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. 

Using AI to gain a competitive advantage over the next few years is not about better forecasts. What is often missed about predictive analytics is the power to learn more about your customers, and how it helps us provide them with better service. Understanding your consumer base is the real benefit of AI, as it empowers decision making that drives loyalty and brand engagement.

Today, algorithms are constantly crunching data, predicting market trends, and responding to market changes in real time. Such advancements are only possible because of AI. All the data from the consumers, the market, and the competition can be consolidated and analyzed. It can be examined historically and now, with the help of  AI technology, forecasted as well.

To gain insight from AI, you need data. Most companies now routinely log every visit to a product page, every call made to an inquiry response center, and every email received. We are seeing data come faster in almost real-time, we are seeing exponential growth of data, and we are also seeing it come from more places.

Loyal Customers & AI

One of the keys to digitalization of consumer insights is the loyalty card, the likes of which were first made popular by grocery and merchant stores. With this kind of card, retailers can now retrieve a record of everything the consumers have purchased and other useful data. Companies have data from millions of users and, along with other correlating data, they have very real and actionable insights.

Gerri Martin-Flickinger, the Chief Technology Officer and EVP at Starbucks, said in 2016 “With about 90 million transactions a week, we know a lot about what people are buying, where they’re buying, how they’re buying. And if we combine this information with other data, like weather, promotions, inventory, insights into local events, we can actually deliver better personalized service to other customers.”

Starbucks use the mass of data from their customer loyalty cards to understand when and why people buy what products, so they can deliver personalized experiences.

Moving Beyond Just Forecasting To Delivering Personalized Experiences

Starbucks along with many other retailers is going from just forecasting what may happen, to using predictive analytics and Artificial Intelligence (AI) to deliver a more personal experience. Predictive marketing is clearly a very big deal right now, and the benefits are clear [Ed: more on Starbucks’ approach to using customer data is available here.]

Companies can claim with ever greater confidence that they know what we’re going to do next

Research conducted in 2018 by the Institute of Business Forecasting (IBF) to  gauge the future of technology, asked “What are the top technology advancements in the next 7 years that will have the largest impact on forecasting and demand planning?” Not surprisingly, close to 70% of the respondents answered Artificial Intelligence and Machine Learning as either their first, second or third choice. We’re looking at a perfect storm of technological and cultural factors, driving companies to claim with ever greater confidence that they know what we’re going to do next – and why.

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Many companies are now turning these insights and forecasts into actions and driving a stronger brand and even new opportunities. They are taking their customers on a journey they want to participate it, and earning their trust and loyalty while improving the organization’s bottom line. What companies are finding is that when it comes to the ways customer experience is delivered (through email personalization, digital advertising or targeted suggestions) consumers are not concerned about being marketed to by AI, as long as the content is personalized.

AI Is Making Things Personal

Micro-targeting and personalized sales are fast becoming the Holy Grail for retailers, as this attention to detail is key to keeping consumers coming through their doors or visiting their websites. Personally relevant, value-added, individualized interactions lead to better customer experiences and retention. With AI and micro-targeting, brands are not only delivering personalized content on a variety of channels, but they are also become more proactive in engaging customers and drawing them into the brand-storytelling process.

Knowing why sales occurred allow us to drive demand patterns instead of just waiting for them to happen

In addition, to strengthen existing shopper-retailer bonds, AI can also speed up the process of acquiring new customers. Algorithms out there are continually learning and can cross channels and consumers, and explain why certain behaviors and sales occurred. It is using these algorithms and a wealth of other variables to not only better predict but also nudge and drive demand patterns instead of just waiting for them to happen. We can use AI to pick the right channels and customize the content being delivered to audiences. Machines are learning to tell stories across a variety of channels, in order to ensure that customers are actively engaging.

It is not just about what sales will be next month but using technology to enable decision making that adds value in a multitude of different areas

The return on relationships generated by establishing this engagement is invaluable. Companies that home in on customer needs and wants through predictive analytics have increased their organic revenue by up to 21% year-on-year, compared to an industry average of 12%. It is not just what sales will be next month but using technology to enable decision making that adds value in a multitude of different areas.

