IDC predicts that in 2026, 55% of the Forbes Global 2000 OEMs will have redesigned service supply chains based on AI. That is just a glimpse of how AI in supply chains, logistics, and workplaces, brings about significant disruptions, unlocking new efficiencies, optimizing processes, and reshaping traditional workflows.
The global supply chain is in a constant state of evolution, seeking to attain optimal efficiency, cost reduction, and customer satisfaction. Yet, it encounters mounting complexities stemming from heightened customer expectations, volatile market dynamics, and an escalating demand for sustainable practices. Artificial intelligence (AI), and generative AIs, has become a transformative force across industries, revolutionizing the way businesses operate. There is no doubt about that at this point in time.
In fact: “Artificial intelligence, particularly generative AI, offers promising solutions to address these challenges. By leveraging the power of generative AI, supply chain stakeholders can analyze massive volumes of data, generate valuable insights, and facilitate better decision-making processes,” said Cem Dilmegani, principal analyst at AIMultiple.
The change is led by tech companies, which current business models are prepared to lead the innovation process in this area. One of those is Microsoft, who introduced in March 2023 Dynamics 365 Copilot. This program capitalizes on the latest developments in generative AI, streamlining monotonous tasks and unleashing the boundless creativity of employees. In April, we highlighted the application of generative AI in critical supply chain processes. Today, we are excited to announce the integration of additional AI-powered assistance across our ERP portfolio, including Microsoft Dynamics 365 Finance, Dynamics 365 Project Operations, and Dynamics 365 Supply Chain Management. To activate these innovative Copilot features within your Dynamics 365 products, collaborate with your IT administrator.
3 Areas where AI will make the most impact
Product Design and Innovation
Generative AI allows supply chain stakeholders to reimagine product design and innovation processes. By leveraging deep learning algorithms, generative AI can analyze vast amounts of data, including customer preferences, market trends, and historical designs. This enables the generation of new, optimized designs and the exploration of innovative possibilities.
For example, generative AI can assist in creating optimized product configurations based on specific customer requirements, optimizing features such as weight, durability, and cost. It can also facilitate the generation of design alternatives, allowing for rapid prototyping and iteration. This capability enhances creativity, accelerates product development cycles, and encourages the exploration of new design concepts that can lead to improved customer satisfaction and competitive advantage.
Demand Forecasting and Inventory Management
Accurate demand forecasting is crucial for supply chain optimization. Generative AI algorithms can analyze historical sales data, market trends, and other relevant factors to generate accurate demand forecasts. By identifying patterns, seasonality, and correlations in the data, generative AI helps businesses predict future demand more effectively. This allows supply chain managers to optimize inventory levels, reduce stockouts, and minimize excess inventory, resulting in cost savings and improved customer satisfaction.
Generative AI also aids in inventory management by optimizing replenishment strategies. By considering demand variability, lead times, and cost factors, generative AI algorithms can determine the optimal ordering quantities and reorder points for different products. This ensures that inventory levels are maintained at an optimal level, avoiding both stockouts and excessive inventory holding costs.
Procurement and Supplier Management
Generative AI can significantly impact procurement and supplier management processes. By analyzing vast amounts of data related to suppliers, including their performance, capabilities, and pricing, generative AI algorithms can assist in supplier selection and evaluation. This technology can help identify potential suppliers, evaluate their suitability based on predefined criteria, and negotiate favourable terms.
Generative AI can also facilitate supply chain risk management by continuously monitoring supplier performance and identifying potential disruptions. By analyzing data from various sources, such as social media, news articles, and industry reports, generative AI algorithms can provide early warnings and alerts regarding supplier-related risks, such as financial instability or delivery delays. This proactive approach allows supply chain managers to take preventive actions and maintain continuity in the supply chain.
These benefits come with their own challenges and considerations, given the powerful changing nature of new AI advancements.
While generative AI offers substantial benefits to supply chains, there are challenges and considerations to be mindful of. Some of these include:
- Data quality and availability: Generative AI relies heavily on high-quality, diverse, and relevant data. Ensuring data integrity, availability, and compatibility across different systems and stakeholders is crucial for the success of generative AI applications.
- Ethical considerations: As generative AI algorithms learn from existing data, biases present in the data can be perpetuated, leading to biased outcomes. Organizations must carefully monitor and address any ethical concerns related to bias and fairness in the generated designs or decisions.
- Implementation and integration: Implementing generative AI technologies requires expertise in AI, data management, and integration with existing systems. Organizations must invest in the necessary infrastructure, talent, and resources to effectively adopt generative AI in their supply chain processes.
AI in Supply Chains: Changing the Nature of Workplaces
In terms of the future of work, AI introduces both opportunities and challenges. While automation may replace certain job functions, it also creates new roles and demands a shift in the skill sets required. Jobs that involve complex problem-solving, data analysis, and decision-making are in high demand as AI systems require human expertise to interpret and act upon their outputs. Additionally, there is an increasing need for AI specialists who can develop, implement, and maintain AI technologies within supply chain environments.
Automation of Repetitive Tasks
AI can automate mundane and repetitive tasks, freeing up human workers to focus on more complex and creative aspects of their jobs. This leads to increased productivity and efficiency, allowing employees to allocate their time and energy to higher-value activities.
AI algorithms can process and analyze vast amounts of data in real-time, providing valuable insights and supporting decision-making processes. By augmenting human intelligence with AI-driven analytics, businesses can make data-driven decisions, identify patterns, and uncover hidden opportunities, leading to more informed and strategic choices.
Rather than replacing human workers, AI has the potential to augment their capabilities. By collaborating with AI-powered tools and systems, employees can leverage AI's computational power and data analysis capabilities to enhance their own performance. This partnership between humans and AI can lead to higher productivity, improved problem-solving, and innovative solutions.
Upskilling and Reskilling
The integration of AI in the workplace will require employees to acquire new skills and adapt to new technologies. Upskilling and reskilling programs will play a crucial role in preparing the workforce for the future, ensuring they possess the necessary expertise to collaborate effectively with AI systems and leverage AI-driven tools.
Job Transformation and Creation
While AI may automate certain job tasks, it also has the potential to create new job roles and opportunities. As AI takes over repetitive tasks, there will be a shift towards jobs that require creativity, critical thinking, and emotional intelligence, which are difficult for AI to replicate. Additionally, the development, deployment, and maintenance of AI systems will create new job opportunities in fields such as AI engineering, data science, and algorithm development.
The implementation of AI in the future of work raises ethical considerations. Organizations must ensure transparency, fairness, and accountability in AI systems to prevent biases, discrimination, and privacy breaches. Ethical guidelines and regulations will be crucial in guiding the responsible use of AI in the workplace.
The introduction of AI may lead to changes in workplace dynamics and collaboration. Employees may find themselves working alongside AI-powered tools and systems, requiring effective communication and coordination. Organizations will need to foster a culture that embraces AI, promotes collaboration, and encourages continuous learning and adaptation.
Implementing AI in the future of work will bring about significant changes. While some job tasks may be automated, AI will also augment human capabilities, enhance decision-making, and create new opportunities. Upskilling, ethical considerations, and adapting to new workplace dynamics will be key factors in successfully harnessing the benefits of AI in the future of work.
Overall, AI empowers supply chains by optimizing operations, enhancing decision-making, and increasing efficiency. While it may impact the nature of work, it also opens up new possibilities and demands a workforce that can adapt to the evolving technological landscape.