2022 Guide: Transforming Supply Chain Management With Machine Learning

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As post-pandemic logistics mayhem continues and fuel and consumer goods prices skyrocket, managing supply chains in retail is becoming a nightmare. The circumstances are beyond our control. But retail companies are seeking ways to regain some by predicting changes and adapting to them in real-time. Machine learning in retail supply chain management just might be the key to solving their problems. 

It sounds a bit bold, but we promise — in less than ten minutes, you’ll become a believer. 

In this piece, we’ll go over the benefits and challenges of introducing ML into your SCM (supply chain management) process. We’ll also offer some practical advice based on Unicsoft’s hands-on experience with the tech.

Let the countdown begin.

ML is paving the way to a more efficient SCM

Unless you’ve been living under a rock, the phrase “machine learning” must ring a bell. In fact, you might feel a buzzing sensation in your head. That’s because this sub-discipline of AI has been attracting a lot of attention lately, with use cases ranging from Tesla’s autopilot to insurance fraud detection.

Gartner predicts that more than 75% of commercial SCM applications will rely on some form of AI by 2026. Machine learning — a form of AI that’s heavily based on data science — falls into that category. Does that make sense? Let’s elaborate.

In short, ML uses complex algorithms to make predictions based on training with historical data. This means the algorithm learns about trends and patterns from historical data and then makes predictions when it gets new data. The best part is, once you’ve trained those algorithms, they can keep working with little human supervision, processing huge amounts of data very quickly. 

Sounds interesting, but how does it relate to retail business? It’s simple: ML intelligently translates your supply chain data and other market insights into actionable predictions, saving you time and money.

When trained with carefully selected datasets, machine learning algorithms can recognize patterns in customer demand, monitor the logistics-related costs and supplier stock, and so on. The SCM system can then accurately forecast demand, optimize delivery routes, and automatically place orders in the most efficient way.

“Wait, but I already have a supply management system in place. It’s not perfect, but it works for my business. Shouldn’t it last me several more years?”

We’re glad you asked.

Limitations of traditional SCM methods

The current methods and tools retail businesses employ to manage their supply chains were designed for a different reality. We don’t know what system you’re using, but it’s likely to have one or more of the flaws typically found in traditional SCM systems:

  • Outdated software (or even MS Excel) that slows down your operations
  • Predefined rules and linear budget-driven approaches that limit efficiency because they can’t quickly respond to shifts in market conditions or sudden disruptions
  • A fragmented view of the chain’s processes due to the lack of comprehensive data analysis (data obtained from different points in the supply chain is analyzed separately)
  • Bureaucratic delays that occur when managers or execs have to personally make and approve every decision

Autonomous AI and ML solutions were created for today’s data-driven markets. They easily outperform traditional methods of SCM and are more future-proof. Here’s why:

  • They’re based on modern technology that’s easy to maintain 
  • They use predictive analytics, combining data from multiple sources
  • They save resources and boost agility because they’re built with automation in mind

We’ll talk more about the benefits of ML in a separate section, but just to sum up what we’ve talked about so far:

Using machine learning in retail supply chain planning, companies can gain an edge over competitors with a more comprehensive approach to data analysis and quicker decision-making. 

Current global supply chains are so complex that no forward-looking retail business can afford to use obsolete SCM solutions. And a change of paradigm is happening: in 2021, 76% of organizations already prioritized implementing AI/ML over other IT initiatives, according to this report.

So what SCM issues can machine learning address? Let’s take a quick look.

Retail supply planning challenges in today’s global economy

Over the past few years, many businesses in the retail sector had to learn the value of supply chain resilience the hard way. The COVID-19 pandemic, the drought in Taiwan, and now the war in Ukraine hit the global economy hard. And supply chains are at the forefront, rolling with the punches.

Not all of the stressors were acts of God (or of warmongering tyrants) — many are inherent to the way we produce, move, and sell goods, which are the key areas where retail businesses may face disruptions:

  • Shifts in consumer demand. Whether it’s consumer electronics, packaged goods, or items of clothing that you sell, it’s important to respond to changes in demand. Seasonal spikes and dips, trends rising and falling — retailers have to optimize their inventory by accounting for factors like these.
  • Unexpected local or global events. At the onset of the pandemic, many manufacturers cut production, anticipating a sharp decline in demand. Shipping companies followed suit. However, sales in many categories quickly picked up when they moved online, and backorders ensued. There are things we can’t foresee but can only react to. What matters is how quickly you can adjust your supply chain.
  • Supplier relationships. Managing multiple suppliers isn’t easy: you need to monitor prices and availability, delivery terms, and keep up with contract renewals.
  • Transportation and logistics. The rising cost of storing and moving goods has had a profound impact on supply chains worldwide. Finding the optimal delivery route is critical, as is resolving issues that may arise along the way.
  • Product complexity. When your retail business involves manufacturing your own products, keeping tabs on all the needed components and materials can cause a headache. Failure to predict supply shortages may lead to pausing or halting production at some point.
  • Financial flexibility. Can your organization handle upticks in demand or increased transportation expenses? Planning your finances in advance is extremely important.

