As the world economy and consumer needs evolved, so did supply chains across the globe. Currently the human population and their purchasing capacity is at an all-time high. Online retailers have done a great job to make almost anything easily available with just one click.
To support this exponentially increasing industry, supply chains need a drastic breakthrough. A supply chain powered by artificial intelligence is a one-stop solution for all your supply chain needs. It can be hard to believe that AI can serve all aspects of something as broad as supply chain. We might not be able to discuss all the use cases, however, we compiled the top ten AI use cases for supply chain and logistics:
Overloaded inventory and an enthusiastic customer leaving the store disappointed are the worst sights for anybody who is responsible for inventory management. Ideally, the inventory capacity of retail stores should be exactly aligned with the demand of customers in a given time frame. But we don’t live in an ideal world and traditional demand forecasting tools lack the capability of taking into consideration fluctuating customer needs, supply situations, and changing demand patterns. But with AI-driven demand forecasting models, you can predict the demand with an accuracy of 90+%, so that situations of under and overstocking are avoided. With Demand ML built on Google, Cloud Pluto7 helped this retail to accurately predict the demand with 80% accuracy for a target set of products.
Gone are the days when markets were localized. Today we can enjoy tea from India sitting in San Francisco. An increase in supply chain efficiency also increased its fragility. Failure in one aspect can lose a customer forever. Modern supply chains face issues like oversupply, undersupply and much more which lead to plummeting profits. To make the modern supply chain defeat these demons, all the data has to act like one entity rather than separated into silos. AI-powered supply ML models deliver meaningful insights from your transformed data to better plan your supply operations. California Design Den (CDD) is reducing inventory carryover by 50% with the help of Supply ML a solution built by Pluto7.
How does running errands on the weekend feel? If you are a person who spends time planning before the execution, then chances are high that in order to save fuel and time you mapped your destination and tried to find the optimum route to visit those 4-5 locations. Imagine if instead of 4 there were 100! This is typically called the “Travelling salesman problem.” Whether it is a ride-share cab, a hauling business, or a grocery delivery, they all face TSP. It involves a huge amount of fluctuating data and demands high-speed analysis which might only be possible for superhumans. But they are fiction! In the real world, we have artificial intelligence and machine learning helping us with optimized logistics routes to deliver our products or services at maximum speed and economical cost. A major fruit retailer used Pluto7’s ML model built to improve the delivery of perishable goods by identifying the optimum delivery route for each target market
Growth is expanding beyond your comfort zone. With today’s technologies, globalization, and trade-friendly law, it has become easier to expand beyond the international border than at any time in the past. Every time a business expands its scope of operations or customer base, the supply chain becomes bulkier and more culturally diverse, which introduces language barriers and siloed data which could result in misunderstandings and inefficient business decisions. With NLP (Natural Language Processing), your team can process and decipher loads of foreign language data and can easily streamline those compliance and auditing actions which seemed impossible before due to the language barrier.
As mentioned before, the efficiency of the supply chain is directly proportional to its fragility. This implies that a small error at any point can translate into catastrophic consequences to your business. One such critical element is your selection of suppliers. For manufacturing and retail, a supplier is probably the first and most important aspect of the supply chain. Wrong decisions could translate into an expansion of lead time resulting in the inability of the business to meet their customer demand. And in the worst-case scenario, it can also mark the doomsday for the entire business. With AI and machine learning models deployed with the data from your supplier relationship management system, accurate predictions can be made on the basis of a credit score or recent interaction with the supplier and can allow you to make more accurate decisions for choosing the appropriate suppliers for the supply needs of the business.
Advanced logistics and sophisticated e-commerce have conditioned customers to expect their orders as early as possible. Apart from price, delivery time can either win or lose you a customer. Many factors can influence your shipment proceedings and heavy traffic could be one of them, which is the driver of wasting fuel, time and customer satisfaction. Artificial intelligence and machine learning can identify peak traffic timings and hot spots in advance so that you can avoid them and keep your customers happy. Machine learning models can analyze historic and external real-time data faster than any other mainstream navigation application, which makes it essential for today’s logistics operations.
Warehouses are a busy and tightly packed environment. Monitoring incoming and outgoing stocks is just the tip of the iceberg when it comes to warehouse management. A simple task of picking up a product from the shelf and giving it to the customer sounds like a no brainer in a small store. Now, imagine the same task has to be done for thousands of orders per minute with shelves spread across thousands of square feet. This raises the need for a solution that can supersonically process information such as quantity ordered and the location of the product in the warehouse and a lot more. With AI and IoT sensors you can execute warehouse operations with higher accuracy which will ensure maintaining stocks at the optimum quantity and keep your customers happy by delivering precisely on their requests.
An organization by definition is a group of people coming together with a common purpose such as business or government department. This implies that we still rely on the human workforce. A growing business will easily attract specialized talent and multi-skilled individuals. The problem may arise when this workforce has to be utilized in an optimum way to generate a maximum result. With AI-driven predictive analysis it becomes extremely easy to divide an employee’s time across workstreams for maximum efficiency. For multi-skilled employees, their bandwidth can be optimized to deliver on workloads, and fluctuation in business needs. For example, a marketer skilled both in email and affiliate marketing can be utilized only for email marketing for nurturing leads. A good real-world example of AI-driven workforce management is Pluto7 helping a renowned furniture retailer to predict store traffic and better utilize their workforce to deliver better customer experience. A similar application can be deployed to manage fleet or warehouse workforce in supply chain management.
There is a well-known saying chanted by sales folks “ You either sell or get sold to”. While most people see victory in selling, a few would win if they sold to at the right time. As a part of inventory management purchases should be made at the right time and for the right product making sure the cost is as low as possible. To fulfill these goals, usually two teams work on making the purchases, one creates the purchase orders and another approves it. Though this process overspending is avoided but it increases the time of purchase completion and hence the lead time. With AI-driven solutions and IoT environment creating purchase orders and their approvals can be automated as well optimized to reduce costs with better inventory management so that the business has regular supplies that are essential for the fulfillment of business goals.
Preventive maintenance at first glance is misunderstood to be useful only for manufacturing plants where machines are working to deliver the final product and any failure would result in slowing down the entire process. But when you view the supply chain from a broader perspective, it too involves lot of machineries. The logistics fleet and warehouse management AGV (Automated guided vehicle) can be few of many other machines that need constant maintenance for them to work at optimum efficiency. Imagine a truck in the fleet breaking down in the middle of a highway when you are already lacking on your lead time. Or an AGV smashing into shelves due to some error. With AI-driven ML models working in IoT environments, all such situations can be avoided by raising timely alerts for the maintenance of individual machines, saving you time and money.
So where should you start?
A typical supply chain has lots of moving parts and associated data silos. It is like a skyscraper where each brick, each column, each rivet is essential and they all are interdependent where one weak element can dethrone you as an industry leader. Currently, all supply chain management solution work in these silos, therefore, your starting point should be to unify all your data into one place, so that all the elements can be analyzed as one entity.
Google has been an unquestionable tech leader and has no intention to give up that title, their products are reliable. That makes Google Cloud the safest and the most robust location for creating your unified data lake. Migration could be a tedious process and that is where Pluto7 comes in to help you through your entire data journey. For any help regarding your supply chain or associated data management write to us at email@example.com or visit our website www.pluto7.com . You can also follow us on linkedin : Pluto7 to stay updated.
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