasting and demand planning are foundational. Welcome to a time of multi-dimensional business thinking. Having seen planning transition from a traditional approach of reverse engineering from the top line to the bottom-line performance, it’s like shifting from 2-dimensional chess to 3-dimensional chess.

Welcome to the 2020’s where Demand forecasting and demand planning are foundational

Shift Away From Short-Term Forecasting

What can we do to leverage the full range of capabilities available to us? Currently, a limiting factor for many is the continued use of spreadsheets. Spreadsheets are still widely used by Demand Planners in companies of all sizes. Spreadsheets require significant time to import data, maintain it, perform modeling, and export data for use in other software. Integrated software solutions within the company should be standard, freeing up time for Demand Planners to create insights of greater value to the company. Innumerable cost-benefit studies support this. Time is money.

It is important that time be dedicated to identifying and analyzing the drivers of demand

A Demand Planner’s time should shift from away purely performing short-term forecasting, for which Machine Learning (ML), and Artificial Intelligence (AI) can be readily deployed. If technology can assist in short term forecasting, what should we do with this extra time? It is important that time and effort be dedicated to identifying and analyzing the drivers of demand (and the forces that affecting these drivers), both now and in the future. This places the Demand Planner in a position to understand and anticipate bigger picture forces impacting the enterprise, and to inform and assist the strategic discussion and decision-making. That is enormously valuable.

Make Collaboration Your Superpower

Given all functions throughout the company plan and depend on demand forecasts and plans, development of relationships across functions is a major value-added. Become familiar with the terminology used by the functional areas participating in and using information from your demand forecasting and planning processes.

Help them to use the information you provide more effectively for their unique roles. Determine how you may be able to reshape information that you provide in a better format or segmentation structure. Ask them to share information with you from their functional area that may add to the effectiveness of the work in demand forecasting and planning, such as industry publications, research reports, market research, and other inside and outside information that they use.

Ask them about their views on events, situations, competitors, and other developments that impact their area of the business. Ask them how they might expect this to affect the company in the future. Ask them how it might affect their function in the future. Ask them to suggest conferences that they attend that they believe would add to the quality of the work that Demand Planners are doing. Ask and listen to inside and outside authoritative sources of information. Be genuinely interested in others and their functions.

Use Technology to Elevate Your Role to Business Partner

Implement technology solutions that enable better operational forecasts more quickly, while serving the company with insights and longer term forecasts that will drive strategic initiatives. Think of yourself as a trusted advisor for the company, aiming to deliver on enterprise goals and objectives and interested in its business success – and well qualified to guide the business in the right direction. This reframing of your role can enhance your value to the company and contribute to its future success and sustainability. Look constantly for ways to improve your functional area and the company as a whole.

Think of yourself as a trusted advisor for the company

As ML and AI develop in the coming years, they will likely be one of our best enablers and sources of rapid research and information for the work of demand forecasting and planning for future company success. The pace of change is rapid and unlikely to slow down in the future. Acquiring tools and applications that enable us to work more effectively and efficiently is essential. We should not fear technological developments but embrace them as they will be fundamental to our success, both as individuals and companies.

 There have been amazing changes for Demand Forecasters and Demand Planners in the past 25-30 years. And more change will come even faster in the next 5-10 years. It will be a great time and opportunity for business forecasting and business planning and for those who do it!

To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.

We all know that machine learning (ML) and AI gets the analytics and data science community excited. Every self-respecting forecasting department is developing ML algorithms to predict who will click, buy, lie, or die (to borrow the title of Eric Siegel’s seminal work on the subject). All analytics conferences and publications are filled with AI buzz words.

But when it comes to real-life implementation, the majority of demand forecasters are somewhat cautious about implementing machine learning. Why is that? Isn’t machine learning all about predicting, which is literally a Forecasters job? Let’s explore the opportunities and pitfalls of applying machine learning in forecasting.

Demand Forecasters & Data Scientists Define ‘Prediction’ Differently

There is a subtle difference in the way forecasting and ML define ‘prediction’. When Forecasters say ‘prediction’ we mean a prediction about the future. Traditional forecasting prediction methods include Time Series modelling, algebraic equations and qualitative judgement calls. As a result traditional forecasting is somewhat manual and time consuming, and may be swayed by human judgement. However, the outputs are easily interpreted and it is an agile process; the Forecaster knows where the numbers are coming from and may easily make corrections as needed. Further, traditional forecasting may be done with limited data.

Machine learning or statistical model ‘prediction’ refers to predicting the past. This sounds a bit counterintuitive, but the idea is to compare the model ‘prediction’ with reality and measure the difference or error. These errors are used to finetune the model to predict the future. Consequently, model predictions are heavily driven by past performance and are almost impossible to finetune. Also, the interpretability of models is very limited. Another factor to consider is that by design ML requires a lot of data. On the upside, machine learning is quick and automated as well as objective, being free from human judgement.

Machine Learning was Built for the Digital World; Forecasters Work in the Real World

Machine learning and AI algorithms were created for a digital world with almost unlimited data on customer clicks, purchases and browsing data. As we know, these algorithms do an excellent job in luring us to make repeat purchases, buy complimentary items, and sign up for loyalty programs. The sunk cost of prediction error (lost sales) is relatively low. In addition, every error is an opportunity for the machine learning algorithm to improve itself.

The real world marketplace is quite different from the digital, marketplace, however. The data here might be limited to cash register sales, loyalty program data, or shipment data. The sunk cost of prediction error can be quite high as restaurants and retailers make procurements in bulk. Also, predictions cannot improve themselves as there is no automatic feedback loop. For these reasons, many brick-and-mortar retailers and their suppliers still rely on traditional forecasting methods. This does not mean that Machine Learning cannot offer opportunities in improving forecasting but there are a few considerations that need to be addressed before venturing into machine learning.

Machine Learning Requires Much More Data Than Time Series 

Any machine learning algorithm requires a lot of data. By a lot of data, I do not mean dates or variables. Machine learning models run on defined observation levels—this can be customer, store etc. You need at least a thousand of those (if not thousands) for machine learning to work. If the sample is limited to only 10 stores, it is probably better to refrain from machine learning and use Time Series techniques instead. Another factor to consider is the cost of maintaining the data. Is it readily available or does it need to be inputted manually? Does the data need to be engineered? Would that be a one-time effort or an ongoing process requiring human and computing resources? What would be the cost of storing data over the years?

