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Jul 2 2024
Streamlining the Chaos: Conquering Manufacturing With Data

The Complexity of Modern Manufacturing

Manufacturing today is far from the straightforward assembly lines of the past; it is chaos incarnate. Each stage in the manufacturing process comes with its own set of data points. Raw materials, production schedules, machine operations, quality control, and logistics all generate vast amounts of data, and managing this data effectively can be the difference between smooth operations and a breakdown in the process.

Data integration is a powerful way to conquer the chaos of modern manufacturing. It’s the process of combining data from diverse sources into a unified view, providing a holistic picture of the entire manufacturing process. This involves collecting data from various systems, such as Enterprise Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), and Internet of Things (IoT) devices. When this data is integrated and analyzed cohesively, it can lead to significant improvements in efficiency, decision-making, and overall productivity.

The Power of a Unified Data Platform

A robust data platform is essential for effective data integration and should encompass analytics, data warehousing, and seamless integration capabilities. Let’s break down these components and see how they contribute to conquering the manufacturing chaos.

1. Analytics: Turning Data into Insights

Data without analysis is like raw material without a blueprint. Advanced analytics tools can sift through the vast amounts of data generated in manufacturing, identifying patterns and trends that might otherwise go unnoticed. Predictive analytics, for example, can forecast equipment failures before they happen, allowing for proactive maintenance and reducing downtime.

Analytics can also optimize production schedules by analyzing historical data and predicting future demand. This ensures that resources are allocated efficiently, minimizing waste and maximizing output. Additionally, quality control can be enhanced by analyzing data from different stages of the production process, identifying defects early, and implementing corrective measures.

2. Data Warehousing: A Central Repository

A data warehouse serves as a central repository where integrated data is stored. This centralized approach ensures that all relevant data is easily accessible, enabling comprehensive analysis and reporting. In manufacturing, a data warehouse can consolidate information from various departments, providing a single source of truth.

For instance, production data, inventory levels, and sales forecasts can be stored in the data warehouse. This unified view allows manufacturers to make informed decisions based on real-time data. If there’s a sudden spike in demand, the data warehouse can provide insights into inventory levels, production capacity, and lead times, enabling quick adjustments to meet the demand.

 3. Integration: Bridging the Gaps

Integration is the linchpin that holds everything together. It involves connecting various data sources and ensuring data flows seamlessly between them. In a manufacturing setting, integration can connect systems like ERP, MES, and Customer Relationship Management (CRM), creating a cohesive data ecosystem.

For example, integrating ERP and MES systems can provide a real-time view of production status, inventory levels, and order fulfillment. This integration eliminates data silos, ensuring that everyone in the organization has access to the same accurate information. It also streamlines workflows, as data doesn’t need to be manually transferred between systems, reducing the risk of errors and saving time.

Case Study: Aeriz

Aeriz is a national aeroponic cannabis brand that provides patients and enthusiasts with the purest tasting, burning, and feeling cultivated cannabis. They needed to be able to connect, manage, and analyze data from several systems, both on-premises and in the cloud, and access data that was not easy to gather from their primary tracking system.

By leveraging the Actian Data Platform, Aeriz was able to access data that wasn’t part of the canned reports provided by their third-party vendors. They were able to easily aggregate this data with Salesforce to improve inventory visibility and accelerate their order-to-cash timeline.

The result was an 80%-time savings of a full-time employee responsible for locating and aggregating data for business reporting. Aeriz can now focus resources on analyzing data to find improvements and efficiencies to accommodate rapid growth.

The Actian Data Platform for Manufacturing

Imagine having the ability to foresee equipment failures before they happen? Or being able to adjust production lines based on live demand forecasts? Enter the Actian Data Platform, a powerhouse designed to tackle the complexities of manufacturing data head-on. The Actian Data Platform transforms your raw data into actionable intelligence, empowering manufacturers to make smarter, faster decisions.

