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Home > Blogs > 6 Ways AI Is Making Supply Chain More Seamless (Supply Chain aka Logistic Industry)
Artificial intelligence (AI) is here, growing and making machines smarter with each day passing. The acceleration we have seen in recent years shows no sign of slowing down. In fact, AI has come roaring out of high-tech labs to become something that we use every day without even realizing it.
Whether it’s any voice-assistant like Alexa booking a cab for you, or the algorithm deciding what news you’ll find most suitable, AI and machine learning stand to benefit many of the world’s biggest businesses, including supply chain & logistics also. According to the report named State of Artificial Intelligence for Enterprises, from Teradata, Supply chain and operations was one of the top three areas where organizations are driving revenue from AI investment.
Furthermore, Forbes Insight Research says, for logistics, supply chain, and transportation, it is an “era of profound transformation”. And, advancements in technology like AI/ML, is among one of the four main transformative forces.
But, why Artificial Intelligence and Machine Learning for supply chain and logistics?
Let’s see some trends accelerating the use of Artificial Intelligence and Machine Learning in Supply Chain management.
1. Big Data
AI and ML require a significant amount of data to show its full power. Logistics, supply chain, and transportation companies produce and use lots of data i.e. big data, from various sources. Moreover, in the past several years, many new types of data have emerged. With an ever-increasing pace of data creation, these companies supply enough data to AI/ML to work to its fullest potential.
2. Computing power
In terms of processing power and efficiency, one of the major breakthroughs in the field of computing, was the development of Graphical Processing Units (GPUs). Such developments enable supply chain and logistics companies to incorporate AI/ML into their operations because the latter one needs such high computing powers.
3. Smart algorithms
In recent years, we witnessed a huge advancement in Machine Learning algorithms. These advanced algorithms can be used to tasks which are impossible for humans or conventional technology alone. For example, AI/ML algorithms can provide valuable information such as the number of vehicles available for delivery ahead of time so the customer can know the price and approximate time frames for future deliveries.
The above trends prove to be the driving progress in AI/ML and making them an increasingly viable technology in almost every field.
Augment or automate?
In Supply Chain Management, there can be many technical applications of AI/ML, but the most common outcome is to either-
a) Augment human decision-making or
b) Automate human decision making
Augmentation of human decision-making, by using AI/ML, generates insights as well as recommended actions for business users but leaves it to the human to analyze, approve, and execute those insights and recommended actions. For example, in order to improve order delivery and service, AI augmentation capabilities can be used to determine the best route a company should take.
Automation of human decision-making, on the other hand, by using AI/ML, generates insights as well as recommends actions but approve and execute them without human intervention. For example, through self-learning and natural language processing, AI/ML can help automate various cumbersome and time-consuming supply chain processes such as demand forecasting, production planning.
To take it a step further even, in this article, we are going to discuss 6 ways AI/ML is making supply chain seamless. Curious to know! So, let’s get started.
1. Streamlining procurement related tasks
The evolution of procurement means that the future of the supply chain is no more limited to ensuring reduced price and timely delivery of goods but also about being mindful of an increasingly complex landscape dominated by legal issues, sustainability concerns, and regularity as well as ethical considerations.
On this note, the procurement leaders or practitioners now must role-play guardians and advocates for their businesses. They would need to acknowledge technology as one of the biggest challenges to disrupt the current supply chain. The reason being, where do we stand in technology today would determine our coping mechanism.
AI/ML is here to rescue, and the entire practice of procurement can run on basic commands of these technologies. Chatbots, through automation and augmentation, helps in streamlining the procurement related tasks.
As of daily procurement tasks, chatbots could be utilized to-
· Communicate with customers during trivial conversations.
· Place purchase request.
· Receiving, Filling, and documentation of invoices.
· Research as well as answer internal questions regarding procurement functionalities.
2. Improving Supply Chain Planning (SCP) using Machine Learning (ML)
Time, cost, and resource constraint-based are some of the major challenges in supply chain planning (SCP) making ML an ideal technology to solve them. Why? Because ML algorithms and models are based on excel at finding anomalies, pattern and predictive insights in large data sets.
ML applied within SCP could help with forecasting of inventory based on demand and supply so that you don’t lose sales due to products not being available. Moreover, facilitating the machine with the right segments of data could revolutionize the agility and optimization of the decision-making processes in supply chain planning.
For example, DHL relying on ML to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify top factors influencing shipment delays.
3. Reshaping warehouse management by improving demand forecasting accuracy
Ask any supply chain or logistics professional and he/she will tell you that the success of Supply Chain Planning (SCP) is heavily reliant on proper warehouse and inventory-based management. Before technological advancements like AI/ML, the technologies couldn’t deliver value in supply chain management because they didn’t consider the factors like demand forecasting. Regardless of demand forecasting, supply flaws like understocking or overstocking can also be a disaster for any logistics company.
ML provides an endless loop of forecasting, which bears a constantly self-improving output based on real-time sales, weather and several other important factors. Having all such capabilities and information, ML could reshape warehouse management, the one we know today with self-driving forklifts, automated sorting, and self-managing inventory systems powered by drones as well as AGV (autonomous ground vehicles).
For example, Amazon has already forged a path in this area by using Kiva systems with its highly automated distribution centers.
4. Streamlining foreign language data using Natural Language Processing (NLP)
In this digital era, one of the key issues in supply chain management is Globalization which presents several critical supply chain management challenges to enterprises and organizations. The language barrier is one of the most critical challenges of Globalization. Moreover, supply chain encounters huge amount of data, it’s important to sanitize this data before it can be used to make important business decisions.
Natural Language Processing (NLP), an element of AI and ML, has the staggering potential for deciphering foreign language data in a streamlined manner so that it can be used for data processing.
Let’s see some other benefits NLP can provide to supply chain:
· Understand and mitigate potential risks with suppliers, manufacturers, and various other supply chain stakeholders by analyzing reports, industry news, and social media posts.
· Ensure compliance with sourcing and ethical practices by monitoring information for potential breaches by supply chain stakeholders.
· Improve information received from supply chain stakeholders through chatbots.
· Optimize the supply chain through querying large & complex datasets using natural language.
5. Enhancing customer experience through voice assistants
AI/ML can also be seen changing the relationship between logistics providers and customers by personalizing them through voice assistants. One such great example is Amazon’s Alexa-powered Echo which can help customers track their orders placed with logistic partner DHL.
The delivery company is offering a voice-based service to track parcels and get shipment information by querying Alexa. In case the customer is facing an issue with their package, Echo users can also ask DHL for assistance.
6. Machine Learning for Supplier Selection and Supplier Relationship Management (SRM)
Supplier selection and sourcing from the right supplier is the key to a sustainable supply chain, Corporate Social Responsibility (CSR) and supply chain ethics. For a globally visible brand, supplier-related risks have become 'ball and chain'. If you make one wrong choice in the operations of a supplier body, your business can suffer.
Here, machine learning can help you. Machine learning and intelligible algorithms provide the best prediction regarding supplier selection and risk management, during every single supplier interaction, by analyzing data sets generated from SRM actions (for instance supplier assessments, audits & credit scoring, etc.).
With the help of ML and intelligible algorithms, passive data related to supplier selection could be made active and useful for business.
And that’s a wrap! Hope this blog post proves to be insightful for you! Want to build a successful career & upskill yourself in Machine Learning? Have a look at Manipal’s Artificial Intelligence & Machine Learning course here!