When it comes to manufacturing operations, one thing is abundantly clear: There are numerous machines and operations set in motion across the production floor to move a product from the idea stage to completion.
Yet, in order for the production process to flow smoothly, data must constantly flow from one section to the next. This data comes from various sources, from the product measurements to equipment performance data. Today, many manufacturers are working to capture and utilize this bag data to spur operational efficiencies and growth.
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Big Data Connectivity
In the past, one of the biggest issues for manufacturers that wanted to use data to gain insight into their production processes was accessibility. Data remained static from one process source to the next without any way for the machines to communicate with one another.
This problem drastically changed with the advances of technology. Many pieces of equipment now come with sensors built into their systems. These sensors can send information wirelessly to computers, other manufacturing equipment and the cloud.
This connectivity has had an outstanding impact on manufacturing processes. Workers can now access data from remote locations in real time. Even the end products are interconnected, sending data regarding customer purchase trends, usage and product lifecycles.
With the vast amounts of data rolling in, manufacturers have a new task: collecting and segmenting the information. Data segmentation is essential so that manufacturing facilities can use the right data at that appropriate time. This useful information could increase performance, identify possible manufacturing issues, predict customer’s changing product demands and stimulate enterprise-wide growth.
The possible applications of big data seem limitless. In fact, many manufacturing facilities that are utilizing big data have instituted advanced analytical methods in the following common operational areas.
Predictive Analytics for Asset Performance
Manufacturing assets are the biggest cost drivers in a facility. These machines may run 24/7 to create low-volume and high-volume product batches, as well as moving finished products down the supply chain. Captured data can reveal the lifecycle of equipment so that maintenance employees can monitor current performance, maintenance requirements and product output rates.
Manufacturers also use this big data to perform predictive analysis. They can gather information from performance logs to understand how equipment is presently performing compared to past performance.
With predictive analysis, manufacturers can understand and plan future maintenance schedules for equipment to prevent potential breakdowns.
They also have a greater ability to understand where a malfunction may occur, allowing them to perform the smaller repairs earlier in the life of the machine to avoid more costly repairs later. This data analysis gives facilities the ability to perform these repairs during scheduled downtimes instead of during full production runs, allowing them to maintain their productivity without producing potential bottlenecks caused by larger equipment shutdowns.
Diagnostic Data Analysis for Production Management
The more logical and streamlined the production process is, the more efficient a manufacturer can run and the more products it can churn out without increasing energy costs or waste. However, a common production issue involves a repetitive or illogical production process.
For instance, say a product must go from one machine to the next and then back to the original machine for another step.
Because of this repetition, the next product on the line would have to be set aside until operators can complete the additional production steps on the first piece. This arrangement ties up resources and could lead to greater inefficiencies.
With diagnostic data analysis, manufacturers can get a snapshot view of how a product travels through the production line. They can gain greater insights and transparency into how equipment is being used. This information could then to be used to determine whether certain production processes could be performed by other equipment further down the production line. This possibility could allow for other workstations to be available to begin constructing the next product to improve production rates.
In addition, data analysis of operations allows manufacturing facilities to use all available floor space. By understanding how the product moves from each workstation, the layout of equipment could be modified for better product flow. Manufacturers could then understand which machines are essential as well as which equipment could be replaced, upgraded or decommissioned.
Descriptive Analytics for Product Monitoring
Products can provide substantial amounts of data while moving through the supply chain. From purchase order insights to customer demand changes, this information can tell a manufacturer about purchase trends and give insights into what customers desire in a product.
With descriptive analytics, a manufacturer can discover possible product patterns and monitor product usage. A manufacturer may decide to allow for product customizations to spur more consumer interest and expand its customer base.
This insight might also allow a manufacturer to analyze customer behavior in regard to its products, spurring possible new product designs and product lines that could be rolled out in the future.
Many manufacturers use descriptive analytics to search for anomalies in their supply chains. Pulling historical data, a manufacturer can monitor raw material supplies to note possible shortages that could create potential bottlenecks. To mitigate this, a facility could institute supplier changes to ensure a more stable procurement line and prevent disruptions.
Descriptive analytics can also help manufacturers further down the supply chain. Product tracking is a large data segment for many facilities. They are monitoring how products travel once leaving loading docks and moving to warehouses, distribution centers and fulfillment centers. Manufacturing facilities can monitor shipping services and 3PLs to track transportation operations and make note of possible issues that require improvement.
Big Data Improving Manufacturing Facilities
Big data is making a big impact in manufacturing facilities across the globe. Manufacturers can now fully leverage data in meaningful ways to improve their companies’ strategic investments, strengthen business relationships and promote operational efficiencies. The amount of data gleaned from operations is usually dependent on the size of the manufacturing facilities and the complexity of their operations. So, a facility may find new opportunities to use the data in other aspects of their enterprise.
AUTHOR BIO: Mariana Vieth is Director of Marketing at WSI, a leading third-party logistics provider that specializes in fulfillment, chemical warehousing, transloading, transportation, and more. Having the 17th-largest 3PL network in the United States, spanning more than 15 million square feet, WSI delivers tailored end-to-end supply chain solutions to customers who seek to increase efficiency, shorten lead times, deliver more reliable performance, and minimize costs.