Data Analytics in the Digital Supply Chain: Unleashing Insights for Operational Excellence

Data Analytics in the Digital Supply Chain: Unleashing Insights for Operational Excellence

Supply chains of the past have been a lot like gambling. The shipper arranges the movement of their freight and hopes this time goes better than the last. They often don’t even know the actual cause of problems in past shipments to try to prevent them in the future. Proactive supply chain management is mostly futile when arranging a shipment is equivalent to a roll of the dice.

Data analytics move shippers beyond throwing the supply chain dice, allowing them to understand their operations and supply chain performance and use the new information to become proactive. Operational excellence that was challenging when seven could come up and cause a shipper to crap out becomes attainable when the shipper can throw the dice out the window and plan their operations.

This article discusses the significant benefits brought to organizations when they stop gambling on shipments and leverage data analytics in supply chain management.

The Power of Data Analytics in the Digital Supply Chain

Supply chain data analytics use the power of AI to standardize and analyze raw data to make conclusions that improve supply chain management. Operations and efficiency improve as the supply chain is better understood and past inefficiencies are uncovered.

Supply chains that are extensive and complex generate plenty of Big Data. Millions of data points are created daily as containers worldwide reach milestones along their journey, resulting in big data which provides valuable, actionable insights when analyzed. Trends are understood, and efficiencies are identified to provide data that drives proactive decision-making.

Shippers gain the ability to know their KPIs, understand past disruptions, review lane efficiency, and rank carriers. Using this new knowledge, they tremendously improve efficiency and supply chain management with the ability to use what works well and avoid what doesn’t. A container currently moving is easily tagged if the potential for an exception exists and a contingency is developed.

When an exception happens, the shipper knows what resolutions have previously succeeded in the specific situation and can implement them accordingly.

In the Digital Supply Chain, Data Analytics Offers Powerful Benefits

Using supply chain management analytics, shippers move faster, become nimble, reduce management time, save money, and keep freight moving. Operational efficiency moves to the next level.

Demand Forecasting and Optimization

When a shipper attempts to analyze their performance without big data analytics, they must contend with a lack of data standardization, the potential to miss important data points, and a likelihood that critical insights will go unnoticed.

Human inefficiency especially holds true for demand forecasting. Whereas demand forecasting was once relegated to a spreadsheet and the use of exponential smoothing equations, big data predictive analytics can move beyond the numbers and math equations to consider all factors.

Supply chain analytics transform and analyze all historical data to create decision-guiding insights that are more predictable. Inventory management accuracy improves as the distance between users and products is shortened while cash flow risks are mitigated.

Two types of data are used by predictive analytics: qualitative and quantitative. Whereas human forecasting is often relegated to numbers, seasonalities, and some social insights, AI gathers data from many sources.

Some examples would be:

  • Qualitative data: Cultural and social trends, news reports, customer feedback and reviews, and competitor and market research
  • Quantitative data: Sales numbers, seasonal changes, inventory levels, and search analytics

With such comprehensive data sets, predictive analytics lets shippers predict future macro trends and market movements more accurately.

Supply Chain Visibility and Real-Time Monitoring

Real-time visibility is one of the most transformative technologies for managing supply chain logistics. Analytics takes visibility beyond a blue dot on the screen by adding the ability to provide data standardization and apply analysis to the shipment as it moves. The shipper doesn’t just see a blue dot on a screen; they can access ETAs, know what is happening with the load, and see how long it’s been there.

With enhanced information in real-time, shippers make impactful decisions as soon as needed. Reactivity is effective and fast with real-time monitoring.

Better reactivity is excellent, but the ability to be proactive is groundbreaking for shippers. More information now opens up the potential to see and prevent problems ahead. Freight keeps moving, and customer expectations are met.

Predictive Analytics for Risk Management

Supply chains present constant risks for shippers, including lost cargo, weather, paperwork hang-ups, insufficient capacity, and port disruptions. The long list of risks often seems out of shippers' control, but predictive analytics that incorporates past events with current and ongoing data help identify and mitigate risks and disruptions.

Comparing what has happened at a port during past disruptions could result in the AI alerting employees of the potential of another disruption and selecting a port more conducive to the container’s movement.

Even more proactive than reshaping a container’s journey, predictive analytics might call for entire lane changes for all freight or eliminate a carrier showing poor performance. The analytics would determine that operational efficiency jumps when certain conditions are met and then recommend a course of action that fosters those conditions.

Implementing Data Analytics Solutions in the Digital Supply Chain

Data analytics transform a shipper’s operations, but the success level depends on a shipper’s considerations and actions before and after implementation.

