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The fact that innovative technologies change our world is now well known to everybody - both kids and grownups. Moreover, it isn’t rocket science to find out how it happens. So today we’ll be talking about how innovative technology (machine learning, to be specific) can impact business and optimize the supply chain.
We’ll tell you everything you need to know about the advantages your business can get from supply chain optimization with ML tech and even a little bit more. Are you excited yet? We hope so!
Supply chain optimization: what is machine learning in this context
To figure this out we need to start from the simplest explanation of what exactly is machine learning. These are specific artificial intelligence methods that are constantly learning in the process of solving similar, analogical tasks. In a world of fantasies, machine learning may take the best seat beneath the window in your office or steal the job from your most annoying colleague who does a routine job.
But in our reality, everything that this technology is capable of for now is raising your company’s business value, improving the brand’s reputation, automating simple tasks, and, most certainly, speed up your work processes. Not bad, huh?
Let’s take a more practical example of a machine learning workflow. Walid Mehanna, head of Data and Analytics at Mercedes-Benz Cars, addresses an interesting question: can or cannot this technology “fix” the ending of Game of Thrones? Unfortunately, that’s unlikely. When these algorithms were provided with all the information about characters and storylines from “A Song of Ice and Fire” George R. R. Martin, the technology turned its ending in ridiculous nonsense (yes, even more than the show did). Artificial Intelligence made Hodor kill the Night King. If you’ve seen the show we bet you got the joke!
This wasn’t the most brilliant idea, however, it was made by a Technology, not the human brain. And it has some sense to a certain point - The Night King wasn’t killed by aliens or time-travelers, he didn’t fall off the cliff, there was nothing too unfitting everything happened in the setting. It was not completely made up, it was a logical solution that ML made based on real data. The machine learning technology shows stunning results in various areas related to analytics and computation. It even managed to write fanfiction, so to speak. Just imagine the optimization of your business with ML!
For example, machine learning analysis algorithms in the supply chain of retail business in the fashion industry can predict which color, fit, and design will be on-demand this season. This way, your losses from insufficient demand for the product may be reduced, and, even more, you’ll be able to make profits by understanding the current trends on the market.
But everything is not so simple, and machine learning also has its imperfections. According to Statista, the most impressive challenge in 2020 was scaling the software with integrated ML - this was faced by about 43% of all companies that have integrated the technology in their software.
Nevertheless, all these problems can be compensated by a successful technical partnership with the development company. They will take on all the challenges that business faces and adapt the technology to the market while optimizing your company’s workflow along with keeping the business at its maximum efficiency.
But what are the challenges of supply chain in particular? How can they be solved by machine learning? Read below to find out!
The challenges of supply chain optimization
Supply chain management has enough issues, which enterprises may face from time to time. These stumbling stones affect both business efficiency and continuity, push customers away, slow down work processes, and as a result, reduce your company's profits.
.1 Unstable demand
Your analysts are probably trying their best to predict the upcoming changes in the market, but they aren’t always doing it perfectly. Finding a unique niche that wouldn’t suffer in any crisis and won’t depend on clients’ demand is almost impossible. Therefore, this problem becomes vital when it comes to saving profits.
.2 Logistical uncertainties
These challenges may arise in the non-transparent supply chain. The person, responsible for logistical processes, may not know exactly what decisions they are authorized to make.
Besides, logistical uncertainties in the supply chain also include unpredicted cases at the enterprise which aren’t covered by insurance: unexpected orders, delay in delivery, equipment failure, etc.
.3 Poor planning
It can happen on the enterprise of any level since people can’t take everything into account, especially considering the amount of data that is involved in the supply chain. Resource planning can be a challenge, since sometimes you may experience the crisis, unexpected sales decrease, etc. Your challenges may be both oversupply of goods that aren’t sold out or, the opposite, an unexpected shortage of supplies.
Analytical instruments handle this list of challenges and do even more. Forecasting and improved planning can make you forget about some of the supply chain complications and hence, perform its optimization. But what analytical instruments do, machine learning does better. Not only because it is innovative AI models, but because it’s a new level of forecasting, planning, workflow optimization, and business processes automatization.
Supply chain optimization with the help of machine learning
According to Gartner, artificial intelligence is the first on the top-trend list of technologies that were used for supply chain optimization in 2019.
Machine learning in supply chain management changed the ways of how this process was done in a lot of enterprises. With this technology implemented in your business, you can have a list of benefits that will make your company more effective compared to what it has been before.
.1 Data gathering and analysis for the most accurate demand forecasting
The methods that most companies use today require human intervention. They don’t estimate the timelines of maximum market demand accurately enough, which has a negative impact on both the analysis speed and quality.
One of the best ways you can use machine learning for supply chain optimization is to make the forecasting more accurate. The technology can take into account large amounts of data, automatically process them, and show more reliable results than manual analytics. Aside from this, machine learning can analyze patterns of customers’ behavior and predict the market trends, which, in turn, enhances the effectiveness of such analysis. Hence, entrepreneurs who integrate this technology into their supply chain have more than real possibility to overcome the competitors by analytics of much better quality.