Machine Learning is far Less Interpretable than Time Series 

By design machine learning is a black box. For example, predictions may be generated by a vote of thousands of decision trees. You can use colorful histograms to depict the weight of each factor in the model. These charts look very smart on presentation slides but are very far from intuitive. If the cost of a wrong prediction is millions of dollars, companies might be more comfortable with Time Series and arithmetic they can understand rather than a slick black box algorithm. This especially applies to new products with no sales data or limited test data.

There are a few workarounds for understanding machine learning. Playing with parameters might be a good indicator of the robustness of results. If one slight change to model inputs or specifications results in significant changes to predictions, this might be a red flag.

At the end of the day model trustworthiness may be proved by testing on new data. We don’t necessarily need to understand the ins and outs of algorithms if we are confident in the end result. Robustness of this argument may depend on your audience. Typically, analytics professionals are comfortable using machine learning predictions as long as they are tested. Supply chain leaders might be more cautious in making business decisions based on black box. A good sanity check is to run traditional forecasting methods in parallel to machine learning. If there is feasible difference between the results there might be either an issue with the model or an important consideration was left out when creating a traditional forecast.

The Cost Benefit of Machine Learning is not Always Clear

It goes without saying that when machine learning is set up right it is wonderfully efficient. All one needs to do is provide inputs and press the button. The ‘setting up right’ piece might be relatively straightforward or extremely difficult depending on prediction goal and data available. Repeat products with abundant history may be easily predicted using even out-of-the box ML packages such as SAS or Azure as long as the data is readily available. New product predictions may require intricate proxy algorithms to solve for limited data. This may require development of ML algorithms from scratch. In addition, there may also be a need to engineer data from different sources to feed the algorithm. This might require significant investment to either hire contractors, expand analytics team or put pressure on existing resources. Before ramping up a data science crew, companies would be well advised to consider how often the algorithm will be used, the efficiency gains, and the computing resources required for the project.

Impacts on Overall Business Planning 

Forecasting is the cornerstone of business planning. Any changes to the forecasting process may have an impact on other areas of the business such as Finance and Supply Chain. Typically, traditional forecasting methods rely on a top-down approach. A forecast is created in aggregate and then broken down by store/time period, etc. These breakdowns may be later used for financial targets or demand planning at store level. By design, ML Forecast utilizes a bottom-up approach. A prediction is created at store/time period level and later aggregated. When switching from traditional forecasting to ML, companies must ensure smooth transition at all stages of business planning. If not done right, this transition may result in discrepancies between the ML prediction vs the financial targets and supply plans.

To summarize, ML is a great instrument to streamline forecasting. As with any tool, it has its applications, benefits, cost, and risks. When utilizing ML for forecasting, companies should consider their data, business need, decision making culture, and planning workflow. A great place to start might be trying out ML on your data using online, off-the-shelf solutions such as Azure and SAS. Most of these solutions have step-by-step training videos that will help fit an ML algorithm to your data. Experimenting with these solutions may help decide whether ML is a good tool for your company’s forecasting, and whether an off-the-shelf solution is sufficient or there is a need for in-house development. Even if it turns out that for whatever reason ML is not a good fit for your company, there is no investment lost and some analytical knowledge will gained.

This article first appeared in the summer 2023 issue of the Journal of Business Forecasting. To access the Journal, become an IBF member and get it delivered to your door every quarter, along with a host of memberships benefits including discounted conferences and training, exclusive workshops, and access to the entire IBF knowledge library. 

I recently spoke to Mariya Sha, Python guru and star of the Python Simplified YouTube channel. I asked her how forecasters and Demand Planners can get started with the Python programming language and leverage it for Machine Learning. I gained some fantastic insights that should inspire all forecasters to take the leap. The following are her responses.

What’s The Best Way To Learn Python?

“The best way to begin is to take a short, introductory course on Udacity, Udemy or Coursera to learn the basic commands. As long as you have a basic understanding of functions, for loops, control flow operations, etc. you have the foundations required to use the language for your specific needs, whether it’s math, ML or whatever you need it to do.”

Does Experience In Excel Help With Learning Python?

“Skills in Excel like VBA (Virtual Basic for Applications) translate directly into Pandas, which is a data science library that is widely used in Python. I believe a lot of things you encounter in other languages can be applied in Python. The difference is that Python is very high level; you don’t need to think about the small details, all the data types. Python takes care of that, unlike other languages like  C++ which requires programming every little detail. By comparison, Python is very simple.

Python cuts to the chase – it allows you to get it done, and get it done fast. If people ty to build a simple application in Python, they will see the difference. Just try it!”

Tips When Starting With Python

“Begin with data types. These are the building blocks of your application. You always need to know which data types to use because they have different methods. Strings have different methods to integers and floating point numbers, for example. Every different data type allows you to do different operations. You need to know which operations you can do with each data type.

When you’re comfortable with that you can move onto control flow operations, like conditional statements, functions, and once you’re comfortable with that you can move to classes and object-oriented programming. Actually, everything is an object in Python – that’s part of why this language is so brilliant.

When you’re comfortable with object-oriented programming, then you can spread your wings and get into what you’re interested in. If you’re interested in Machine Learning, you’d then start looking for Machine Learning frameworks and libraries, if you’re into data science you’ll dive into Pandas.”

What are Objects In Python?

“If you’re creating a windmill for instance, it’ll have a height, width, speed, color etc. This is the data about the windmill. It’ll also have functions like ‘spin’ and ‘stop’. The data and functions combine into an object, which in this case is a windmill.”

What Is Python Library?

“A library is ready blocks of code that somebody else made that you can use for yourself. Take, for example, a SoftMax function which is an algorithm we use in ML. Instead of writing the entire formula, you write SoftMax and it’s done for you. It’s basically using simplified parameters instead of writing code. Every library has incredible documentation, with a lot of support and forums where you can ask questions. If you have an issue, somebody will help you out.

The most important libraries are NumPy (for mathematics), Pandas (for data science), and Matplotlib (for plotting graphs and charts). These are 3 main libraries that we all use. If you’re into ML/AI you’d probably go for PyTorch and TensorFlow.”

Using Python For Machine Learning

“Python is a very minimalistic language. You only specify the most basic things. With AI, it’s almost the opposite where everything consists of long formulas – not complicated math, but there’s a lot of it and it’s sequential. Python gives you the easiest syntax for ML. Processes like radiant descent can be summarized in a single command.

Inference, for example, sounds very complicated with predicting and loading a pre-trained neural network and exposing it to an image it’s never seen before. It all seems very complicated but with 30 minutes you can do all of this.