Assessing Data Readiness and Infrastructure

Before implementing a data analytics solution, assessing existing systems and their readiness and capabilities is crucial. Will adding analytics have little impact considering how the current infrastructure is designed, or are systems in place that will allow for data maximization?

Identifying the organization’s data and integration requirements is essential to know if an analytics implementation and deployment will be successful. Pre-deployment planning will commonly include testing and validation activities and define a transition period to ensure optimal results.

Selecting the Right Analytics Tools and Technologies

As shippers move to add the transformative power of analytics to their operations, many tools and technologies have been introduced to the market. It is critical to explore the different devices, their capabilities, and ease of deployment. No one wants to start an implementation only to find out the product doesn’t perform as needed.

Shippers need to ask, will this solution scale with us, do end users find it easy to navigate, and is it compatible with our existing tech stack?

Building Data Analytics Competency and Culture

At the center of transformation through data is the end user. A shipper’s people need to be prepared for a new solution and allowed time to transition effectively.

Technical implementation begins with communication. Employees need to see how the analytics solution aligns with current strategies and benefits the company and their work. The big picture needs to be embraced. Helping employees see the perks and conveniences of their individual workflows will foster validation.

A roll-out that includes some key employees first helps assuage the concerns of others as they see the benefits their co-workers receive and that the solution is easy to use.

Ultimately, the organization needs to think of itself as becoming a data-driven culture, training employees in data literacy, enacting continuous improvement, and extolling the value of improved customer service. This constant improvement culture must be integral to daily operations, empowering employees to utilize their new actionable insights to create innovative solutions.

Data Analytics Are at the Center of the Digital Supply Chain

Deploying data analytics transforms operational efficiency for shippers. As the entire supply chain becomes visible and analyzable, actionable insights result. Forecasting and proactive planning have become possible, allowing organizations to optimize inventory levels, undertake effective risk management, and change the ineffective parts of their supply chains.

Data analytics result in agile, cost-efficient, proactive, and customer-focused shippers.

Vizion API is the premier provider of analytics solutions for shippers, with an easy-to-integrate and deploy API solution. Shippers use Vizion API to gain new operational efficiencies from real-time visibility, exception notifications, past supply chain operations analysis, and data standardization.

Book a demo with Vizion API today and see what happens when supply chains move from being a throw of the dice to becoming understood.

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Data Analytics in the Digital Supply Chain: Unleashing Insights for Operational Excellence

September 12, 2023
supply chain analytics

Supply chains of the past have been a lot like gambling. The shipper arranges the movement of their freight and hopes this time goes better than the last. They often don’t even know the actual cause of problems in past shipments to try to prevent them in the future. Proactive supply chain management is mostly futile when arranging a shipment is equivalent to a roll of the dice.

Data analytics move shippers beyond throwing the supply chain dice, allowing them to understand their operations and supply chain performance and use the new information to become proactive. Operational excellence that was challenging when seven could come up and cause a shipper to crap out becomes attainable when the shipper can throw the dice out the window and plan their operations.

This article discusses the significant benefits brought to organizations when they stop gambling on shipments and leverage data analytics in supply chain management.

The Power of Data Analytics in the Digital Supply Chain

Supply chain data analytics use the power of AI to standardize and analyze raw data to make conclusions that improve supply chain management. Operations and efficiency improve as the supply chain is better understood and past inefficiencies are uncovered.

Supply chains that are extensive and complex generate plenty of Big Data. Millions of data points are created daily as containers worldwide reach milestones along their journey, resulting in big data which provides valuable, actionable insights when analyzed. Trends are understood, and efficiencies are identified to provide data that drives proactive decision-making.

Shippers gain the ability to know their KPIs, understand past disruptions, review lane efficiency, and rank carriers. Using this new knowledge, they tremendously improve efficiency and supply chain management with the ability to use what works well and avoid what doesn’t. A container currently moving is easily tagged if the potential for an exception exists and a contingency is developed.

When an exception happens, the shipper knows what resolutions have previously succeeded in the specific situation and can implement them accordingly.

In the Digital Supply Chain, Data Analytics Offers Powerful Benefits

Using supply chain management analytics, shippers move faster, become nimble, reduce management time, save money, and keep freight moving. Operational efficiency moves to the next level.

Demand Forecasting and Optimization

When a shipper attempts to analyze their performance without big data analytics, they must contend with a lack of data standardization, the potential to miss important data points, and a likelihood that critical insights will go unnoticed.

Human inefficiency especially holds true for demand forecasting. Whereas demand forecasting was once relegated to a spreadsheet and the use of exponential smoothing equations, big data predictive analytics can move beyond the numbers and math equations to consider all factors.

