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Manufacturing Insight: How to Use Analytics to Improve Manufacturing Operations

Imagine you have just landed your dream job at a manufacturing company as Operations Manager. The company has a long history and a good reputation but recently, manufacturing issues have begun to impact productivity and profitability. You were hired to identify and resolve the underlying problems. You know the answer lies in the data, but how can you root it out? What data do you need to accomplish this? And what visualizations will provide the actionable analysis of that raw information?

In today´s post, we will explore the relationship between data analytic visualizations and manufacturing efficiency. Insightful visualizations can be created by leveraging the dynamic capabilities of business intelligence tools like Power BI to build simple but powerful data dashboards.

Back to our new hypothetical job, to identify and address our manufacturing concerns, we will employ multiple data sources and integrate them into insightful visualizations. We’ll start with the most fundamental data patterns, increasing the complexity of our visualizations until we can make data-backed recommendations for how to improve manufacturing operations:


1.      First, we’ll review historical trends in customer demand and requested manufacturing.

2.     We’ll then examine the current Open Orders management system and how to improve it.

3.     Lastly, we’ll delve into specific metrics from the Manufacturing Execution System (MES) data.

Of the many stages of Supply Chain, manufacturing is perhaps the most critical. Manufacturing is the central purpose of all goods companies and is the function that necessitates all the surrounding Supply Chain activities. To transform raw materials into finished products, manufacturing teams must meet customer demand efficiently, manage costs, maintain quality, and optimize inventory levels.

Unfortunately, achieving visibility into all aspects of manufacturing can be challenging. Manufacturing operations often involve legacy systems. These older manufacturing plant systems were not designed for modern analytics, making data difficult to access and analyze. Furthermore, the volume and variety of data comes from numerous sources such as sensors, machines, or human inputs. Handling and organizing this fragmented data requires complex transformations and calculations. Managing manufacturing issues with traditional, manual files can result in further data fragmentation, limited collaboration, version control challenges, complexity, confusion, and data security risks. Because of this, business intelligence tools for visibility and analysis of the manufacturing process are essential for optimizing operations, improving quality control, and enabling data-driven decision-making.

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Enhance Planning by Analyzing Historical Trends

Visualizing historical Manufacturing Orders (MO) over time offers numerous advantages for our business. For instance, in the following image, we can identify seasonal trends that impact our production schedules and inventory management. We discovered that during the early summer months, we typically experience a surge in orders due to increased demand. By analyzing historical order data, we can anticipate these peaks, adjust our production schedules accordingly, and ensure that our inventory is sufficient. The below image shows the behavior of customer orders next to manufacturing orders. We can see there was a peak in May for customer orders and a subsequent increase in (rushed) manufacturing orders to replenish inventory. If this seasonality pattern is consistent across years, we can use that information to proactively stock up in advance of next year’s spring rush!

Another benefit of visibility into manufacturing order trends is improved capacity planning. This ensures that our manufacturing facilities have the necessary resources to meet production demands efficiently. By anticipating a surge in manufacturing orders for a particular item, we can allocate additional resources such as manpower, machinery, and materials to meet that demand. Conversely, during periods of low demand, we can scale back production capacity to avoid overproduction and minimize holding costs.

Prioritize Open Orders by Urgency and Importance

Understanding the priority of manufacturing orders is crucial for our production managers. It enables them to effectively schedule production activities, meet customer deadlines, and maintain high levels of customer satisfaction. By prioritizing orders based on their importance and urgency, we can ensure that our customers receive their orders on time, every time. The ability to filter open orders by priority level increases operational visibility, enhances prioritization, and improves resource planning. By leveraging business intelligence tools, we can create visualizations like the one below that highlight priorities and draw users' attention to crucial orders. The below image uses color highlighting prioritization in a line chart and a detailed table where icons such as exclamation marks point to the most urgent open orders.

Identify Efficiency Challenge with MES

To address our manufacturing efficiency challenges, we can implement a Manufacturing Execution System (MES) to facilitate the measurement of facility and personnel performance. MES provides real-time data on production rates, machine utilization, and resource availability. This enables the identification of imbalances or bottlenecks in resource utilization, allowing for adjustments in allocation as needed. By analyzing this data, we can easily project if our current capacity will meet upcoming demand or if additional capacity will be required. Adding this feature to our visuals could be helpful.

Another valuable metric is run-time variation, which is crucial for reducing inefficiencies when tracked by employee and facility. This metric is defined as the fluctuation between estimated and actual time required to complete tasks during the production process. These variations can occur due to changes in materials, machine performance, environmental conditions, or human factors. If scheduled hours are consistently falling short of the plan in certain resource groups or facilities, you can investigate the root causes of the delays and implement measures to improve efficiency and productivity.

Ask yourself: Are all activities consistently completed within the estimated time? Are estimates achievable? Is there room for optimization within existing resources?


In the above example, we compare efficiency between employees and facilities. Knowing the runtime variation for different operators provides actionable insights for performance evaluation, comparison, root cause analysis, improvement initiatives, and target setting within the manufacturing process. Negative runtime variation may result from streamlined processes, well-trained employees, or optimized equipment. Positive runtime variation may be caused by equipment breakdowns, skill gaps, or inefficient workflows.

Negative runtime variation may result from streamlined processes, well-trained employees, or optimized equipment.

As Operations Manager, it is essential to have clear visibility of the manufacturing process. By now, we discovered that through historical manufacturing and demand data, we could identify trends and adjust our production schedules to meet customer demand. Also, prioritizing manufacturing orders enables us to ensure that our customers receive their orders on time, improving our customer satisfaction levels. To enhance our production, run-time variation comes in handy when we need to identify bottlenecks or inefficiencies.

Keep in mind, this is just a small glimpse of what Power BI can do for the manufacturing industry!


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