By handing over much of the heavy lifting to AI, organizations can concentrate on developing insights which help differentiate their brand and drive more sales. By successfully combining technological innovation with a real desire to improve customer-brand interactions, companies will finally be able to gain the loyalty of their audience.

I’ll be speaking at the Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from May 6-18, 2019. It’s a 2-day conference with an optional data science workshop, designed to get you up to speed with basics of predictive analytics, or get you to the cutting edge of the field with the the latest methodologies, tools and best practices.

It is Friday evening and Taylor Swift tweets an endorsement for your product #lovinIt. Soon after, your cloud based analytical processor identifies this along with text analytics from comments using your web crawlers and artificial intelligence. It then immediately adjusts the forecast for the next 36 hours.

In addition, in areas where inventory may become an issue micro targeting campaigns kick in, automatically offering similar products at discount to shift demand. On Monday morning you come in and waiting for you is an automated report letting you know everything that occurred and the potential estimated impact on retail replenishment for the next period, what may have been cannibalized and what was incremental volume, and how this affects your financials for the year. Technology eh? The tools we have to predict demand are truly remarkable.

Forget Twitter, I’m Still Using POS Data

This is wonderful, but I live in a different world. In my world we have POS data, not Twitter comments from Taylor Swift. Our POS data is not directly tied to our planning system yet. We would love to forecast even true orders, but with system limitations and idiosyncrasies in our key customers’ ordering behavior, we rarely have an accurate picture of original customer demand. Our data is messy, and we struggle with multiple platforms that do not communicate well. I spend way too much time still educating and selling demand planning to others in the organization and explaining why the forecast is always wrong.

As great as it would be, we’re a far cry from using AI to interpret unstructed data like Twitter to produce automated, real-time demand insights and gauge impact on inventory. No matter where you are on the spectrum of maturity though, there are still some fundamental principles we all must follow. This is important – wait this is a VERY important point to understand.

Transitioning From The Basics To Advanced Forecasting & Planning

There is a lot of talk (admitting I am one of the biggest culprits) about the future of demand planning and at the same time we seem as a profession to still be stuck and not progressing forward. There are a multitude of reasons for this, but I believe one of the main ones is that we still struggle with embracing, understanding, or anchoring to a few basic laws of demand planning. Business forecasting, like everything, has its own principles that you must know if you want to use it to add value to your business. As you start from the basic world most of us live in to the advanced analytics world, I encourage you to use these principles as your guiding star and reference.

5 Unshakable Laws Of Demand Planning

Here, I would like to present the five basic and most important principles of business forecasting. These principles must be forefront in the mind of every planner and communicated often to every executive.

• Demand planning includes uncertainty. Remember that you want to forecast the future, which is something unknown. So, you cannot expect that you will predict the future reality with 100% accuracy. Because it is expected that your forecasting will be wrong, the real question is, “by how much?” Forecasts can be wrong and demand plans can be accurate if you include probability and/or an estimate of error.

• Demand Planning is less precise and less accurate as you get more detailed or granular. Product families or product groups will have less uncertainty and more accuracy than at an item-location level. For the dimension of time, forecasts will deteriorate and have more error as you go from months to weeks to days. It is more important though to plan demand at the right levels that meet the needs of the use of the forecast with the least amount of uncertainty. The way you forecast and plan demand is unique to what question you would like the forecast to answer.

• Demand Planning is more precise and accurate the closer you are to the demand. Tomorrow is more predictable than three months from today or one year from now. It also stands to reason that point of sale or actual customer demand are closer to real demand than retailer or distributor orders are. Retail or distributor orders are in turn closer to real demand than shipments from a factory. The objective is to get closer to consumer sentiment and true demand. Recognize that the closer you get to the true demand signal, the better the forecast will be.