Supply chain analytics transform and analyze all historical data to create decision-guiding insights that are more predictable. Inventory management accuracy improves as the distance between users and products is shortened while cash flow risks are mitigated.

Two types of data are used by predictive analytics: qualitative and quantitative. Whereas human forecasting is often relegated to numbers, seasonalities, and some social insights, AI gathers data from many sources.

Some examples would be:

  • Qualitative data: Cultural and social trends, news reports, customer feedback and reviews, and competitor and market research
  • Quantitative data: Sales numbers, seasonal changes, inventory levels, and search analytics

With such comprehensive data sets, predictive analytics lets shippers predict future macro trends and market movements more accurately.

Supply Chain Visibility and Real-Time Monitoring

Real-time visibility is one of the most transformative technologies for managing supply chain logistics. Analytics takes visibility beyond a blue dot on the screen by adding the ability to provide data standardization and apply analysis to the shipment as it moves. The shipper doesn’t just see a blue dot on a screen; they can access ETAs, know what is happening with the load, and see how long it’s been there.

With enhanced information in real-time, shippers make impactful decisions as soon as needed. Reactivity is effective and fast with real-time monitoring.

Better reactivity is excellent, but the ability to be proactive is groundbreaking for shippers. More information now opens up the potential to see and prevent problems ahead. Freight keeps moving, and customer expectations are met.

Predictive Analytics for Risk Management

Supply chains present constant risks for shippers, including lost cargo, weather, paperwork hang-ups, insufficient capacity, and port disruptions. The long list of risks often seems out of shippers' control, but predictive analytics that incorporates past events with current and ongoing data help identify and mitigate risks and disruptions.

Comparing what has happened at a port during past disruptions could result in the AI alerting employees of the potential of another disruption and selecting a port more conducive to the container’s movement.

Even more proactive than reshaping a container’s journey, predictive analytics might call for entire lane changes for all freight or eliminate a carrier showing poor performance. The analytics would determine that operational efficiency jumps when certain conditions are met and then recommend a course of action that fosters those conditions.

Implementing Data Analytics Solutions in the Digital Supply Chain

Data analytics transform a shipper’s operations, but the success level depends on a shipper’s considerations and actions before and after implementation.

Assessing Data Readiness and Infrastructure

Before implementing a data analytics solution, assessing existing systems and their readiness and capabilities is crucial. Will adding analytics have little impact considering how the current infrastructure is designed, or are systems in place that will allow for data maximization?

Identifying the organization’s data and integration requirements is essential to know if an analytics implementation and deployment will be successful. Pre-deployment planning will commonly include testing and validation activities and define a transition period to ensure optimal results.

Selecting the Right Analytics Tools and Technologies

As shippers move to add the transformative power of analytics to their operations, many tools and technologies have been introduced to the market. It is critical to explore the different devices, their capabilities, and ease of deployment. No one wants to start an implementation only to find out the product doesn’t perform as needed.

Shippers need to ask, will this solution scale with us, do end users find it easy to navigate, and is it compatible with our existing tech stack?

Building Data Analytics Competency and Culture

At the center of transformation through data is the end user. A shipper’s people need to be prepared for a new solution and allowed time to transition effectively.

Technical implementation begins with communication. Employees need to see how the analytics solution aligns with current strategies and benefits the company and their work. The big picture needs to be embraced. Helping employees see the perks and conveniences of their individual workflows will foster validation.

A roll-out that includes some key employees first helps assuage the concerns of others as they see the benefits their co-workers receive and that the solution is easy to use.

Ultimately, the organization needs to think of itself as becoming a data-driven culture, training employees in data literacy, enacting continuous improvement, and extolling the value of improved customer service. This constant improvement culture must be integral to daily operations, empowering employees to utilize their new actionable insights to create innovative solutions.

Data Analytics Are at the Center of the Digital Supply Chain

Deploying data analytics transforms operational efficiency for shippers. As the entire supply chain becomes visible and analyzable, actionable insights result. Forecasting and proactive planning have become possible, allowing organizations to optimize inventory levels, undertake effective risk management, and change the ineffective parts of their supply chains.

Data analytics result in agile, cost-efficient, proactive, and customer-focused shippers.

Vizion API is the premier provider of analytics solutions for shippers, with an easy-to-integrate and deploy API solution. Shippers use Vizion API to gain new operational efficiencies from real-time visibility, exception notifications, past supply chain operations analysis, and data standardization.

Book a demo with Vizion API today and see what happens when supply chains move from being a throw of the dice to becoming understood.