If somebody explains it to you in simple language, I think everybody can understand it. It’s not as intimidating when you understand how simple it is. I think people are afraid of ML because they read these academic style articles and assume they’re not smart enough to do it. At the end of the day, it’s very simple math. Unlike other languages like C++, Python takes care of the details, and allows you to do ML in plain English.”

Parting Thoughts

“There are no rules when it comes to using Python. Don’t listen to what people say is the right way to doing things – it just limits your imagination. The best way to go is trial and error. Try to be creative – it’s the best way to learn.”

You can find Mariya Sha and more data science and computing insights at her YouTube channel, Python Implied.

Have you ever heard the Chinese proverb about the farmer? I originally heard it on a podcast featuring Dan Bilzerian.

The story goes:

A farmer and his son had a horse who helped the family earn a living. One day the horse ran away and the farmer’s neighbors said, “Your horse has run away, what terrible luck!” “Maybe,” replied the farmer.

Sometime later, the horse returned with a group of wild mares. The neighbors then said, “Your horse has returned with several other horses, what great luck!” “Maybe,” replied the farmer.

Later that week, the farmer’s son was trying to break one of the mares and she threw him to the ground, breaking his leg. The neighbors said, “Your son has broken his leg, what terrible luck!” “Maybe,” replied the farmer.

A couple of weeks later, the army marched through town recruiting young men. They did not take his son as he was recovering from his injury. The neighbors said, “Your boy was spared, what great luck!”

“Maybe,” the farmer replied.

The point of the story is that you never really know if something is bad or good because you don’t know how it’s going to affect the next step in your life.

I think about this when it comes to the future of demand planning as it relates to Artificial Intelligence (AI) and Machine Learning. How is it going to affect the next step in our career or even our supply chain?

Will AI change demand planning for the better? “Maybe”

AI-Generated Insight Isn’t Enough

About a year ago Daniel Fitzpatrick wrote an article titled Beware the Pitfalls of AI in Demand Planning, published on this website.

He mentions a few concerns but the one that caught my eye was “Forecasts as Proxies for Success”. In the article he says “Forecast accuracy is only a proxy for improved business performance. Without an effective supply chain to support more accurate forecasting, much of the value that an advanced algorithm might add may be lost. An excessive focus on improving forecast accuracy may draw attention and resources away from other constraints that are actually causing larger problems.”

“An accurate forecast does not reduce demand volatility.”

Let’s think about this for a minute. So even with advanced algorithms and heavy investment in AI, an accurate forecast does not reduce demand volatility. To tackle that, you are going to need to understand the root cause, some of which can be prevented and some are completely out of our control. Let’s separate the two.

On one hand, you have the preventative. Communication is at the top of the list. It’s not going to help when your sales team keeps market or customer knowledge to themselves. How about promotions and if they aren’t planned out correctly? They could drive bad consumer behavior, a self-inflicted wound of huge swings in demand. A third is inventory levels. Whether it’s through safety stock or customer inventory-level agreements, with the right strategy, both can help lead to smoother, more forecastable demand.

“Machine Learning Algorithms can only take you so far.”

On other hand, you have the uncontrollable. There’s weather, labor market and wages, raw materials costs and availability, and industrial production specifically as it relates to commodities. The list goes on, a ripple effect that affects all of our supply chains. The majority of my career has been in the CPG space and as much as I would like to claim to say I have seen it all, I am sure you could come up with a story that tops mine. The point is, AI and Machine Learning Algorithms can only take you so far.

“AI improves forecasts based on real-time, internal and external data”

Demand Planning isn’t simple and we can all envision putting ourselves on the imaginary process line of evolution ranging from the basic to the advanced. But unless you are on the top of the food chain, AI and machine learning can likely help. Machine learning can take you to the next level; it enables improved forecasts based on real-time data using internal and external data sources and can turn the uncontrollable into the measurable.

External Data Is Our Friend

External data is our friend and modern machine learning algorithms combined with our supply chain networks can likely outperform processes that are managed solely by Demand Planners. Think about new products. What if AI helped users identify products with similar characteristics ultimately leading to better predictions? Demand Planners can be turned into Super Demand Planners.

Recently I have been getting the impression that there is going to be a shift in our world of Demand Planning. Maybe it’s about finding efficiencies in processes, maintaining lean personnel, or maybe it’s about preserving inventory levels and increasing cash flow. Either way, change is coming and likely in the name of AI.

So, is Artificial Intelligence (AI) to the Rescue for Demand Planning? Maybe.

Maybe we can expand our set of tools and work smarter, not harder. Maybe there is always going to be the uncontrollable and too much data isn’t always a good thing.

The integration of Artificial Intelligence and Machine Learning into the S&OP process allows companies to become more agile in response to changes in demand. Data sources that previously were hard to analyze due to complexity or sheer volume are becoming standard planning inputs.

For example, using these tools, demand planners can now use data from sources such as online reviews, competitors’ advertising, and even Tweets.

With all that AI/ML can do to enhance the planning process, does this mean that the current role of demand planning is doomed? Should those of us who are currently working in the demand planning arena begin looking for new jobs?

Not so fast. While AI and ML offer our planning processes new and powerful ways of managing inputs to demand, they also have some significant limitations. And I believe human Demand Planners will be required to ensure that AI and ML are truly effective in the planning process. To better understand how these technologies and human Demand Planners can complement each other, let’s begin by looking at some of the often-overlooked limitations of these tools.

1. Patterns, Patterns Everywhere

The primary way that these tools can assist us in planning is by finding patterns in large amounts of complex data. As companies gather more and more data about their customers and their businesses, the quantity of data that they need to analyze to make good decisions becomes more than human Demand Planners can manage. By training AI/ML systems to find patterns that are buried in these mountains of data, companies can exploit data that was previously inaccessible due to volume or complexity.

But patterns can be a trap. Just because a customer bought a large quantity of product every September for the last 6 years does not mean that this will happen again this year. We still need to assess other factors that might influence this pattern such as pricing, product features, and competitive products. So, while AI/ML are good at indicating that these sales might recur, it will take some human input and research to determine if it makes sense to bet on it happening again this year. In time we may be able to train these systems to incorporate these potential sources of input; but in the meantime, we will need human insights to fill in these gaps.

2. Intelligence Vs Common Sense

Where human beings can infer things from common sense, AI/ML can be stumped. Look at this scenario: A man went to a restaurant. He ordered a steak. He left a big tip. If I asked a friend what this man ate, he would say a steak. But most AI/ML systems would struggle to get this answer because nothing in these statements explicitly describes what the man ate, only what he ordered. From experience, we know that what we order in a restaurant is usually also what we eat. This sort of common-sense extrapolation based on context is difficult for AI/ML systems.

