Sudipta Ghosh, Partner – Supply Chain Transformation, and Leader – Data and Analytics and Nitin Soundale, Partner – Supply Chain Transformation from PwC India discuss the challenges posed by the increasing complexity of supply chains and the importance of end-to-end visibility for making quick and agile decisions.
They introduce the concept of a supply chain control tower which has four pillars – visibility, exceptions, integration, and automation to add flexibility and resilience to the supply chain process. The components that enable tech-enabled solutions are nudges and scenario capabilities. The establishment of a supply chain control tower is an ongoing journey and requires continuous refinement and optimization, making step-by-step improvements for organizations to undertake to sustain this initiative and add value to their business.
technology
Manufacturing has faced many challenges in recent years. The ongoing effects of the pandemic and, more recently, the war in Ukraine have exposed supply chain vulnerabilities, forcing companies to adapt fast. Those who don’t face the unwelcome prospect of being unable to get their goods on store shelves or being left with a costly overstock in their warehouses.
In spite of the challenges, the manufacturing industry continued to grow in 2022. In addition, a survey conducted by the Manufacturing Alliance finds that the majority of manufacturing CEOs believe supply chain challenges that have been troubling them for a couple of years are improving. Growth is possible.
Only those manufacturers that continue to adapt will thrive, and AI plays a major role in revolutionizing operations. It’s all about finding it. Technology is the key to everything in a rapidly changing world, from increasing efficiency and boosting productivity to enhancing sustainability and accelerating growth.
Operational efficiency
AI can significantly enhance visibility and efficiency by keeping track of the circulation of information, services and goods in the entire manufacturing process. This leads to more reliable forecasting as it is based on data from numerous productive resources, including robotic packing machines. Additionally, with machine learning used to optimize supply and demand, the consistency observed can help manufacturers manage the instability common in today’s operating environment.
Technology can be a mighty aid in facilitating companies become more streamlined and less susceptible to uncertainty. Artificial intelligence, for one, offers manufacturers an integrated perspective of the supply chain and eliminates obstructions in operations with real-time analytics. Augmented and virtual reality can also be used to mimic processes and undergo trials, enabling enterprises to spot (and get ready for) potential threats, weaknesses, and mistakes before they affect output lines.
BMW has developed its virtual factory in collaboration with NVIDIA to train robots to work more efficiently before goods go into full production. A fifth of manufacturers are also experimenting with or actively developing metaverse platforms for their products and services.
As one of the largest expenses for manufacturers, shipping and warehousing are no exception. With the advent of IoT devices and sensors, firms are now able to collect and analyze a vast amount of vehicle performance data and provide complete product tracking from factory to dealer and retailer. Using these insights, they can boost agility, streamline costs, and optimize their logistics operations.
Taking care of the environment and saving money
The potential benefits of AI go beyond just operational concerns, directly impacting two important areas of manufacturing.
The first is profitability. According to recent Accenture research, only 12 percent of firms have advanced their AI maturity sufficiently to achieve superior growth and business transformation. Companies that do could double their revenues by 2024 by 24 percent, boosting their bottom line significantly and benefiting their employees, shareholders, and customers.
ESG is an area in which AI offers a major advantage. The International Energy Agency states that industry consumption of energy and emissions account for a large share of global carbon emissions. Consequently, companies have a duty to act responsibly and provide clear data concerning their outcomes. AI algorithms can generate reasonable advice regarding how to keep energy utilization and raw material usage balanced. There are also other AI-driven methods for monitoring emissions across the entire scope of a corporation’s value chain. Both of these answers guarantee organizations take steps to reduce their environmental impact while adhering to stringent regulations.
One automation company used artificial intelligence to discover that 40 percent of energy consumed by one of its machines occurred when it was not producing anything, according to Blake Moret, CEO of Rockwell Automation. With this single insight, the company was able to drastically reduce its costs and emissions by turning off the equipment when it wasn’t in use.
Relationship building
The focus on yields, factory efficiency, and optimal warehousing levels can make it easy to overlook the importance of customer experience. Nevertheless, CX too is ripe for AI innovation.
In order to succeed as a manufacturer, you need strong relationships with your suppliers, distributors, and customers. However, customer loyalty remains a major challenge for most firms. In Zendesk’s research, 61 percent of customers walk away after a single negative experience, and 72 percent of manufacturing leaders agree that customer service is a critical business priority.
