Deep Dive: POS at lightspeed
Can data, artificial intelligence, and machine learning change the way B2B partners share data for mutual advantage?
This newsletter edition takes a giant leap. I’ve thought about one example of how distributors share data with manufacturers and have posited a new approach that is radical in its scale, scope, and aspirations. I am inspired by a book, Competing in the Age of AI, and have taken the authors’ ideas and run with abandon. I am admittedly and decidedly a layperson when it comes to big data, artificial intelligence (AI), and machine learning. But I am also something akin to a kid in a candy store. I have designed and optimized channels for more than 30 years. I see the potential for tremendous new approaches for creating the exchange of value between B2B companies and their customers, and the opportunity to redesign the traditional value chain to compete with disruptors and beat them. And so, I ask you to read the book, consider my ideas, and add to the conversation with your perspectives.
Once upon a time, in a value chain not so far away
Years ago, I did a brisk consulting business designing distributor point of sale (POS) programs for manufacturers. Suppliers valued distributors for their coverage but suffered from direct access to customer identities and a lack of visibility to purchase behaviors, limiting their ability to base decisions on customer knowledge. Most often, this meant adding discounts or rebates to an overall channel compensation program. In return for an economic incentive, distributors would provide POS data, usually as a flat-file or spreadsheet. Suppliers agreed to use the data for precisely defined purposes, including business planning, market share calculations, product development, and trend analysis. Every distributor’s data was considered confidential and not shared with other distributors. The system worked, sort of, but the data was usually disorganized, unclean, and never provided in real-time.
While reading Competing in the Age of AI, by Marco Iansiti and Karim R. Lakhani, I was struck by this passage:
AI Is becoming the universal engine of execution. As digital technology increasingly shapes “all of what we do” and enables a rapidly growing number of tasks and processes, AI is becoming the new operational foundation of business—the core of a company’s operating model, defining how the company drives the execution of tasks. AI is not only displacing human activity, it is changing the very concept of the firm.
I wondered: If AI is redefining operating models within a business, can it also redefine operating models between firms? Can AI change the concept of B2B collaborations? I ran with this idea and imagined a modern POS program for the digital age—one that takes advantage of AI and machine learning to provide game-changing benefits for distributors and suppliers that are an order of magnitude beyond old-fashioned POS programs. My idea, described below, builds on several ideas shared in previous newsletter editions: data partnerships will reinvent the value chain; a new theory of B2B innovation to replace disruption and disintermediation will emerge; and value chain partners will collaborate around projects staffed with workforces from multiple companies. (I discuss these perspectives here, here, and here.)
May the (data) force be with us
Imagine a new corporation co-owned by a group of distributors and manufacturers. The company functions as a data repository and applies big data, analytics, AI, and machine learning to benefit its owners. In some applications, the data coordinates and deploys collaborative programs and activities for mutual benefit. In others, individual owners access the data for proprietary innovations and differentiated services. Data confidentiality and practical use doctrines are strictly maintained by operating as an independent company guided by contractual obligations and continuous oversight.
The data provides unheard of value because it includes customer data (purchase patterns, price sensitivity, customer service issues, customization or project specifications, firmographics, and more) and product data (product performance, technical specifications, application configurations, operational objectives, upgrade status, maintenance/repair history, and more.) The data set is exhaustive because it includes proprietary information from manufacturers and distributors across a large and varied range of customers, applications, markets, and industries. The data set is more comprehensive than any individual manufacturer or distributor can assemble. Therefore, it is a foundation for unmatchable competitive advantage against non-participating companies and, perhaps, disruptive buyer/seller marketplaces (Amazon, Alibaba, and so forth). Specific benefits achieved through the co-owned data company may include:
Price optimization and product suggestions. Because of the data set’s diversity and scale, co-owner benefits go far beyond what can be achieved by a single company capability. As a result, one-off price adjustments and product offerings will be more accurate. More importantly, prices and products can be offered in bundles tailored to the customer’s situation, perhaps with total cost of ownership (TCO) guarantees.
Predictive maintenance and fulfillment service optimization. The system allows for design, customization, and, importantly, integration of delivery, product utilization, inventory management, and technical support experiences through a collaboration of co-owners. For example, program variables may be designed around product lifecycle and planned upgrades, service level guarantees to incorporate contributions from multiple co-owners, or customer-defined business cycles around seasonality or economic conditions.
Product and service opportunity discovery. As predicted for the algorithm economy and fourth-generation retail (introduced here), B2B companies can leverage data and analytics to spot niche opportunities, offer virtual/shared brands, time product (and service) transitions for maximum profitability and competitiveness, and more. Designing a continual stream of new products and services at scale is a massive competitive differentiation against outside disruptors that compete on an endless aisle assortment in a buy box environment.
Supply chain resiliency. The data company’s information can be provided to co-owners for their individual supply chain planning needs. But a much more robust data application would be to manage the supply chain up and down the value chain, and horizontally as members share inventory and collaborate to support customer experiences. Data dashboards provide customized direct access for customers. The data company enables self-service and real-time information, as it guarantees supply chain performance and risk management for the next global economic crisis, pandemic, extreme weather events, and other acts of God. Importantly, this supply chain can be run with a higher degree of automation, replacing manual planning on spreadsheets with supply chain supervisors.
Ideas for innovating B2B
My configuration of a collaborative and co-owned data company and ideas for game-changing customer and owner benefits are entirely open for debate, redesign, and expansion. Perhaps my most significant takeaway from Competing in the Age of AI is that while artificial intelligence is not new, it is coming into its own. As B2B users of artificial intelligence become comfortable with its application and confident in its power, they will redesign their organizations (and ecosystem alliances) as technology-enabled, data-driven power players.
I invite you to read Iansiti and Lakhani’s insights, consider their foresight, and then make your plans. Specific concepts (with my interpretations) you may want to apply include:
Software, networks, and AI. The future is not just about AI. It’s about companies operating more like software companies and leveraging connected data to achieve network effects. Incumbent business leaders are not of this world and should include experts and thought leaders in their futuring initiatives to create the best possible foresight.
Scale, scope, and learning. Much of the power of AI is captured in these three variables. As B2B companies and the B2B value chain transform to run on data, the traditional approach of achieving scale through acquisition will be replaced by scaling through data. Service across lines of trade will become routine. Suppliers eliminate territory restrictions. Companies will compete on reaction time, customization of integrated product/delivery customer experiences, and uber-resiliency.
The AI factory. The core of every business will be a scalable decision factory. This capability will replace the product factory for manufacturers and the warehouse for distributors. AI factories will be remarkably similar across every B2B company, so the strongest companies will win by using data for planning, execution, and course correction.
Rearchitecting your firm. Incumbents often resist change because they cannot (or will not) undo their organizational structure and power centers. This concept also applies to how roles, responsibilities, and margins are allocated among the extended organization, known as the traditional value chain. Much work is being done, often outside the B2B ecosystem, around the future of work and learning, leadership mindsets, organizational concepts, and more. B2B companies must seek out, find, and apply revolutionary concepts.
Join the journey
If you find my ideas in this newsletter outlandish, over-the-top, or otherwise improbable, I make no apologies. It is an attempt to stretch our imagination. This newsletter’s impact will be more powerful if you add your voice. Don’t be a stranger. Add your comments, experiences, arguments, and ideas. If you like, reach out directly at mark.dancer@n4bi.com.