But it doesn’t stop there. The Actian Data Platform’s robust data warehousing capabilities ensure that all your critical data is centralized, accessible, and ready for deep analysis. Coupled with seamless integration features, this platform breaks down data silos and ensures a cohesive flow of information across all your systems. From the shop floor to the executive suite, everyone operates with the same up-to-date information, fostering collaboration and efficiency like never before. With Actian, chaos turns to clarity and complexity becomes a competitive advantage.

Embracing the Future of Manufacturing

Imagine analytics that predict the future, a data warehouse that’s your lone source of truth, and integration that connects it all seamlessly. This isn’t just about managing chaos—it’s about turning data into a well-choreographed dance of efficiency and productivity. By embracing the power of data, you can watch your manufacturing operations transform into a precision machine that’s ready to conquer any challenge!

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Author: Kasey Nolan

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Feb 22 2024
Measuring and Reporting on Supply Chain Sustainability the Right Way

In an era where sustainability is not just a buzzword but a strategic imperative, the supply chain plays a pivotal role in shaping an organization’s environmental and social footprint. Here are some ways to guide your business on the essential aspects of measuring and reporting sustainability within the supply chain, focusing on data management, goal and metric definitions, and adherence to reporting standards.

Data Management: Unraveling the Threads of Sustainability

In the intricate web of supply chain operations, data serves as the thread that weaves together the fabric of sustainability. Comprehensive data management is essential for measuring, monitoring, and optimizing sustainability initiatives within all aspects of your organization’s supply chain.

The first step in sustainable data management is collecting relevant information across the organization. Some examples of this data include energy consumption, water usage, waste generation, emissions, and social impact factors such as labor practices and community engagement. The challenge, however, is gathering data from diverse sources—including suppliers, manufacturers, logistics partners, and internal operations. Strategies for overcoming this include implementing data-sharing agreements with vendors, conducting regular audits, and leveraging emerging technologies like Internet of Things (IoT) sensors, blockchain, and the API integration capabilities of your data platform to track and trace environmental and social performance throughout the supply chain.

Once collected, sustainability data must be organized coherently and structured to facilitate fast analysis and decision-making. This means establishing a clear taxonomy and data schema that categorizes information according to relevant sustainability indicators, like carbon emissions or waste generation. This is where data visualization tools and dashboards come in handy because they will help present the information in a user-friendly format.

Defining Goals and Metrics: Charting a Course for Sustainable Success

Once the data is collected and integrated, the next step is to establish goals and metrics for meaningful action and measurable progress. By breaking down silos and integrating data from various departments, sources, and stakeholders, organizations can gain a comprehensive understanding of their environmental and social impact across the entire supply chain. This integrated approach allows you to identify and establish goals that address the most significant areas of opportunity and risk.

Implementing policies to act on the data requires a strategic and proactive approach that aligns with your defined goals and metrics. Best practices include setting ambitious, yet achievable, targets based on data-driven insights and industry benchmarks. These targets should provide clear direction and accountability for sustainability efforts. Additionally, your organization should develop policies and procedures to track progress toward these targets, leveraging technology and data analytics to monitor performance in real-time to course correct as needed.

Establishing a culture of continuous improvement and accountability is essential, with regular reviews and updates to policies and targets based on evolving data insights and stakeholder expectations.

Reporting Standards: Navigating the Landscape of Transparency

Established reporting frameworks such as the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB) play a crucial role in guiding organizations toward transparent and consistent sustainability reporting. These frameworks provide comprehensive guidelines and standardized metrics for measuring and disclosing environmental, social, and governance (ESG) performance.

Adhering to recognized reporting standards helps organizations enhance credibility and comparability in the eyes of stakeholders—including investors, customers, employees, and regulators. Consistent reporting enables investors to make informed investment decisions, customers to make ethical purchasing choices, and regulators to enforce compliance with environmental and social regulations.

The emergence of integrated reporting represents a paradigm shift in how organizations disclose their performance and make holistic decisions, moving beyond traditional financial metrics to encompass broader value creation for all stakeholders. Integrated reporting seeks to present financial and sustainability performance cohesively, acknowledging the interconnectedness between financial success and environmental and social impact.