In a planning scenario this can be a major problem. For example, a customer always orders a large quantity of a certain product at Thanksgiving, and later returns about 20% of the product since it has not sold. Does this mean that the customer doesn’t understand how to purchase product correctly? Or does it mean that they need excess quantities to ensure that their displays are full throughout the selling season? While AI/ML can’t answer these questions, human planners can contact the customer and asses what the real issue might be.

3. AI Has Limited Adaptation

One of the strengths of human intelligence is that the human mind can easily adapt to new information. If I tell you that a customer just went bankrupt, from experience you will know what impact this might have on your business. You can quickly adjust your processes to accommodate this change. AI/ML can’t react that quickly. These systems would need to be retrained to know what to do in this situation.

And since each situation would be slightly different, any training provided for one scenario would have only limited application to later ones. These systems need ongoing training to be truly agile and adaptive.

4. AI Has No Understanding Of Cause & Effect

Humans from experience instinctively understand cause and effect. If I drop a glass on a hard floor it will shatter. But my dropping the glass does not cause it to shatter. The glass striking the floor causes this. Here’s another example: We know from experience that roosters crow when the sun rises. AI/ML have no trouble understanding this relationship. But if we ask whether the rooster’s crowing causes the sun to rise or vice versa, these systems are stumped.

Using the customer bankruptcy example above, from our experience we can usually easily assess the possible causes: lack of sales, high expenses, loss of funding, better competition, etc. Our experience allows us to make these mental jumps easily. However, without extensive training, AI/ML systems would struggle to relate the causes to the effects.

5. AI Lacks Ethics

AI/ML systems will reflect the biases and perspectives of the humans that trained them. They can’t tell right from wrong. Programming these systems to reflect the complexity of human values and how these adapt to different and changing situations is extremely difficult. Therefore, allowing them to make certain types of decisions can be dangerous.

For example, in planning how much credit to extend to a customer, we can train a system to analyze the business factors that make a customer a good or a bad business risk. But these systems can’t tell us how well these customers manage their environmental or societal impact. If these factors are important to our decision, we need human input.

Complementary, Not Competitive

With the limitations of AI/ML that we have discussed here, it might seem that these tools are less useful that we might at first have thought. The truth is that they are extremely useful when we are dealing with large amounts of data, and where we have the time, skill, and resources to train them properly. What they lack is what human planners can provide. In the best case I believe the combination of properly trained AI/ML systems and experienced Demand Planners can be extremely effective in drawing out all the insights hidden in the data.

To make the most of this relationship, demand planners will need to develop some new skills. While we can leave a large part of the data analysis to systems, human insights based on broad experience will be required if we are to make the most of the analysis these systems provide. Additional human soft skills such as relationship-building, listening, innovating, and thinking strategically — together with the input of AI — can make our planning both more agile and more effective.

This article originally appeared in the Fall 2021 issue of The Journal of Business Forecasting. Become an IBF member and get the Journal delivered to your door quarterly, plus discounted entry to IBF conferences and events, members only tutorials and workshops, access to the entire IBF knowledge library and more. Get your membership. 

As we integrate artificial intelligence (AI) and machine learning (ML) into our demand planning processes, I am sure that we will see improvement in our ability to anticipate demand rather than react to it. However, accurate forecasting is only one element in an effective supply chain. So, while I am very much in favor of making good use of these new tools, I have some reservations about their impact on improved demand planning.

My concerns fall into five categories:

• The nature of algorithms
• Algorithms don’t execute
• Gaming and overrides trump algorithms
• Forecasts as proxies for success
• Implementing advanced algorithms may initially make things worse
• Implementing advanced algorithms may reveal significant supply chain inefficiencies

1. The Nature of Algorithms

Algorithms are models of reality, and all models fall short of representing reality with perfect accuracy. Understanding a model’s limitations is key to its proper use. Certainly, AI and ML algorithms will allow us to better model potential demand, but we will also need to be aware of their limitations.

“all models fall short of representing reality with perfect accuracy”

Bad data, incorrect or biased interpretations of the data, and ignoring data that does not agree with corporate direction remain significant risks in any planning process. Adding reliable processes to validate both the data itself as well as any interpretations of the data will improve the effectiveness of these advanced algorithms.

2. Algorithms Don’t Execute

Poor supply chain execution will undermine any algorithm, no matter how accurate it is. Relying on improved algorithms alone will not improve a supply chain that is riddled with ineffective practices and siloed teams. In fact, more accurate modeling may reveal just how detrimental these poor practices are. An accurate forecast that correctly anticipates future demand will be worthless if the product can’t be produced and shipped in time to meet the demand.

And a more detailed view of future customer behavior will be worthless if the company cannot focus the necessary resources on planning the development, production and shipment of products that satisfy the customers’ expectations. Adding performance metrics to key supply chain processes will allow for discovery of potential constraints. And setting up processes to address these constraints as they appear will allow for continuous improvement throughout the supply chain.

3. Gaming and Overrides Trump Algorithms

A reliable algorithm that no one trusts will be of little value to a company. When individuals or teams believe that their view of the future is more accurate than a system’s predictions, and they are allowed to game or override the algorithm, most if not all of the value of the algorithm is lost. In my experience, most companies have a significant number of S&OP team members who distrust the systems they use to plan.

“A reliable algorithm that no one trusts will be of little value to a company”

Unless this is addressed, this underlying lack of confidence in any system will severely limit any algorithm’s impact on improving forecast performance. Overrides should be documented and validated by product performance and gaming should be clearly discouraged and called out when it does occur.

4. Forecasts as Proxies for Success

In applying AI and ML algorithms to our business, we need to ask what our true goal is. Is it really a more accurate forecast? Or is it a more robust and agile process for responding to customer demand?  It is possible to improve forecast accuracy without also improving service levels and on-time delivery.

Forecast accuracy is only a proxy for improved business performance. Without an effective supply chain to support more accurate forecasting, much of the value that an advanced algorithm might add may be lost. An excessive focus on improving forecast accuracy may draw attention and resources away from other constraints that are actually causing larger problems.

5. Implementing Advanced Algorithms May Reveal Significant Supply Chain Inefficiencies

There is no guarantee that implementing advanced forecasting algorithms will improve business performance. A more accurate forecast may reveal that the company can’t actually respond quickly as market and customer preferences change.