• Demand Planning improves the more you know or can see. Demand planning relies on historical data and external environmental factors. Historical data is an important starting point for many forecasts. At the same time, you can’t expect to have the same results from forecasting if you do not have enough history or there is some change in environmental conditions. We want as big and complete a picture as possible – this will be better than a narrow picture from a small sample. Lots of interlocking weak information may be vastly more trustworthy than a point or two of strong information.

• Demand planning is a process. With that, no matter if you need to estimate the result of 2+2 or predict the future impact of a Taylor Swift Tweet in real time, it uses the same basic steps and principles. It will require collection, transformation, modeling, and analysis of data with the goal of gaining consensus of a future forecast. Most importantly though, demand planning is a collaborative process, not a test of statistical algorithms. In many ways we manage assumptions and expectations more than we manage numbers. The analytics provide a solid foundation to work with, but the real value comes from answering questions or enabling others to make decisions.

Artificial Intelligence has been widely praised for its ability to transform the supply chain for the better, from more accurate demand forecasting to real-time, automated inventory management. Zeroing in on that critical component of demand planning, an accurate forecast, AI-powered forecasts outperform traditional models, in some cases driving double-digit percentage gains in accuracy.

Sounds like a silver bullet, right? And it is. So why are many businesses still operating with poor, outdated forecasting processes? In IBF’s June 2018 survey, 70% of respondents said AI will be the future of demand planning in 2025. Forecasting practitioners are well-aware of the benefits, but the pitfalls of AI forecasting projects are preventing widespread adoption. Without coding or data science skills, it can be difficult for planners, analysts, and others tasked with improving forecasting processes to implement the most advanced AI-powered forecasting solutions.

Do Demand Planners Need Data Scientists To Exploit The Power OF AI?

When a demand planner has a centralized data science organization to collaborate with they have an opportunity to access the skills to exploit AI but typically, development cycles are long, costly and complicated. Many projects fail completely, often with a hefty price tag. At a leading Fortune 500 company I work with, the planners on the forecasting team are currently debating whether to rely on the company’s centralized data science team or hire their own data scientists. The strategic discussions have focused on these two options. Now, with new software platforms that automate the process of building AI models, they have a third option of incorporating advanced AI—Automated Machine Learning (AutoML) and Automated Deep Learning (AutoDL)—into their workflow.

These allow a demand planning function to achieve the same objective that they would get in options one and two, but faster and with less cost and risk associated with the project. The forecasting practitioners and demand planners don’t necessarily have a data science background but that’s not a limitation as these platforms empower them to take advantage of the latest in AutoML/DL to create process improvements and drive forecast accuracy—not in 2025, but now.

How Deep Learning Solves Common Forecasting Challenges

Tools powered by AutoML and AutoDL not only solve common, complex demand forecasting challenges with ease, but do so in a fraction of the time. Manufacturers of fresh, perishable goods create their own algorithms that precisely forecast daily sales for each SKU in each store, reducing wasted inventory and creating efficiencies in transportation and logistics. Apparel brand manufacturers are utilizing these platforms to improve forecast accuracy for new product introductions that have little historical data, mitigating risk in a way not possible with traditional forecasting methods. Fast food chains are predicting sales of individual menu items down to half-hour increments with greater accuracy than ever before, in turn allowing for better staffing and inventory management.

The biggest factor impacting accuracy is the data, and unlike traditional methods, Machine Learning and Deep Learning-powered methods allow forecasters to incorporate different types of data, structured or unstructured, and combine those data sets into the forecasting model. This holistic approach incorporates all data that could impact future sales, leading to major gains in accuracy.

Automation Reduces Tedious Manual Work

Automation is a critical component with any Machine Learning or Deep Learning software as without it, development processes are often still lengthy and failure rates high. AutoML and AutoDL forecasting platforms can automatically factor in seasonality, holiday and trend effects removing the need for tedious manual work for planners.