This, one after the other, may lead to the optimization of the customer service and expansion of the client base. Knowing how your clients behave, you can choose a unique approach to each of them.
Example: the situation with the demand in the market is different for every business. If the Ritz hotel lacks champagne, guests will be dissatisfied, the brand will face reputation losses, and eventually, it’ll lead to an apocalypse. But if there is a shortage of ham in Walmart, no one will be upset: it's better to have this kind of product redeemed sooner than to keep it in the oversupply warehouse.
If, say, you have a restaurant and your supply chain is not optimized by machine learning, you may encounter some mistakes in demand analysis. For example, you will run out of the product that was popular all week and you'll have in excess the one that is no longer bought. So some of the foods you’ve previously purchased will expire. Machine learning, which can analyze customer behavior, may help you understand where to invest right now. It's like a horoscope, only the predictions of machine learning do come true.
.2 Inventory planning
More thoughtful inventory management can be achieved through the quality demand analysis we mentioned above. This will allow you to predict in advance the decision to fill the warehouse or buy less of not on-demand goods. This will make significant optimization both to your supply chain and your business efficiency, reducing losses by up to 65%.
Machine learning in the supply chain will be able to balance out delay losses by providing a solution to optimize the chain itself. Companies using this technology can improve inventory management in the warehouse and optimize business efficiency.
Example: During the Olympics in Brazil (2016), there were difficulties with the slow passage of equipment across borders. The customs process took too much time because of paperwork even though the documents were prepared correctly. How can machine learning solve this kind of challenge in the supply chain? It can analyze the average time spent at the border by each product and thus give you a recommendation on when to send the goods and how to properly plan the supply chain.
.3 Ensuring full end-to-end supply chain transparency
As you can see above, supply chain transparency is also one of the major challenges this industry faces and should be considered during the optimization. About 69% of companies can't claim full transparency in their supply chain operations. Fortunately, this is one of those aspects that can be fixed with machine learning. With this technology, the supply chain will be transparent from the manufacturer to the customer.
With machine learning analyzing the full amount of data that the workflow produces, you can track the relationships between the different components in the supply chain. Thanks to this, you will be able to see the shortcomings or even to anticipate them preventing malfunctions and understand what processes need to be optimized in the first place.
Example: if your employees are unsure of what is happening at each stage of the supply chain, they cannot coordinate its effectiveness to achieve company goals. Someone responsible for one part of the job may make the potentially wrong decision, as the UK government once did with the construction of new fire departments (they’ve started the project even though there was no need for it). This solution returns to your business in the form of major losses for many years, while a single software solution would help to avoid these difficult situations.
.4 Qualitative analysis of the suppliers
Quite a few companies work with external suppliers, not everyone creates components for their products themselves. Analyzing your suppliers' products and their compliance with market requirements can be a critical factor in supply chain optimization and determining the quality of your final product.
The manual analysis may result in missing or omitting important data because of human factors. All this, as a result, can lead to failure in supply chain management.
By supply chain optimization with machine learning, you get a real opportunity to create a supplier rating by selecting the best of the best. Technology algorithms that analyze all the info about different suppliers will help you determine the quality of the delivered product, as well as minimize the possibility of fraud.
Example: imagine that you, for example, are manufacturing game consoles. You had your hopes put into the new supplier, thinking that with them your business will sky-rocket to success. You invested in this new product after looking through all the advertisements and reviews but when the time came you figured out that it was just a nicely done promo campaign. New inventory appeared to be much more expensive and had poorer quality than your previous one.
You could’ve omitted them by optimization of the supply chain with machine learning. This way you would predict the quality of this supplier's goods by analyzing how they deliver compared to other candidates you've had.
.5 Supply chain security
Using the complex software for supply chain optimization you automatically raise its security level. Without machine learning, you'll have to manually analyze everyone you give access to your data, as well as constantly check the security of your systems.
Using machine learning algorithms, you can do this automatically by teaching systems to evaluate those who request access and to analyze the data.
Example: In 2015, Amazon thought about acquiring a startup Elemental Technologies, which could help them in their work on video hosting, now known as Amazon Prime Video. The company hired an expert to check the security level of the startup, during which were detected failures in companies using Elemental Technologies chips. They could’ve done perfectly even without an expert if their supply chain had machine learning optimization. It would evaluate the security level and track the pattern of failures in companies using startup services faster than experts would do manually. This would have allowed them to work with access without worrying about data security.
So, to sum up, we want to say that machine learning in the supply chain can make a revolutionary breakthrough, with optimization of not only its speed and efficiency but also the safety and quality of the final product.
Of course, the work of technology has its shortcomings and challenges, but they can be compensated by cooperation with a reliable technical partner.
We believe that optimization of your business and innovative technologies implementation will give you a chance to outperform your competitors and become a market leader. And we would be glad to be the one who will lead you to success one day.