Rarely has a crisis accelerated the adoption of a technology in the manner that is occurring today with AI in the food industry. The business of selling food to consumers is being disrupted to a degree not since the last pandemic, over 100 years ago. It is increasingly apparent that our food system was ill prepared ('anti-fragile') for this Covid-19 induced crisis. With restaurants shuttered, a dramatic return to home cooking, a re-ignition in the meal-kit movement, shut-downs of meat factories and office canteens, and explosion of home delivery it may seem as though the world will never be the same again. This too, of course, will pass, but instead of being a 6 month blip, the continued deconstruction and automation of the food supply process makes it clear that we are entering a new norm, and that returning to the world as we knew it won't be possible.
Artificial Intelligence is benefiting to various industries including healthcare, education and manufacturing. But what is Artificial intelligence (AI)? In Layman language, a simulator of human intelligence, which makes the decision after analyzing various data utilizing a collection of different intelligent technologies including machine and deep learning, analytics and computer vision. The fourth industrial revolution is employing AI to enhance its overall efficiency. The technology is not only helping to reduce manufacturing cost as well as it is improving productivity and quality. Manufacturing is a capital-intensive process, and once a plant is a set-up, replacing, removing or renovating is exorbitantly expensive. New machines improve performance; reduce redundancies, while improving overall quality metrics. AI is proving an alternative route to achieve all this and at extremely competitive price points. Instead of now replacing machines, manufacturers are adding AI/ML tools to pre-inspect raw materials identify defects, perform quality evaluations, and a lot more.
The production and distribution of consumer goods and food products are not as cut and dry as it may seem to spectators. With the numerous challenges and complexities throughout the supply chain, artificial intelligence is rightfully standing at the forefront of technological solutions. Here are some additional points of consideration for businesses considering investing in AI to restructure supply chains. The abundance of algorithms fed to machines teaches each machine the operational standards and proper systems within the supply chain, allowing them to spot errors with a higher level of accuracy than humans. But the true impact of artificial intelligence in the supply chain goes even further once businesses deploy the technology, explaining why internationally respected companies are already using AI.
Efficiency and cost-effectiveness are the biggest challenges facing supply chain management today. Businesses are continually striving to reduce costs, enhance profit margins, and provide exceptional customer service. In such a competitive market, disruptive technologies like Machine Learning (ML) and Artificial Intelligence (AI) have opened up exciting opportunities for companies. Are you grabbing these opportunities? Artificial Intelligence and Machine Learning have recently become buzzwords across different verticals, but what do they mean for modern supply chain management?
If the vision of Industry 4.0 is to be realized, most enterprise processes must become more digitized. A critical element will be the evolution of traditional supply chains toward a connected, smart, and highly efficient supply chain ecosystem. The supply chain today is a series of largely discrete, siloed steps taken through marketing, product development, manufacturing, and distribution, and finally into the hands of the customer. Digitization brings down those walls, and the chain becomes a completely integrated ecosystem that is fully transparent to all the players involved -- from the suppliers of raw materials, components, and parts, to the transporters of those supplies and finished goods, and finally to the customers demanding fulfillment. This network will depend on a number of key technologies: integrated planning and execution systems, logistics visibility, autonomous logistics, smart procurement and warehousing, spare parts management, and advanced analytics. The result will enable companies to react to disruptions in the supply chain, and even anticipate them, by fully modeling the network, creating "what-if" scenarios, and adjusting the supply chain in real time as conditions change. Once built -- and the components are starting to be developed today -- the digital supply "network" will offer a new degree of resiliency and responsiveness enabling companies that get there first to beat the competition in the effort to provide customers with the most efficient and transparent service delivery. At most companies, products are delivered to customers through a very standardized process. Marketing analyzes customer demand and tries to predict sales for the coming period. With that information, manufacturing orders raw materials, components, and parts for the anticipated capacity.