By integrating financial and non-financial data into a single, comprehensive report, organizations can provide stakeholders with a holistic view of their long-term value-creation strategy. Integrated reporting encourages a more balanced and sustainable approach to business decision-making, where financial considerations are complemented by environmental and social considerations. As organizations increasingly recognize the importance of holistic value creation, integrated reporting, and integrated data in general, is the key for communicating sustainability performance and demonstrating long-term resilience and viability.

Integration is Hard, but Actian Can Help

The Actian Data Platform offers invaluable capabilities to companies striving to enhance their ESG efforts and reporting accuracy. By providing a unified platform for data management, integration, and analytics, Actian empowers organizations to access, analyze, and leverage sustainability-related data from across the entire supply chain in real-time.

With these real-time insights into key ESG metrics, your company can make informed decisions that drive sustainable practices and optimize resource usage. Actian’s advanced integration capabilities empower your organization to identify trends, patterns, and opportunities for improvement, facilitating proactive interventions to minimize environmental impact and maximize social responsibility. Moreover, by streamlining data collection and aggregation, Actian enhances confidence that sustainability reports are comprehensive, accurate, and timely, bolstering credibility and trust with stakeholders.

Measuring and reporting sustainability in the supply chain requires a strategic and holistic approach. By mastering data management, defining clear goals and metrics, and adhering to reporting standards, businesses can not only enhance their environmental and social impact but also build trust with stakeholders. By making data easy, the Actian Data Platform enables you to drive and monitor sustainability initiatives across your entire supply chain.

The post Measuring and Reporting on Supply Chain Sustainability the Right Way appeared first on Actian.


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Author: Actian Corporation

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Aug 23 2023
Why Predictive Data Analytics Will Drive the Supply Chain of the Future

It’s hard to imagine a crucial aspect of business that’s been more disrupted, unpredictable, and volatile over the last few years than supply chains. As a result of worker shortages, geopolitical issues, manufacturing shutdowns, unstable fuel prices, and myriad other factors, supply chains across almost all industries were unable to make on-time deliveries and meet customer demand.

These problems—and the impact on consumers who were forced to wait weeks, months or longer for products—have caused organizations to rethink their supply chains.  We’ve detailed how to navigate managing the complexity and volume of your supply chain in this downloadable eBook Your Supply Chain Future is Now with Predictive Analytics. Companies have realized that legacy technologies and basic forecasting methods are not sufficient to predict and meet demand, especially as many consumers now expect next day or even same day delivery.

The ability to accurately predict demand and ensure the right products are available at the right time in the right place and with a favorable price point is increasingly difficult. Even seasonal demand that used to be fairly easy to predict based on previous buying habits is breaking long-standing patterns and becoming more unpredictable. Everything has changed, and organizations that aren’t modernizing and using supply chain analytics will be at a significant disadvantage.

Supply Chains of the Future Demand Predictive Analytics

To say that supply chains of the future will require new data analytics capabilities, including predictive analytics and supply chain analytics, is certainly true. But today’s supply chains need them too. Predictive analytics and other innovative technologies enable supply chains to be automated, respond faster, become more resilient, and be more sustainable.

The need to modernize supply chain processes by integrating data for predictive analytics will define one of the priorities for Chief Supply Chain Officers (CSCOs). This role will also be tasked with implementing the right data platform to manage ever-growing data volumes, build new data pipelines easily, and deliver analytic insights at scale.

As supply chains stretch around the world, having visibility into processes and suppliers, along with managing risk, is critical, but difficult. A scalable data platform is needed that can integrate, manage, and analyze data—and perform predictive analytics—at the speed modern businesses require, while also offering strong price performance.

A Look into the Future of Supply Chains

Modern supply chains must be efficient, agile, and connected. They must also have advanced forecasting capabilities to deliver granular insights into supply and demand, ensuring there’s enough product on the shelves to meet consumer needs, but not too much or it results in costly inventory, storage, or waste.

