Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Logistics, distribution and the whole supply chain management (SMC) is an essential part of many industries globally, relying on its operation to function.
In this era of high supply risk, demand uncertainty, sky high gas prices, and increasing competitive intensity the distribution, logistics, and supply chain industry hinges on their organisation’s ability to apply and integrate processes that will enhance their speed, reliability, and profitability.
Given the complex and fast-paced nature of supply chain management, there is an increased need to better manage data and information to leverage and make better business decisions. In fact, it is in a significant position to invest in artificial intelligence and utilise its capabilities to improve business outcomes. The large amount of data collected by the industry in warehousing, transportation, and logistics, has a lot of potential for use cases and adoption of AI in supply chain management and distribution.
AI in logistics and supply chain enables smart machines to perform tasks, automate processes, and analyse data to provide actionable insights to either improve processes or uncover opportunities to improve profitability.
This aims to provide instruments to provide uninterrupted real-time data flow from IoT devices, connect all data points to extract insights for better decision making, and establish intelligent and smart assumptions through advanced analytics and predictive modeling.
Trends Accelerating use of AI in Supply Chain and Logistics
Big Data
Over the last several years, supply chain and logistics companies have collected a vast amount and variety of data from operations, customers, and suppliers. This data and the fast-paced flow of information is what fuels machine learning and artificial intelligence to achieve its potential to help improve the industry and SMC operation.
Computing Power and Speed
Significant breakthrough in processing power and efficiency in computers is accelerating the adoption of AI in different stages of operation in an SMC.
Algorithmic Advancement
Advancement in detection of patterns and discovery of correlation in fueling the adoption of AI in logistics and distribution. Smart algorithms for example offers valuable information to the truck fleet requirement for deliveries ahead of time so the organisation can prepare and customers will have an approximate price and delivery time frame.
Use Cases
Demand Forecasting for warehouse supply and demand management
Identify patterns and factors in supply chain data providing warehouse managers the capacity to make data-driven decisions about inventory stocking. Predictive analysis provides deep levels of insights and forecasting loop.
Production planning and factory scheduling
Using AI predict demand and optimise the flow of critical parts to keep production moving smoothly. Analyse possible constraints and optimise for them especially for custom and build-to-order production. AI also helps business reduce supply latency for parts utilised in highly customised or most popular products.
Increasing efficiency and profitability of loading process
SCM requires detail-oriented analysis including techniques in loading and unloading goods to determine the most efficient and fastest means to get goods on and off delivery vehicles – aircraft, ships, and trucks.
Optimisation of route efficiency and delivery logistics
AI-optimised routes and GPS tools provide logistics companies the most efficient routes the ensure timely deliveries to customers and clients.
Improvement of Health and Longevity of Transport Vehicles
IoT devices data and in-transit supply chain vehicles information provide data on the health and longevity of expensive equipment and transport vehicles used in moving goods and supplies. Machine learning makes failure predictions and establish maintenance recommendation to keep the vehicles in tip top shape.
Simplified Inventory Management
Efficient and well-organized inventory management is the foundation of a supply chain. AI help minimise manual input, create accurate forecasts, analyse and interpret real-time data thus providing accurate inventory.
Uncover cost savings and revenue optimization
Moving goods is becoming increasingly expensive. Bloomberg reported that in 2020 shipping increased by 12%. AI help organisations negotiate better shipping and procurement rates, pinpoint better delivery options for higher profit, and improve carrier contracts.
Improved Customer Experience
Personalised customer experience is driving the differentiation for delivery companies. Apart from traditional parcel tracking, there are voice-based service shipment tracking as well as pro-active customer service assistance should there be delays and issues.
Benefits of AI in Supply Chain Management and Logistics
- End-to-end visibility enhanced with near real-time data
- Predictive Analytics
- Actionable analytic insights based on pattern identification, at scale, far exceeding the abilities of conventional supply chain systems
- Reduced manual human work
- Informed decision making augmented by machine learning (ML), AI-driven predictions and recommendations based on analyzing multiple potential scenarios
- Production planning and scheduling
- Improved supplier selection and supplier relationship management
- Improved customer experience
Improved Customer Experience
Personalised customer experience is driving the differentiation for delivery companies. Apart from traditional parcel tracking, there are voice-based service shipment tracking as well as pro-active customer service assistance should there be delays and issues.
