The Future of Decision Intelligence in Manufacturing: How to Navigate your Company in the Coming Decade
The Rise of Decision Intelligence
Decision intelligence brings together data analytics, artificial intelligence, and human expertise to enable informed decision-making in complex industrial environments. By analyzing vast amounts of data from sensors, machines, and production processes, decision intelligence systems not only interpret data but through predictive analytics, can identify patterns, trends, and anomalies that would otherwise go unnoticed. Real-time insights enable businesses of all sizes to optimize production schedules, predict equipment failures, and proactively address potential disruptions.
Several key trends are contributing to the growing adoption of decision intelligence tools across organizations of any industry and size including:
Democratization of AI: AI tools are becoming increasingly user-friendly, making it easier for non-experts to access and use AI-powered decision-making capabilities. This is enabling businesses to make better decisions across a wider range of functions, from marketing and sales to operations and finance.
Integration with existing workflows: data science and AI tools are being integrated into existing business workflows, making it easier for users to access and apply AI-powered insights without disrupting their existing processes. This is helping to ensure that AI becomes a seamless part of the decision-making process.
Real-time decisioning: decision intelligence tools are increasingly being used for real-time decisioning, enabling businesses to make decisions based on up-to-the-minute data and insights. This is particularly important for businesses operating in dynamic environments where decisions need to be made quickly.
Adaptive decisioning: the latest AI tools are being used to develop adaptive decisioning models that can learn and improve over time. This is enabling businesses to make better decisions as their data and environment evolve.
Defining the New Data Frontier
According to Bibby Financial Services’ 2023 Global Business Monitor report, inflation, rising material and energy costs, and an uncertain economic environment have contributed to a weakening of the global manufacturing sector. From volatile market conditions to supply chain disruptions and sustainability mandates, the stakes for companies in the manufacturing industry are higher than ever.
Adopting smart manufacturing and green industry practices, characterised by the following, promises to herald a new era of efficiency and adaptability to help organizations weather uncertainty and strengthen business resilience:
Integration of the Internet of Things (IoT): Connecting machines, devices, and sensors to gather and analyse data in real time.
Advanced Robotics and Automation: Enhancing precision and reducing manual intervention in repetitive tasks.
Implementation of 5G Technology: Accelerating data exchange rates for instant communication and decision-making.
Artificial Intelligence (AI) and Machine Learning (ML): Providing actionable insights and predictive maintenance to minimise downtime.
Adoption of Cyber-Physical Systems (CPS): Creating a seamless interface between physical operations and digital control.
Utilisation of Augmented Reality (AR): Assisting in complex assembly, maintenance, and training processes.
Sustainability Practices: Embedding environmental considerations into manufacturing processes to meet regulatory and societal expectations.
The evolution to smart manufacturing requires the integration of data from multiple sources and the use of AI to empower human decision-making to identify and act upon opportunities for optimization, improved production performance and drive business impact and outcomes.
As we look ahead to the next decade, manufacturers must embrace decision intelligence as a core competency. By investing in data-driven decision-making practices and leveraging innovative technologies, they can position themselves for success in an era of unprecedented change, becoming champions of Industry 4.0 and the green agenda.
The Manufacturing SME Landscape
Constituting 90% of the world's businesses, SMEs are critical engines of economic growth and have been significantly impacted by current economic conditions in addition to cashflow concerns.
SMEs also typically lag behind large enterprises in the adoption of data and AI-driven solutions that can overcome operational problems, advance business strategy and resilience. An unprecedented opportunity presents itself to SMEs willing to take advantage of the growing ‘cognitive revolution’ powered by data and AI to improve their operations and drive sustainable growth in their businesses.
SMEs are often characterised by their nimble nature and adaptive capabilities, recognised as the birthplace of ground-breaking products and processes and yet challenges remain to embrace decision intelligence as a tool to amplify their competitive edge.
Current SME Challenges in Adopting and Scaling Data and AI
For SMEs, recognising the benefits of data-driven strategies is quintessential yet challenges exist that must be overcome.
Cost Prohibitions: Frequent assumption that data and AI are prohibitively expensive for small-scale operations.
Complexity Concerns: The seeming intricacy of implementing and maintaining new technological systems.
Talent Scarcity: A shortage of in-house expertise to navigate the data-driven landscape.
Cultural Resistance: Hesitance towards change, rooted in a preference for traditional methods.
Data Governance: Navigating the legal and ethical dimensions of data use necessitates robust frameworks.
SMEs often face a lack of skilled talent/employees proficient in AI and data technologies. This challenge is exacerbated by the high demand for such expertise in the market, making it difficult for SMEs to attract and retain qualified personnel. SMEs also struggle in establishing a clear vision for AI implementation and communicating its importance across the organization to effectively build a business case and stakeholder support. Additionally, the organizational culture may resist change, hindering the adoption of new AI and automation technologies for fear of negatively impacting the existing workforce.
Even with a clear vision for the future and executive sponsorship for change, SMEs face the day-to-day realities of trying to source and harness data from a variety of sources, many of which have not been digitized nor governed, as well as understanding and documenting process knowledge and operational expertise that remain locked within a core group of its mature workers.
Overcoming the above requires close collaboration between an ecosystem of technology partners and SMEs willing to experiment and co-create new experiences that leverage human-centric design, elevated by the latest advances in data engineering, AI and generative AI tools, and focused on business outcomes.
The infusion of decision intelligence into SME manufacturing operations transforms intuition-based approaches into data-empowered strategies. It equips leaders with the tools to make substantiated decisions that are aligned with long-term objectives, fostering a culture of informed risk-taking and innovation that is indispensable for staying ahead of the industrial vanguard.
Benchmarking Data and AI Capabilities in Manufacturing Today
SUSI seeks to help SMEs transform their data into growth opportunities through surprisingly easy, outcomes-focused AI-driven decision intelligence. We are committed to empowering SMEs to lead the green industrial revolution and advance the circular economy.
And we want to hear from you. Take a moment to complete our benchmarking survey and share your insights. Learn how your organisation stacks up against your peers in industry and receive a tailored report as well as get access to other learning opportunities and future insights to data and AI trends and updates in manufacturing. Together, we can chart a course for the future and make a meaningful impact in the world of manufacturing.