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As part of Solutions Review’s Contributed Content Series<\/a>\u2014a collection of contributed articles written by our\u00a0enterprise tech thought leader community<\/a><\/span>\u2014<\/strong><\/em>Duncan Angove, CEO of Blue Yonder<\/a>, talks about the technology solutions currently transforming the supply chain.<\/b><\/p>\n Legacy database technologies and computing limitations have compelled supply chain solution providers<\/a> to settle on aggregating data and applying simplified rules-based algorithms to deliver more responsive and cost-effective solutions to common supply chain problems. Unfortunately, this approach further exacerbated siloed decision-making due to the inevitable latency in communication between systems and the myriad of failed attempts to standardize communication protocols across trading partners. Supply chain technology vendors added fuel to the fire by focusing on “fixing” point solutions rather than going to the source of the problem\u2014the disconnected data and the infrastructure.<\/span><\/p>\n The ramifications of the industry’s sub-par, disconnected supply chain tools are far-reaching, with businesses and consumers alike feeling the effects of the chasm between upstream and downstream functions. For example, inventory management<\/span> remains a challenge<\/span><\/a> for many retailers, resulting in inventory shortages in markets of high demand while simultaneously creating waste through excess inventory in markets that exhibit low demand; we’re still seeing<\/span> devastating shortages<\/span><\/a> of life-saving medication, and consumers<\/span> often face a trade-off<\/span><\/a> between sustainability and convenience. The reality is global supply chains continue to pose challenges across business verticals despite some<\/span> high-profile declarations<\/span><\/a> to the contrary.<\/span><\/p>\n The COVID-19 pandemic dramatically accelerated the rate of technological innovation as businesses rushed online\u2014altering supply chain flow paths, taxing labor and talent availability, and heightening the uncertainty of decision-making. As we look forward, the fallacy of continuing with point solutions and existing underlying technologies can no longer be denied.<\/span><\/p>\n Three critical pieces of technology will upend conventional thinking and catalyze a global supply chain transformation: the data cloud, generative AI, and the underlying, near-infinite intelligence of microservices architecture seen on the platforms of today’s public clouds.<\/span><\/p>\n Modern-day data clouds solve the problem of storing and sharing massive volumes of data\u2014even at the lowest level of granularity\u2014without the high cost, poor performance, and latency. This is because, unlike traditional databases or even databases in the cloud, data clouds can distinguish between storage and computing work processes so that each can be consumed and scaled independently.<\/span><\/p>\n Leveraging a single data cloud to make and execute supply chain decisions eliminates integration complexities and associated costs. Data movement and latency are replaced with the ability to transform data into the desired format cloud-native services can consume without moving the data around to the supply chain applications. External data sharing becomes as simple as one business granting access to another, reducing latency between trading partners, like suppliers and carriers, and enhancing coordination across the supply chain. This allows supply chains to collaborate, plan, and execute in near real-time while referencing a standard data set.<\/span><\/p>\n With direct access to data marketplaces, supply chain teams can more readily enhance the existing internal data on their customers, distributors, product catalog, and processes with external third-party data\u2014from commodity prices and weather patterns to consumer price elasticity. This results in more robust, relevant data sets to inform and collaborate on scenarios and to map out potential business disruptions.\u00a0\u00a0<\/span><\/p>\n At the heart of the problem with supply chains is data. For supply chain solutions and teams, the data cloud means data acquisition, storage, and sharing is faster, cheaper, and more accessible. Between its sheer computational power and architectural benefits, the data cloud is poised to give supply chain teams a tremendous boost in productivity and efficacy\u2014with much-needed data now only a query away.<\/span><\/p>\n Properly trained and with the proper guardrails, generative AI can be a trusted navigator to augment decision-making and guide staff through complex processes. Supply chain technology providers can harness large language models (LLM) by training them on a company’s unique supply chain practices and their own intellectual property (e.g., how a solution provider semantically constructs a digital twin).