AI Helps Forecast New Products

While Machine Learning has provided many improvements over legacy demand forecasting methods, Deep Learning is uniquely well-suited for forecasting. Deep Learning acts like a long-term memory function and is better at learning patterns over time, and when historical data is incomplete or non-existent, like forecasting new products, AutoDL excels by quickly iterating to fill in these gaps.

AI Does Something Statistical Models Can’t Do – Identify Non-Linear Relationships

Deep learning solutions have greater capabilities to handle unstructured data and learn the correlations by understanding complex non-linear relationships. Sophisticated models are better suited for this type of task, however these advanced Deep Learning and AutoDL models require significant processing power. Largely made possible by GPU (Graphics Processing Unit) computing, these advanced forecasting algorithms can discover much finer patterns in the data than traditional methods.

AutoML and AutoDL solutions put the power of these unique capabilities into the hands of non-technical users while also solving pain points for data science personnel, like automating feature engineering, and model design, training and deployment.

When considering how to best incorporate the latest AI technology into forecasting, practitioners are no longer restricted to either partnering with an internal data science team, hiring their own, or simply relying on traditional methods. AI platforms that leverage AutoML and AutoDL can be valuable not only for data scientists but also for those business users who are fluent in data but perhaps not coding. This new breed of software is putting the power of Machine Learning and Deep Learning directly into the hands of planners and analysts, so they can build better forecasts today without the overhead, creating more accuracy and better decision-making throughout the supply chain.

IBF’s 2018 research report entitled The Impact Of People and Process On Forecast Error revealed that forecasting error of cause-and-effect models (AI) is more than four percentage points lower than Time Series models (20.79% vs. 24.86%). Click the link to download. 

Predictions are big business. The market for predictive analytics is expected to reach $12.4 Billion by 2022 as Artificial Intelligence (AI) becomes more widespread in Demand Planning organizations. But we’re not losing our jobs – don’t worry – because even though a lot of the analytical work will be automated, new roles are emerging to implement and manage AI, and interpret the findings. Here are the four types of AI in Demand Planning, and the new roles required to leverage them.

1. Limited Memory AI

Today, most Limited Memory Artificial Intelligence (AI) applications rely on an age-old premise: use vast stores of historical data together with current observations to predict future outcomes. A Demand Planner calculating the base forecast on a product is likely to use past sales to trend as a starting point. Depending on the number of products in a portfolio, this task could be executed efficiently without the use of AI applications, say for instance in a small enterprise. However, for large organisations such as multinationals, Limited Memory AI could execute this task with speed and accuracy providing live or on demand recommendations for review by the Demand Planner.

For any organization, investments in technology must provide a Return on Investment, with any decisions on AI implementation first requiring an extensive review of data stores to ensure they meet the required standard. Garbage in garbage out still applies and if you want quality data, you need a human.

Working In Reactive AI: Demand Planners with an interest in the technical aspects of the role may specialize as Demand Planning Scientists with a focus on Data Management and algorithm development, optimisation and maintenance. This function would support the initial preparation of data, selection and fine-tuning of the organization’s specific algorithms.

2. Reactive AI

Reactive AI, a second type of application, could also support Demand Planners. Scenario Planning and one-off decisions, which combines multiple information sources together, benefits launch and promotional planning where historical trending is not possible. Reliability of this proposition would depend on the extent of connectivity between the organization and it’s customers or key partners. Inputs could be drawn virtually from various stakeholders when scenarios are triggered as part of automated workflow processes.

Working In Reactive AI: Demand Planners could specialize in this area as Launch & Promo Planners. Successful collaboration with other departments has always been a differentiator of top talent in this area. Weak AI deployed in Limited Memory and Reactive applications do not offer direct substitutes for this aspect of human capability.

3. Theory Of Mind AI

Developed social characteristics of human communication and trust have proven difficult to replicate in AI. The Theory of Mind category of these applications deliver outcomes based on predictions of how people will behave within a given environment through perception of that environment and those within it. Where technological advancement of this type enables AI to read body language and interpret non-verbal cues to inform decision-making, opportunity may arise in collaborative forecasting processes.