Organizations that don’t have a strategy for supply chain modernization will likely experience higher costs, spend more time on manually intensive processes, face ongoing inefficiencies, and fall behind their forward-looking peers.

The supply chain of the future will be more automated, with the use of technologies such as the Internet of Things (IoT), predictive analytics, blockchain, robotics, and even drones. These technologies will enable real-time tracking and monitoring of supply chain processes such as procurement, manufacturing, and distribution of goods. This eBook can help businesses like yours:

  • Implement predictive analytics and deliver trustworthy insights
  • Understand and build the supply chain of the future
  • Improve supply chain sustainability
  • Evolve the role of the CSCO to meet current and future needs
  • Manage diversification in your supply chain
  • Create a roadmap for ongoing success
  • Make data analytics easy to use with the right cloud data platform

Get Easy-to-Use Supply Chain Analytics You Can Trust

Supply chain success starts with the right platform. Customers trust Actian for data and analytics, including predictive analytics, for visibility, insights, and data-driven automation to better manage and optimize their supply chains. The Actian Data Platform is easy to manage, enabling you to transform your supply chain and your business by simplifying how you connect and analyze data. Forrester recently recognized Actian as  one of the top 15 cloud data warehouse providers. See why and find out how Actian can help modernize your supply chain.

Get the eBook to learn more about how the Actian platform can make managing your supply chain easier.

Related resources you may find useful:

·      The Power of Real-time Supply Chain Analytics

·      The Top Data and Analytics Capabilities Every Modern Business Should Have

·      How Your Peers Are Experiencing Their Journeys to the Cloud

The post Why Predictive Data Analytics Will Drive the Supply Chain of the Future appeared first on Actian.


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Author: Steve Lennon

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May 23 2023
Unlocking Business Insights: How Supply Chain Analytics Measures Your Company’s Health

In today’s highly competitive business world, companies are constantly looking for ways to improve their supply chain operations. One of the most effective ways to do this is by measuring supply chain performance using real-time analytics. By understanding the performance of each aspect of the supply chain, companies can identify bottlenecks, reduce lead times, and improve customer satisfaction. By implementing real-time supply chain analytics, you can gain valuable insights into your company’s health and identify areas for improvement.

Key Performance Indicators in Supply Chain Analytics

Before diving into the benefits of supply chain analytics, it’s essential to understand the key performance indicators (KPIs) that are typically used to measure supply chain performance. These metrics are vast, but three that are common examples are:

Inventory Turnover: This KPI measures how quickly you are selling your inventory. A low inventory turnover rate can indicate that you are carrying too much inventory, while a high rate can suggest that you are not keeping enough stock on hand.

Order Cycle Time: This KPI measures the time it takes from when a customer places an order to when the order is fulfilled. A longer order cycle time can lead to dissatisfied customers, while a shorter cycle time can improve customer satisfaction.

Perfect Order Rate: This KPI measures the percentage of orders that are delivered on time, in full, and without any errors. A low perfect order rate can indicate that you have issues with your order fulfillment process, which can lead to lost sales and dissatisfied customers.

Using Data Analytics to Improve Supply Chain Performance

One of the most effective ways to improve supply chain performance is using data analytics. By collecting and analyzing data from various aspects of the supply chain, companies can identify patterns and trends that can be used to optimize operations. Data analytics can be used to identify areas where supply chain operations are inefficient or ineffective, such as high inventory levels or long lead times. It can also be used to identify opportunities for improvement, like reducing transportation costs or improving manufacturing efficiency. Some specific areas where supply chain analytics can improve performance include:

  1. Improved forecasting accuracy: By analyzing historical data and trends, you can improve your forecasting accuracy. This can help you better anticipate demand for your products and avoid overstocking or understocking.
  2. Better inventory management: By analyzing inventory turnover and other metrics, you can optimize your inventory levels to reduce carrying costs while still meeting customer demand.
  3. Increased supply chain visibility: By using analytics tools, you can gain more visibility into your supply chain operations. This can help you identify bottlenecks or inefficiencies and make data-driven decisions to improve your supply chain.
  4. Faster order fulfillment: By analyzing order cycle times and perfect order rates, you can identify areas where you can streamline your order fulfillment process. This can help you deliver products to customers faster and improve customer satisfaction.
  5. Reduced risk: By analyzing your supply chain, you can identify potential risks and take steps to mitigate them. For example, you may identify a supplier who is at risk of going out of business, and you can take steps to find a new supplier before a disruption occurs.

