Supply chain: A leg up with AI
The digital evolution of SCM
In the current era, the supply chain encompasses the whole world. The entire process from raw material sourcing to product delivery is complex, involving a network of individuals and organisations, and collaborations between them. And, when the process is complex, the challenges are also daunting - from sourcing raw materials and manufacturing to distribution, compliance, and security, with many other in-betweeners.
Till the big-data uprising, large applications, which were centralised to specific domains of supply-chain management such as manufacturing and logistics, were prioritised for digitisation.
However, in the present age of artificial intelligence (AI), supply chain has become more dynamic, making organisations richer. Most of the AI-driven revenue in businesses is from supply chain and operations.
AI granted boons for SCM
Reduced operational costs
Enhanced overall productivity
Accurate demand forecasting
Improved production planning
Effective supplier selection
Any supply chain involves apparent costs such as production and transportation expenses, as well as obscure costs that include spending in sorting parcels, identifying damaged goods and other such related activities. The operational costs, though obscure, are significant. A tab needs to be maintained on them, and, where possible, curtailed too. AI comes as a blessing for reducing unnecessary operational expenditure.
By employing Intelligent Robotic Sorting, letters, parcels and palletized shipments can be sorted in an agile manner, more effectively and with high accuracy.*
Also, AI-Powered Visual Inspection spots damaged goods by reading photos of cargo that the tool takes using special cameras. Additionally, it also identifies a suitable corrective action.
AI’s ability to process and analyse large volumes of data is unparalleled. It can measure and determine factors affecting the supply chain’s performance, which is an important step in improving productivity.
For example, AI-enabled tools can detect fraud, make accurate forecasts, optimise transportation and facilitate real-time decisions, among numerous other things. They incessantly monitor, analyse, identify and resolve issues.
IBM’s Watson and One Network’s Neo are examples of AI-enabled supply chain assistants that use AI to boost insights and productivity in supply chain management.
Demand forecasting is a nightmare for any supply chain professional. It is full of uncertainty, with all influencers constantly changing. Thanks to AI, these factors can now be tracked and measured. This way, demand can be predicted with greater accuracy and on a real-time basis. The forecast gets continuously updated based on real-time data on sales, weather, customer preferences, logistics and other factors.
This is closely related to demand forecasting. Before AI technology took centre-stage, production planning and factory scheduling were not managed very efficiently. However, AI is changing this scenario for the better, particularly for make-to-order products with specific part requirements and configurations.
For example, along with demand forecasting, AI can calculate the need for required parts and optimise their flow. This moves production smoothly and reduces supply chain delays.
Imagine the damage to a company’s reputation if the consumer received a defective product, resulting from faulty raw material. It is a fact that supplier-related risks are a significant concern for logistics professionals.
With AI, it is possible to evaluate suppliers based on all the influencing factors and use this information to choose better suppliers in the future. To put it simply, no more putting the company’s reputation at stake.
Consider this - you want to track an order placed on Amazon, which has already been shipped. You can now do it on Amazon’s site using Echo, thanks to AI.
Asking Alexa is all that you have to do. “Alexa, ask DHL Express where my package 1234567891 is. ” Or, “Alexa, tell DHL Express to track my shipment.” Additionally, In case of an issue with the shipment, users could ask to be redirected to the customer assistance department of DHL.
The future: More power to edge ecosystems
AI has pervaded almost all industry verticals. It is considered somewhat of an elixir to all challenges faced by businesses — any problem, check for AI solutions. Be it meteorology, medical or food, no industry is left untouched by AI. And all these businesses need an efficient supply chain management system.
Even with many facets of the supply chain already being positively impacted by AI, there are areas for improvement. One such is ‘edge ecosystems’, that is, physical locations like distribution centres where data originates from operators, devices, sensors etc. In the future, a lot of AI-enabled supply chain decisions will be made at ‘edge ecosystems’.
It appears that ‘edge ecosystems’ are ideally suited to streamline workflows if they are enabled with data processing and communications abilities since they have access to real-time information.
*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.