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The Role of Business Intelligence in Supply Chain Management

 

Business intelligence helps enterprises manage the volumes of information that come from supply chains. They’re valuable tools for organizations that handle data from multiple stages of the supply chain and multiple storage solutions. Business intelligence and big data analytics reduce enterprise information silos, link seemingly disparate pieces of data together, and give organizations tools to improve supply chain operations.   

What role does business intelligence play in supply chain management?

BI and big data applications help enterprises remove guesswork from business decisions, visualize each stage of the supply chain, and track SCM-related KPIs and goals.

Removing guesswork from business decisions 

Without a set of accurate, processed information backing business decisions, supply chain managers can only guess how to improve shipping times, order the right supplies, and satisfy customers. Business intelligence and big data strategies bring all relevant supply and inventory data together so that information isn’t split into silos. When more data is available, business leaders have a more comprehensive view of their supply chain. In contrast, missing pieces mean missing insight. 

For example, without the data from a fleet’s Internet of Things (IoT) devices, a business might never know that a faulty storage unit is leading to broken equipment. Or without using information from the legacy database that the organization still uses for its customer data, the business may not be able to determine why customers are canceling orders in droves. 

Visualizing each stage of the supply chain

With business intelligence and data analytics applied to their supply chain, businesses can monitor each stage. Available insights for the supply chain include: 

  • Inventory levels
  • Orders placed
  • Orders fulfilled
  • Orders shipped
  • Customer data
  • Third-party suppliers or partners
  • Schedules 

Visualizing each stage of the supply chain also helps organizations see how one step affects another. With dashboards, charts, and numbers, they may finally realize that orders aren’t being fulfilled because one of the company’s suppliers only delivered half the inventory that was scheduled. And using their data solution, the company can find the original order placed for inventory to learn that a thousand parts were ordered but only 500 were shipped. 

Tracking SCM-related KPIs and goals 

A business intelligence solution gives businesses a single location to track their supply chain-related key performance indicators (KPIs) and goals. The dashboards and reports available in BI software not only help teams visualize their supply chain but also manage each step of their goals and performance indicators. 

For example, a team might set a KPI for 80 percent of orders placed to be fulfilled in a two-week time period. Using a BI tool that draws data from their supply chain or ERP software, they can see exactly how many orders have been fulfilled within their parameters. They can then use reports to present monthly or quarterly progress to company leaders or other teams.

Learn more about supply chain management: What is the Supply Chain Management Process? 

How do enterprises apply business intelligence to supply chains?

To develop a BI strategy, make stored data available, embed BI in existing supply chain software, and ensure the data you’re using is clean and accurate. 

Store data in reliable and accessible places 

To gain accurate, clear business insights, pull data from as many storage locations within your organization as you can. The more clean, prepared data businesses can use, the more reliable their conclusions will be. Solutions like these include data lakes and data warehouses, but they can also include applications like Google Sheets. 

Embed BI within SCM systems 

With an embedded software solution, BI tools are automatically available within other business applications connected to the supply chain, such as ERP and CRM. What does this look like when applied? 

For example, when integrated with an ERP solution, business intelligence software has access to specific supply chain data such as total inventory, customer details, and shipping information. Analysts use that data to create dashboards revealing information like shipped inventory, completed orders, or most frequently ordered supplies. 

Embedded in a CRM, business intelligence software can flag an increase in customer complaints or a sharp drop in orders for a popular product. This data helps supply chain teams realize that their shipped goods aren’t satisfying customers or decide whether they need to order fewer supplies for the formerly popular product. 

Visualizing data trends that come from existing supply chain applications helps organizations wrap their minds around the things they need to change or continue doing—it better organizes and distills data points for leaders to understand.  

Use accurate data

To ensure the interpretation of your enterprise’s data is accurate, your data teams will need to clean and prepare data. Cleaning data removes duplicates and old, outdated information that might cause erroneous calculations.

Additionally, sometimes it takes months to collect enough data to come to a viable business conclusion—not all reliable decisions can be made in a day. It’s unsafe to rely on business intelligence to solve all business problems without first making sure the data is accurate. 

Questions to consider when implementing business intelligence 

Organizations planning a BI strategy for their supply chain processes should consider the following questions before selecting software or signing off the final draft of the plan. 

Does the big data or BI solution process unstructured data? 