It may also show that more resources will be needed to support the execution of a more accurate forecasting process. In the long run these are useful lessons that the company can use to address constraints to improve the entire supply chain to take advantage of more accurate forecasting. So the expectation needs to be that these advanced models will be part of a continuous improvement process that will require all the links of the supply chain to become more effective by addressing constraints as they are discovered.

6. Stepping Back to Step Forward

The success of any supply chain is dependent, in large part, on the people in it understanding their roles and executing effectively. As AI and ML models are more integrated into the demand planning process, the key practices of good communication, ongoing training, executive support and continuous improvement will also need to be supported. Without these basic practices, the value of improved forecast accuracy may be limited.

By themselves these new algorithms will only show us what is possible. It will be up to each member of the S&OP teams to make sure that the possible consistently becomes reality through consistent execution guided by reliable performance metrics.

To find about more about practical applications of machine learning models, pick up a copy of Eric Wilson’s new book, Predictive Analytics For Business Forecasting & Planning. Written in easy-to-understand language, it breaks down how machine learning and predictive analytics can be applied in your organization to improve forecast accuracy and gain unprecedented insight. Get your copy. 

Even before the pandemic, foward-thinking retailers leveraged AI and machine learning technology (ML) for demand forecasting. In the post-Covid world, however, those ML models have failed to provide accurate predictions because they don’t know that the data they use is now obsolete due to changing demand patterns. How can we upgrade them to the new reality?

There are six possible ways to get a more accurate forecast:

1 – Gathering data on new market behavior: As dynamics within the new market stabilize, use that data set to create a new model for forecasting demand.

2- Use a feature engineering approach: Track external data sources like price indices, market states, latest news developments, exchange rates, and related financial/economic factors. Using these, models can generate more accurate predictive outputs.

3 – Factor in up-to-date POS data: Analyzing recent POS data can allow us to observe and react to real-time shifts in patterns of demand, improving forecast reliability. Depending on a given product’s classification, the appropriate range for a POS data set might be between a month to two months.

4 – Use the transfer learning approach: If we possess any data sets relating to historical pandemics or behavior based on similar principles, we’re able to use that data within the context of this present-day pandemic.

5 – Utilize a model for information cascades: Merge the cascade modeling with current POS data sets to create a demand forecasting model that is able to recognize aggregated consumer behavior patterns and predict herd patterns for future sales.

6 – Leverage NLP (natural language processing) technology: NLP analyzes actual consumer comments and posts from an array of social media sources, from media platforms to popular social media sites. NLP can use sentiment analysis algorithms to collect and analyze conversations and discussions from real customers. This gives an unfiltered look at consumers’ behavioral patterns, preferences and attitudes.

If you are looking for a way to improve your current ML models and thinking of building a demand forecasting feature from scratch, this will help you to choose the best approach depending on your business type.

A data scientist generally works with historical data, and it’s impossible to predict such drastic changes as a worldwide pandemic. But as a general rule, you should prioritize flexibility in retraining your models, add more external factors as predictors, and account for a short-term perspective as long-term models become less relevant.

We tackled this problem with pre-Covid models for a restaurant business. Here is an example from our dashboard:

Forecasting dashboard showing normal revenue before the pandemic.

Forecasting dashboard revealing revenue post-lockdown.

In this case, we rebuild our models from scratch, losing a degree of accuracy. At this point, the historical data are not relevant anymore and we wait for new statistics and patterns inside the data.

Not every demand forecasting article on the internet fits the particular needs of your business and industry. Effective approaches vary dramatically depending on business types – here are some of the distinctions and varying ways to address demand forecasting:

Small vs Large Businesses

Small and large businesses should be approaching forecasting in completely different manners. First of all, the acceptance criteria for huge businesses is significantly smoother than for small businesses. We can have a higher error for prediction in quantity, as high sales volume allows for greater tolerance for error. When it comes to historical data, large businesses have a higher volume of collected data, making it easier to identify patterns in customer behavior. With small businesses, it’s often necessary to test your hypothesis to prove the correlations between sales volume and predictors.

Demand for a product at a large business, showing clear demand patterns.

Demand for a small business showing erratic sales patterns.

Online vs Offline

Online sales allow for a greater range of predictors and external factors in your modeling. It’s not necessary to have a POS (point of sale) system, as you’re able to collect all relevant data from the website. You know more information about customers and collect their historical purchases. With different predictors, you can apply a wider range of machine learning models: Gradient Boosting, Random Forest, SVR, Multiple Regression, KNN, etc. With offline sales, you are often limited to historical sales only. The best approach here is to use Time Series Analysis.

The USA vs Europe

Regional differences play a huge factor in predictive modeling, as varying locations will have different behaviors both in specific sales and general cultural factors. It’s worth noting that certain regions’ consumers are influenced to different degrees by marketing campaigns. Additionally, holidays vary by region, and you’ll need to decide whether to add this feature to the model or not. Take into account different legal constraints (limit for product amount) etc.

Perishable vs Non-Perishable Products

Finally, it’s crucial to factor product type within your demand modeling. With perishable products, you need to set up the right metrics to penalize the model when the prediction is much higher than the real value, as the consequences for excess inventory are significant. You need to be careful with data preparation and work with outlier detection because this prediction should be extremely sensitive to any changes.

Conclusion

The pandemic has made us all hyper-aware of the limitations and constraints of forecasting models, but the best practices in forecasting are the same whether we’re in a pandemic or not. You should always be striving to fit your particular business model and business needs to your forecasting tools, to attract new customers, increase your revenue, and expand your market share.

To find about more about practical applications of machine learning models, pick up a copy of Eric Wilson’s new book, Predictive Analytics For Business Forecasting & Planning. Written in easy-to-understand language, it breaks down how machine learning and predictive analytics can be applied in your organization to improve forecast accuracy and gain unprecedented insight. Get your copy. 

The following is an extract from Eric Wilson’s new book, Predictive Analytics For Business Forecasting & Planning, written by Eric Wilson CPF. It is your guidebook to the predictive analytics revolution.  Get your copy. 

Most of what is discussed around predictive analytics is in terms of a new way to forecast where you are not just looking at your internal sales history, but also you are bringing in more data, different drivers, and other external variables to improve your forecast.

We have focused on using predictive analytics to help in discovering “Why do things happen?” and the benefits of translating this into “What could happen if…?” With advances in technology, we now have advanced models and methods that can better enable predictive analytics.

