How to improve your company’s supply chain with Machine Learning?
AI & Machine Learning
Both large corporations and enterprises from the SME segment are currently undergoing a real digital revolution. It is therefore not surprising that retailers and companies producing consumer goods are also intensively preparing for technological changes. In many organizations, technologies such as artificial intelligence (AI) and machine learning can revolutionize key aspects of operations, while facilitating redefinition of existing consumer experiences and the acquisition of new ones. By changing the front and back-office processes, retailers are able to better meet the growing demands of consumers and get profitable returns on investment. Using artificial intelligence mechanisms, companies now have 360-degree insight into their supply chain and a lasting impact on the following processes:
Availability of the products
Due to the huge variety of products available on the market, supply networks are becoming more and more complex. The need to coordinate deliveries with many subcontractors is currently one of the main challenges for demand planners. For this reason, many companies implement solutions based on artificial intelligence, to improve their ordering systems, ensure timely product availability and efficiently process orders. One example of this is to base the demand management on predictive algorithms that operate on large data sources.
Let us assume that a system based on AI which collects data such as visited websites or searched phrases predicted greater consumer demand for laptops. Thanks to this information, the manufacturer can increase production with certainty that they will find the demand for manufactured products. Transport companies know in advance how many trucks will be needed to deliver computers at certain times. The retailer knows that they need to order enough goods, increase the advertising and exhibition space on store shelves and prepare for a jump in online orders.
How can the system assume that more consumers intend to buy a given product? The model is similar for every company - based on the collected data (time spent on a given website, frequency of searching for a given word, recently purchased products, etc.) it is possible to create links, schemes and deviations from the norm. The analyzed data is used to determine the potential behavior of the client - on this basis, the system sends information about the need to increase the supply of products.
Logistics and delivery
To ensure timely delivery of undamaged goods, companies must implement solutions that will control and monitor the supply chain in a holistic way. Threats related to delivery, such as inefficient use of the fleet and negligence during transport, require monitoring both the delivery process and the current status of the shipment. In this case, AI helps to avoid wasting time and money and enables effective fulfillment of emerging customer expectations. Using IoT based systems, retailers have a real--time view of the current location of products and can compare planned and current logistics flows to respond quickly to unexpected conditions and departures from schedules.
In this case, AI activities are based on data obtained from sensors placed in intelligent machines and vehicles. The collected information is analyzed, and then any anomalies are caught. Thanks to this, it is possible to detect the problems of delivery at an early stage.
With AI-based platforms, retailers can automate inventory management which is an important part of reducing product losses, whether it's vandalism, theft, expiration of products or their deterioration.
To this end, some stores already use video-monitoring that sends the collected data to a system based on artificial intelligence mechanisms. In this way - by using machine learning processes - sellers can quickly detect which products are invisible or displayed incorrectly and quickly initiate remedial actions. Warehouse managers can be automatically notified about the need to properly organize or supplement shelves, thus ensuring effective satisfaction of customers' demand while limiting losses in available resources.
Today's IoT-based devices generate huge amounts of data with built-in sensors, which is an excellent opportunity to continuously use machine learning and transform this data into assets that create value for the enterprise.
Thanks to this information, retailers can set a detailed plan for the maintenance of equipment prior to their actual failure. Especially in the case of retail companies, the lack of maintenance of machines, or even their temporary failure may result in huge losses for the company.
Imagine a situation in which a van crashes. It entails many - costly for the company - effects, including failure to deliver goods on time, excluding the vehicle from the current schedule of work, the need to quickly find a replacement car for the driver and inform the warehouses of delays. Thanks to AI, this situation can be effectively avoided. It is sufficient that the sensors embedded in the car will send the information about the current condition of the parts in the vehicle to the system. With the help of machine learning algorithms, it will be possible to catch any anomalies and provide the central office with the information about the impending need for a repair, automatic booking of another car for the driver and re-planning the delivery. As a result, before a failure occurs, the vehicle will be repaired, used parts replaced, and the delivery process undisturbed.
This example is one of many possible applications of AI and machine learning in predictive maintenance of equipment. Similarly, artificial intelligence can be used to maintain any other infrastructure. According to estimates by Manufacturing Business Technology, predictive maintenance of AI-based equipment could bring companies $ 360 billion in savings over the next 14 years.
According to the Walker Research's "Customer 2020" report, by 2020 the customer's expectations and experience during shopping will become a more important criterion for consumer decision-making than price. At the same time, using technologies that combine natural language processing with consumer purchase data and their history, it becomes possible to accurately profile each client and create an offer ideally suited to their needs. It is not surprising that companies are trying to use machine learning to provide automated solutions for customer service.
Thanks to systems that enable consumers to ask questions and receive accurate and timely responses, the response time is shorter and employees can devote attention to other tasks. In addition, food and drink producers, retailers and restaurants now use AI technology to monitor conversations about their products or services on social media. Modern platforms that use machine learning can effectively analyze consumer moods, to help companies make decisions about choosing the best products and creating offers for diverse clients.
In order to stay ahead of the competition, meet consumer demands and ensure greater efficiency throughout the supply chain, companies must take appropriate steps by introducing innovations and implementing systems based on modern technologies. According to a report prepared by the consultancy company McKinsey, companies that actively implemented the AI strategy had a 5% higher profit margin than those not using artificial intelligence.
With the right digital strategy combined with tailor-made tools such as artificial intelligence and machine learning, these companies can be more successful than ever before.
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