Working With Theory Of Mind AI: AI assistants trained to work with teams may be well placed to provide a second opinion. Managerial and strategic skills would enable Demand Planners to thrive in such scenarios. Those individuals with the required capabilities may be qualified to be promoted to Demand Manager, overseeing AI decision recommendations, validating them and making the last call.

4. Self-Aware AI

AI in the fourth category is differentiated from the others based on its self-awareness capability, which draws on internal concepts of feelings and uses these additionally in the outcomes produced. This type of AI does not exist in the full capacity of that category definition, although strides are being made. To replace Demand Planners, AI would need those skills and more. Instinct, intuition, creativity to generate new ideas, social energy and charisma to influence are characteristics humans themselves are unable to quantify – far less replicate. These allow new ways of doing things and decisions to be taken outside of information that is available or could be generated. These include, for example, unprecedented events and rapid changes in consumer tastes including those for food or emerging fashion trends.

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Working With Self-Aware AI: There’s is no new Demand Planner role for the type of AI (yet, anyway). That’s because in these circumstances, data does not usually exist. In professions where errors translate into costs such as lost sales and inventory builds, AI would excel in driving quality of information, automating mundane tasks, providing secondary opinions and recommendations. It could succeed under supervision, which would create a new Demand Planning role. How much time is committed to this would determine the viability of the application and the role. For those aspects of human nature, which enable social interaction, AI would fail and create a void in organizational cultures that could be detrimental to creativity, engagement and performance.

[For jobs in Demand Planning, Predictive Analytics and S&OP, check out IBF’s jobs board]

When it comes to Machine Learning in Supply Chain Planning, how many of us can say we are ready to reap the benefits? If we are not quite there yet, what do we have to do get started? The use of Artificial Intelligence (AI) and Machine Learning in Supply Chain Planning has started and early adopters are already optimizing supply chain processes and gaining greater insight for smarter decision making. Here’s how you can get started with Machine Learning in your company.

Should You Believe The Machine Learning Hype?

Results from early Machine Learning successes are driving the hype to a fever pitch. Typing “Machine Learning AND Supply Chain” into Google delivers more than 3.8 million results in less than half a second.  There’s no question that Machine Learning is a topic that supply chain planning people are thinking, talking, and writing about. The real question is are we, as a profession, ready to embrace Machine Learning?” If so, what does that mean and how do we get there?

What Exactly Is Machine Learning?

While most organizations are still in the early stages of exploring machine learning, a thorough understanding of what it is and how it can be applied is vital. Terms like data science, advanced analytics, Artificial Intelligence and cognitive computing have been used interchangeably with Machine Learning in current periodicals. So what exactly is ‘Machine Learning’?

Machine learning is a type of artificial intelligence where computers have the ability to learn without being explicitly programmed. Machine Learning programs teach themselves to grow and change when exposed to new data.

Machines that could learn were first introduced in the 1960s with much of the early focus on probabilistic algorithms and forecasting. However, it wasn’t until the late 90s with the emergence of more powerful PCs that computer programs that analyzed large amounts of data and drew conclusions became more widespread. Some of the early successes came in the form of Neural Network programs. In more recent years, the explosion of ‘Big Data’ and the exponential increase in computer power led to significant growth of available solutions offering Machine Learning capabilities. Companies have discovered, however, that it takes more than abundant data and advanced computer capabilities to be successful with Machine Learning initiatives. Success also depends on having the appropriate talent, infrastructure and business focus.

A basic, foundational component of any effort to mature supply chain capabilities is to identify, secure and organize the necessary executive support. Real change happens from the top down, and it is imperative to secure an executive sponsor that has both the organizational power and vision for Machine Learning capabilities. It is important to ensure that any envisioned machine learning capabilities align with higher-level business goals and objectives.

