According to Gartner, “79% of CSCOs [Chief Supply Chain Officers] are developing training to drive adoption of advanced analytics.” Supply chain analytics can help organizations reduce costs, improve the speed and accuracy of their operations, and increase customer satisfaction. It can also help companies respond to changing market conditions and mitigate the impact of disruptions on their supply chain. To succeed with supply chain analytics, companies need to build a strong data foundation, invest in the right technology and talent, and develop a clear business case for its adoption. Read on to learn more about the role of data in supply chain analytics, use cases, benefits, challenges, and trends.
What is supply chain analytics?
Supply chain analytics is the process of analyzing data to improve and optimize supply chain management. The goal of supply chain analytics is to enable companies to make better decisions about their supply chain, ultimately leading to increased profitability and competitiveness. This process can involve collecting and analyzing data from various sources within the supply chain, such as suppliers, customers, inventory, and production, to gain insights into how the chain is performing and identify areas for improvement. This data can be used to make decisions about pricing, inventory levels, production processes, and other supply chain operations. Your teams can also start by analyzing data on supply chain operations, logistics, and procurement. The adoption of supply chain analytics can include everything from tracking the movement of goods and materials to predicting demand for products and analyzing the performance of suppliers.
The role of data in supply chain analytics
One key aspect of supply chain analytics is the use of data and technology. As with many other evolving fields in the 21st century, data is the lifeblood of supply chain analytics. You can’t analyze anything without having a clear understanding of what data you have access to, what it measures, and what additional data you can collect. By gathering and analyzing large amounts of data from various sources, companies can gain insights into their supply chain operations and identify areas for improvement. This may involve using advanced analytics tools and techniques, such as machine learning and predictive modeling, to extract meaning from the data and make predictions about future events.
Beyond the data about shipping costs, time delays, manufacturing costs, manufacturing plant information and more, you also need the metadata–the data about the data. Has the data undergone any transformations from its most raw state to the form that your analyst is working with? When was the data last collected? How often is new data collected? Where is the data stored? Who has access? Once you know where the data is living, you need a way to work with it.
Einblick and supply chain analytics
At Einblick, we’ve built a visual data science canvas that is innately collaborative, saves time to insight by operationalizing repetitive code and tasks, and matches your mental model. In a canvas, you can branch out into different thought processes easily and visually, comparing code and output side-by-side. This makes data cleaning, exploratory data analysis, and model building easy and fast. These are all key functions required for a successful supply chain analytics pipeline.
Below, you can check out a canvas focused on understanding sensor data. This gave our clients more insight into how the sensors were functioning, to prevent failure or downtime in the future. When applied to the supply chain, this could mean monitoring notifications from suppliers, weather conditions that might impact shipping time, and market conditions that may affect the volume of purchases from customers, among other processes that collect important data.
For example, one of our enterprise clients, a major car manufacturing company, collected data on all of the various car parts, where they came from, where they needed to end up, cost of shipping by boat or air, how critical the parts were, whether the part was for a new car model or an existing car model, among other things. Then they used Einblick to start building predictive models to determine best courses of action to prevent production slowdowns. The next step will be to use prescriptive analytics to automate next steps based on the results of the predictive analytics.
Supply chain analytics use cases
As illustrated above, there are many ways that your team can utilize data and analytics to optimize supply chain operations and management. We’ve compiled a list of a couple additional use cases that you can apply to your supply chain. It’s likely that you’re doing some manual inspection of these categories already, to better understand your business and its pain points. But data and analytics can optimize your current operations significantly in the following areas.
- Forecasting Demand: supply chain analytics can be used to forecast demand for products. This allows companies to accurately plan inventory and production levels to meet customer needs.
- Improving Logistics: supply chain analytics can be used to improve logistics by analyzing data from different stages of the supply chain. This allows companies to identify areas of inefficiency and develop strategies to improve efficiency and reduce costs.
- Optimizing Inventory Levels: supply chain analytics can be used to optimize inventory levels by analyzing historical data and predicting future demand. Companies can use this information to determine the optimal number of items to keep in stock and when to order new inventory.
- Monitoring Supplier Performance: supply chain analytics can be used to monitor supplier performance by tracking order fulfillment and delivery times. This allows companies to identify areas where suppliers are not meeting their commitments and take corrective action to improve performance.
- Analyzing Risk: Supply chain analytics can be used to analyze risk by identifying potential threats and vulnerabilities in the supply chain, such as production or delivery slowdowns. This allows companies to proactively address any potential risks and ensure the continuity of their operations.
Benefits of supply chain analytics
Now that we’ve gone over some of the areas where data science and machine learning can assist in supply chain operations, we’ll talk through some of the benefits of implementing supply chain analytics.
