Harnessing the Power of Predictive Intelligence for Resilient Supply Chains
Ravikiran Pothukuchi is a visionary business leader with more than 20 years of experience in driving growth and transformation in enterprise software and digital manufacturing. At Dassault Systèmes, he assists industries in connecting their value networks through AI, virtual twins, and PLM solutions. Known for his consultative selling approach, innovation-driven strategies, and ability to build high-performing teams, he helps organizations achieve efficiency, sustainability, and a lasting competitive advantage.
In a recent interaction with M R Yuvatha, Senior Correspondent at siliconindia, Ravikiran shared his insights on how AI-driven predictive intelligence is enabling resilient and efficient supply chains.
In today’s world, global manufacturing is significantly influenced by geopolitical tensions and macroeconomic decisions that are reshaping supply chains across the globe. The volatility arising from regulatory changes and international conflicts is often unpredictable, leading to considerable impacts on sourcing and production. While no organization can fully anticipate these risks in advance, advanced technologies now enable the industry to model and analyze these risks with greater accuracy and speed.
To illustrate how advanced technologies can help mitigate risks such as the negative effects of raw material price volatility, it’s essential to understand the challenges manufacturers face. Raw materials are often the most impacted component during disruptions, particularly since manufacturers source inputs from various countries. Price fluctuations can be driven by geopolitical factors and foreign exchange conditions.
For example, in the automotive sector, companies must maintain stable consumer prices, even when the costs of raw materials are unpredictable. Industry solutions now utilize global raw material indices to evaluate how price changes affect specific models across different regions.
This allows organizations to assess profitability impacts and make informed decisions regarding production, inventory, and profit margins. Overall, this demonstrates how AI-driven predictive modelling can enhance resilience within the industry.
The New Age of Intelligent Supply Chains
The development of supply chain management highlights its pioneering role in adopting analytics, a practice that began in the late 1960s and 1970s under the term 'operations research'. During this period, businesses across various sectors faced significant challenges due to the inherent volatility and unpredictability of consumer demand. Simultaneously, they struggled with constrained operational capacity, leading to a constant balancing act between meeting fluctuating customer needs and managing limited resources.
Initially, organizations employed descriptive analytics, which allowed them to analyze and understand past performance and trends. However, this retrospective approach fell short when it came to predicting future sales or operational conditions, leaving companies ill-prepared for upcoming shifts in demand.
With the emergence of advanced technologies in data science, artificial intelligence, and machine learning, industries have gained the ability to make more accurate forecasts of future scenarios. These modern analytical tools enable businesses to not only analyze historical data but also leverage complex algorithms to anticipate market trends and capacity requirements.
By integrating these advanced methodologies, organizations can better align their supply chains to respond proactively to both current customer demands and future market dynamics, ultimately improving efficiency and customer satisfaction.
Also Read: Leveraging Virtual Twin as an Extension to Improve Products and Services of the Real World
Driving Faster Conversions Through Predictive Intelligence
Traditional statistical forecasting methods, which rely exclusively on historical data, typically achieve only 65-70% accuracy in predicting demand. This limitation arises because these methods do not incorporate real-time variables that can significantly influence consumer behaviour. In contrast, AI-driven demand sensing technology effectively bridges this gap by analyzing a wide range of data points, including real-time weather patterns, sudden disruptions such as natural disasters or significant geopolitical events, and localized occurrences like regional promotions or store openings.
By harnessing these insights, companies can better anticipate short-term fluctuations in demand, ensuring they maintain optimal inventory levels. This strategic alignment helps to significantly reduce working capital requirements and minimizes the risks associated with unnecessary stockpiling of goods.
AI can amplify human capabilities, but nuanced judgment and ethical oversight remain irreplaceably human.
On the operational side, AI-enabled predictive maintenance is revolutionizing how organizations manage their equipment. Rather than following rigid, pre-scheduled maintenance timelines, this advanced technology allows companies to proactively identify and predict potential equipment failures before they occur. By leveraging data analytics and machine learning algorithms, organizations can optimize their maintenance schedules based on actual equipment performance and usage patterns. This proactive approach not only minimizes unexpected downtime but also enhances overall operational efficiency, allowing businesses to operate smoothly and reliably.
Together, these innovations AI-driven demand sensing and predictive maintenance are fundamentally reshaping global supply chains and modern manufacturing operations, leading to more agile, responsive, and efficient systems that are better equipped to meet the demands of today’s dynamic market environment.
The Authenticity Challenge in an AI-Driven World
In the contemporary global landscape of technology and business, a significant discussion revolves around finding the right equilibrium between artificial intelligence (AI) and human intelligence. This intricate topic is receiving considerable attention from scholars, researchers, and industry experts.
Artificial intelligence possesses extraordinary abilities that can significantly enhance human capabilities. For instance, AI can generate synthetic data, which is invaluable for fortifying analytical models and improving predictions in various fields such as healthcare, finance, and marketing. However, the rise of AI also presents complex challenges concerning contextual understanding, ethical considerations, and legal accountability. From an organizational standpoint, businesses must carefully evaluate how much input and intervention from AI and human workers are optimal for specific tasks.
For instance, highly technical and repetitive tasks, such as programming, coding, and data entry, can be efficiently executed by AI systems, leading to increased productivity and reduced human error. In contrast, AI plays a transformative role in creative sectors, where it can assist in generating unique content and exploring innovative ideas. This collaboration between AI and human creativity can lead to ground-breaking developments in fields like entertainment, advertising, and design.
Nevertheless, the need for human oversight remains critical, particularly in areas that require nuanced ethical judgment and contextual awareness. Issues like bias in AI algorithms, implications of data privacy, and the moral consequences of automated decisions highlight the limitations of AI. Human intelligence is essential in navigating these complexities and ensuring that technological advancements align with societal values and ethical standards.
Ravikiran states, “Dassault Systèmes recognizes the importance of using artificial intelligence responsibly. The industry is fully aware of both the power and responsibility that AI entails, and therefore adheres to ethical standards and global regulations. They have also signed an agreement with the European Union to reinforce their commitment to responsible AI usage and to ensure compliance across all regions”.