Best Practices for Implementing Supply Chain KPIs

Implementing KPIs in a supply chain can be a complex process, but there are several best practices that companies can follow to ensure success. These include:

  1. Defining Clear Objectives: Before implementing KPIs, it’s important to define clear objectives that align with overall business goals. This ensures that KPIs are relevant and meaningful.
  2. Choosing the Right KPIs: Not all KPIs are created equal, and it’s important to choose KPIs that are relevant to specific aspects of the supply chain. This ensures that KPIs provide meaningful insights.
  3. Collecting Accurate, Data: KPIs are only as good as the data that is used to measure them, so it’s important to collect accurate and reliable data. That means that the data must be consistent, complete, and correct, and that data must be available in a timeframe that allows your business to react to changes.
  4. Communicating Results: KPIs should be communicated to all stakeholders in a clear and concise manner. This ensures that everyone understands the importance of KPIs and how they contribute to overall business success.
  5. Continuously Improving: Supply chain operations are constantly evolving, so it’s important to continuously review and improve KPIs to ensure they remain relevant and effective.

By analyzing key performance indicators, businesses can identify inefficiencies, improve customer satisfaction, and reduce costs. Supply chain analytics can provide valuable insights into overall business health when they are built using KPI’s that are directly tied to overall business objectives. Use these resources to learn how the Avalanche Cloud Data Platform is helping to deliver real-time data for supply chain analytics:

The post Unlocking Business Insights: How Supply Chain Analytics Measures Your Company’s Health appeared first on Actian.


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Author: Traci Curran

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May 11 2023
What is Supply Chain Analytics?

Supply chain analytics uses data and advanced analytics to analyze and optimize various aspects of the supply chain, including procurement, manufacturing, and logistics. The main goals of supply chain analytics are to improve efficiency, lower costs, and increase revenue. Supply chain analytics can also provide real-time insights that help businesses adjust to changing conditions quickly and effectively.

Using supply chain analytics, you can ask the right questions, find the right answers, and realize the benefits of a well-optimized supply chain.

Frequently Asked Questions

There are four primary types of supply chain analytics: descriptive, diagnostic, predictive, and prescriptive. These advanced analytics techniques may sound complex, but you should find this simple business-level overview of what each type reveals with examples to be straightforward.

What events have happened?

Descriptive analytics mines historical data to identify trends and relationships. Examples include identifying excess inventory and late deliveries.

Why did these events happen?

Diagnostic analytics examines trends and correlations between variables to determine the root cause of a supply chain event. This type of analytics can diagnose events such as why there was too much stock and why deliveries were late.

What might happen in the future?

Predictive analytics uses supply chain data to predict future outcomes, such as forecasting demand or anticipating possible transportation bottlenecks.

What should we do?

Prescriptive analytics uses data to prescribe the best course of action, such as decreasing production or using alternative shippers.

Benefits of Supply Chain Analytics

Answering these types of questions provides a myriad of benefits. Below are just a few of them:

  • Improved efficiency and cost savings: Through using supply chain analytics to streamline processes, reduce waste and optimize operations. Examples include optimizing routes and schedules, reducing manufacturing downtime, using less fuel and better sourcing of materials, and many more opportunities.
  • Increased visibility and transparency: Allow organizations to identify potential problems early on and take proactive measures to address them.
  • Better risk management: By highlighting interdependencies and uncovering areas along the supply chain where disruption can lead to failure.
  • More accurate planning: Gain better insight into sourcing, manufacturing, and distribution to meet customer demand.
  • Better customer experience: Real-time insights into customer demand can improve how you manage inventory levels and ensure that products are in stock when customers want them.
  • Less environmental impact: Normalize analyzing energy consumption, waste, and other sustainability factors.

