“Uncertainty is the only certainty there is,” mathematician John Allen Paulos once wrote. The procurement and supply chain management industries are no exception, but next-generation technology provides trends worth watching. 

With time, the supply chain has shifted from the back-office function it once was to a strategic driver of business growth. Contemporary business models, technological advancements and innovative processes have made supply chains efficient and agile.

Supply chains will become more complex and internationally dispersed. Procurement leaders need to build on newer capabilities to help them navigate the changing business landscape and adapt quickly.

At this rate, what will supply chains look like in the year 2030? Big data, cloud computing, artificial intelligence (AI), robotic process automation (RPA) and the internet of things (IoT) will help procurement leaders, contractors and supply chain managers meet future demand.

While big data in logistics is still in its infancy, it’s the foundation on which AI, cloud computing and RPA become more accurate and effective in simplifying tasks and relegating them to automated systems. Big data expands the dataset for analysis beyond the traditional internal data in supply chain management systems and software. It also applies statistical processes to new and existing data sources. Now, most companies lack the tools and knowledge to explore and utilize big data in their supply chains. In the future, these tools will be more accessible. 

Cloud Computing and AI

Supply chains generate big data, and cloud-based AI turns that data into insights. Cloud computing coupled with AI has transformed how supply chains operate, and its abilities will only increase in complexity over the next 10 years. Through predictive analytics, cloud and AI systems can use past trends and market indicators to facilitate the following processes:  

• powering process automation
• informing supplier selection
• improving customer support
• streamlining supplier onboarding and automating supplier management
• providing real-time information on shipments
• analyzing carrier performance
• anticipating trends in operational issues

Progressive companies already utilize supply chain knowledge management systems to respond to supply chain difficulties in real-time. With a cloud-based, mobile-enabled solution, supervisors input information from the worksite, immediately notifying operators.

Companies can build transparent supplier relationships by automating the information exchange between an organization and its suppliers and contractors. Organizations can easily manage their vendors down to each individual worker across geographically dispersed worksites. Workers can complete site-specific orientation and training online before they set foot on site. Operators can track the completion status of the training curriculum and assess knowledge retention through online evaluations.

Analytics can help companies monitor supplier/vendor capabilities and track data on a supplier’s compliance or performance. Traditionally, different departments compiled this information through paper records. Decision-makers had to sift through piles of papers or electronic files to find this information. Today, advanced analytics allows operators to define supplier attributes to categorize them into logical profile sections. Detailed supplier profiles make it easier for operators to quickly retrieve, process and validate supplier information in a matter of seconds.

Once a new supplier is onboarded, collecting, verifying and storing supplier data will ensure responsible supplier risk management. A high-end analytics engine can analyze this data to generate supplier performance insights in real-time. Such insights empower sourcing professionals to easily monitor the supplier and vendor pool, their credentials such as certificates of insurance (COIs) and their compliance status.

Delivering tangible cost savings has always been a critical task for procurement and will continue to be a high priority in the next decade. Considering this, procurement leaders will have to look for newer ways to achieve cost efficiency. One way is through supplier analysis. Critical supplier information is often trapped in varying data management systems. Consolidating that data into one common repository helps operators get better visibility into spending across the entire value chain. A centralized data framework, complemented by an analytics engine, for example, can help decision-makers identify expensive or low-performing suppliers. A new central data management system can be seamlessly integrated with the legacy system through application programming interfaces (APIs).

Robotic Process Automation

Robots are expected to see "strong growth over the next five years, particularly within supply chain operations that include lower-value, potentially dangerous or high-risk tasks,” according to Deloitte. With the massive growth in e-commerce, this should not surprise anyone in the logistics world. Robotic technology applications include automated vehicles like drones, trucks and trains, last-mile deliveries and storage and retrieval systems (ASRS).

The increased usage of autonomous robots can achieve the following objectives:

• increase efficiency and productivity
• reduce re-work and risk rates
• improve employee safety
• perform mundane tasks so humans can work more strategic efforts
• increase revenue by improving order fulfillment and delivery speed, leaving customers satisfied

New pricing structures will enable companies to invest in automation, making the leap into robotics much more feasible. Using a RaaS-type model (Robotics as a Service), providers lease units through a monthly service contract instead of customers paying an up-front capital expenditure. 

Internet of Things 

An emerging trend for supply chain managers is asset tracking through IoT to save time and money and enable data-driven decision-making. 

The IoT is made up of interconnected physical devices that can monitor, collect and send data to cloud-based software for analysis via Wi-Fi. IoT devices have improved quality management in supply chains through GPS tracking of shipments and monitoring parcel conditions. RFID chips, smart devices and mobile sensors can track and authenticate products, measure temperature, humidity, light levels, movement, handling, speed and other environmental factors of shipments. 

The growing pace of technological innovation propels digital supply chain management solutions. Thankfully, embarking on the technical journey will become more accessible and cost-effective as more technologies emerge. Organizations that rapidly adopt these emerging solutions while incrementally replacing legacy systems will better navigate this decade with greater insight and efficiency. 

Danny Shields is vice president of industry relations at Avetta, a provider of cloud-based supply chain risk management technology. 