The Demand Planner or predictive analytics professional blends forecasting and business intelligence. They merge techniques and methods including machine learning to support the business’s needs. A common misconception is that machine learning, business forecasting, advanced business intelligence, and all things predictive analytics are synonymous.

The diversity of opinion reflects the fluidity of how we understand the defining language of the field. Business forecasting is the process to extract information and provide insights. Machine learning is a subset or application of AI and is more of an approach than a process. Business intelligence is the different types of analytics and outputs.

Where they overlap is the intersection of process, approach, and insights of predictive analytics.

Business Intelligence

Business Intelligence or BI focuses on infrastructure and output. The use of the term business
intelligence can be traced to the mid- to late-1860s, but it would be a century later before consultant. Howard Dresner was credited with coining the term. His definition was, “Business Intelligence: An umbrella term that covers architectures, databases, analytical tools, applications, and methodologies used for applying data analysis techniques to support business decision-making.” Despite this definition, historically, when people thought of business intelligence, most considered it as a fancy way of talking about data reporting. It has always been much bigger than just a dashboard and, through the years, people have begun to better understand the breadth and uses of BI to inform data-driven business decisions.

Fig b | Infographic depicting intersection of AI, BI, and business forecasting

Predictive analytics in BI has become a natural and needed progression of decision-making capabilities and insights. Where most of BI focused on visualization of data and descriptive type analytics, with predictive analytics we are asking more what could happen or even what we can make happen as an organization. Predictive analytics helps in presenting actionable information to help executives, managers, and other corporate end-users to make informed business decisions. Overall, predictive analytics can help discover why things happen and use this knowledge to reveal what could happen in future.

Business Forecasting

Business Forecasting: The process of using analytics, data, insights, and experience to make predictions and answer questions for various business needs. It is a process of breaking something down into its constituent elements to understand the whole and make predictions. Where BI is about the tools and representation, business forecasting is the analysis and procedures.

Predictive analytics in business forecasting has become a more advanced process that encompasses more and different types of data, more forward-looking causal type models, and more advanced algorithms and technology. It uses several tools, data mining methodologies, forecasting methods, analytical models (including machine learning approaches), and descriptive and predictive variables to analyze historical and current data, assess risk and opportunities, and make predictions.

Instead of just historical sales, we are trying to better understand the factors or the likely purchase behavior of the buyer. Predictive analytics is a new way to forecast where you are not just looking at your internal sales history, but you are bringing in more data, different drivers, and other external variables to improve your forecast.

Machine Learning

Machine Learning involves different approaches and methodologies. It is a subset of AI and is a
collection of different techniques, methods, modeling, and programming that allow systems to learn automatically.10 Machine Learning: An algorithm or technique that enables systems to be “trained” and to learn patterns from inputs and subsequently recalibrate from experience without being explicitly programmed. Unlike other approaches, these techniques and algorithms strive to learn as they are presented with new data and can forecast and mine data independently.

For predictive analytics, machine learning has opened new opportunities and provides more
advanced methods for it to use. Predictive analytics in machine learning is a category of approaches to achieve better forecasts, improved intelligence, automation of processes, and a path to AI. Some new advanced models and methods can be incorporated to further enable predictive analytics.

Predictive Analytics

At the intersection of advanced business forecasting, mature business intelligence, and some machine learning techniques, is predictive analytics. Predictive Analytics: A process and strategy that uses a variety of advanced statistical algorithms to detect patterns and conditions that may occur in the future for insights into what will happen.

Predictive analytics used to be out of reach for most organizations. However, recent advances
in professional skills, increased data, and new technologies, including machine learning and AI
techniques, have made it much more accessible. Predictive analytics utilizes many advanced business and planning processes to provide more information with less latency and improved efficiency. This is not just about advanced analytics outputs and business intelligence; it also offers more mature organizations a view of what and why things occur. Finally, while predictive analytics may use some machine learning techniques, it is only a portion of the planner’s toolbox, along with other statistical and data mining techniques.

This article is an extract from the book Predictive Analytics For Business Forecasting & Planning, written by Eric Wilson CPF. It is your guidebook to the predictive analytics revolution. Get your copy.

Business forecasting and predictive analytics are merging to leverage Big Data as a growth driver.

Predictive analytics does not have to be complicated and Demand Planners can learn these models and methods to drive business insight.

Organizational processes to support the application of predictive analytics insights are arguably a bigger challenge than the models. 

IBF spoke to Eric Siegel, author of Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie, Or Die and former Columbia Professor, who revealed just what predictive analytics is and how it crosses over into business forecasting.

“Predictive analytics is basically applications of machine learning for business problems”, says Siegel. Machine learning learns from data to render prediction about each individual [thing being examined].” That individual thing can be a customer, product, machine, or any number of things.

When asked why predictive analytics is the latest evolution in information technology, Siegel responded “Because predicting by individual case is the most actionable form of analytics because it directly informs decision for marketing, fraud detection, credit risk management etcetera”.

But How Does Predictive Analytics Actually Work?

“Data encodes the collective experience of an organization so predictive analytics is the process of learning from that experience. You know how many items you sold, which of your customers cancelled, or which transaction turned out to be fraudulent.”

Siegel continued, “You know all this – that’s the experience, and you learn from that experience and the number crunching methods derive patterns. And those patterns are pretty intuitive and understandable. They could be business rules. For example, if a customer lives in a rural area, has these demographic characteristics and has exhibited these behaviors, then they might have a 4 times more likely chance of buying your product than the average.”

“That may be a relatively small chance but when improving something like mass marketing, finding a segment that is 4 times more like to buy than the average, that has a dramatic improvement on business performance.”

It is clear then, that by identifying patterns in data, predictive analytics can reduce risk and identify valuable commercial opportunities.

Predictive Analytics Meets Business Forecasting

“There is a continuum between forecasting and predictive analytics”, Siegel notes. But he does highlight key differences in their current applications:

• Forecasting is about a singular prediction, i.e., about sales in the next quarter or who will win a political election.
• Predictive analytics renders a predictive score for each individual whether it is a consumer, client or product, and as such provides insight into how to improve operations relating to marketing, fraud detection, credit risk management etc. more effectively.

Siegel laments the current disconnect between the two fields, “There should be a lot more interaction between what are two very siloed industries but have a lot of the same concepts, a lot of the same core analytical methods, and a lot of the same thinking. Both belong under the subjective umbrella know a as ‘data science’”.

Ultimately, both forecasting and predictive analytics serve to gain business insight but approach it from different starting points. Every business decision starts with a lag between what you know now and what occurs. Whether you’re forecasting sales or the likelihood someone will buy something in response to a marketing initiative, you’re generating a prediction.

