Digital and physical transformation for supply chains resiliency

Organizations are pursuing leaner operations and new strategies to meet customer demands, lower costs, and increase resiliency against external forces. Every industry has faced an increasing number of supply chain disruptions over the past decade due to natural disasters, weather events, labor shortages, trade disputes, and geopolitical issues. The rate of supply chain disruptions is unlikely to ease anytime soon given the ongoing geopolitical tensions, the effects of climate change, and persistent labor challenges.

The restrictions in the Black Sea caused by the Ukrainian war have left ships stranded at port and increased prices for commodities such as wheat and nickel. Similarly, companies have been navigating away from the Red Sea to avoid the risk of attacks from the Houthi rebels in the region. These incidents have further highlighted the fragility of global supply chains and motivated business leaders to prioritize supply chain resilience.

In addition to addressing supply chain disruptions, organizations are also pursuing ESG (environmental, social, and governance) initiatives due to shifting customer behavior and investor sentiment. Customers now care more about the environmental impact of the products they buy and the businesses they partner with. However, customer expectations for faster deliveries are increasing and expected to increase last-mile deliveries by 78% by 2030. As last-mile deliveries are considered less environmentally friendly, these competing priorities will be a challenge for logistics firms hoping to be carbon neutral in the coming years.

Organizations must re-evaluate their supply chain strategies and implement measures to mitigate risks, enhance resilience, and address new requirements. This may involve diversifying their supplier base, increasing inventory levels, and exploring alternative transportation routes or modes. By proactively addressing potential disruptions, companies can better ensure the continuity of their operations and meet customer demands, even in the face of unforeseen events.

This blog post describes some popular mitigation strategies used by global organizations to address supply chain issues. It includes both digital and physical solutions, including AWS Supply Chain. AWS Supply Chain is a cloud application that mitigates risk and lowers costs with unified data, machine-learning-powered actionable insights, and built-in contextual collaboration.

Supply chain transformation strategies

Organizations are pursuing supply chain transformations to enhance supply chain resilience, de-risk external factors, balance costs, drive efficiency, and improve performance. These transformations can be broadly classified into two main strategies: digital transformation and physical transformation.

Organizations are pursuing digital transformation strategies that leverage advanced technologies to enable supply chain innovation across functions. These strategies balance the cost to implement with increased resiliency, improved efficiency, and increased productivity. Digital solutions improve visibility and connectivity, empowering organizations to make faster, more agile decisions while optimizing critical key performance indicators (KPIs). The technology underpinning these solutions – whether artificial intelligence (AI), machine learning (ML), or the internet of things (IoT) – enables new levels of automation, control, and insight.

Organizations are also exploring physical transformation strategies to enhance supply chain resilience, de-risk external factors, balance costs, drive efficiency, and improve performance. One prominent approach is repositioning supply chains, such as near-shoring, which involves shifting manufacturing and distribution nodes closer to the end market, thereby reducing transit times and mitigating the effects of some supply chain disruptions. Complementing this effort, organizations are investigating “micro-fulfillment centers” at the local level to support last-mile delivery to end customers.

Physical transformation also includes automation opportunities. Warehouse and distribution center automation with robots and autonomous vehicles can help increase capacity, reduce labor costs, and improve efficiency across operations. The use of autonomous vehicles for middle mile or last mile transportation is also in consideration to reduce transportation costs and fuel consumption. Meanwhile manufacturing automation is becoming increasingly interesting as companies seek to offset rising labor costs. With incentives from various government programs, companies are investing in automated manufacturing operations, including the deployment of humanoid robots on factory floors to support near-shoring efforts and reduce reliance on low-cost labor markets.