- Improved forecasting accuracy: supply chain analytics can help improve the accuracy of forecasting by combining historical data, such as sales and inventory levels, with predictive analytics to create more accurate forecasts. Supply chain analytics can help businesses identify trends and anticipate changes in customer demand or supplier performance.
- Increased visibility: supply chain analytics can provide visibility into the entire supply chain, from suppliers to customers. This can help identify potential problems, such as late shipments or production delays, before they become major issues.
- Improved customer service: by having better visibility into the supply chain, companies can better anticipate customer needs and respond quickly to customer inquiries, needs, and concerns. This can occur via automated email campaigns, customer service outreach, and more.
- Reduced costs: supply chain analytics can help identify areas where costs can be reduced and help companies make more cost-effective decisions.
- Improved inventory management: supply chain analytics can provide insights into inventory levels, helping companies better manage their inventory and reduce costs associated with carrying excess inventory.
Ways to implement and optimize supply chain analytics
In order to benefit from supply chain analytics, you need to start somewhere. This may mean choosing the right tools for your business, or hiring or training the right people to take on particular roles as data scientists, analysts, and other professionals. Remember that analytics is just a tool, and domain expertise in your specific supply chain is also a critical part of effectively utilizing analytics tools.
- Build a strong data foundation: in order to get the most value from supply chain analytics, it's important to have accurate and comprehensive data. This means investing in the technical and business skills needed to maximize the value of data.
- Invest in the right technology and talent: to effectively use supply chain analytics, businesses need to invest in the right technology and have the right talent in place. This can include developing training programs to help your existing talent pool with advanced analytics adoption, or outsourcing to consultants to get started.
- Develop a clear business case: to get buy-in from stakeholders and accelerate the adoption of supply chain analytics, it's important to clearly articulate the business case and how it will benefit the organization.
- Test different analytics techniques: to understand which analytics techniques are most effective for your organization, it's important to test a variety of approaches. There is no one-size-fits-all data solution, algorithm, or model. You have to try different approaches and see what works for you. This is true even after you’ve built a more robust analytics foundation, as your company’s and team’s needs may change over time.
- Focus on continuous improvement: supply chain analytics should be viewed as an ongoing process, rather than a one-time project. By regularly reviewing and updating your analytics strategy, you can continuously improve your supply chain operations.
Data-driven decisions in supply chain analytics
In terms of applying analytics to supply chain management, you can start with the four main kinds of analytics: descriptive, diagnostic, predictive, and prescriptive analytics.
- Descriptive analytics involves analyzing past data to better understand what has happened in the past. This type of analytics can be used to identify trends and patterns in supply chain operations and can help companies identify areas for improvement. Descriptive analytics can help you get a clearer understanding of what’s been going on in your supply chain–for example, is one supplier always late or always early? Is there a particular part that is used in multiple products?
- Diagnostic analytics is a type of analytics that involves analyzing data to identify the root cause of a problem or issue. It answers the question In the context of supply chain analytics, diagnostic analytics can be used to identify problems or bottlenecks in the supply chain and determine the underlying causes. For example, if a company is experiencing delays in the delivery of goods to customers, they could use diagnostic analytics to identify the root cause of the delays. This could involve analyzing data on transportation routes, inventory levels, and supplier performance to identify bottlenecks or other issues that are causing the delays.
- Predictive analytics uses statistical modeling and machine learning techniques to make predictions about future outcomes. It answers the question, what will happen? In the context of supply chain analytics, predictive analytics can be used to forecast demand for products, identify potential bottlenecks in the supply chain, and optimize inventory levels.
- Prescriptive analytics goes beyond predicting future outcomes and provides recommendations for actions to take based on the insights gained from the data. In the context of supply chain analytics, prescriptive analytics can be used to optimize routes, identify the most cost-effective suppliers, and streamline operations. You can read more about predictive and prescriptive analytics on our blog.
Challenges in implementing supply chain analytics
Given the many benefits of implementing supply chain analytics, it is important to start with a strong foundation, with an awareness of the challenges and roadblocks you may encounter. We’ll go over these in brief here, and focus on how to combat two of the most common challenges.
- Data Quality: poor data quality is one of the biggest challenges when implementing supply chain analytics, or any analytics framework or strategy. Data needs to be accurate, complete and up-to-date in order for it to be useful for analysis. It’s important to be monitoring the data, doing regular data quality checks, and collecting and maintaining metadata to understand where the data has come from, if any changes have been made, how often it’s updated, and what it’s intended use is.
- 2. Technology: implementing the right technology to support analytics is also a challenge. It is important to choose the right software, hardware, and infrastructure that can handle the data and analytics. Part of choosing the right technology is ensuring that you have the right support in place to empower your team to use the technology effectively.
