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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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Jun 13 2023
6 Steps to Leveraging Supply Chain Data to Inform Predictive Analytics

Predictive analytics is a powerful tool to help use supply chain data to make more informed decisions about the future. This might involve analyzing data about inventory, order fulfillment, delivery times, manufacturing equipment and processes, suppliers, customers, and other factors that impact your supply chain. Predictive analytics can help you deal with some of your supply chain challenges more effectively, including demand volatility, supply shortages, manufacturing downtime, and high warehouse labor costs.

Six Steps to Inform Predictive Analytics

Knowing what’s going to happen in the future can help you transform your supply chain, but you’ll need to first understand how to leverage your supply chain data to inform predictive analytics. Here are some foundational steps to help you get started:

  1. Collect Data

Predictive analytics relies on historical data to predict future events. How much data you’ll need depends on the type of problem you’re trying to solve, model complexity, data accuracy, and many other things. The types of data required depend on what you are trying to forecast. For instance, to forecast demand, you would need to gather data on past sales, customer orders, market research, planned promotions, and more.

  1. Clean and Pre-Process Data

Data quality is key for predictive analytics to make accurate forecasts. Your data collection process needs to ensure that data is accurate, complete, unique, valid, consistent, and from the right time period.

  1. Select a Predictive Analytics Technique

Machine learning uses algorithms and statistical models to identify patterns in data and make predictions. You need to select the appropriate machine-learning technique based on your data and the nature of your use case. Here are the major ones to choose from:

  • Regression Analysis: Finds a relationship between one or more independent variables and a dependent variable.
  • Decision Tree: Type of machine learning used to make predictions based on how a previous set of questions were answered.
  • Neural Networks: Simulates the functioning of the human brain to analyze complex data sets. It creates an adaptive system that computers use to learn from their mistakes and improve continuously.
  • Time-Series Analysis: Analyzes time-based data to predict future values.
  • Classification: Prediction technique that uses machine learning to calculate the probability that an item belongs to a particular category.
  • Clustering: Uses machine learning to group objects into categories based on their similarities, thereby splitting a large dataset into smaller subsets.

  1. Train the Model

Training a machine learning model is a process in which a machine learning algorithm is fed with data from which it can learn.

  1. Validate the Model

After training, you need to validate the model to ensure that it can accurately predict the future. This involves comparing the model’s predictions with actual data from a test period.

  1. Use the Model to Forecast the Future

Once you have validated your model, you are ready to start using it to forecast data for future periods.

You’ll also need the right machine learning platform to execute these six predictive analytics steps successfully. Our blog “What Makes a Great Machine Learning Platform” helps you to discover how to evaluate a solution and learn about the Actian Data Platform’s capabilities.

Try our Actian Data Platform Free Trial to see for yourself how it can help you simplify predictive analytics deployment.

 

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Author: Teresa Wingfield

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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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May 2 2023
Do you Need AI to Transform your Supply Chain?

I recently shared a blog called “The Power of Real-time Supply Chain Analytics” that discusses how manufacturers can use real-time supply chain analytics to reinvent their supply chain across sourcing, processing, and distribution of goods. Its focus is mostly on understanding events as they unfold in real-time. This time around, I’m providing an overview of using predictive analytics to understand what will happen in the future.  

As a subset of artificial intelligence (AI), predictive analytics makes predictions about future outcomes using historical data. Predictive analytics also helps businesses transform their supply chain to increase efficiency, reduce risk, and grow revenue. Let’s look at four areas in which predictive analytics can revolutionize the supply chain: demand forecasting, procurement, supply chain risk management, and customer experience.   

Demand Forecasting 

Demand forecasting in supply chain management refers to the process of planning or predicting the demand of materials to ensure delivery of the right products in the right quantities at the right time to satisfy customer demand, without creating excess inventory. 

The benefits of being able to anticipate customer needs and buying behavior are tremendous. With this knowledge, manufacturers can better ensure that they have the right levels of inventory, plan for the optimal production schedule, and obtain the most cost-effective and efficient logistics. An accurate demand forecast can also help businesses determine the best price to charge for their product to make the most profit.  

Intelligent Procurement 

Manufacturers that implement intelligent procurement can gain better insight into and control over their spending and sourcing higher quality production components. Using predictive analytics, purchasing departments have a better understanding of what, where, and when to source based on their past purchases, commodity prices, and other industry trends.  

