Machine Learning in Supply Chain
Machine learning has been one of the most promising technologies due to its multiple capabilities important for business success in terms of making accurate predictions, recognizing patterns, to name a few. Machine learning is applicable in training computers so they could imitate human thought processes. It leads to generating accurate solutions quicker even when faced with huge amounts of data. Machine learning begins with two sets of data: Training Data and Test Data. Training data is used for teaching the machine in order to review the data's correlations while creating a mathematical model afterward. Test data is the dataset needed to be analyzed as it includes unknowns that must be understood for further evaluation.
The modern supply chain is becoming more complex by the day. Businesses continue to struggle with keeping their supply chain under control, but hidden risks still pose a significant threat to the industry. Even with all the new technologies making their way to the industry, businesses must be aware of these hidden risks and understand how to react appropriately. supply chain management can potentially face several challenges such as:
? Fluctuation in demand
? Inadequate inventory planning
? Backlogs of orders
? Uncertainties in logistics
? Communication gaps within the supply chain
? Shortages in supply
Advanced technologies like machine learning as a branch of artificial intelligence is the optimal solution in addressing these business issues across various industries. it is important to know that though Machine Learning is versatile, it is not a 'general purpose' solution that can be used in all data. Instead, machine learning can only successfully work in cooperation with skilled data scientists and business leaders for accurate data selection and validation.
Implementing AI and machine learning algorithms in the supply chain for your business proves to be a success in the following cases.
Transportation Management - Companies actively acquire Transportation Management Systems to promote freight savings and provide a more competitive service while determining the impact on performance.
Machine learning gives companies the opportunity to access the potentially insightful data and spot the answer to the questions pertaining to company's performance:
? Are service level standards met in terms of delivery and schedule?
? Which lanes are associated with more delays in the service?
? What are the 'stops' that cause delays to shipments?
Having all this information, the company can find solutions to conflicts in the future as machine learning promotes high service levels and a better understanding for shippers on how to deliver results efficiently.
Warehouse Management – Machine learning provides more accurate inventory management that helps predict demand of growth and its drops. Machine learning is used in warehouse optimization assisting in detection of excesses and shortages of stocks in your store on time. This is essential in preventing sales losses due to the ability to pinpoint familiar patterns, inspect storage and check the inventory every now and then in a more accurate way.
Supply Chain Planning – Using machine learning in supply chain planning makes decision-making processes optimized through the application of AI algorithms from analyzing massive data sets. It leads to ensuring wider planning functionality, producing accurate results and making it a powerfully reliable tool in your business.
Demand Prediction – Machine learning-powered demand prediction algorithm provides a more improved demand forecasting function. By analyzing customer behavior tendencies, businesses can easily match potential buying habits and shape the customer portfolio with precision. With predictive analytics in the supply chain, businesses are able to control manufacturing and logistics in the prevention of supply shortages and excesses.
Logistics Route Optimization – It is important to incorporate machine learning for route optimization which analyzes existing routes for faster delivery of goods. Enabling this function also prevents delays in delivery and helps enhance customer satisfaction.
Workforce Planning – By using existing production data, machine learning is capable of creating a more appropriate environment that can naturally adjust to various condition changes in the future. It is applicable in recruitment, retention, employee development, and performance management. Automation of the processes in gathering data, making inferences, and generating ready-to-use insights can be done when machine Learning is utilized in workforce management. Thus, managers get the reliable tools for maximizing the overall workforce performance.
End-to-End Visibility – Machine learning algorithms play a key role in providing end-to-end visibility from suppliers and manufacturers to stores and customers and eliminating the probability of conflicts as the technology can accurately identify inefficiencies that require immediate response. There's a huge amount of data involved with a wide network of IoT sensors in combination with advanced analytics. With the use of machine learning to analyze this data, hidden interconnections between various processes in supply chain management can be discovered without fail.
Top Companies using Machine Learning Technology in their business.
Baidu
IBM
Machine learning is gaining such momentum that the total funding allocated to ML worldwide during the first quarter of 2019 was $28.5 billion. Moreover, it's been found that 49% of companies are exploring or planning to use ML.