These days, a highly adaptive supply chain management system is vital for a retail business. It’s impossible to build one without harnessing the power of data, and machine learning is the right technology to achieve that goal. The sooner you implement an ML CMS, the sooner you’ll see the many different benefits for your business.

Applications of ML in retail supply chain management

The value of machine learning in retail supply chain management is immense. Thanks to autonomous algorithms that can quickly process massive amounts of data, ML is perfectly suited for the data-rich environment of supply chains.

Depending on your needs, your budget, and the richness of your data, ML-enhanced software can deliver major improvements in the following areas.

Demand forecasting

Using historical data and applying predictive analysis, ML-powered solutions can forecast demand with a high degree of accuracy. The software can then build projections of sales and revenue for a given period of time. Armed with this knowledge, retail businesses can plan procurement, financial resources, and marketing activities down to a T.

As more new data comes in, algorithms can improve the accuracy of its forecasts, which can further drive the efficiency of your business without additional investment. 

Inventory and warehouse management

Accurate demand projections can help you efficiently manage inventory, which prevents overstocking and minimizes backorder queues. You can also use ML in combination with smart sensors for automatic monitoring of storage conditions, letting the system adjust settings based on data analysis. 

How about reliably tracking your goods through their entire journey by recognizing objects by their shape and size? ML can do that too.

ML-based systems can also optimize the locations of goods in your warehouse based on the popularity of items. After recording the trips forklifts regularly take to pick up different positions and analyzing the data, software can suggest where to place them for maximum efficiency.

Logistics and transportation management

Thanks to ML’s ability to analyze data and continuously improve projections, it can unlock a number of benefits in this area:

  • Automated selection of the best transportation partner
  • Real-time route planning and adjustment
  • Accurate delivery predictions
  • Constant optimization of resources like electricity and fuel
  • Improved fleet maintenance thanks to predicting malfunctions

Here’s an example of how machine learning can help predict incidents in supply chains. Read the story about an exception prediction tool that Unicsoft built for a leading provider of supply chain consulting, software, and fourth-party logistics services. Spoiler: the accuracy of forecasts reached 80%.

As you can see, implementing ML can really make a difference when it comes to delivering your goods promptly. Your customers will definitely appreciate it.

Anomaly detection

Machine learning is no slouch when it comes to detecting anomalies, which allows you to identify defective goods or predict maintenance needs and prevent equipment failure, saving valuable time and avoiding unnecessary losses and shutdowns. 

Fraud prevention is another interesting use case for machine learning in retail supply chain management. ML algorithms trained with data from legitimate and fraudulent transactions can learn to identify irregularities and flag such transactions for review by humans.

Supplier relationship management

ML had countless applications in this domain. For instance, you can use it to automate contract renewal. Pre-trained with multiple contracts, ML can take on the job of compiling, filling out, or renewing routine contracts.

ML-enhanced software can also monitor prices and offers. Having absorbed the logic of human operators on previous deals, your SCM system can unmistakably identify the best offer.

But the exceptional data processing capabilities of ML-enhanced software let you achieve much more than that. Want better resilience? Try supplier diversification. 

Say you have your own line of products, and there’s a predicted shortage of components or raw materials. With the push of a button, your manufacturing facilities can seamlessly change suppliers without degrading quality. All the numbers have already been crunched and projections laid out for you by algorithms. This is a major advantage in a world where supply lines are disrupted by natural disasters, and production lines of key players are booked for years ahead.

Delta Air Lines Inc., with its yearly budget of over $7 billion to buy uniforms, as well as food and other items offered during flights, manages a total of 8,000 suppliers. Impressive, right? Without an ML-enabled solution, this would have involved a lot of manual work and billable hours spent on thousands of tiny decisions.

Intelligent automation

As one of the main benefits of ML, automation ensures you can save employees from having to repeat the same mundane tasks over and over. This can include things like filling out repetitive paperwork and performing quality inspections — but where ML really shines is the automation of decision-making.

Using algorithms that learn from your data and key market indicators, you can set up decision scenarios that will improve over time. For example, your ML solution can offer suggestions for important choices like when you should consider switching suppliers or a subcontractor and what alternatives you should consider. 