Siegel said of the similarities between forecasting and machine learning, “the methods on the business application side include decision, trees, logistic regression, neural networks and ensemble models while forecasting uses time series modeling, but there are ways these two classes really do interact and really build on one another”.

Predictive Analytics Isn’t Scary

When challenged that complex predictive analytics methods can scare people off, Siegel insists that “they’re totally intuitive” and that machine learning and predictive analytics can be “accessible, understandable, relevant, interesting, and even entertaining”. That should reassure Demand planners looking to adopt predictive analytics methods and models.

Talking of the apparent complexity of machine learning models, Siegel commented that even neural networks, which represent the more advanced modeling on the predictive analytics spectrum, are modular and each if its components are in fact very simple.

Even if the model as a whole is difficult to fully understand (even for the people who invented them) you can test them and see how well they work, meaning that regardless of how complicated the models are to understand, their actual application is relatively straightforward.

Whether it’s through his Dr. Data YouTube channel (complete with rap videos), his book, or his Coursera program, Siegel is on a mission to make predictive analytics accessible. When it comes to the data that predictive analytics uses, he again highlights the simplicity, “It can be simple as a two-dimensional table on an Excel spreadsheet where each row is an example and each column is an independent demographic or behavioral variable”.

How Can Demand Planners Start Using Predictive Analytics?

It goes without saying that training in data science and predictive analytics is necessary when it comes to demand planners applying these techniques. Most of the training available on predictive analytics is technical, however, and that’s just part of equation warns Siegel, “There’s another side to machine learning if you’re going to make business value out of it which is the organizational process – the way you’re positioning the technology so it’s not just a cool, elegant model but is actually actionable and will actually be deployed.”

That’s a theme that Demand Planners will recognize all too well and it’ll come as no surprise that supporting process and culture are vital to leveraging predictive analytics insight in an organization, “Organizational requirements like planning, greenlighting, staffing, and data preparation are foundational requirements.”

Click to order your copy now.

One of the key themes raised by Eric Siegel is that forecasting and predictive analytics are merging to meet the business needs of today. To find out more about the future of these fields and how they impact demand planners and forecasters, check out Eric Wilson’s upcoming book, Predictive Analytics For Business Forecasting, published by the Institute of Business Forecasting, which is available to preorder now.

To get up to speed with the core concepts underlying predictive analytics, head over to Eric Siegel’s Machine Learning Course on Coursera.

IBF spoke to Dr. Eric Siegel, author of the best-selling book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, and host of the Dr. Data Show on YouTube. He reveals why predictive analytics has become a critical best practice for all businesses and how – when misused – it can further marginalize society’s most vulnerable groups.

What is predictive analytics?

Let me give three definitions. The first is that it’s the business application of machine learning. The second, more common functional definition, is that it’s technology that learns from data to make predictions about an individual person or case in order to improve company performance. The third definition—where predictive analytics meets forecasting— is that it’s the deployment of machine learning methods such as decision trees, log-linear regression, ensemble models and neural networks. These methods serve to predict demand. When applied for forecasting, rather than generating a prediction for an individual customer, corporate client, or transaction, which is what predictive analytics is usually employed to do, it simply provides a forecast for the item and time range in question.

How does predictive analytics work?

Imagine you have rows of data available from which to learn. In the case of a classical business application, each row corresponds to a customer and contains the outcome or behavior about the customer you’d like to predict—such as whether they committed fraud, whether they canceled their subscription, whether or not they turn out to be a good credit risk, whether they defaulted on their debt, or whatever outcome is relevant to your business.

In the case of forecasting, each row includes or summarizes everything you knew up to this point in time about a particular SKU or whatever you’re trying to predict demand for, along with the outcome, i.e., what the actual demand ended up being over the next week or next month. These rows compose your training data from which correlations are discovered between the data points you hold about the product and the outcome.

Are machine learning and predictive analytics synonyms?

It depends on the context, but, when you’re talking about these kinds of business problems, they’re entirely synonymous. A lot of confusion surrounding this technology is due to nomenclature. A few years ago, the industrial world started using the word machine learning. Before that, the term was only common within academia and research and development.

When it comes to this technology and demand forecasting, you can just as well call it machine learning as predictive analytics. Whether we’re predicting the likelihood of somebody defaulting on a loan or predicting demand for a product, the same exact core analytical methods apply.

To complicate things further, what about prescriptive analytics?

Prescriptive analytics is a superfluous term, which has never been agreed on as far as its definition. The way it’s usually used is to imply that predictive analytics is not enough and now you need additional technology in order to translate what you’ve predicted into actual action to be taken. But predictive analytics has all along been intended to directly inform actions to be taken such as whether to make marketing contact, whether to audit for fraud, or approving someone for a credit card application. The term prescriptive analytics implies there’s an entirely new field or technology. There isn’t. Predictive analytics as a field already includes field deployment, i.e., the translation into action.

How does predictive analytics differ to more traditional statistical modeling?

Machine learning is statistical modeling. It’s just that we don’t use the word statistics because it sounds boring and it kind of undersells it because the core methods have advanced so much since the term statistical modeling originated. Predictive analytics is just the use of those methods for business applications. They all make use of historical learning cases, i.e., data, and they all find patterns that map from the things we do know to the things that we’d like to predict.

What impact has predictive analytics had on businesses over the last few years?

Predictive analytics has become a critical best practice for all businesses that operate at scale. It empowers businesses to execute on mass-scale operations more effectively. Business is a numbers game. Mass marketing, for example, is mostly just junk mail. Investigating fraud is mostly looking at transactions that are not fraud. Most resources and time in that sense are intrinsically wasteful.

The question is, how can you make all these mass operations more effective? By predicting. If you can predict for each individual case, that is, put a probability on it, then you know whether or not to risk spending $2 to send somebody a marketing brochure, or the risk of not auditing somebody for fraud. So predictive analytics generate probabilities, which allows us to manage risk. If you take a marketing campaign and improve its return on investment by a factor of three, that’s a valuable business outcome.

How can companies get started in predictive analytics?

E: There needs to be a champion at the company who becomes familiar with the field by reading or undergoing some preliminary training. Then it’s about defining the right pilot project. Where’s the lowest hanging fruit, the greatest opportunity? What’s the large-scale operation that occurs repeatedly— whether it’s a forecasting function or marketing or what have you—that could stand to benefit? Then you just need to define the scope for the project, do a preliminary analysis of the data, and, usually, engage with an external resource like professional services to get that first project off the ground.