“Uncertainty is the only certainty there is,” mathematician John Allen Paulos once wrote. The procurement and supply chain management industries are no exception, but next-generation technology provides trends worth watching. 

With time, the supply chain has shifted from the back-office function it once was to a strategic driver of business growth. Contemporary business models, technological advancements and innovative processes have made supply chains efficient and agile.

Supply chains will become more complex and internationally dispersed. Procurement leaders need to build on newer capabilities to help them navigate the changing business landscape and adapt quickly.

At this rate, what will supply chains look like in the year 2030? Big data, cloud computing, artificial intelligence (AI), robotic process automation (RPA) and the internet of things (IoT) will help procurement leaders, contractors and supply chain managers meet future demand.

While big data in logistics is still in its infancy, it’s the foundation on which AI, cloud computing and RPA become more accurate and effective in simplifying tasks and relegating them to automated systems. Big data expands the dataset for analysis beyond the traditional internal data in supply chain management systems and software. It also applies statistical processes to new and existing data sources. Now, most companies lack the tools and knowledge to explore and utilize big data in their supply chains. In the future, these tools will be more accessible. 

Cloud Computing and AI

Supply chains generate big data, and cloud-based AI turns that data into insights. Cloud computing coupled with AI has transformed how supply chains operate, and its abilities will only increase in complexity over the next 10 years. Through predictive analytics, cloud and AI systems can use past trends and market indicators to facilitate the following processes:  

• powering process automation
• informing supplier selection
• improving customer support
• streamlining supplier onboarding and automating supplier management
• providing real-time information on shipments
• analyzing carrier performance
• anticipating trends in operational issues

Progressive companies already utilize supply chain knowledge management systems to respond to supply chain difficulties in real-time. With a cloud-based, mobile-enabled solution, supervisors input information from the worksite, immediately notifying operators.

Companies can build transparent supplier relationships by automating the information exchange between an organization and its suppliers and contractors. Organizations can easily manage their vendors down to each individual worker across geographically dispersed worksites. Workers can complete site-specific orientation and training online before they set foot on site. Operators can track the completion status of the training curriculum and assess knowledge retention through online evaluations.

Analytics can help companies monitor supplier/vendor capabilities and track data on a supplier’s compliance or performance. Traditionally, different departments compiled this information through paper records. Decision-makers had to sift through piles of papers or electronic files to find this information. Today, advanced analytics allows operators to define supplier attributes to categorize them into logical profile sections. Detailed supplier profiles make it easier for operators to quickly retrieve, process and validate supplier information in a matter of seconds.

Once a new supplier is onboarded, collecting, verifying and storing supplier data will ensure responsible supplier risk management. A high-end analytics engine can analyze this data to generate supplier performance insights in real-time. Such insights empower sourcing professionals to easily monitor the supplier and vendor pool, their credentials such as certificates of insurance (COIs) and their compliance status.

Delivering tangible cost savings has always been a critical task for procurement and will continue to be a high priority in the next decade. Considering this, procurement leaders will have to look for newer ways to achieve cost efficiency. One way is through supplier analysis. Critical supplier information is often trapped in varying data management systems. Consolidating that data into one common repository helps operators get better visibility into spending across the entire value chain. A centralized data framework, complemented by an analytics engine, for example, can help decision-makers identify expensive or low-performing suppliers. A new central data management system can be seamlessly integrated with the legacy system through application programming interfaces (APIs).

Robotic Process Automation

Robots are expected to see "strong growth over the next five years, particularly within supply chain operations that include lower-value, potentially dangerous or high-risk tasks,” according to Deloitte. With the massive growth in e-commerce, this should not surprise anyone in the logistics world. Robotic technology applications include automated vehicles like drones, trucks and trains, last-mile deliveries and storage and retrieval systems (ASRS).

The increased usage of autonomous robots can achieve the following objectives:

• increase efficiency and productivity
• reduce re-work and risk rates
• improve employee safety
• perform mundane tasks so humans can work more strategic efforts
• increase revenue by improving order fulfillment and delivery speed, leaving customers satisfied

New pricing structures will enable companies to invest in automation, making the leap into robotics much more feasible. Using a RaaS-type model (Robotics as a Service), providers lease units through a monthly service contract instead of customers paying an up-front capital expenditure. 

Internet of Things 

An emerging trend for supply chain managers is asset tracking through IoT to save time and money and enable data-driven decision-making. 

The IoT is made up of interconnected physical devices that can monitor, collect and send data to cloud-based software for analysis via Wi-Fi. IoT devices have improved quality management in supply chains through GPS tracking of shipments and monitoring parcel conditions. RFID chips, smart devices and mobile sensors can track and authenticate products, measure temperature, humidity, light levels, movement, handling, speed and other environmental factors of shipments. 

The growing pace of technological innovation propels digital supply chain management solutions. Thankfully, embarking on the technical journey will become more accessible and cost-effective as more technologies emerge. Organizations that rapidly adopt these emerging solutions while incrementally replacing legacy systems will better navigate this decade with greater insight and efficiency. 

Danny Shields is vice president of industry relations at Avetta, a provider of cloud-based supply chain risk management technology.