If companies’ supply chains, warehouses, or delivery trucks use Internet of Things (IoT) devices like sensors to track product status, they’ll need to find a solution that processes unstructured data. They should also consider if they’re going to need IoT devices in the near future, even if the organization hasn’t installed any yet. The ability to scale will help the business be more prepared when the time for IoT comes. 

Aside from IoT devices, much enterprise data is still unstructured. A BI solution that readily supports both types of data will help teams in the long run if they anticipate having large unstructured data volumes. 

To learn more about unstructured data in BI strategies, read How Businesses Use Unstructured Data for Business Intelligence.

Which features does the business absolutely need? 

Companies that need more advanced analytics tools may need to implement a full BI software platform. For smaller businesses, a cheaper solution with fewer features may be sufficient. Note that if the business is likely to grow quickly in the next few years, it may need a BI or data solution that can scale based on data sources, the number of users, and processing requirements. 

Does the business have executive and leader buy-in?

A BI solution is only successful if teams prepare for it thoroughly. Decision makers in the supply chain process need to be on board before an organization launches a business intelligence or data analytics strategy. Leaders and employees must be committed to keeping data accurate and safe; otherwise, they risk deriving invalid or harmful insights from the data.  

Specific benefits of BI in supply chain management 

Business intelligence helps teams focus on specific segments from large volumes of data that would otherwise be difficult to parse. Multiple factors hidden within enterprise data affect supply chain goods and processes. When properly applied, business intelligence can be utilized to improve manufacturing and customer experiences. 

Using data to trace food quality 

Terri Jordan, EVP of global business development and president at Aryballe, uses odor data as one example. Using information about smells—which can be highly subjective—requires modern digital tools. 

“Digital olfaction solutions that mimic the human sense of smell can provide insights to solve a number of challenges, such as identifying malodors, accelerating RD projects, and ensuring product consistency, all of which lead to optimal customer experiences,” Jordan said. “Digital olfaction (the digital capture of aromas) can be used as part of successful traceability solutions Enterprises need to use big data to trace goods for improved supply chain tracking in particular. According to Jordan, the reliability of supply chains is impacted When properly positioned and studied, enterprise data can provide an overhead view of business risks and help companies mitigate and use them. But tracking and analyzing that data well is challenging, according to Mark Holmes, the senior advisor for global supply chain at InterSystems. 

Enterprise systems collect data from almost every part of the supply chain process, which creates massive volumes of information that the enterprise can use. “Unfortunately, this flood of data is typically poorly tracked and analyzed, resulting in unidentified opportunities, lost profits, or worse, loss of business continuity and customer trust,” said Holmes. Enterprises need a more comprehensive approach to their overall supply chain so they can identify risks to their SCM process and develop a “risk chain” based on the data they capture. 

Data fabrics work to prevent data loss and to keep data clean, accurate, and available for business analytics. They can be an advantage specifically for enterprise supply chains, according to Holmes. 

“In today’s disruptive supply chain ecosystem, a combination of real-time connected data, self-service, and a high degree of automation, speed, and intelligence represents a powerful competitive advantage,” Holmes said. 

A single source for data reduces the silos that naturally develop within organizations, helping teams see a clearer map of the overall supply chain. “Not only does this streamline data and decision making—allowing each section of an organization to have a full picture of the business—but it also provides a single platform to view inventory optimization, production footprint analysis, transportation route optimization, and more,” Holmes said. Previously siloed supply chain tasks can be streamlined with a single data hub like a fabric. 

Graphing clearer relationships between pieces of data

Graphing data is another method of handling large and disparate volumes of supply chain data, according to Maya Natarajan, the senior director of product marketing at data platform provider Neo4j. 

“Modern supply chains are complex networks that encompass everything from factories to suppliers to distribution centers to transportation and much more,” she says. “With all of these parts in motion, this means companies are managing large amounts of data. 

That data encompasses ERP information, sensor-collected data, inventory statistics, and other related information, which is challenging to analyze, according to Natarajan.   

Graph databases store data in relationships, indicated “Now, organizations can optimize these large volumes Because supply chain management covers such a broad range of enterprise resources and processes, it’s challenging for businesses to harness their data for effective decision-making. Using business intelligence and analytics technology to study supply chain data helps enterprises see relationships between disparate products, sales, transportation, and manufacturing operations. 

 

 

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