What is big data and why is it valuable for businesses?

Big data and data science are simply umbrella terms that mean we have a lot of data and let’s do something useful with them. Big data did originally mean data that is too big for traditional database technology, but that’s not how it’s commonly used now. These days, both big data and data science allude more to a culture than any particular technology or value proposition. When we talk about using big data, we’re just saying, “We’ve got smart people who are working on using data to derive value”.

What kind of data do companies typically hold that could be used for predictive analytics?

The good news is that anything you do repeatedly is the exact thing that creates the data you need to improve that process. In marketing, it’s simple—you’ve got all the demographic profile and behavioral data about that customer, like what they’ve bought up to that moment and other data points. For forecasting, it’s very similar. Everything that’s been going on with the sales of this particular product up to this moment in time, as well as other external factors which you believe could influence demand, can be leveraged by predictive analytics.

One of the most famous cases of a company benefitting from predictive analytics was when Target marketed to women they identified as likely to be pregnant. How did they figure that out?

That’s an unusual case because normally in marketing applications, you’re predicting for a particular product or a set of products. This is a little different because they’re trying to predict something about the current state of the customer rather than their future behavior. If we think about training data with all the things we know about the customer, Target shored that up with data from customers who voluntarily told Target that they were pregnant. Then the analytics process ends up just being the same thing as for any of these other problems we’ve been discussing. They analyzed all the demographic and profile information about the individual customer and the purchases that they’ve been making, especially more recently, to identify which customers are likely to be pregnant. Then they market appropriate products to that group.

When it comes to implementing predictive analytics, is the challenge not so much building or accessing the technology itself but building a culture around it?

Yes. That’s a parallel challenge and is often the greater challenge. Predictive analytics needs to be seen as an organizational process and the business value needs to be the leading motivator. The actual deployment has to be an integral part of the project from inception. To that end, you need to lead with that value proposition. Let’s say we’re selling this idea internally to marketing. We tell them we’re going to market to only the top 30% predicted most likely to buy and in so doing, we’ll save 70% on marketing costs and here’s our estimated ROI.

These metrics are why we’re doing it—you don’t lead with machine learning or the analytical methods, you lead with a value proposition, the functional purpose of the project. Then, to the degree they need to how the underlying technologies work, you explain the technical aspects that we’ve been discussing.

Do businesses need Chief Data Officers?

I’m actually agnostic about that. It certainly can’t hurt in terms of improving visibility of the analytics function and encouraging a general culture of using data and finding ways to do it. Of course, it depends on the individual organization. The very first Chief Data Officer was at Yahoo. It turns out the job title of Chief Data Officer was first suggested as a joke and everybody in the room laughed. But it stuck and it became a whole new thing.

In your book you say it’s impossible to predict with high accuracy. In business, what kind of accuracy do you need?

Well, accuracy is not the right measure. Rather, you want measures that tell you how much better than guessing your predictions are. As previously mentioned, business is a numbers game.  You want to tip the odds in your favor by predicting better than guessing. That can have a dramatic effect even if it’s not “accurate” in the conventional sense.

For example, I can identify the 20% of customers that are three times more likely than average to buy, but it might be that those people overall have just a 6% chance of buying. I don’t have high confidence that any one customer’s definitely going to make a purchase. But knowing that group is three times more likely to buy than the average tells you that the model has learned something. Instead of prediction accuracy, we should ideally measure the value in business terms like return on investment or overall profit of the project or marketing campaign. If you can translate it into those terms, which you often can, that’s the number that matters, not the predictive performance of the underlying model.

There are now off-the-shelf predictive analytics/machine learning tools that are very cheap. Are these black box systems and can they be trusted?

Most of the software products out there are not black box. There are well-known standard machine learning methods such as the decision trees and neural networks methods we have mentioned. The software products have lots of different bells and whistles and variations to them, but more or less they use known methods or algorithms, and they don’t hide how they work. There are exceptions. There are some machine learning products that build branding around having a “secret special sauce.”

For more technical users and experts, including myself, that creates suspicion and raises questions regarding credibility. After all, if you want to sell a product to experts in the field, it doesn’t make sense to keep the core analytics a secret and market the product as some magical tool. Transparency, as well as cost, user interface, and how well it integrates with existing operational systems and databases should be the selling points.

Many people are still forecasting in Excel when these tools are now easily accessible. What do you think is stopping widespread adoption?

There’s a lot of complexity in moving from Excel to proper machine learning. It’s a completely new endeavor and you have to get together training data, apply methods that may be new to you, and then be able to interpret the outcome, results and performance. It’s a big project. The thing is, there has to be a business reason or incentive to do it. How much ROI would there be if your forecasts were 10% better, for example, and how much is that worth to you?

What is the persuasion paradox and why is it important?

The paradox is that, although we can’t perceive or record individual cases of causation or influence, we can predict it. You can’t conclusively observe persuasion, even in retrospect. If you look at a customer, why did they buy the product? Was it because of your marketing initiative? You can never be entirely sure because they might have bought the product anyway. But we can predict the likelihood of causation over large numbers of individuals.

On the other hand, in forecasting, observing influence is generally is not a concern. We just want to know how many widgets are going to be sold next week. We don’t care why we’re going to sell a lot of widgets next week. We just want an accurate estimate of sales.

What are some examples of unethical uses of data?

The first one that comes to mind is using a protected class such as the race, gender, or religion of an individual in order to derive a consequential decision such as whether they get approved for a loan or sentencing in prison. Let’s imagine that a manager is interviewing a candidate for a job, and they use predictive analytics to gauge the candidate’s likelihood of success in the position. The manager might reject the candidate based on the results provided by the system. When the candidate asks the manager for the basis of their decision, they would have to say, “Well, for one thing, our predictive model penalized you by seven points because you’re black.” Normally, protected classes are disallowed as direct inputs, but there are experts, including university professors involved in the legal justice system, campaigning to purposefully introduce then as direct inputs. The risk is real.

What excites you about predictive analytics?

I’m a former academic and I think that technology that learns from examples and, therefore, from experience to draw generalizations that hold in future cases is the most interesting kind of any science and technology. It’s the most exciting science period. The fact that it naturally ends up being valuable only makes it that much more exciting.

To read the full interview with Dr. Eric Siegel, download the latest issue of the Journal of Business Forecasting. When you become an IBF member, you’ll get the Journal delivered to your doorstep quarterly. Get your IBF membership.