Artificial intelligence (AI) has been spearheading the rapid digital transformation of the manufacturing sector in recent years. Since Industry 4.0 and the proliferation of smart manufacturing technologies, AI has been playing an increasingly important role in improving manufacturing functions like supply chain management and production optimisation.
Together with cloud-based supply chain management tools and intelligent solutions, AI has empowered manufacturers to track and manage the physical flow of goods, operational performance, sustainability metrics and more. AI has also enabled hyper-personalisation in the supply chain to meet changing customer needs.
With the demand for personalisation and customisation growing, how is AI-driven hyper-personalisation evolving the manufacturing supply chain?
Impact of AI-driven hyper-personalisation on the manufacturing supply chain
Reduced costs and waste in the supply chain
AI-driven hyper-personalisation can help manufacturers reduce costs and waste in the supply chain by intelligently forecasting the raw materials required and utilised before the production process. This way, there is less waste thanks to predictive analytics that identify only what customers need, rather than creating products that might not sell. This can also lead to increased profitability and sustainability. In addition, hyper-personalised supply chains can optimise delivery, logistics and labour costs thanks to real-time data provided by AI.
Increased supply chain agility and resilience
Hyper-personalised supply chains can leverage advanced analytics to recognise changing customer requirements more efficiently. This enables increased supply chain agility, leading to quicker turnarounds, and increased supply chain resilience as AI-driven supply chain forecasting is reported to reduce errors by about 20% to 50% and excess stock by about 50%.
Additionally, AI provides end-to-end visibility that allows manufacturers to evaluate and identify risks in the supply chain. Company leaders can use this data to make the right decisions and increase resilience across the entire value chain.
Improved customer satisfaction and loyalty
Hyper-personalisation in manufacturing supply chains will ultimately lead to greater customer satisfaction and loyalty thanks to AI providing deeper customer insights that help companies address concerns and needs more accurately.
Instead of conventional one-size-fits-all strategies of traditional factories, modern cloud-based and AI-driven production lines and supply chains can take advantage of advanced machine learning systems to enable autonomous manufacturing adjustments. This helps manufacturers attain hyper-personalisation that will enhance the customer experience.
The future of manufacturing supply chains
The potential of AI-driven hyper-personalisation to improve manufacturing supply chains cannot be overstated. New data insights, machine learning and autonomous and predictive solutions allow manufacturers to create customised products and services that meet the unique needs of individual customers, leading to increased customer satisfaction and loyalty, profitability and improved supply chain agility and resilience.
While supply chain resilience is paramount in the rapidly evolving manufacturing industry, achieving new levels of resilience and efficiency will not be easy if manufacturers do not have the right roadmaps and frameworks in place as guidelines. Manufacturing leaders must be able to identify their weak points to fast-track their digital transformation and improve their sustainability.
With structured maturity assessments and frameworks like the Index voor de gereedheid van de slimme industrie (SIRI) En Consumer Sustainability Industry Readiness Index (COSIRI), manufacturing leaders can easily analyse their developmental progress and find the right path towards advanced digital transformation and improved sustainability. Learn more about Index voor de gereedheid van de slimme industrie En COSIRI, and how they can help you get further in the future of smart manufacturing.