According to research:
One-third of IT leaders intend to use ML for business analytics.
25% of IT leaders plan to use ML for security purposes.
16% of IT leaders want to use ML in sales and marketing.
With these mind-boggling statistics in mind, businesses are forced to dive deeper into the concept of ML and learn how this technology can help them remain relevant.
Applications using Machine Learning
Image, Facial and Speech Recognition
Many companies' core business today is based on machine learning and image/speech recognition. Google, for example, uses ML in image recognition for Google Photos and speech recognition for Google Home and Google Assistant.
Another company that heavily relies on ML is Apple. Today, millions of people talk to Siri, Apple's own virtual assistant. The company has recently extended the application of its virtual assistant through its newer product HomePod, a smart home device. Additionally, Apple has been investing in ML startups, such as Vocal IQ, a platform for voice interfaces, and Emotient, a leader in emotion detection.
Apple has also been using deep learning for face detection since 2016 with the release of its iOS 10. And with the release of their Vision framework, iOS developers can use the technology in their apps.
Personalization & Search
In the last few years, forward-thinking companies have completely changed the online experience. Thanks to businesses such as Netflix and Spotify, customers can now enjoy the ultimate customer experience, one-on-one personalization. Personalization is an essential ingredient of Netflix. It allows each subscriber to have a different view of the content. By using a range of machine learning and recommendation algorithms, the homepage adapts to the subscriber's interests and can help expand their interests over time. They are continuously conducting online A/B testing and offline experiments to improve subscribers' unique experiences further.
Spotify is another company that relies on personalization. Up until recently, Spotify's curated playlists didn't include any personalization. There was one official playlist, and it appeared on everyone's screens. However, now Spotify is making those playlist part curated and part personalized. Meaning, human editors will pick and choose which songs are perfect for which list, but not every song will appear to every listener. Instead, Spotify will automatically adjust the playlist to each listener's preferences.
Customer Service
Customer service is critical for the success of any business. Why? Because it's an essential ingredient for retaining customers and increasing profits. In fact, one study has found that an increase in customer retention of 5% can lead to an increase in profit of at least 25%.
We can see many companies using support-focused customer analytics tools enabled with machine learning to provide a higher level of convenience for their customers. One such company is Zendesk.
The company's customer service focuses on self-service. Why? Because according to research, 81% of customers prefer to help themselves rather than speaking to an agent. Thanks to the advances in ML, their customers can now get support from chatbots, virtual assistants, and other AI-enhanced tools. These AI-enhanced tools can simulate conversations with customer service agents and help customers find more accurate and helpful answers.
Zendesk has also been using ML to aid with content creation. It analyzes the data that comes in from support tickets and uses that to create actionable tips in the form of help articles. Additionally, it's been using machine learning to add a predictive element to some support analytics. By using predictive customer service analytics, the company can use the previous data from its users to determine what the quantitative results may be in the future.
Security and Fraud Detection
In the last decade, there's has been an explosion in the number of people entrusting companies with their personal data. However, there also has been an explosion in the number of data breaches and hacks.
In 2016, 3 billion Yahoo accounts were hacked in one of the largest breaches of all time. In that same year, Uber reported that hackers stole the information of over 57 million riders and drivers.
With its power to analyze millions of files, companies have been heavily investing in machine learning to help them uncover threats and automatically get rid of them before they can wreak havoc.
Cisco is one of those companies. They've managed to significantly improve their security and fraud detection by using the machine learning and analytics engine called Cognitive Intelligence. The engine it's used to detect threats on a network by continually monitoring the behavior of the network for anomalies. In addition to detecting threats, it can also offer endpoint malware protection by analyzing the attributes and behavior of older malware.
Another company that's been making smart investments in machine learning is American Express. As it has 100+ million cards in operation and processes $1 trillion transactions, they rely on data analytics and machine learning algorithms to detect fraud in real-time. By doing so, they're saving millions of dollars in possible losses.