The good news is that viable solutions exist. Manufacturers can ramp up their operations and revamp their CX with AI-driven automation and self-service tools. An omnichannel support platform that unifies brands, contact centers, and support channels into one comprehensive solution helps modernize the customer experience—cultivating loyalty, advocacy, and ultimately fueling growth. To illustrate this point: Siemens, the largest European manufacturer in industry, energy, healthcare and infrastructure partnered with Zendesk to consolidate and analyze all of its customer data.
Future-shaping
The manufacturing industry should not expect smooth sailing in the years ahead, due to the ongoing economic and geopolitical uncertainty, as well as the transition to a hybrid future of work. Since many of these factors are beyond manufacturers’ control, it is even more critical that they take proactive measures to shape their own future.
Using data and technology, they can accomplish this. By deploying AI-based tools and solutions, firms are able to improve nearly every aspect of their operations without incurring huge overheads, whether it is optimizing operations, sharpening decision-making, enhancing sustainability performance, or improving customer experiences.
Smart infrastructure is the solution to complex supply chains today. And that requires artificial intelligence.
In an era marked by rapid technological advancements and shifting market dynamics, the automotive industry stands at a pivotal crossroads. With challenges ranging from raw material scarcity to the rise of electric vehicles (EVs), automakers and original equipment manufacturers (OEMs) are reimagining their supply chain strategies. This transformation is not just a response to current trends but a proactive move towards a more resilient and intelligent future.
Navigating New Terrain: The Shift to Smart Supply Chains
Despite the end of the pandemic, the automotive industry continues to face significant supply chain challenges. These include net-zero commitments, the burgeoning EV market, geopolitical conflicts, evolving trade agreements, natural disasters, and high interest rates affecting consumer behavior. To stay agile and competitive, automakers and suppliers are now rethinking their supply chain management strategies, moving away from reactive consumer demand models to more resilient, digitally enabled supply chains.
The Roadblocks to Smart Supply Chain Adoption
A recent Capgemini report reveals that only 53% of auto suppliers and OEMs have mature intelligent supply chains. Key challenges in scaling intelligent supply chains include:
- Raw Material and Resource Scarcity: Ongoing shortages of critical materials, like rare earth minerals and semiconductors, are major hurdles.
- Transition from Offshoring to Nearshoring and Reshoring: Many companies are moving operations closer to home, a trend accelerated by the pandemic.
- Unattainable Stock Levels: The pandemic has led some OEMs to overstock inventory, creating financial burdens and potential market oversupply.
- Lack of Circularity: Despite the growing demand for sustainable practices, only 53% of automotive executives followed a circular economy strategy in 2022.
- Deteriorating Supplier-OEM Relations: The pandemic has strained relationships, with issues around trust, transparency, and communication.
Strategies for Future-Proofing Supply Chains
To address these challenges and future-proof their supply chains, automakers should consider several tactics:
- Invest in Data Strategy and Capabilities: Utilizing AI and analytics tools for better data integration and prediction.
- Adopt Inventory Optimization Technology: Leveraging predictive analytics and digital twins for better demand forecasting and disruption anticipation.
- Prepare the Workforce for Digital Transformation: Upskilling and reskilling workers to align with evolving business models and technological advancements.
- Develop New Supplier-OEM Relations Policies: Establishing clear communication and trust-building measures.
- Integrate Circularity Practices: Implementing circular supply chain processes to enable repair, resell, reuse, or recycle goods.
The Road Ahead: Building Resilient and Intelligent Supply Chains
By implementing these strategies, automakers can streamline operations, optimize inventory, improve production rates, and mitigate issues. This leads to reduced costs, faster time to market, and potentially more sales. Additionally, intelligent supply chains can significantly reduce emissions and carbon footprints, enhancing customer experience and loyalty, especially among environmentally conscious consumers.
Conclusion:
The journey towards resilient, connected, intelligent supply chains is ongoing. Automakers must embrace trust, transparency, and data-driven intelligence to overcome current challenges and secure their place in the future market. The time to act is now, with the industry at a crucial juncture, facing material scarcity, geopolitical conflicts, and the imperative to decarbonize.
Your Thoughts?
How do you see the future of supply chains in the automotive industry? What role will technology and sustainability play in shaping this future? Share your perspectives and join the discussion below!
The Top Asian Startups Revolutionizing Transportation Management Systems
Transportation Management Systems (TMS) are critical for supply chain professionals, playing a pivotal role in logistics operations. They offer a platform for coordinating and managing the movement of goods, which in turn reduces costs, improves efficiency, and enhances customer service. With the rise of digitalization, numerous startups companies in Asia are leveraging this technology to revolutionize the logistics industry.
