As technologies such as digital twins, machine learning (ML) and the Internet of Things (IoT) continue to evolve and proliferate, companies around the world may start doing things that weren't possible before. These advanced technologies, working in conjunction with people, can help companies create an intelligent supply chain that predicts and monitors the impact of nearly every decision they make, allowing them to balance the three key outcomes that today's supply chain leaders need to consistently and simultaneously achieve. . Three key results: Relevance Relevance to customers and employees to ensure future growth. According to Accenture Technology Vision 2020, 76% of leaders agree that organizations need to fundamentally change the experience that brings technology and people together, and is more human-centered. Such experiences help create more engaged employees and more loyal customers. Sustainability Operational resilience that ensures profitability and viability in a crisis-prone post-COVID world.
A CxO Pulse survey conducted by Accenture in July 2020 found that three-quarters of supply chain executives want to rethink their supply chains (including processes and operating models) to make them more resilient, which is not surprising given the massive disruptions they have experienced in Last year. Duty Business responsibility that promotes prosperity for all without destroying the planet. A sustainable sustainable supply chain is a major value proposition for companies as consumers become more aware of what products they consume: where they come from, how they are made, and how they are made. recycled. These are all great ideas, but are there companies that actually work this way? According to a recent study by Accenture 1 , there is. The small group of leaders we identified in our research—13%—excelled at simultaneously being relevant, sustainable, and accountable. We found that in all industries studied, these companies significantly outperform others in overall financial performance as measured by enterprise value and EBITDA (earnings before interest, taxes, depreciation and amortization).
These Leaders give us a glimpse of what human-machine collaboration makes possible for all companies. Augmented analytics and artificial intelligence are the keys to simultaneously addressing the challenges of relevance, sustainability, and accountability. Our research shows that leaders are embracing these powerful tools at scale and, in the process, are taking advantage of the significant opportunities created by human-machine collaboration. For example, at least half of the leaders said they are investing more than $5 million in embedded AI networking products, AI virtual assistants, advanced data analytics, intelligent automation, industrial IoT sensors, and embedded AI networking products. Just under half said the same about ML/deep learning and sentiment monitoring analytics. In addition, 90% or more of both Executives and Others agree that profiting from these investments will require building and expanding ecosystem partnerships with a wide range of players, acquiring and maintaining analytics and AI skills, and tapping into key digital platforms . Analytics and AI: Three Critical Use Cases for Immediate and Significant Value There are many use cases for supply chain analytics and artificial intelligence, and the number continues to grow.
But not all use cases are the same. Some are harder to scale than others, and their impact on key business priorities may vary by use case. This is why companies that want to increase their spending on and use of these technologies should focus their initial efforts on getting the best return on their investment. We believe that three use cases in particular make the most sense as a starting point, and each can play an important role in helping companies maximize relevance, sustainability, and accountability. 1. Advanced Scenario Modeling One use case that has become increasingly important in the wake of COVID-19 is scenario modeling, often done with a digital twin. A digital twin is a virtual copy of a supply chain that represents assets, warehouses, logistics and material flows, and merchandise items - essentially a live online version of a company's backbone network that can be used to simulate the operation of the supply chain, including all its complexities.
leads to loss of value and risks. Digital Twin can be created for an end-to-end supply chain or for specific functional areas for targeted improvements. These AI and cloud-based digital twins can help companies build resilience by identifying potential vulnerabilities and optimizing key areas of their supply chain. For example, a digital twin can serve as the basis for a supply chain stress test, similar to the one developed by Accenture and MIT. The test uses digital twin scenario modeling to assess potential operational and financial risks and impacts caused by major market disruptions, natural disasters, or other catastrophic events. The test can allow companies not only to understand how resilient their supply chains and operations are, but also to identify the weakest links and quantify the impact of those link failures on their role. This analysis, in turn, can help companies develop mitigation measures to improve resilience, and can also be used to reallocate resources from low-risk areas to save money during difficult times.
Digital twin modeling allows companies to design a network that optimizes costs and customer service levels while analyzing its carbon footprint. This ensures that companies can achieve sustainability goals by providing the best service to their customers. For example, a company can develop a network that reduces delivery times by minimizing the distances trucks have to travel, and thus reducing fuel consumption and emissions. 2. Unified demand planning A deep understanding of the source of demand - individual customers - for the most accurate satisfaction has never been so difficult, as customer expectations are rapidly changing and becoming more diverse. And as we saw in the early days of COVID-19, getting good on-demand processing during disruptions is next to impossible without the right information. The good news is that the data and AI-powered tools that companies need to understand demand are now available.
That's what Single Demand is all about: integrating all available internal and external (and often real-time) data across every process and every function within an organization to revolutionize the way demand is forecasted and planned. With this new approach, organizations end up with a unified view of demand and a reproducible planning process that improves accuracy and provides new insights to make more meaningful decisions across the business. For example, Accenture uses internal data (for example, from the supply chain and trade), external data (for example, consumption data, mobility, macroeconomic factors, brand attitudes, weather, and COVID-19 cases) and advanced algorithms to predict customer-level consumption . and shipment at the location level. As a result, companies are better able to meet demand, avoiding unexpected disruptions or changes in conditions, and even eliminating unnecessary supplies and thus fuel consumption and emissions. Unified demand highlights a truism from human history: better information leads to better decisions—for both customers and businesses. Today, two overlapping factors allow companies to get this "better information" when it comes to predicting what customers want to buy.