Supply Chain Risk Management 

Supply chain risk management is the implementation of strategies to manage everyday and exceptional risks along the supply chain based on continuous risk assessment with the objective of reducing vulnerability and ensuring continuity.  

While there are many market disruptions that are typically unpredictable, such as natural disasters, pandemics, and cyber and terrorist attacks, there are many risks that can be forecast. Manufacturers can apply predictive analytics to their data for early detection and remediation of:  

  • Equipment and product issues at factories 
  • Capacity constraints at warehouses 
  • Late deliveries by logistics providers
  • Financial distress of supplies and customers.  

 Customer Experience 

Delivering a great customer experience entails timely delivery of quality goods and keeping customers informed. The sooner a manufacturer can see a potential disruption to its supply chain, the faster it can react to avoid the interruption or at least lessen its impact. Even when the manufacturer can’t prevent the disruption, it can warn its customers of issues so that they aren’t blind sighted at the last minute. 

Your Bottom-Line Deserves AI 

A manufacturer does indeed need AI to optimize its supply chain. By using predictive analytics to optimize inventory levels, sourcing, transportation routes, and many other aspects of the supply chain, manufacturers can improve their bottom-line and provide better service to their customers.  

Interested in more information? You can learn more about how Actian’s solutions are used in manufacturing here and review this related blog for further details on real-time supply chain analytics.

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Author: Teresa Wingfield

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Mar 30 2023
Deciphering the Data Story Behind Supply Chain Analytics

When it comes to supply chain data, there’s an intriguing story to be told. If businesses have access to accurate data in real time about their supply chain operations, they have tremendous opportunities to increase efficiency, reduce costs, and grow revenue. Here’s a look at some of the types of supply chain data and the data story that supply chain analytics can reveal.

Procurement Data

This includes information about the type, quality, quantity, and cost of raw materials and components used in the production process. Analyzing spend can help businesses identify areas where they can reduce costs and make data driven decisions about how to best allocate their budget. For example, real-time comparisons of supplier pricing can help sourcing teams negotiate more favorable prices.

Supplier Data

This includes data about suppliers, such as their performance history, delivery times, and product quality. Supplier data is key to reducing order fulfillment issues and to identifying and proactively planning for supply chain disruption. Companies are increasingly leveraging supplier data in real-time to enhance their environmental, social and governance efforts.

Production Data

This includes data about manufacturing processes, including production schedules, output levels, and equipment utilization and performance. Faster insights into production data can help optimize material availability, workforce and processes needed to keep production lines running. Businesses can also more quickly spot quality control issues and equipment problems before they lead to costly downtime.

Inventory Data

This includes data about the quantity and location of inventory, inventory turnover and safety stock requirements. Demand forecasting using predictive analytics helps to determine the right level of inventory. Real-time visibility is essential to dynamically adjust production up or down as demand fluctuates and to offer promotions and sales for slow-moving inventory.

Transportation Data

This includes data about the movement of goods from one location to another such as shipment tracking, transit conditions and times, and transportation costs. Predictive analytics can estimate transit times to determine the best possible routes. What’s possible today was inconceivable a decade ago: using sensors to track things such as temperature and safe transportation at any point in time to protect goods and improve driving habits.

Customer Data

This includes customer data such as order history, purchase behavior, and preferences. Companies can meet customer expectations and increase sales when they understand and anticipate what their customers need – and when they are able to create personalized experiences and quickly adjust the supply change based on constantly changing customer behavior.

Sales Data

This includes sales data such as revenue, profit margins and customer satisfaction. Companies use demand forecasting based on past sales to help them adjust production, inventory levels, and improve sales and operations planning processes.

Create Your Data Story

What’s your supply chain data story going to be? It all depends on the data platform you choose to process your supply chain analytics. The platform will need to be highly scalable to accommodate what can be massive amounts of supply chain data and must support real-time insights into supply chain events as they happen so decision makers can form next-best actions in the moment.

The Avalanche Cloud Data Platform provides data integration, data management, and data analytics services in a single platform that offers customers the full scalability benefits of cloud- native technologies. The Avalanche platform provides REAL, real-time analytics by taking full advantage of the CPU, RAM, and disk to store, compress, and access data with unmatched performance.

The post Deciphering the Data Story Behind Supply Chain Analytics appeared first on Actian.


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Author: Teresa Wingfield

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