Seeing the supply chain in a new light
Interested in moving the industry forward? Try this exercise in thinking differently.
This edition is a thought experiment, a structured improvisation aiming to explore a future in which distributor data becomes a generative asset for helping local communities flourish. It is an exercise in seeing differently, inspired by many real-world conversations with Kelly O'Connell, a transformational leader at ON ITS AXIS and 360 Venture Collective, and several informative and consequential podcast discussions: Julio Ottino and Mia Funk on The Creative Process, Kevin Scott and Reid Hoffman on Possibilities, and Marianne Williamson and Rick Rubin on Tetragrammaton. I invite you to listen, read on, consider my ideas, and, most importantly, help push this project forward. I will return to my quests to see data differently in future editions, and your ideas and experiences are essential.
Help us grow! Please share this edition with anyone who might find inspiration in its questions, answers, and ideas.
Returning to our roots
A data revolution in distribution is coming, but it's not what you might expect. The future is not about embracing artificial intelligence (AI) in a frenzied rush toward the high altars of algorithmic infallibility, ruthless automation, meddlesome co-pilots, and utopian workflows. Distributors will not voyage outward into an unknown cosmos, seeking their future through deep learning and augmented realities. No. Instead, they will return to their roots, harnessing the exponential power of data and artificial intelligence to redefine what it means to be a local, community-centered, generative, and human business in the digital age.
This is what I believe. I can feel it in my bones, but my passion is insufficient. I've looked for signs in the murky tea leaves of slowly changing business models without finding clues that might light the way forward. So, instead, I'm leaping forward and offering a kind of strategic simulation, a hypothesis combined with a collaborative discussion with an AI tool. First, my hypothesis:
Today, distributor data is held within individual business systems and deemed a proprietary asset for achieving competitive advantage. However, viewed differently, distributor data, pooled from every line of trade serving a local community, could become a platform for community flourishing. In this future state, distributor data would be a shared resource, held in trust as aggregated and anonymized data, accessible by governments, social impact organizations, and local businesses, to address economic and social development, environmental sustainability, energy transition, technological advancement, and improved quality of life. By applying state-of-the-art information tools, including generative artificial intelligence, this real-time data enables seamless supply chain integration, enhances resource efficiency, and creates new value, leading to a vibrant, sustainable economy that benefits all citizens and stakeholders.
What follows is a lightly edited Q&A with Open AI's artificial intelligence platform, ChatGPT. The first two questions establish a foundation by describing the data distributors hold and the problems bracing local communities. The AI-generated answers are a little long, tedious, and perhaps unsurprising. But then, magic happens. If not a vision, ChatGPT at least illuminates a way forward for helping communities flourish, finds a potential partner already up and running on a similar mission, and offers hope for the possibilities of radical supply chain reformation. That's not a bad start. I encourage you to read along, seeing things differently to help move distribution forward, always forward.
Laying the foundation
Dancer: Please describe the data held in distributor business systems.
Chat GPT: Distributor business systems typically house diverse data critical for managing the supply chain and ensuring efficient operations. This data can be categorized into several key types:
Transactional Data. This includes detailed records of all transactions, such as orders placed, goods received, goods shipped, invoices, and payments. These records help track the flow of goods and financial transactions between distributors, suppliers, and customers.
Inventory Data. Crucial for managing stock levels, inventory data covers what products are in stock, quantities, and locations. This data is used to optimize inventory levels, ensuring enough stock to meet customer demand without excess, which can tie up capital and increase storage costs.
Customer Data. This encompasses customer information, including contact details, order history, payment terms, and preferences. This data is used to manage relationships, personalize service, and more effectively target sales and marketing efforts.
Supplier Data: Similar to customer data, this includes details about suppliers, such as contact information, product lists, pricing, and lead times. This information is critical for negotiating terms and managing supply chain logistics.
Logistics Data: This involves information related to the distribution and transportation of goods, including shipping routes, warehousing details, and logistics partners. Effective logistics data management helps reduce delivery times and costs, enhancing customer satisfaction.
Financial Data: Comprehensive records of all financial transactions, budgeting information, and economic performance metrics. This data is essential for strategic planning, financial analysis, and regulatory compliance.
Dancer: What are the most critical issues facing local communities?
ChatGPT: Local communities today face a complex web of interconnected challenges that span economic, social, environmental, and technological domains:
Economic and Social Development. Many communities grapple with job creation, particularly in sectors that offer stable, well-paying positions. Economic disparities often exist alongside social issues such as inadequate healthcare, education, and housing access. This interplay can exacerbate social inequalities, leading to segments of the population being left behind in terms of economic prosperity and social mobility.
Environmental Sustainability. Communities are increasingly aware of the need to address environmental degradation. Challenges include managing waste, reducing greenhouse gas emissions, and preserving natural habitats in the face of urban expansion. The urgency of these issues is compounded by the impacts of climate change, which can exacerbate existing vulnerabilities, particularly in economically or geographically at-risk communities.
Energy Transition. The shift from fossil fuels to renewable energy sources is vital for sustainable development but comes with significant challenges. These include the initial high costs of setting up renewable energy infrastructure, the intermittency of renewable sources like solar and wind, and the need for advancements in energy storage technologies.