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You Need A Machine Learning Champion To Define Requirements And Drive Things Forward

Who will lead the initiative and what skills do they need? Finding or assigning the right person to champion the initiative is critical for success. This leader needs a diverse set of skills including communication and influence to build support for adoption; team-building skills to convince existing employees of the benefits; and the ability to recruit new talent as needed. To establish organizational credibility, this Machine Learning champion needs to understand the various available technologies and their applications to the supply chain.

Working together, the executive sponsor and initiative leader can now envision how supply chain roles need to change to embrace an analytics driven supply chain. What new roles are needed (Database Engineers, Data Scientists, etc.) and are the required skills available within the company? How should the team be organized to efficiently run the business while also driving innovation?

What infrastructure or foundational components are required to enable the envisioned machine learning capabilities? Often the first place to start is to determine whether you have the data. What will it take to build accurate and consistent data to support the envisioned machine learning applications? How will the data be maintained and who is responsible for doing so? Do you need a Master Data Management (MDM) solution for supply chain data?

The next step is to determine whether your supply chain platform supports the envisioned Machine Learning capabilities. If so, how are these capabilities enabled? If not, what solutions are available on the market and is there budget available to acquire and implement a new solution? A critical step at this point might be to build an ROI based business case to obtain the funds needed to invest in a solution that enables machine learning capabilities.

Don’t Be Cutting Edge, But Copy What Cutting-Edge Leaders Are Doing

If your company is ready to explore machine learning capabilities, where should you start? Although it can be an advantage to be on the leading edge, I believe it is more productive to first review what has worked for other companies. Building Artificial Intelligence capabilities like machine learning is an evolutionary process. Getting started is probably more important than where you start. As you build experience, continue to explore new areas where additional applications of Machine Learning can add value. A few examples of how your company could get started with Machine Learning in the supply chain are provided below.

Application 1: Best-Fit Algorithms

Considering the fact that forecast accuracy continues to be a problem for many companies, a high value application of Machine Learning could be in the area of a “Best-Fit” algorithm for forecasting. Best-Fit algorithms automatically switch to the most appropriate forecasting method and parameters based on the latest demand information, to ensure you create the best forecast for every product at every stage of its life cycle. The algorithm evaluates forecast error for each cycle and recommends or automatically changes to the forecasting method that will produce the best result.

Application 2: Supply Chain Optimization

Another high value application area of Machine Learning is found in applying algorithms that continually analyze the state of your supply chain and recommend or automatically execute changes to meet customer requirements while maximizing company objectives. Optimization driven by algorithmic planning has also been in practical use for many years. Supply chain optimization relies on a set of provided information (supply chain facilities and capacities, transportation lanes and capacities, customer service requirements, profit requirements, etc.) and real-time operational updates (orders, shipments, unplanned events, etc.) to suggest optimal responses to planned and unplanned events.

Application 3: Multi-Echelon Inventory Optimization

One powerful application of Machine Learning is Multi-Echelon Inventory Optimization (MEIO), which automatically adjusts inventory parameters to meet stated customer service requirements while minimizing inventory investment. Using the latest demand and inventory projections and variabilities, MEIO seeks the optimal balance of component, Work In Process and finished goods inventory at the right locations. Embracing MEIO can reduce total inventories by upwards of 30% while maintaining or improving customer fill rates.

The Key To Success? Learn To Crawl Before You Walk

Attaining the full benefits of Machine Learning capabilities will be an evolutionary process. We must learn to crawl, then walk, then run. The introduction of Machine Learning into most supply chain organizations will take time, but that shouldn’t stop supply chain professionals from planning for the future or taking advantage of Machine Learning capabilities available today. Implementing foundational data and system platforms and taking advantage of existing algorithmic planning optimization technologies builds the expertise and experience needed to pursue more advanced Machine Learning in the future.

Are you considering applying machine learning solutions to your supply chain?  If so, what steps are you taking to get there?

Learn to create your own Machine Learning Models at IBF’s Demand Planning & Forecasting Bootcamp with w/ Hands-On Data Science & Predictive Business Analytics Workshop. Join us for this world-class training event in Chicago, Illinois on 14-16 March 2018.