Getting Started

Supply chain analytics provides a data-driven way for businesses to optimize their operations, with its ability to provide real-time visibility, highlight risks, reduce costs and inefficiencies, better plan for customer demand, improve the customer experience, and reduce environmental impact.

To get started, you’ll need the right data platform to run your descriptive, diagnostic, predictive, and prescriptive supply chain analytics. The Avalanche Cloud Data Platform can help you transform your supply chain, by simplifying how you connect, manage, and analyze data. Using the Avalanche platform, you can easily aggregate and analyze massive amounts of supply chain data to gain data-driven insights in real-time, for optimizing supply chain operations.

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Author: Teresa Wingfield

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Apr 25 2023
Best Practices for Using Data to Optimize Your Supply Chain

When a company is data-driven, it makes strategic decisions based on data analysis and interpretation rather than mere intuition. A data-driven approach to supply chain management is the key to building a strong supply chain, one that’s efficient, resilient, and that can easily adapt to changing business conditions.  

How exactly you can best incorporate data and analytics to optimize your supply chain depends on several factors, but these best practices should help you get started:     

#1. Build a Data-Driven Culture 

Transitioning to a data-driven approach requires a cultural change where leadership views data as valuable, creates greater awareness of what it means to be data-driven, and develops and communicates a well-defined strategy that has buy-in from all levels of the organization.  

#2. Identify Priority Business Use Cases 

The good news is that there are a lot of opportunities to use supply chain analytics to optimize your supply chain across sourcing, processing, and distribution of goods. But you’ll have to start somewhere and should prioritize opportunities that will generate the greatest benefits for your business and that are solvable with the types of data and skills available in your organization.  

#3. Define Success Criteria 

After you’ve decided which use cases will add the most value, you’ll need to define what your business hopes to achieve and the key performance indicators (KPIs) you’ll use to continuously measure your progress. Your KPIs might track things such as manufacturing downtime, labor costs, and on-time delivery.  

#4. Invest in a Data Platform  

You’ll need a solution that includes integration, management, and analytics and that supports real-time insights into what’s happening across your supply chain. The platform will also need to be highly scalable to accommodate what can be massive amounts of supply chain data.  

#5. Use Advanced Analytics 

Artificial intelligence techniques such as machine learning power predictive analytics to identify patterns and trends in data. Insights help manufacturers optimize various aspects of the supply chain, including inventory levels, procurement, transportation routes, and many other activities. Artificial intelligence uncovers insights that can allow manufacturers to improve their bottom line and provide better customer service.  

#6. Collaborate with Suppliers and Partners 

Sharing data and insights can help develop strategies aimed at improving supply chain efficiency and developing innovative products and services.  

#7. Train and Educate Employees 

The more your teams know about advanced analytics techniques, especially artificial intelligence, and how to use and interpret data, the more value you can derive from your supply chain data. Plus, with demand for analytics skills far exceeding supply, manufacturers will need to make full use of the talent pool they already have.  

Learn More 

Hopefully, you’ve found these best practices for using data to optimize your supply chain useful and actionable. Here’s my recommended reading list if you’d like to learn more about data-driven business and technologies:   

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Author: Teresa Wingfield

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Mar 17 2023
How to Use Data to Get More Visibility into Your Supply Chain

Supply chains have undergone—and continue to experience—major changes and disruption. Worker shortages, rapidly changing customer demands, logistics problems, transportation bottlenecks, and other factors have all contributed to challenges. Even sales patterns that used to be easy to predict, such as those based on holidays and seasonal buying, have become much harder to understand, amplifying the need for visibility across the entire supply chain. 

