“Supply chain is like nature, it is all around us” – Dave Waters

Mcdonalds sells more than 75 hamburgers every second. As simple as it may sound, but a hamburger needs various ingredients: bread crumbs, ham, vegetables, oil etc. Imagine going and collecting each ingredient from the source and getting it supplied with an intelligent supply chain system. Probably we would have pre book our burgers 2 months in advance. This was about the consumers, imagine a world without supply chain for retailers, manufacturers and service providers. 

A world without supply chain management or the concept altogether will result in sinking of most of the businesses. Manufacturing, retail or even healthcare heavily rely on sophisticated supply chains. Without raw materials there will be no consumer goods to meet the increasing demands. An emergency ward in any hospital would witness the biggest nightmare without adequate supplies of medicines, oxygen cylinders etc. And this is just the tip of the iceberg.

As the world economy and consumer needs evolved, so did supply chains across the globe. Currently human population and their purchasing capacity is on all time high. Internet and e-retailers have done a great job to make almost everything easily available with just one click. But to support this exponentially increasing industry, supply chain needs a drastic breakthrough. And a supply chain powered by artificial intelligence is a one-stop solution for all the supply chain needs. It could 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: 

 

  • Demand forecasting

An overloaded inventory and an enthusiastic customer leaving the store disappointed are the ugliest 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 in consideration the fluctuating customer needs, supply situations and changing demand patterns. But with AI-driven demand forecasting models, one can predict the demand with an accuracy of 90+%, so situations of under and overstocking avoided. With the demand ML model built on Google, Cloud  Pluto7 helped this retail to accurately predict the demand with 80% accuracy for target set of products.

  • Supply forecasting 

Gone are the days when markets were localized. Today we can enjoy tea for India sitting in San Francisco, credits goes to the supply chains. But an increase in supply chain efficiency also increased their fragility. Failure of one element can make you lose a customer forever. Modern supply chains face issues like oversupply, undersupply and lot more which lead to plummeting profits. To make modern supply chain defeat these demons, all the data has to look like one entity rather than 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 solution built by Pluto7.

  • Logistics route optimization (TSP)

How does running errands on the weekend feel like? If you are a person who spends some time on planning before 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 they were 100 !! This is typically called a “Travelling salesman problem” . Whether it is a sharing cab hauling business or grocery delivery one, they all face TSP. It involves 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 ML model built by Pluto7 to improve the delivery of perishable goods by identifying the optimum delivery route for each target market

  • ML for foreign language data analysis

Growth means 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 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. Resulting in 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. 

  • Supplier selection and relationship management

As discussed 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 the 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 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 on your data from supplier relationship management system, accurate predictions can be made on the basis of let’s say credit score or recent interaction with the supplier and can make you make more accurate decisions for choosing appropriate suppliers for your supply needs of the business. 

  • Peak hour predictions in logistics

Advanced logistics and sophisticated e-commerce have conditioned customers to receive 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 in today’s logistics operations. 

  • Warehouse monitoring and planning

Warehouses are 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 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 demand for a solution that can supersonically process information such as quantity ordered and location of the product in warehouse and a lot more. With AI and IoT sensors you can execute warehouse operations with high accuracy which will ensure maintaining stocks at the optimum quantity and keep your customers happy by delivering precisely on their requests.

  • Workforce planning

An organisation 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 a  human workforce. A growing business will easily attract specialized talent and multi-skilled individuals to work for it. The problem arises 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. 

  • Automated purchase orders

There is a famous saying chanted by sales folks “ You either sell or get sold to”. While most people see a victory in selling, a few would win if they get 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 least as possible. To fulfill these goals usually two teams work on making the purchases, one creates the purchase order and another approves it. Though this avoids overspending, 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 cost reduction and better inventory management making sure the business has regular supplies that are essential for the fulfillment of business goals.

  • Preventive maintenance in supply chain

Preventive maintenance as 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 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 highway when you are already lacking on your lead time. Or an AGV smashing into shelves due to some errors. With AI driven ML models working in IoT environment, all such and many more hazardous situations can be avoided by raising timely alerts for maintenance of individual machines, saving time and money for you. 

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 any supply chain management solution works in these silos, therefore, your starting point should be to unify all your data in one place, so that all the elements can be analysed 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 as the safest and the most robust location for creating your unified data lake. It 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 contact@pluto7.com or visit our website www.pluto7.com . You can also follow us on linkedin : Pluto7 to stay updated.