Digitalization for improved visibility and performance

Organizations are increasing investments in digital technologies to increase supply chain visibility and improve collaboration between supply chain teams and their stakeholders. Consulting firms like BCG emphasize the necessity for companies to embrace digitization, leveraging tools like AI and machine learning to enable agile decision-making and adaptability, ultimately leading to a resilient, sustainable, and cost-efficient supply chain. EY supports this sentiment, urging supply chain leaders to accelerate digital investments in pursuit of an autonomous supply chain. This global push toward a more digitized supply chain is fueled by the need for improved supply chain management, which is anticipated to witness growth in areas such as machine learning, generative AI, and digital tools aimed at enhancing critical use cases like demand forecasting, inventory management, production scheduling, and early detection of supply chain disruptions. McKinsey further highlights that with the advent of advanced digital technologies offering greater visibility and capabilities, organizations will be better positioned to manage intricate and diverse supply chains while also identifying vulnerabilities related to data security, operational visibility, and adequate oversight and governance.

Physical transformation to offset increasing labor costs

Organizations are exploring physical transformation strategies that can apply automation to offset increasing labor costs. China’s attractive low labor costs are becoming less appealing because of rising labor wages and geopolitical challenges. While countries like India, Mexico, and Vietnam offer the lowest labor costs, US-based organizations are choosing to keep operations local and in-country. New funding and tax incentives through the CHIPS and Science Act, the Inflation Reduction Act, and the Build Back Better Infrastructure Act will further accelerate this shift. Robotics and mechanical automation in manufacturing and transportation are also emerging to reduce operating costs and increase efficiency. Autonomous mobile robots (AMRs) and autonomous guided vehicles (AGVs) are types of mobile robots that transport goods around warehouses with minimal human intervention, reducing labor costs and increasing operational efficiency. While AMRs use sensors and algorithms to navigate, AGVs follow pre-defined paths around the warehouse space and have the built-in capability to interact safely with infrastructure and people. Additionally, robotic sortation and packing systems enable faster and more accurate order fulfillment while minimizing errors and damage. These collaborative robots work alongside human operators, improving efficiency and reducing the risk of injury. Robotic assembly in manufacturing like BMW’s and Tesla’s deployment of humanoid robots on factory floors will likely also be more prevalent to help companies nearshoring their manufacturing operations offset high labor costs, provide consistent capacity for output, and improve safety for their valued employees.

Additionally, Internet of Things (IoT) devices can be utilized for proactive maintenance of warehouse equipment, reducing downtime and repair costs, improving energy management, and enhancing safety and security. There is also growing potential and interest in using drones for inventory management within warehouses. Outside the warehouse, in the yard area, companies are deploying autonomous yard trucks to move trailers and cargo, further streamlining operations.

Logistics and transportation are the next sectors of the supply chain poised for automation, with autonomous vehicles expected to play a significant role, particularly in logistics operations. Examples like the increase of autonomous taxis in San Francisco and planned expansion to additional cities indicate a positive signal the emergence of this technology and should increase interest in autonomous vehicles for middle-mile and last-mile logistics applications. This technology has the potential to increase supply chain capacity while reducing transportation costs and associated fuel consumption. Logistics firms are currently facing a shortage of drivers to move goods, and autonomous trucks can potentially alleviate this issue by removing the need for an 11-hour driving limit per day imposed by the Hours of Service regulations. Autonomous trucks, which are designed to operate without fatigue, stress, or impairment, could potentially drive longer hours while reducing the number of accidents on the road. Along with improving the safety of drivers and riders on the road, autonomous trucks can alleviate trucking companies from the financial impacts of driver-related accidents.

Global efforts to achieve carbon neutrality

Companies continue to adopt alternative fuels in their transportation systems to achieve sustainability targets. Given that transportation accounts for 17% of emissions, this is an opportunity for companies to contribute to global efforts in mitigating the impacts of global warming. Despite global alignment in reversing the trends of climate change, the path to achieve it will be regional as we expect Europe and parts of US (primarily ZEV states) to be at the forefront of this trend. Customers will have a variety of options to select in terms of reducing their transportation emissions which include hydrogen, battery, and natural gas. Each fuel source will have its strengths and weaknesses which will lead to challenges around 1) network design to accommodate each fuel source range and associated uptime, 2) having the right infrastructure and capabilities to maintain a fleet with different fuels, and 3) tracking carbon credits for reporting. These challenges can potentially be mitigated with greater levels of digital tools and automation previously mentioned in this article.

