Machine learning is changing the future of supply chain management. According to a recent study by Mckinsey Global Institute, advanced AI technologies have the potential to unlock a global economic impact of $10-15T across all industry segments. Gartner recently projected that by 2020, 95% of supply chain planning vendors will rely on supervised and unsupervised machine learning for their solutions.
Increasing costs, Revenue losses, Bad customer service, and reducing profits are all By-product of operational inefficiencies. For the Supply chain business to survive in today’s competitive and complex market, Machine Learning (ML) and Artificial Intelligence (AI) are considered as the most promising technologies available.
The ability for the system to analyze the data, learn, and improve automatically from experience, without any programming is done through Machine learning. The future of supply chain technologies will be highly automated and highly responsive. Machine learning is used to identify missing, rogue, or duplicate data points and uses history and historical actions to correct the data.
Why and How does Machine Learning is ideally suited to transform supply chain management? The answer is such that Machine Learning algorithms can be best used to effect for detecting patterns and predictive insights. By doing so, the supply chain companies can forecast error rates, reduce costs, improve demand planning productivity, and increase on-time shipments.
Here are 5 ways that a Supply Chain Management can be transformed by Machine Learning Technology:
- Predictive Analytics
- Reducing Cost and Response Time
- Improve Customer Experience
- Scheduling Maintenance
- Fraud Prevention
- Predictive Analytics
Predictive analytics techniques allow organizations to identify patterns and trends hidden in their data to understand market trends, identify demand, and establish appropriate pricing strategies. A study by the Council of Supply Chain Management Professionals revealed that 93% of shippers and 98% of third-party logistics firms feel like data-driven decision-making is crucial to supply chain activities, and 71% of them believe that big data improves quality and performance. The predictive Analytics technique has the advantage of enabling real-time decisions based on statistical estimates of future outcomes. It has the potential to enhance strategic thinking and overall performance. - Reducing Cost and Response Time
An increasing number of B2C companies are leveraging machine learning techniques to trigger automated responses and handle demand-to-supply imbalances, thus minimizing the costs and improving customer experience. The ability of machine learning algorithms to analyze and learn from real-time data and historic delivery records helps supply chain managers to optimize the route for their fleet of vehicles leading to reduced driving time, cost-saving and enhanced productivity. Further, by improving connectivity with various logistics service providers and integrating freight and warehousing processes, administrative and operational costs in the supply chain can be reduced. - Improve Customer Experience
Machine learning techniques can be used to enhance the customer experience by improving supply chain visibility and achieve faster delivery commitments. The historical data from various sources are analyzed by Machine learning models along the supply value chain. Machine learning techniques, including a combination of deep analytics, IoT, and real-time monitoring, can be used to improve supply chain visibility substantially, thus helping businesses transform customer experience and achieve faster delivery commitments. - Scheduling Maintenance
One of the most exciting applications of this technology is proactive machine maintenance scheduling. Advanced ML algorithms study the signs of machine failure and predict in advance the breakdown or malfunction in the machines in the near future. By making this prediction, the ML algorithm lets the planners schedule downtime in advance before a breakdown occurs. Logistics and various other elements of the supply chain can also benefit from this technique - Fraud Prevention
Machine learning can reduce the potential for fraud in the supply chain in addition to reducing risk and improving product and process quality. Machine Learning algorithm provides insights that instantaneously reduce the risk of fraud.
Gartner predicted that at least 50% of global companies would be using Artificial Intelligence (AI) -related transformational technologies in supply chain operations by 2023. According to a Statistic survey, visibility is a significant organizational challenge for 21% of supply chain professionals. If you have had questions about machine learning within supply chain management (or) need to create a process for supply chain using ML technology, please reach us at [email protected]
Self-driving vehicles, mechanical associates, and robotized sickness determination are altogether the results of an arising AI insurgency that will reshape how we live and work.
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We have already seen and heard more about the impact of AI in different industries, like retail, logistics, healthcare, and so on.
Today we are going to discuss how AI is transforming the Pharmaceutical industry.
The use of technology in the pharma industry helps them to deliver safe and reliable drugs to market in time.
Statistics show that the biopharma market is projected to reach $10B BY 2024 (supplements101.ne).
The US and Asia is the largest pharmaceutical market globally.
New AI innovations, for example, Robotics and Microfluidics, blended in with Big Data Analytics, can fasten the drug development and approval process, thereby shortening the time to market frame.
The market value of AI in the healthcare industry is expected to reach 6 6.6 billion by 2021.
AI technology is also rapidly entering hospitals.AI applications are concentrated in three major investment fields: digitization, engagement, and diagnostics.Looking at some examples of artificial intelligence in health care, it is clear that there are tremendous advances in the inclusion of AI in medical services.Let’s explore some amazing applications of AI that revolutionize health care.Also Read: The Future of Healthcare Sector Will be Around “AI”Robot DoctorsNothing is more exciting than AI robots.
However, these are not human-like droids from sci-fi films.
the same year, the first semi-automated surgical robot was used to stitch narrow blood vessels up to 0.03 mm.Clinical DiagnosisAI algorithms diagnose diseases faster and more accurately than physicians.
They are particularly successful in detecting diseases from image-based test results.Late last year, Google’s DeepMind trained a neural network to accurately diagnose 50 types of eye diseases by analyzing 3D rental scans.
Some types of cancer, such as various types of melanoma, are difficult to detect at an early stage.AI algorithms can scan and analyze biopsy images, and MRI scans 1,000 times faster than doctors.
Understanding and intuition of machine learning can support our understanding of the challenges and solutions facing Google engineers while also opening our minds to the broader implications of machine learning.
The benefits of gaining a general understanding of machine learning include: Gain empathy for the engineers, who are ultimately trying to establish the best results for the users.Understand what problems machines are solving, their current capabilities, and the goals of scientists.Understand the competitive ecosystem and how companies use machine learning to drive resultsPrepare for what many industry leaders call a significant change in our society (Andrew Ng refers to AI as a "new electricity")Understand basic concepts that often appear in research (it has helped me understand certain concepts that appear in Google Brain research)Growing up as an individual and expanding your horizons (you might really enjoy machine learning!
)When the code works, data is produced; it is a rewarding and empowering feeling (even if it is a very humble result) Let’s have a look at these prominent machine learning tutorials.
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