Here are some of the top Asia-based startups that are driving innovation in the TMS space:
Freterium is a logistics platform based in Morocco (but with operations in Asia), that helps shippers and carriers optimize their operations. Founded in 2017, the platform offers route optimization, real-time tracking, and predictive analytics among its features. For more details, click here.
SuperProcure
SuperProcure is a logistics tech startup based in Kolkata, India. It provides a cloud-based platform that helps businesses automate their logistics and supply chain operations. The platform offers features like real-time tracking, freight discovery, and vendor management. For more details, click here.
Haulio
Singapore-based Haulio, founded in 2016, aims to transform the region’s port logistics by enabling hauliers through technology. The platform is used by 90% of truckers and drivers in the cities where it operates. For more details, click here.
Fleetx
Fleetx, founded in 2017, provides an intelligent fleet management platform that uses AI and machine learning to help fleet owners improve efficiency. While their key clients are undisclosed, they have raised $26.1M in total funding. For more details, click here.
RoaDo
RoaDo is a Bangalore, India-based logistics tech startup helping enterprises, and LSPs to digitalize their end-to-end operations. It is a procurement-to-pay platform with a private marketplace for sourcing vehicles. It allows stakeholders to manage, automate and digitalize their entire logistics and supply chain operations in a single place. For more details, click here.
Movex
Founded in 2020 by a team dedicated to transforming how people and things move, Movex offers mobility solutions for enterprise transport businesses. While information about their funding is not available, their core functionalities include advanced routing, auto dispatch, multi-location trips, and AI-powered analytics. For more details, click here.
moveonline
Based in Saudi Arabia, moveonline was founded in 2017 and operates a ride-hailing app that connects users with local drivers. The company does not disclose its funding or key clients, but it aims to change the way the world works by making transportation more community-driven. For more details, click here.
Pando
PandoCorp is a logistics management company based in Chennai, India. Founded in 2017, the company provides a platform for businesses to manage their logistics and supply chain operations. It’s backed by investors like Chiratae Ventures and Nexus Venture Partners. For more details, click here.
oTMS
oTMS (One Transport Management System) is a Shanghai-based provider of SaaS logistics solutions. Founded in 2013, the company offers a cloud-based platform that helps businesses manage transportation logistics more efficiently. For more details, click here.
Grab Haulier
Grab Haulier is a digital platform based in Malaysia that connects shippers and hauliers. Founded in 2017, it offers services including freight matching, real-time tracking, and electronic documentation. For more details, click here.
When evaluating these solutions, look for features that meet your specific needs, consider their scalability as your business grows, and assess the quality of customer support. Ensure the TMS solution integrates seamlessly with your existing systems and processes.
The growth of TMS technology in Asia is a testament to the region’s burgeoning digital economy and its impact on the logistics industry. These companies are not only improving efficiencies in the supply chain but also introducing innovative solutions that are changing the landscape of transportation management.
Looking for the best transportation management systems to support your operations in APAC? Check out the latest #TMS solutions from the most innovative technology vendors on https://www.chaintech.net
What are your thoughts on this list? Are we missing any company? We invite you to share your feedback and engage in a thought-provoking discussion in the comments section below.
Leveraging the Metaverse for Industrial Efficiency: A Deep Dive into Transportation, Supply Chain, and Logistics
The metaverse is no longer a buzzword confined to the realm of consumer applications. This article explores a report by Ernst & Young and Nokia, shedding light on how the industrial and enterprise metaverses are revolutionizing the transportation, supply chain, and logistics sectors.
The metaverse, a term often associated with gaming and social networking, is making significant strides in the industrial and enterprise sectors, particularly in transportation, supply chain, and logistics. A recent report by Ernst & Young and Nokia, titled The Metaverse at Work, provides a comprehensive analysis of the current state and future potential of the metaverse in these sectors.
Contrary to the popular belief that the metaverse is a futuristic concept, the report reveals that the industrial and enterprise metaverses are already here and growing rapidly. The study, conducted across six geographies and four industries, presents a consistent global picture, demonstrating how metaverse use cases are being practically planned and deployed across key verticals.
The report highlights that the benefits realized by early adopters generally exceed the expectations of those in pre-deployment stages. It underscores the true challenges they’ve faced, often hinged on technical infrastructure, and reveals that the partners they are employing to face these challenges are not necessarily the companies seen as driving the advancement of the metaverse.