How AWS Supply Chain enables digital transformation

AWS Supply Chain can help customers achieve their digitization, automation, and sustainability objectives. AWS Supply Chain combines Amazon’s nearly 30 years of supply chain experience with the resilience, security, and business continuity of an AWS service to help customers optimize their supply chains. It first unifies data from disparate supply chain silos into a supply chain data lake (SCDL) to increase supply chain visibility. With centralized data, machine-learning-powered insights, recommended actions, and in-application collaboration capabilities, AWS Supply Chain enables faster decision-making and improved customer experiences.

AWS Supply Chain enables digitalization strategies by enhancing critical supply chain processes from planning and visibility to sustainability tracking. Advanced analytics, machine learning models, and seamless data integration empower organizations with data-driven insights, automated decision support, and tighter cross-functional alignment. Digitalization begins with a dedicated supply chain data lake (SCDL), which is a flexible, scalable data infrastructure that provides pre-built capabilities to ingest, standardize, and integrate data across fragmented systems. It aggregates and associates supply chain data into a high-quality, unified data asset, enabling organizations to tap into the collective intelligence of their enterprise. AWS Supply Chain also includes a new generative AI-powered data onboarding agent built using Amazon Bedrock, that automatically transforms customers’ data from any native format to the AWS Supply Chain data model, increasing the speed and ease of onboarding data by minimizing manual data integration.

This centralized approach enhances inventory visibility across all locations, ensures data consistency across channels, and facilitates effective decision-making driven by machine learning-powered insights. Organizations can leverage these capabilities without the need for costly re-platforming, upfront licensing fees, or long-term commitments. AWS Supply Chain’s advanced Demand Planning feature harnesses the power of ML models to deliver precise demand forecasting across various sales channels. This capability enables organizations to maintain optimal inventory levels, minimizing the risks of overstocking or stockouts, and swiftly responding to demand fluctuations and supply disruptions. The Supply Planning feature complements Demand Planning by optimizing inventory positioning, ensuring the right products are available at the right locations at the right time. The N-Tier Visibility feature simplifies communication and data sharing with multiple tiers of suppliers. This functionality is crucial for managing the diverse demands of multi-channel distribution, improving accuracy in inventory planning and execution. By facilitating collaboration throughout the supply chain, organizations can respond more effectively to changing market conditions and customer expectations. AWS Supply Chain provides a centralized sustainability data repository, facilitating sustainable practices through consolidated reporting and auditing of environmental data.

The next phase of supply chain management evolution will include generative AI to automate manual tasks, improve information accuracy, and increase resource productivity. Amazon Q in AWS Supply Chain, an interactive generative AI assistant that will be released later this year, analyzes data to identify business risks and opportunities, provide explainability, and help customers evaluate trade-offs. It can proactively identify important exceptions, provide recommended actions to mitigate issues quickly, and answer customer questions, replacing the traditional approach of manually collecting and analyzing data from disparate systems.

Conclusion

The global supply chain landscape faces persistent challenges from geopolitical tensions, climate change impacts, and labor shortages. Organizations must prioritize resilience, sustainability, and digital transformation to navigate these complexities. This blog post explored digital strategies leveraging artificial intelligence, machine learning, and the Internet of Things for enhanced visibility, predictive analytics, and data-driven decisions. It also covered physical strategies like near-shoring, warehouse automation, and autonomous vehicles to address labor issues, reduce costs, and drive efficiency.

AWS Supply Chain offers a comprehensive solution, combining Amazon’s supply chain experience with AWS resilience and security. It unifies disparate data into a centralized supply chain data lake, enabling machine learning-powered demand forecasting, optimized inventory positioning, multi-tier visibility, and seamless collaboration. The generative AI capability, Amazon Q in AWS Supply Chain, automates manual tasks, improves accuracy, and increases productivity.

As organizations pursue both digital and physical transformation strategies, AWS Supply Chain positions itself as a powerful enabler, supporting the adoption of advanced technologies and the implementation of sustainable practices. With its comprehensive capabilities, AWS Supply Chain equips organizations with the tools and insights necessary to enhance resilience, drive sustainability initiatives, and optimize performance across their global supply chain operations. Please visit AWS Supply Chain to learn more and get started. For a self-paced technical overview, visit AWS Workshops.