Harnessing Data Analytics for Supply Chain Optimization

TL; DR: Data analytics can be used to optimize the entire supply chain by providing extensive insight into the current state of processes and helping to predict the future. There are different ways to capture data and different categories of data. The final choice of the best data analytics strategy will depend on each company’s priorities, but the research stage will be worth it. The information you receive will be crucial in improving inventory management, demand forecasting, budgeting, and more.

At the start of 2024, Cash Flow predicted that professionals with expertise in supply chain statistics and analytics will be in high demand, and Supply Chain Brain posted about the merits of data analytics for supply chain optimization. It is clear that the topic of data-driven decision making will only gain momentum and evolve, requiring the rest of the industry and the experts within it to catch up to the overall digitalization.  

Whether you are new to data analytics in supply chain or wish to deepen your knowledge, if you want to optimize your business, this article is for you.  

Information is power. In the world of supply chain specifically, accurate and relevant data grants companies the power to better control their businesses. Analytics are helpful when making informed decisions and striving for continuous improvement. After using data analysis, supply chain professionals can identify which strategies drive their business forward, and which inefficiencies drag them down.  

Data analytics can help companies enhance customer satisfaction by boosting order accuracy and responsiveness to changing market demands, or simply by listening to their customer feedback. With the power of data analytics on their side, organizations can achieve greater efficiency, agility, and competitiveness in today’s supply chain ecosystem overall.  

All of this might seem too good to be true, but when it comes to supply chain, data truly is a gift that keeps on giving. The best way to start understanding it is by learning the basics. 

Data Analytics is an industry in its own right, and it is by no means monolithic. Different types of analytics can target the past, present, or even future results, and develop different areas within your supply chain. 

• Descriptive Analytics summarize historical data to see what happened, assess the current state of supply chain, and draw simple matter-of-fact conclusions.  
• Predictive Analytics take descriptive data a step further, using historical insights to forecast future events in the supply chain. 
• Prescriptive Analytics go beyond prediction to recommend actions that can be taken to optimize outcomes. It uses algorithms and optimization techniques to better face the risks or lean into the growth opportunities. 
• Diagnostic Analytics focus on identifying the root causes of problems in the supply chain. It investigates past patterns to pinpoint inefficiencies.  
• Real-time Analytics process data as it is generated to provide immediate insights and support real-time decision-making in the supply chain.  
• Network Analytics analyze the relationships between various entities in the supply chain network (such as suppliers, manufacturers, distributors, and customers) to improve their collaboration. 
• Sentiment Analysis assess textual data from sources such as social media, customer reviews, and feedback to gauge customer preferences and react to feedback. 

Before data analytics can grant you a better-informed approach to running a supply chain business, you will need to examine what types of data acquisition align with your objectives. 

Let’s start from the top, examining what kinds of data your supply chain can produce for analysis.  

• Supply data. The supply is easy to monitor via an inventory management system, where you will see how many items of goods you have on site. Various management systems offer different tools for supply monitoring. For example, in addition to its IMS, Oracle offers Manufacturing and Purchasing software, to help you track the incoming supply as it is being made or bought.  
• Demand data. The demand calculation is powered by the number and frequency of orders your business receives. In a cloud-based management system, client-facing employees, inventory managers, and transportation workers can all be notified of the upcoming orders automatically in seconds. A non-cloud system will need a manual hierarchical pipeline to notify staff and input data on each shipment. 
• Cost data. Delivery of every item costs your company something. Tracking this information is paramount for staying within your budget and evolving to spend less and earn more. You can measure the cost of your supply chain in cost per load, cost per mile, or other, more niche metrics. Coyote Logistics provides an extensive breakdown of supply chain costs. 
• Performance data. Informed by your company’s priorities, this subset of data is the most subjective and refers to how well your supply chain performs based on the categories that mean the most to you. Surgere will measure the difference between planned and actual inventory and the time “a carrier waits before processing orders for pickup and delivery”, while Celonis aims broader, with metrics like the time of the entire supply chain cycle and cash-to-cash time,  “between when a company sends cash to suppliers and when it receives cash from customers”. 

Now that you know which data can be tracked and used for data analytics, how do you go about it? A hint: the most popular routes companies take are powered by the industry 4.0 technologies, also known by the term “smart”. 

This approach gathers data through various smart methods, including sensors, IoT devices, and integrated software systems. These technologies continuously collect data from equipment and operations, and generally target changing qualities, not quantities, inside of your warehouse: temperature, humidity, sound, proximity, and more. Sensors embedded within machines and devices capture information after each change that occurs inside of your supply chain and store that data in the cloud for later use.  

Systems such as RFID tags, barcode scanners, and GPS devices capture data at different stages of the supply chain, and data processing systems digest it. These technologies are a bit more quantitative: they react to certain events and provide real-time visibility into inventory levels, shipment status, and the progress of logistics operations. They, too, are automated with machine learning algorithms, but take an extra step in processing data, instead of just keeping it stored. 