Business and consumer needs change faster than ever, which has a ripple effect across supply chains that are trying to keep up. On top of this, global supply chains have become increasingly complex, making them more susceptible to delays caused by everything from inclement weather to shipping problems to raw materials shortages.  

Keeping the supply chain moving without interruption places new demands for data and analytics to provide visibility and insights. A supply chain that’s driven by a modern approach to data and analytics enables new benefits, such as improved operations, enhanced demand forecasting, increased efficiencies, reduced costs, and better customer experiences.   

Supply Chain Analytics Keep Modern Supply Chains Running 

Data that can provide visibility into supply chains is coming from traditional, new, and emerging sources. This includes enterprise resource planning and point of sale systems, a growing number of internet of things (IoT) devices, inventory and procurement solutions, and more.  

Customer-centric supply chains integrate additional data to better understand the products and services consumers want. This entails data across social media, purchasing histories, and customer journeys to have insights into customer behaviors and sentiment.  

Supply chain analytics and enterprise data management capabilities are needed for organizations to know where their products and materials are at any moment and identify ways to optimize processes. These capabilities, for example, allow companies to track and trace products—from parts to sub-assemblies to final builds—as they move from one location to another through the supply chain until they arrive at their final destination. That destination could be a retail store or a customer’s front doorstep.  

Supply chain visibility helps organizations minimize risk while identifying opportunities, such as improving planning to avoid higher cost next-day shipping to meet tight timeframes. Better planning allows companies to use less expensive shipping options without causing unexpected downtimes in factories.  

Visibility is also essential for building resilience and agility into the supply chain, allowing the business to pivot quickly as customer needs change or new trends emerge. The enabler of visibility, and for insights delivered at every point across the end-to-end supply chain, is data. When all relevant data is brought together on a single platform and readily available to all stakeholders, businesses not only know where their parts, components, and products are, but they can proactively identify and address potential challenges before they cause delays or other problems.   

A Growing Need for Supply Chain Resilience  

Although companies need a resilient supply chain, most are not achieving it. According to “Gartner Predicts 2023: Supply Chain Technology,” by 2026, 95% of companies will have failed to enable end-to-end resiliency in their supply chains. “Due to the last few years of major and minor supply chain disruptions, many companies are looking to drive more resiliency into their supply chains,” according to Gartner. “They see this as a key means to help them buffer against the impacts of these ongoing disruptions more effectively.” 

Improving resiliency requires the business to move from analysis on basic forecasting data to connecting and analyzing all data for real-time insights that produce more accurate and robust forecasts, uncover opportunities to improve sustainability, and meet other supply chain goals. The insights help organizations identify macro- and micro-level issues that could impact the supply chain—and predict issues with enough time for the business to proactively respond.  

Manual processes and outdated legacy systems that won’t scale to handle the data volumes needed for end-to-end insights will not give organizations the resiliency or visibility they need. By contrast, a modern cloud data platform breaks down silos to integrate all data and can quickly scale to solve data challenges.  

This type of platform can deliver the supply chain analytics and enterprise data management needed to reach supply chain priorities faster. For example, manufacturers can know where raw materials are in the supply chain, when they’re due to arrive at a facility, and how a change in transportation methods or routes can impact both operations and profitability. Retailers can know when items will be available in warehouses to meet customer demand, fill orders, and nurture customer journeys.  

Easily Connect, Manage, and Analyze Supply Chain Data 

Organizations that have the ability to bring together data from all sources along the supply chain and perform analytics at scale can gain the visibility needed to inform decision making and automate processes. With the right approach and technology, organizations can turn their supply chain into a competitive advantage. 

The Avalanche Cloud Data Platform makes data easy. It simplifies how people connect, manage, and analyze their data to modernize and transform their supply chain. With Avalanche’s built-in data integration, businesses can quickly build pipelines to ingest data from any source. Anyone in the organization who needs the data can easily access it to make informed decisions, gain insights, expand automation, and optimize it for other supply chain needs.  

Related resources you may find useful: 

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Author: Brett Martin

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