An explosion of relevant data provides much deeper insight. Historically, when companies tried to understand demand, they had to rely primarily on their own sales data: what they sold over what period of time. It was helpful but incomplete. Consumer-facing companies have been able to improve their understanding of their end consumer with greater access to point-of-sale data from retailers and the advent of syndicated market data, but they are still falling short. But now, with the ubiquity of AI-based solutions to help organizations collect and use this information, a whole new world of data has opened up for companies to help them truly understand what drives demand at an increasingly granular level and meet those demands. . demand more efficiently, perhaps by predicting demand before customers even know what they want.
For example, machine learning algorithms learn demand patterns and predict which product categories a consumer will need in a particular store based on relevant data, thus improving customer satisfaction and loyalty. The cloud provides a mechanism for collecting and analyzing data All this data that is now available would still not be very useful if it could not be collected and used. This is where the cloud comes to the rescue. The cloud provides companies with a platform to equivalence of information. This allows a company to access and consolidate a wide range of relevant data sources—both external and internal—triangulate the data it needs and make data compatible across business functions. Instead of individual functions (such as sales, marketing, finance, or supply chain) creating separate forecasts based on their specific data stores, everyone can apply analytics and AI to data to gain non-linear insights into what is really affecting demand and how changes some elements—for example, weather, economic activity, and government actions—will affect it. Armed with data-driven forecasts, supply chain leaders can more intelligently and proactively decide how to respond to and satisfy demand, including identifying the most appropriate actions in production, pricing, promotions and order fulfillment.
3. Monitoring and resolution of supplier risks A deep understanding of demand is only half the battle. Getting a similar view of the full supplier base is also critical, as a company can understand how its suppliers are performing and see potential risks across the entire supplier base. We saw the importance of greater transparency in the supplier base in the early days of the pandemic, which caused massive supply disruptions in virtually every industry around the world. Most companies couldn't see anything but a few large suppliers - they were actually flying blind - so they couldn't know which suppliers were closed or which orders were in line. This was particularly challenging due to the global nature and complexity of most vendor bases. But a company doesn't need a pandemic-sized disruption to bring down a normally functioning supply chain if the company doesn't have access to vital information.
Even a relatively minor problem—for example, a delay in one delivery of raw materials from one upstream supplier—can worsen throughout the supply chain, causing potentially huge complications further down the chain from supplier to supplier—and ultimately to the final consumer. is exposed to. This so-called "whip effect" has been around for decades, but now the data and technology is available to finally do something about it. Analytics, artificial intelligence and the cloud play an important role here, allowing companies to constantly monitor and respond to disruptions in the multi-stage supply chain. As we discussed about demand, more accurate information about what's going on throughout the supply chain allows you to make smarter and more informed decisions. For the first time, companies can actually collect data from multi-tiered supply chains, consolidate it in the cloud, and apply robust AI models to it to give companies real-time insight into the health of their suppliers. With this data, companies can proactively identify where certain suppliers pose a risk—for example, a supplier's precarious financial situation that could lead to bankruptcy, or a supplier's inability to obtain vital raw materials—and predict the resulting impact on the entire supply chain.
Scenario modeling can then help the company identify the best alternatives so that the organization is prepared if a disruption does occur. And they can promote their responsibility agenda by ensuring, for example, that suppliers' carbon footprints meet agreed levels and that suppliers purchase and manufacture materials in a sustainable and responsible manner. Access to real-time supplier data could allow companies to hold suppliers accountable for where and how they source materials, allowing brands to weed out suppliers that do not meet ethical or environmental standards. Chart the path to a more relevant, sustainable and responsible future Over the past five years, analytics and artificial intelligence have become increasingly important for the business of many companies. These powerful tools enable companies to automate tasks they have not been able to do before, while at the same time providing much deeper information that companies can use to make faster and more efficient decisions to improve business performance. And "business performance" today requires the simultaneous achievement of traditionally competing key performance indicators such as customer satisfaction, revenue, efficiency, cost control and carbon emissions. But until now, companies have only scratched the surface of the possibilities of analytics and artificial intelligence.
Accenture 1 research points to a growing body of evidence that some companies are now starting to use these tools to help them do what was previously impossible: become more relevant, sustainable and responsible all at the same time. These companies are demonstrating that the old compromises they used to make when considering these three outcomes are disappearing as collaboration between people and machines becomes more common. And as our research shows, striking the right balance between these outcomes is the winning formula for faster growth and greater enterprise value. Other companies need to step up their game so they don't get left behind. Focusing on a few key use cases such as scenario modeling, unified demand planning, and supplier risk management is a good way for companies to start incorporating supply chain analytics and artificial intelligence into their operations to inform every person and every decision across the business. These are very manageable first steps that can put companies on the path to smarter operations that can help them compete effectively with the organizations currently setting the bar. .