Additionally, enterprise software systems like ERP (Enterprise Resource Planning) and SCM (Supply Chain Management) platforms collect data from internal and external sources like suppliers and customers. This data includes orders, delivery schedules, and other important bits of client or manufacturer history that can tell the company a lot about the changing dynamics within their business. 

The most straightforward iteration of this type of data collection is gradually becoming obsolete as the world digitalizes. It involves only human intervention to record information using various methods. This can include manual entry of details into digital systems and may involve visual inspection and handwritten documentation. 

Data capture that is partly human-powered, however, is still very popular. Barcode scanners or handheld devices used by personnel to scan barcodes or RFID tags to gather data minimize human error, but still require human involvement to initiate and validate the data capture process. 

Manual data capture can be prone to errors and inefficiencies, but it remains a common practice when automation is not feasible or cost-effective, or instances where human judgment and discretion are necessary, such as quality control inspections or exception handling. 

Even the most sophisticated tools of data analysis can prove useless in the wrong hands, and the opposite is just as true: modest data harvesting technologies will yield great results in the hands of a skilled analyst.  

Here are the best practices to ensure your data analytics paint the full picture: 

• Data Quality Management. Ensure that data collected across the supply chain is accurate, reliable, and consistent. Implement processes for data validation to maintain high data quality standards. 
• Data Integration. Integrate data from various sources and systems within the supply chain, including ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), SCM (Supply Chain Management), IoT (Internet of Things) devices, and external partners. 
• Advanced Analytics. Apply predictive modeling, machine learning, and optimization algorithms to extract insights from supply chain data.  
• Real-time Data Monitoring. Implement systems for live monitoring of key supply chain metrics and events. Respond to changes, disruptions, or anomalies ASAP. 
• Data Visualization and Reporting. Use design tools and dashboards to present supply chain data in a clear and intuitive manner. Even the simplest charts can help stakeholders better understand complex data patterns. 
• Collaboration and Information Sharing. Foster teamwork among supply chain partners by establishing data-sharing agreements. This collaborative approach enhances visibility, coordination, and responsiveness across the entire supply chain network. 
• Data Privacy. Implement robust data security measures to protect sensitive supply chain data from cyber threats. Ensure compliance with data privacy regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act). 
• Continuous Improvement. Evaluate and refine data-related processes and correct your mistakes. By embracing a culture of ongoing growth, organizations can adapt to evolving business needs and leverage data more effectively over time.

You’ve discovered how to collect and handle data effectively. Next, we’ll look at the parts of the supply chain that can benefit greatly from those data analytics.

Automated algorithms gather sales data, customer behavior data, market trends, and seasonal fluctuations to help you better plan your inventory. Ideally, this information will empower you to stock just enough goods to meet your customers’ demand without entering the overstocking territory.

Additionally, monitoring customer behavior and market trends manually gets more difficult as your business expands into new countries; human findings can be subject to stereotypes and biases, while automatically gathered insights present you with objective facts.

In the industries that deal with perishable goods (healthcare, food produce) orf fast-changing trends (fashion), overstocked items are premeditated waste as much as they are a failed investment. Data Analytics help to avoid reckless stocking of goods that will stay on the shelves past their expiration point to meet the error-prone Minimum Stock Level. Certain customized systems can take the commitment to sustainability even further, allowing you to calculate the ways to minimize carbon footprint.   

Today’s predictive models can reveal how your supply chain will be affected by various natural disasters and geopolitical conflict, using a combination of news tracking, historical data, and even social media signals. They can also warn you about certain suppliers by conducting research on their delivery history to see how trustworthy they are. 

This category refers to the freight as much as to the warehouse design. For transportation, automated GPS insights and access to traffic information can save your truckers plenty of time on the road. Risk Management comes into play here, as well. Signals about road damage due to weather or construction work will take a lot of time to track manually, but with automation you’ll be the first to know.

Lengthy routes that are riddled with potential threats to your goods exist not only beyond the walls of your warehouse. Inefficient layout of a storage facility can result in confusion during order picking and packing process, as well as human errors and employee trauma. According to Hy-Tek, historical data for metrics like ‘average distance traveled to fulfill order’ or ‘picks per hour’ can help to analyze and optimize the pick path for maximum efficiency and safety.

At the end of the day supply chain is a business that has to be profitable. Despite this simple truth, in a poorly managed supply chain human error can lead to a lot of unnecessary spending. Data analytics can show you the price of the entire supply chain journey and help you improve your margins by drawing conclusions from that information.

Data analytics is a bustling industry that only promises to grow. For supply chains that want to keep their competitive edge, integrating automated algorithms for data capture is not a matter “if”, but “when”. Do your research on what parts of your supply chain require more attention and open a window into perfectly transparent inventory management, warehousing, and freight.

Innovecs has been optimizing the supply chains of our partners for over a decade, and our growth never stops. If you are open to an interview or a discussion that can potentially deepen our expertise, feel free to reach out and we will happily feature you on our blog. We can help your business reach new heights through the power of innovative technology and data-driven software solutions or just have a beneficial chat.