Behind every successful manufacturing company is an efficient supply chain. The supply chain acts as the backbone of the manufacturing process, generating vast amounts of data. While this data is valuable, it can be challenging to collect, process, and extract actionable insights to optimize the supply chain. This is where supply chain analytics comes into play.
Analytics offers businesses the opportunity to make data-driven decisions based on clear, reliable data that is often visualized through graphs, reports, and other tools. Supply chain analytics helps to sift through massive datasets, identify patterns, and produce actionable insights that can enhance decision-making. These insights are crucial, especially in the coming years when the use of analytics will rapidly increase. Leveraging data effectively leads to better operational planning, streamlined supply chains, and ultimately, greater profitability.
Why Are Supply Chain Analytics Important?
Imagine having access to a tool that could predict future trends—would you use it? While supply chain analytics cannot predict the future with 100% certainty, it can identify patterns and predict future trends with a high degree of accuracy. By analyzing current and historical data, supply chain analytics can forecast market trends, quantify demand, and help businesses make informed decisions about pricing strategies and supply chain adjustments.
Here are the key reasons why supply chain analytics is crucial for manufacturing success:
Improve Supply Chain Planning
Supply chain analytics helps businesses forecast future demand with a high level of accuracy. This allows companies to plan their operations better, adjusting production and inventory based on predicted shifts in demand. Analytics also helps businesses understand typical customer behaviors after initial orders, offering valuable insights for inventory management and demand forecasting.
Increase Flexibility within the Supply Chain
In the modern manufacturing environment, supply chains need to be dynamic and adaptable. Analytics-powered business intelligence enables greater flexibility by predicting machine maintenance schedules, tracking changes in customer behavior, and adjusting production schedules accordingly. This flexibility allows businesses to optimize costs and maximize profitability, making operations more agile and responsive to market demands.
Understand Trends or Risks within the Supply Chain
Supply chain analytics makes it easier to spot trends and risks across the entire supply chain. By aggregating data from multiple sources, analytics can alert companies to potential issues before they escalate. Identifying risks early allows businesses to take corrective action, saving time and resources and preventing costly disruptions.
Gain Efficiency in Staffing or Sourcing
With a strong analytics strategy in place, businesses can optimize their resource scheduling. Analytics tools help coordinate vendor deliveries, manage employee workloads, and keep everyone informed in real time. This efficiency minimizes operational disruptions and helps businesses maintain smooth, continuous operations.
Understand Key Aspects of the Business
Once supply chain analytics are implemented, businesses can streamline critical operations such as planning accuracy, order management, procurement, and risk mitigation. These improvements contribute directly to a company’s bottom line. Supply chain analytics also provide valuable insights that enable businesses to identify operational inefficiencies and make data-driven adjustments.
How Do Supply Chain Analytics Work?
Supply chain analytics collects data from various sources, including internal systems, external sources, IoT devices, and other emerging technologies. The goal is to improve decision-making at all levels of the supply chain, from operational to strategic processes. By offering real-time visibility into critical supply chain activities, analytics helps businesses identify trade-offs between customer service levels and cost.
The implementation of a supply chain analytics initiative often begins with data scientists. These experts analyze data on cash flow, inventory, waste levels, and other key areas of the business to identify trends and correlations. They create predictive models that help optimize supply chain processes. After testing and refining these models, the resulting analytics strategy is used to enhance supply chain performance.
In practice, data scientists, engineers, and business users work together to continually refine the analytics process, ensuring that data-driven insights are effectively applied and lead to improvements in supply chain management.
Types of Supply Chain Analytics
Supply chain analytics offers several opportunities to enhance operational performance. These opportunities generally fall into four main types:
Descriptive Analytics
Descriptive analytics helps provide visibility into the supply chain and offers a reliable source of information from both internal and external systems. This type of analytics uses dashboards and reports to analyze historical data and communicate past, present, and future trends. Descriptive analytics answers questions such as, “How have our inventory levels changed over time?” or “What is the ROI of this particular investment?”
Diagnostic Analytics
Diagnostic analytics helps identify the reasons behind certain outcomes, acting as a tool for troubleshooting. It answers questions like, “Why did we run out of inventory this month?” or “Why haven’t we achieved the same level of inventory turnover as other industry leaders?”
Predictive Analytics
Predictive analytics helps forecast future events based on current and historical data. This type of analytics was especially useful during times of uncertainty, such as the pandemic, when companies needed insights into demand fluctuations. Predictive analytics helps businesses understand the potential impact of emerging trends and mitigate risks or disruptions before they occur.
Prescriptive Analytics
Prescriptive analytics provides actionable insights that help businesses optimize operations and solve complex challenges. This type of analytics enables collaboration between various stakeholders—such as logistics partners and vendors—to improve efficiency and reduce waste. It answers questions like, “What is the best strategy for shipping our products?” or “When should we launch a new product?”
Supply Chain Analytics Data Sources
Where does the data for supply chain analytics come from? The answer is simple: data is everywhere. Supply chain analytics pulls data from various sources, such as:
- Social Media Listening: This unstructured data is valuable for demand planning and forecasting, offering insights into customer behavior and market trends.
- Geo Data: Location-based data helps predict demand fluctuations by region, enabling more effective resource allocation.
- Inventory Data: Accurate inventory data helps optimize inventory management, reducing waste and ensuring timely restocking.
- Employee Workload Data: Data-driven scheduling helps optimize staffing levels and enhances operational efficiency.
- Production Rate Data: Analytics can adjust inventory levels in real time based on changes in production rates, minimizing inefficiencies.
- Shipping Data: Real-time data from IoT-enabled devices ensures that shipments are optimized for the most efficient delivery schedules.
Supply Chain Analytics History
The history of supply chains has evolved significantly, especially in the past century. Early supply chains were localized, defined by geography and manual processes. However, with the advent of the internal combustion engine, trucks, forklifts, and other innovations in the early 1900s, the supply chain landscape began to change.
During World War II, the need for efficient logistics led to advancements in supply chain engineering. The 1950s brought the revolutionary concept of containerization, which transformed global trade by standardizing shipping practices.
The 1960s to the 1990s saw the introduction of computerized systems for inventory management, including real-time data tracking and forecasting. The term “Supply Chain Management” was coined in 1983, marking the beginning of a more formalized approach to managing global supply chains.
Today, we stand on the cusp of a new era in supply chain optimization, fueled by data analytics, AI, and the Internet of Things. While only a small percentage of companies have fully integrated advanced technologies into their supply chains, the future promises further innovations that will shape the way businesses manage their operations.
The Future of Supply Chain Analytics
Looking ahead, supply chain analytics is set to evolve rapidly with the introduction of advanced technologies like 5G, IoT, and machine learning. These innovations will further automate and optimize supply chain processes, leading to even greater efficiencies.
While the adoption of these technologies is still in its early stages, the benefits of advanced supply chain analytics are clear. Businesses that embrace these tools now will be well-positioned to stay ahead of the curve and capitalize on the next wave of supply chain transformation.