If you are looking to take your manufacturing business to the next level, employing advanced data analytics can help in a variety of ways including improving the quality of your product, increasing productivity, minimizing waste, and reducing variability. This can be accomplished by making slight adjustments to your manufacturing process that have a nominal impact on how you conduct your business while delivering streamlined results.
Although manufacturers may not have tapped into the power of data analytics in the past, in today’s fast-changing environment, business owners looking to make the most informed decisions can no longer avoid the valuable information that can be derived from incorporating advanced data analytics as a critical part of the process in their day-to-day operations. Currently, accessing and utilizing advanced data analytics is easier than ever before as the combination of emerging technologies and low-cost computing power make it possible to quickly collect and analyze large amounts of data from both internal and external sources.
Digital twin technology
Digital twin data technology has helped NASA monitor mission-critical machines for decades. We saw the beginnings of this technology in the movie Apollo 13, when NASA used simulators to mimic what was happening on the damaged spacecraft to evaluate how to get the crew back home. Today, digital twin technology uses cloud-connected sensors embedded in machines to upload real-time operational data, producing up-to-date, virtual simulations of real-world machines. The internet of things is key to implementing digital twin technology. The increasing affordability of sensors and the data throughput capacity of cloud computing make the application of digital twin modelling affordable for a large portion of manufacturers. Armed with this “big data,” you can identify patterns, trends and correlations among various manufacturing process steps and inputs. This analysis can lead to valuable and often unexpected insights that you can leverage to improve your manufacturing process and boost profits.
Predictive maintenance systems
Although it is impossible to prevent machines and equipment from breaking down, predictive maintenance systems allow you to anticipate and prepare for these events, translating to less downtime for your business. Predictive maintenance systems use a combination of embedded sensors, artificial intelligence, and advanced data analytics to gather historical information on between hundreds and thousands of parameters. This helps the systems identify and monitor the factors most closely correlated with service interruptions due to system malfunctions.
Predictive maintenance systems allow you to schedule maintenance for times that will impact your operations the least, such as nights or over a holiday weekend. You will also be able to minimize downtime by having the necessary personnel, parts, and materials on hand when you need them.
Even if you have minimized or eliminated unscheduled downtime, other operating inefficiencies may be more difficult to detect. Advanced data analytics can reveal hidden inefficiencies and bottlenecks by examining hundreds of production parameters that affect efficiency and throughput and applying sophisticated modeling techniques.
Often, relatively simple adjustments to these parameters can help you streamline operations and maximize your output. For example, simply rearranging the plant floor or relocating often-used parts to be more accessible can help eliminate bottlenecks.
How big data can improve supply chain management
The COVID-19 pandemic, the lingering effects of BREXIT, and the Ukraine conflict, as well as effects from climate change have contributed to the new normal of supply chain disruptions. Big data analytics can provide manufacturers with greater control over their supply networks. According to a special report from Thomson Reuters, the biggest factors are traceability (knowing where goods are), predicting potential problems, having plans in place to address these issues, and carrying out customer service.
Investing in a global trade management solution (GTM) allows a company to capture supply chain data in a central, standardized data format which will improve the effectiveness of data analytics. In the end, supply chain advanced data analytics has the potential to benefit your organization not only through cost savings and operational efficiency, but also, with the proper analytic tools, through the reduction of mundane, manual tasks. Ultimately, this allows you more time to focus on strategic planning.
Keeping your ERP system current
Most manufacturers use some form of enterprise resource planning (ERP) system. These software systems help centralize and integrate the management of various interdependent business activities, such as sales, marketing, finance, human resources, customer relations, procurement, and supply chain operations. By creating a centralized repository of business data, ERP systems and advanced data analytics can work hand-in-hand to help manufacturers optimize performance.
Because analytics are only as effective as the underlying data being analyzed, manufacturers wanting to take advantage of these techniques should ensure that their ERP systems are not obsolete. Your ERP software may be due for an upgrade if it:
- Does not provide access to real-time data
- Requires certain manual processes
- Does not communicate with your suppliers’ and customers’ systems, or
- Relies on multiple platforms
It is also important to consider whether your system is user friendly and whether the vendor supplies needed support.
Leveraging the power of big data
Manufacturers that collect and analyze data about their operations can enjoy significant improvements in productivity, efficiency, and profitability. In addition to the areas discussed above, advanced data analytics can be used to optimize a variety of other processes, including finance and budgeting, scheduling, marketing, and customer service.
For more insights from our Manufacturing and Distribution Practice, reach out to our specialists today or contact Mark Fagan at firstname.lastname@example.org.
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