Logistics and retail are connected together in a supply chain. It takes careful planning and timing to ensure there is no disruption or overstock and that a fleet is utilized to its optimum capacity for logistics. It goes even further in retail where one must keep track of products, competition and customer preferences based on analysis of real time data and historical data. This is where artificial intelligence development can transform both retail and logistics, helping companies gain over $ 1.3 trillion per year according to estimates.
AI transforms retail
Retail operators must always balance inventory with demand on the one hand and keep customers happy with exemplary services at all stages of the journey. Integrating artificial intelligence development into sales, CRM, customer recommendations, and the tracking and supply chain works wonders. Real time big data analytics deliver granular predictive capabilities such as being able to predict customers’ changing preferences, seasonal trends and external influences. AI in CRM helps derive intelligence on customer behaviors and preferences, thereby leading to opportunities to cross sell or to address grievances and engender loyalty and deliver happy experiences. Pepper, a humanoid robot, works as a customer greeter in over 140 Softbank mobile stores. When introduced to stores in Santa Monica and Palo Alto, it led to an increase of 70% in foot traffic.
Moving on to the backend operations, artificial intelligence in retail warehousing and logistics yields considerable improvements. AI combines with robotics into cognitive automation. An automated system keeps tracks of goods in the warehouse, robots locate track and pick inventory for shipment, resulting in accuracy and reduced load on employees. The system loops into sales giving customers and sales managers precise status details. AI does not end there. It reflects in logistics too.
Artificial intelligence in logistics
GPS, IoT and sensors and fast internet as well as reduced cost of artificial intelligence development radically change the way logistics operate. On one side you have fleet management in which AI plays a crucial role in analyzing requirements, predicting changes in demand in various regions and helping fleet managers assign vehicles. It helps in air freight too with DHL being a prime example of how AI with machine learning helps predict transit time a week in advance. Its system monitors online and social media posts to pinpoint supply chain problems and understand sentiment of conversations to improve supply side operations.
An Australian fleet operator serving a petroleum company uses a centralized AI system that tracks each vehicle and each driver with up to the minute information on the condition of each truck, routes, how long it takes to transit, fuel consumed and other parameters. Use of sensors to check tires, parts of the truck and engine helps in predictive maintenance, reducing on road breakdowns and increasing driver safety. The fleet operator can promise a delivery and keep it.
Speed and agility
Retail is highly dependent on logistics to thrive in an extremely competitive market. The scenario is complex with retail operators stocking goods in their warehouses that may be located in different areas besides arranging to pick up from specific stores spread across various cities.
Manual operations would lead to unacceptable delays whereas an artificial intelligence powered system handles all the data about product availability, location and logistics, giving prospective buyers precise information leading to a buy decision. Cognitive automation takes care of all data, identifies bottlenecks and delivers real time accurate information about availability and delivery.
Amazon is a prime example with its product page giving visitors information on availability, price options and expected delivery time for their location. This would never be possible with standard IT solutions and just not practical with manual operations in this day and age. And the good thing about such a system is that it does not become obsolete; constant machine learning makes it smarter over time.
Where do you start?
It is one thing for Amazon and DHL to implement AI. They have bulk operations to support the investment in artificial intelligence solutions and it is necessary but what about retail and logistics that operate on a smaller or localized scale. They could rope in artificial intelligence development services for specific areas of their operations that will lead to a marked improvement and cost reduction.
Ecosmob, an artificial intelligence solutions company, is of the opinion that medium sized and smaller retail stores can start by introducing artificial intelligence into their inventory systems and thus avoid one problem which is that of dead stock that must be disposed of at throw away prices.
Then there are trucking operators who could benefit by the use of AI to track each truck and driver in one area, so as to know precise location. In another area, AI can be used to derive information about fuel consumption, miles traveled, duration, routes and engine performance that would lead to reduction in costs of repairs. A lot is possible, according to Ecosmob, but the important thing is to get started and progressively implement AI to cover end to end operations in a phased manner.