<\/span><\/p>\n Using well-trained and curated Generative AI solutions, users will have an expert navigator to provide recommendations or offer specific, secure, and verifiable guidance. This navigator also serves as an agent on behalf of the user to identify, evaluate, and provide options to address disruptions to normal supply chain operations. <\/span>Time-sensitive problem-solving could be an excellent use case for eventually granting agency to generative AI for recurring or predictable events. <\/span><\/p>\n For example, suppose a logistics planner needs to follow a minimum set of viable alternatives when troubleshooting for a delayed shipment, such as evaluating an alternate carrier, expediting the shipment, or altering the route. In that case, the logistics planner can preemptively off-load to the navigator to prepare and evaluate those options when the <\/span>next <\/span><\/i>shipment is delayed and select the optimal choice based on current market conditions. Rather than being interrupted when the delay is identified, the logistics planner can delegate agency to their trained generative AI navigator. The navigator conducts all the necessary work, and all that’s required from the planner is a final sign-off. The magnitude of disruptions in today’s supply chains makes this technology an ideal tool to address these challenges at scale.<\/span><\/p>\n Generative AI not only allows the most novice of planners to leverage the most experienced planners’ know-how but also frees up overall human intellectual capital to focus on problems that require collaboration, decisioning, and execution outside of the norms. Additionally, this technology up-levels teams’ critical thinking and absorbs countless tasks that are easily automated or replicated. These are just a few examples, but the accelerating proliferation of data in supply chain planning makes it an ideal environment for more generative AI use cases to emerge.<\/span><\/p>\n Operating in today’s disruptive, complex environments can be challenging for any supply chain. With canceled flights, delayed shipments, significant port delays reducing imports, truckers facing border rules, and winter storms slamming logistics networks, it has never been more important to anticipate and quickly react to volatile market disruptions. Immense pressure is placed on delivering precise, effective plans; making the right decisions in real-time requires access to meaningful data and a deep understanding of market trends and customer behaviors. But acquiring, managing, and interpreting vast amounts of data is daunting.<\/span><\/p>\n The recent converging advancements in data availability, computational power, and algorithmic innovation present an optimal opportunity to alleviate these challenges. Enterprises are shifting away from monolithic applications in droves, moving to microservices-based architecture to reap the benefits of increased productivity and scale. Because microservices are self-contained, independent deployment modules, the cost of scaling is comparatively less, and they can run in an isolated fashion, which means jobs can be processed in parallel for increased speed and efficiency.<\/span><\/p>\n Running on the world’s largest elastic supercomputer\u2014the internet\u2014businesses can spin up infinite computing power and run hundreds or even thousands of simulations in minutes without compromising accuracy. This collapses the time horizon between planning and execution to nearly zero, resulting in incredibly smart, fast, agile, and responsive supply chains. Taking this a step further, recent innovations in advanced analytics, artificial intelligence (AI), and machine learning (ML) are capitalizing on this increased computing power to deliver infinite intelligence.<\/span><\/p>\n These advancements in computing power and ML<\/a> mean that teams can consider all relevant data, leaving no stone unturned and ensuring analyses reflect market and business realities without burdening individual team members.<\/span><\/p>\n The future of the supply chain is a world of connected, cognitive applications that interoperate seamlessly on a single data cloud, unburdened by corporate boundaries, to deliver the most sustainable, resilient, and effective supply chains for global commerce. Corporations and technology providers that don’t adopt the data cloud, generative AI, and infinite intelligence through public cloud computing platforms will not have access to the supply chain information highways of tomorrow. They will be at risk of losing their competitive footholds.\u00a0\u00a0<\/span><\/p>\n The future of the supply chain will be driven by cutting-edge technology, and it’s now finally within our grasp.<\/span><\/p>\n As part of Solutions Review’s Contributed Content Series\u2014a collection of contributed articles written by our\u00a0enterprise tech thought leader community\u2014Duncan Angove, CEO of Blue Yonder, talks about the technology solutions currently transforming the supply chain. Efficient, effective, and resilient supply chains, by their very nature, require the busting of silos. Paradoxically, supply chain planning and execution […]<\/p>\n","protected":false},"author":1056,"featured_media":6820,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[5],"tags":[893,2963,3325,3324,2857,3323],"acf":[],"yoast_head":"\n![\"\"](\"https:\/\/solutionsreview.com\/enterprise-resource-planning\/files\/2023\/10\/insightjam_logo.jpg\"){kind=logo}
<\/a>Efficient, effective, and resilient supply chains, by their very nature, require the busting of silos. Paradoxically, supply chain planning and execution technologies have historically fallen short of this requirement. In contrast to ERP<\/a> and CRM<\/a> tools, which were successfully platformed years ago for the enterprise, supply chain management tools have largely remained as point solutions. Some vendors may claim to have “changed the game” regarding supply chain planning, but it certainly doesn’t feel that way from a customer perspective.<\/span><\/p>\nData Cloud<\/b><\/h3>\n
Generative AI<\/b><\/h3>\n
Infinite Intelligence<\/b><\/h3>\n
What Does the Future of the Supply Chain Look Like?<\/b><\/h3>\n
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