CeMAT Insider

Can machine learning really create a better supply chain?

Posted by CeMAT Team on 09-Mar-2017 12:18:42

Can machine learning really create a better supply chain?

"Within an organization, the words 'Demand Planning' stir emotions. Usually, it is not a mild reaction. Instead, it's a series of emotions defined by wild extremes including anger, despair, disillusionment, or hopelessness." 

Lora Cecere understands the anguish of many business owners who are just trying to make their supply chain more efficient. The above quote is from her book Bricks Matter, and it gets to the heart of why supply chain management is always an evolving and changing beast.

So the question we want to ask in this post is: is there a technology out there that gives businesses back the reigns of their supply chain? We think that, just maybe, machine learning is the future of effective supply chain management that can hopefully take the despair out of inventory planning.

 

 

The challenges of demand forecasting

 

Demand variability is a complex to figure out. Identifying trends through demand data and working out all the demand drivers manually leaves a lot of this hard work open to human error, persistent bias and poor planning.

As bad as that all sounds, if we remove human error and use the most highly regarded forecasting techniques, it’s often found that they produce disappointing results if your methods cannot keep up with changing consumer trends and differing buying patterns.

Controlling the movement of different kinds of goods across a supply chain takes serious planning and management. This is the very reason why demand forecasting has been heralded as one of the largest pain points in supply chain management because the slightest miscalculation in forecasting could have devastating economic effects. 

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Even if you’re doing your best within your business to accurately forecast your demand, many of the technologies available don’t learn as they go and often produce inaccurate results.

Relying on historical data has also been proven to be an ineffective method to predict demand as changing consumer behaviour leaves business demand open to fluctuations on very short notice.

None of these challenges should be taken lightly. Constant demand forecasting errors could be holding your business back from significant growth. So if traditional methods of demand forecasting aren’t really helping, is adaptive machinery a viable option?

 

 

The power of machine learning in the supply chain

 

You may be reading this thinking your forecasting software is perfectly capable of keeping on eye on your business’ demand fluctuations. You might even have an algorithm in place to solve the puzzle of your supply chain, and just maybe, you’ve been getting great results.

Without wanting to be the bearer of bad news, you also might find that one day your algorithm won’t predict a sudden peak in demand. This is where Artificial Intelligence (AI) comes into play.

Machine learning is part of AI where learning is facilitated through continuous advancement of computing. The AI is constantly exposed to new scenarios and the machine can learn, test and adapt in order to improve decision-making.

Many people often bring out their inner skeptics when it comes to AI, but we here at the CeMAT team are persistent. Ask yourself what you trust more to do a complicated math equation: your own brain, or the calculator on your smart phone? Most people would most likely trust their calculator more, and the great thing about AI isn’t just than it can do that hard equation you never paid attention to in high school, but it will learn and adapt to similar equations too.

AI technologies can easily understand the internal and external factors that turn your demand forecasting from a straightforward series of numbers into convoluted chaos. Machine learning can reliably model the numerous causes of demand variations.

 

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Lots of new products = lots of new calculations

 

The rise in eCommerce has changed the traditional structure of many businesses. You’ll see that long gone are the days where a stand-alone single shop location is the main source of your income. Today you can have a physical store, as well as an online portal that caters to many customers across the globe.

With the rise in eCommerce, many businesses are finding that selling many products at low costs can drive growth. This is just one example where machine learning can assist with the sudden introduction of a range of new products that all require their own demand forecasting. 

Added complexity to the supply chain in this way makes demand very hard to predict, and the last thing a business wants to do is not have stock in a sudden spike and then be left with a bunch of ‘dead stock’.

 

 

Data-overload from extreme complexity

 

New technologies have brought about a proliferation of data, and managing information from your market and logistics operations is often more than most business owners and supply chain managers can handle. In fact, if you read our previous post on micrologistics you should know all about the complexity of information streaming through our supply chains. 

As companies grow to introduce more brands or begin to utilise a variety of delivery modes, basic software applications often can’t adjust to such intense growth. The data that is being created from all these new transactions, tracking technologies, makes it hard to integrate it meaningfully into other business intelligence systems.

A modern supply chain needs a modern solution to manage all this complexity, and one thing is for sure, AI technologies are designed to handle and adapt to such complexity.

 

 

Reflections

 

There is no question that businesses want machines that are better, faster, more powerful, and most of all, interactive. AI solutions have already begun to speak to us through our phones, they are powering space shuttles, diagnosing medical conditions and are guiding our driverless cars. So why not let it help you make demand forecasting easier? 

What do you think of AI applications for the supply chain? Let us know your thoughts in the comments below.

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Topics: Supply Chain Management