- Change management: supply chain analytics requires changes to processes and operations. It is important to manage the change process and ensure that all stakeholders are on board with the changes.
- Security: security is also a challenge when implementing supply chain analytics. Data needs to be protected from unauthorized access and theft.
- Cost: cost is also a challenge when implementing supply chain analytics. It can be expensive to purchase the necessary technology and infrastructure, as well as to hire the necessary personnel.
Limited access to accurate and comprehensive data
When implementing supply chain analytics, or any other analytics strategy, you have to start with the data–access, quality, security, and comprehensiveness. No matter how advanced your algorithm is, if you train the algorithm on bad data, the predictions will be bad too. You might hear this phrase “garbage in, garbage out” frequently. This is similar to manufacturing a product–if your raw materials are low quality, the finished product might fall apart easily, rust, or stop working suddenly. You need high quality raw materials (data) to create a high quality product (analysis). Here are some initial steps you can take to improve your data.
- Define data standards and governance policies: establishing clear standards for data collection, storage, and use can help ensure that data is consistent and accurate.
- Regularly review and cleanse data: it's important to periodically review data to ensure that it is complete and accurate. This may include identifying and correcting errors, removing duplicate records, and standardizing formats.
- Use data validation checks: automated data validation checks can help identify errors and inconsistencies in data as it is being entered or updated.
- Establish data ownership and accountability: assigning ownership of data to specific individuals or teams can help ensure that data is kept up to date and accurate.
- Invest in data management tools: tools such as data quality management software can help automate the process of identifying and correcting errors in data.
Limited availability of talent and skills in the field
Next, even if you have the best quality data, you need some technical expertise and domain expertise so that your team can work with the data. If you’re just starting to build out your data and analytics strategy, you might be unsure how to allocate your existing talent’s time, or whether you should hire someone new, or work with a consultant. But, there are several ways that companies can leverage their existing talent to improve supply chain analytics, even if they don't have a large team of data analysts or data scientists:
- Utilize cross-functional teams: involving employees from different departments in the analytics process can bring a diverse set of skills and perspectives to the table.
- Train and develop existing staff: providing training and development opportunities can help employees gain the skills they need to contribute to the analytics process. This can include courses on data analysis, statistics, and visualization tools.
- Utilize self-service analytics tools: self-service analytics tools can enable employees who may not have a background in data analysis to explore and analyze data on their own.
- Partner with external experts: if in-house resources are limited, companies can consider partnering with external experts, such as consulting firms or data science service providers, to support their supply chain analytics efforts.
Trends in supply chain analytics
Lastly, it’s important to look ahead. We’ll go over a few trends in supply chain analytics we’re anticipating in the coming years:
- Increased Use of AI and Machine Learning: AI and machine learning are becoming more widely used in supply chain analytics. Companies are using these technologies to improve forecasting, inventory management, and demand planning.
- Greater Focus on Data Quality: companies are beginning to recognize the importance of having accurate data when it comes to supply chain analytics. Companies are investing in data cleansing and data enrichment initiatives to ensure their data is as accurate and up-to-date as possible.
- Automation of Processes: automation is becoming more prevalent in the supply chain. Automated processes help to reduce costs, increase accuracy, and improve efficiency.
- Increased Use of Predictive Analytics: predictive analytics are being used to help companies anticipate and respond to customer needs. Companies are using predictive analytics to forecast demand and improve inventory management.
- Increased Use of Real-Time Data: companies are leveraging real-time data to gain insights into their supply chain operations. This helps them to identify potential problems and take corrective action before they become too serious.
Overall, supply chain analytics is an important tool for companies looking to optimize their supply chain operations and stay ahead of the competition in today's fast-paced business environment. By leveraging data and technology, companies can gain valuable insights and make better decisions that drive efficiency and improve their bottom line.
- Start with assessing your data, ensuring you have systems in place to monitor quality, and movement of data from one place to another.
- Train your existing staff, who have domain expertise, to use analytics tools.
- Analytics comes in a few forms: descriptive, diagnostic, predictive, and prescriptive. Start with the foundations of descriptive analytics to answer the question “what happened?” and then you’ll be able to move forward towards predictive analytics and prescriptive analytics to determine “what will likely happen” and “what should be done?”
- Test out different analytics tools and techniques to figure out what is best for your company and team right now. Then iterate on your processes as needed. The data space is all about continual improvement and learning, so don’t be afraid to try something new if what was working, stops working.
Einblick is an agile data science platform that provides data scientists with a collaborative workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick customers include Cisco, DARPA, Fuji, NetApp and USDA. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.