Now that you’ve read about the applications of ML in supply chain management, you must be ready to pull the trigger on investing in it. Hold that thought. The next section highlights the importance of knowing the possible pitfalls.

The complexities of ML implementation

Let’s briefly touch upon the common problems of implementing ML techniques in SCM solutions. You may encounter some of these obstacles, but keep in mind that you can overcome most of them with the right technical expertise and experience:

  • Poor quality/insufficient quantity of training data. Machine learning algorithms are sensitive to data quality and need enormous amounts of high-quality data to learn. Datasets need to be selected carefully and prepared for processing by data scientists.
  • Requirement for powerful hardware. You’ll need a lot of computing power and storage to train your ML algorithms, and you need to know which options would work best for your use case.
  • Complex business models. Standard ML algorithms may not be the perfect fit for your needs, so you may need to combine multiple algorithms or build one from scratch to create a custom solution. 
  • Lack of developer staff. If your in-house team lacks the required expertise, you may have to hire data scientists from the local labor market or take the outsourcing route.
  • Delayed gratification. Gathering data and building, training, and testing ML models can take quite some time. But these are essential phases of the project if you want accurate predictions. Follow the lead of your team or software partner so you can find an optimal price/performance ratio. 

It’s true: adopting machine learning in your SCM workflow can be challenging, and you can’t do it without the help of experts. But it’s totally worth the effort. 

With the help of ML, you can achieve better financial transparency across your operation’s entire supply chain. Accurate predictions based on previous transactions can enable you to move from a reactive to a proactive approach in managing your finances. Automation of routine tasks and decision-making reduces cost, frees up resources, and increases ROI.

But don’t take our word for it. Ask Schneider Electric, winner of Gartner’s 2022 Supply Chain Award. They adopted ML-driven supply chain management and saved over €100 million!

ML is a great business tool when it’s properly implemented. But before the first line of code is written by developers, you’ll need to do some work as well.

Do you need a hand to implement machine learning in your supply chain?

Practical advice on implementing ML for SCM optimization

We recommend following this tried and true three-step process. It starts with a high-level assessment of your existing supply chain. A clear understanding of your needs is a prerequisite for a successful transformation.

Analyze your current supply chain

Take an objective look at these aspects of your current SCM situation:

  • Map out all the critical processes and components of your supply chain.
  • Scrutinize your supplier network to identify bottlenecks, risks, and redundant nodes.
  • Assess your financial position and technological maturity.
  • Conduct market research to compare your supply chain operations and KPIs with those of the competition.
  • Determine your supply chain weak points and what measures you’ll need to take to achieve the desired improvements.

Next, start planning your transformation.

Set measurable goals

At this stage, you have to do two things:

  1. Translate your business goals into specific ML-powered features and technical requirements. You need to have a concrete vision of how implementing machine learning will achieve your objectives and what tangible benefits it will bring. You’ll require your tech team’s help with this.  
  2. Set KPIs and calculate expected ROI. As soon as you understand how your goals can be converted into actual ML features and workflows, you can start measuring performance indicators so you can define your objectives in numbers and estimate profitability.

Finally, you’ll start building your ML-enhanced SCM solution.

Proceed with engineering and development

The process of building software is complex and multifaceted, and there’s no need to describe it in detail here. So here’s a simplified version to save you some time:

  • Assemble your team.
  • Discuss and confirm requirements.
  • Decide on architecture, design, and tech stack.
  • Procure quality data in required quantities.
  • Develop, train, and test ML models.
  • Deploy your solution and monitor performance.

Now, all that’s left to do is to enjoy your new software, reap the benefits of machine learning, and keep an eye out for the next technology to revolutionize supply chains.

Wrapping up

Before we call it a day, let’s quickly go over the key takeaways:

  • ML is a powerful tool that can be customized to fit the needs of retailers of any specialization or size.
  • It brings along many local and global benefits, and the ability to make sense of your data in real-time is at the heart of it.
  • An ML-enabled solution can tie disparate elements of SCM data into a coherent system, providing a holistic end-to-end view of your supply chain.
  • With the power of machine learning in retail supply chain management, you can automate most of the decision-making, improving the agility and financial performance of your business.
  • To get a working ML-based system with properly trained algorithms and the right functionality, you’ll need the help of experienced developers with a strong background in data science. 

In case you’re looking for a tech partner to help you realize your vision, Unicsoft is ready to step up. We specialize in building ML-powered solutions for a variety of applications. Our team’s AI/ML expertise, combined with superior programming skills and a problem-solving attitude, make Unicsoft the best choice for projects of any complexity and scale. 

To learn even more about the benefits of using ML in retail, check out this piece on our blog.