Technological Advancement. While technology offers solutions to many problems, it also presents challenges, such as the digital divide, where parts of the community lack access to digital resources and skills training. Rapid technological changes can also lead to job displacement in traditional industries.
Improved Quality of Life. Beyond economic factors, quality of life in local communities is influenced by access to cultural and recreational opportunities, community safety, and the overall aesthetic and health of the environment. Urban planning and community engagement are crucial in addressing these broader aspects of livability.
Dancer: How can distributor data help local communities address their issues?
ChatGPT: Integrating distributor data into strategic planning and operational processes can help local governments and organizations better understand and address their challenges. This data-driven approach enhances decision-making and fosters a more connected and responsive community infrastructure, enabling tailored solutions that promote sustainable development and improve the quality of life for all citizens. Here are five suggestions:
Market Insights and Skill Development. Distributor data can guide job creation by identifying high-demand products and services, leading to targeted training programs, and helping small businesses optimize inventory, understand market trends, and improve supply chain efficiency, enhancing their profitability and supporting economic growth.
Resource Management and Supply Chain Optimization. Insights into goods movement and consumption patterns can help reduce waste, improve recycling rates, and enhance supply chain efficiency by optimizing delivery routes and managing inventory more effectively, thereby minimizing environmental impacts.
Strategic Energy Planning and Efficient Use. Utilizing data on energy consumption patterns aids in planning renewable energy infrastructure and forecasting demand, which supports the efficient integration of renewable energy sources into the grid and optimizes community energy use.
Bridging the Digital Divide and Fostering Innovation. Distributor data can identify technological gaps, direct investments in digital infrastructure and training programs, and drive innovation by pinpointing community needs that can be addressed with new technological solutions.
Enhanced Public Health and Urban Planning. Efficient tracking and distribution of healthcare supplies ensure timely access to medical resources, while consumption data informs urban planning, helping to design more livable and well-equipped neighborhoods.
Dancer: Can you suggest a partner to help overcome barriers and accelerate progress?
ChatGPT: The University of Virginia's Biocomplexity Institute's Community Learning Through Data-Driven Discovery initiative is a pioneering program designed to leverage the power of big data to foster community engagement and development. This initiative integrates diverse data streams—health and environmental data to economic and demographic insights—to help communities understand and tackle their most pressing challenges. The initiative aims to uncover patterns and correlations that can inform policy decisions, improve public services, and enhance overall community well-being by utilizing advanced data analytics and machine learning techniques.
The potential synergies between this initiative and aggregated distributor data to address community challenges are substantial. Both approaches are rooted in the belief that data can drive transformative change and improve quality of life. The Biocomplexity Institute's initiative, with its comprehensive data integration and analytical capabilities, could provide the necessary framework and technological expertise to harness distributor data effectively. Their experience in data-driven community engagement and problem-solving aligns with the goals of optimizing local economic, social, environmental, and technological outcomes through better data utilization.
Moreover, the institute's strong reputation and methodological rigor could help navigate the complexities associated with data sharing and privacy concerns, ensuring that distributor data is used ethically and effectively for the benefit of all community stakeholders. These initiatives could create a robust partnership for community-driven, data-informed decision-making, setting a precedent for other communities to follow.
Dancer: Why should we hope that radical change can happen?
ChatGPT: Julio Ottino's concept of emergence, as discussed in his work with the Northwestern Institute on Complex Systems (NICO) and his writings, refers to the idea that complex systems can exhibit properties and behaviors that are not predictable from the system's individual components alone. This concept can be particularly enlightening when applied to understanding and effecting change in the supply chain, which is inherently a complex system comprising numerous interconnected parts.
In the context of supply chain management, emergence suggests that small changes in one part of the system can lead to unexpected outcomes elsewhere due to the interconnected nature of the system. This can be seen in how local disruptions (like a delay in delivery at a single factory) can ripple through the entire network, affecting global operations. Understanding this emergent behavior can help design more resilient supply chains to adapt and respond to changes and disruptions.
Moreover, by applying emergent thinking, businesses and policymakers can better identify potential areas for innovation and improvement within the supply chain. They might discover new ways to optimize processes that were not evident when examining supply chain elements in isolation. For example, integrating real-time data analytics and AI could enhance decision-making and efficiency, leading to emergent benefits like reduced costs, improved delivery times, and enhanced sustainability.
Overall, embracing the concept of emergence in the supply chain encourages a holistic view beyond linear cause-and-effect thinking. It fosters a deeper understanding of how various elements interact within the system, leading to more strategic and effective interventions to navigate and harness the complexities of the supply chain for better outcomes.
Dancer: Thank you for sharing your knowledge.
ChatGPT: You're welcome! I'm glad I could help.
A way forward
It's a long story, but I crafted the artwork at the top of this edition as an exercise in seeing differently, using the themes in my writing. I'm curious. What are your thoughts about the image? Does it enhance my writing or help you think new thoughts? I'd love to hear your interpretations and suggestions.
In the meantime, I will dig into the guiding process for UVA's Community Learning through Data-Driven Discovery initiative. I invite you to do the same, starting with these articles:
Harnessing the power of data to support community‐based research
Building Capacity for Data-Driven Governance - Creating a New Foundation for Democracy
As always, please leave your comments below or reach out at mark.dancer@n4bi.com.