One of the key findings of the report is that companies believe in the power of the metaverse, with only 2% of respondents viewing it as a buzzword or fad. Furthermore, 58% of companies planning to enter the industrial and enterprise metaverses have already done so through at least a pilot program.
In the context of transportation, supply chain, and logistics, the metaverse offers significant potential. For instance, visualized predictive maintenance and autonomous/remote-controlled maintenance robotics are two use cases that have shown promising results. By overlaying sensor data on a digital twin, information can be made more actionable for technicians, leading to improved process efficiency and sustainability due to minimized machine downtime and extended machine lifetimes.
Moreover, the use of autonomous or remote-controlled robotics for maintenance and repairs allows for prompt interventions for vehicles on the move. This not only results in capital expenditure (CAPEX) reduction and sustainability benefits due to extended machine lifetimes but also improves safety by reducing the need to put humans in higher-risk environments for certain repairs.
The report also emphasizes the importance of technical enablers that allow for enhanced collection, transmission, storage, and processing of data, which are foundational to launching metaverse use cases. These enablers include cloud computing, AI/ML, industrial data collection, and network. For instance, cloud computing enables the storage and processing of the large quantities of data inputs and outputs required to orchestrate metaverse use cases. AI/ML is indispensable for modeling digital twins and creating realistic environments and simulations and interpreting data/making predictions.
However, the deployment of metaverse use cases is not without risks. The primary concerns for metaverse use cases are cyber, information security, and data privacy, with over 60% of respondents citing these as primary risks. Cyber and information security risks are paramount across a range of digital use cases but are amplified by the drastic increase in data, that industrial and enterprise metaverse use cases will produce. Data privacy risks can prove especially complex, particularly as companies expand the scope and scale of use cases to reflect a more interwoven, data-driven virtual ecosystem, incorporating valuable, potentially sensitive, data assets.
Takeaways:
- The industrial and enterprise metaverses are already here and growing rapidly, with significant potential for the transportation, supply chain, and logistics sectors.
- Visualized predictive maintenance and autonomous/remote-controlled maintenance robotics are two promising use cases in these sectors, leading to improved process efficiency, sustainability, and safety.
- The benefits realized by early adopters of the metaverse generally exceed the expectations of those in pre-deployment stages, underscoring the tangible value of the metaverse in the industrial and enterprise sectors.
We’d love to hear your thoughts on the application of the metaverse in the transportation, supply chain, and logistics sectors. How is your organization leveraging the metaverse? What challenges and opportunities do you foresee? Share your insights and join the conversation in the comments section below.
Introduction to AI
In recent years, AI has become increasingly popular in various industries and business functions due to its ability to improve efficiency and productivity. In the supply chain industry, AI is being used in various areas such as planning and forecasting.
Planning is a critical function in the supply chain industry as it determines the most efficient way to utilize resources and minimize costs. AI can help with this by analyzing large amounts of data and identifying patterns that humans may not be able to see. This information can then be used to make better decisions about things like production levels and inventory management.
Forecasting is another important function in the supply chain industry as it allows businesses to anticipate future demand and plan accordingly. AI can again help here by analyzing data and identifying trends that could impact future demand. This information can then be used to make more accurate forecasts, which can help businesses avoid stockouts and other problems associated with inaccurate forecasting.
AI in Supply Chain Planning and Forcasting
The use of AI in supply chain planning and forecasting can help organizations to optimize their operations and reduce costs. By automating the process of data collection and analysis, AI can provide accurate and up-to-date information about demand patterns, inventory levels, and other factors that impact the supply chain. This information can be used to improve planning and decision-making around production, warehousing, distribution, and delivery. In addition, AI can help to identify potential disruptions in the supply chain and suggest mitigation strategies.
How AI Can Help With Supply Chain Planning
AI can help with supply chain planning in a number of ways. For one, AI can help to identify patterns and trends in data that can be used to forecast future demand. Additionally, AI can be used to optimise routes and schedules for transportation, and to predict delays or disruptions. Ultimately, AI can help to improve the efficiency and accuracy of supply chain planning, making it a more reliable and effective tool for businesses.
Conclusion
AI in supply chain planning and forecasting is quickly becoming a necessity for companies of all sizes. With the ability to analyze large amounts of data and make decisions faster, more accurately, and more efficiently than ever before, it can help organizations save time and money while also improving customer satisfaction. As technology continues to evolve and become even more advanced, we will likely see continued growth in the use of AI-based solutions within supply chains around the world.