Imagine a world where manufacturing machines predict their own breakdowns, where production inefficiencies are spotted and rectified in real-time, and where every decision is backed by a wealth of data. How close are we to this reality, and how is Big Data propelling the manufacturing industry towards this future? Big Data refers to massive data sets utilized for strategic decision-making. In the manufacturing industry, Big Data involves collecting data at every production stage, including information from machines, devices, and operators. All these insights help organizations optimize their operations, improve product quality, and reduce costs.
Dive into this article to discover our current position in the Big Data journey and the potential goldmine it promises for those ready to harness its power for substantial profits.
Current Scenario
Manufacturers have used data analysis to improve efficiency and advance their market share for many years. But the most significant change today is how data is collected. A mere 39% of manufacturing executives have successfully scaled data-driven use cases beyond a single product’s production process. Many companies still use fragmented, traditional methods for data capture, with staff manually checking and recording factors, filling forms, and writing down operation and maintenance histories for the machines on the floor. Unfortunately, these methods are highly inaccurate due to human errors. They are also time-consuming, open to bias, and do not generate the quality of analysis required for accurate decision-making.
Future Scenario – What Optimizing Your Data Could Reveal
Preventive maintenance programs have been around manufacturing for decades. The idea is that through use-based or time-based programs, unplanned breakdowns are less likely to occur. By applying analytics, real-time data can be leveraged to do more than prevent breakdowns. Yield-energy-throughput (YET) analytics can be used to ensure that those individual machines are as efficient as possible when they are operating, helping to increase their yields and throughput and reduce the amount of energy they consume. Even small percentage improvements in operational efficiency can significantly enhance earnings before interest and tax (EBIT).
It can predict with high accuracy the likelihood of a breakdown and the moment it will occur. This reduces costs, overall downtime and productivity by allowing technicians to perform repairs at the machine’s optimal time and stage parts.
Profit-per-hour (PPH) maximization analytics, meanwhile, scrutinizes the thousands of parameters and conditions that have an impact on the total profitability of an integrated supply chain (from raw materials purchasing to final sales), providing intelligence on how best to capitalize on given conditions.
Together, these advanced analytics approaches can deliver EBITDA (earnings before interest, taxes, depreciation, and amortization) margin improvements of as much as 4 to 10 percent. They can also boost ongoing continuous improvement efforts at a time when manufacturers have seemingly exhausted other options for increasing productivity.
Moreover, they offer a lever for competitive advantage, even for companies with overcapacity, by helping them better manage their production systems and optimally reallocate resources in real time.
Case Study
Real World Impact:
For example, a major metal plant has used a combination of advanced analytics tools as the foundation of a continuous improvement program. Real-time performance visualization in operators’ stations has enabled the company to increase production rates in one of its lines by 50 percent. Engineers are gaining new insights into the failure characteristics of major pieces of equipment and are making ongoing improvements to increase reliability. The company expects to improve total production by 30 percent, without a substantial increase in operating costs by using condition monitoring and predictive maintenance in conjunction with process controls and automated material tracking.
How to Implement Big Data in Manufacturing:
For those looking to harness the power of Big Data in your organization, here are some actionable steps to guide the journey:
- Enhance Data Management:
- Objective: Create a robust data infrastructure.
- Action: Prioritize the structuring and categorization of data to make it analytics-ready.
- Investment: Allocate resources to IT teams skilled in data aggregation, storage, and management. Consider cloud storage solutions for scalability.
- Initiate Pilot Programs:
- Objective: Test and validate analytics solutions.
- Action: Identify critical manufacturing processes that can benefit from analytics. Implement pilot projects to gauge the impact and refine the approach based on feedback.
- Evaluation: Regularly review the outcomes and iterate for improvements.
- Establish Dedicated Analytics Labs:
- Objective: Foster innovation and continuous learning.
- Action: Create specialized labs or innovation hubs within operational units. Equip them with the latest tools and technologies.
- Benefits: These labs will serve as centers of excellence, driving best practices and scaling successful analytics initiatives across the organization.
- Revamp Existing Business Processes:
- Objective: Ensure processes are analytics-compatible.
- Action: Audit and map out existing processes. Identify areas where analytics can be integrated for better decision-making.
- Training: Organize workshops and training sessions to familiarize teams with the new analytics-driven processes.
- Commit to a Culture of Continuous Transformation:
- Objective: Ensure long-term success with analytics.
- Action: Promote a culture where analytics is seen as a continuous journey, not a one-time project. Encourage teams to constantly seek new data-driven insights and refine their approaches.
- Review: Set up periodic reviews to assess the impact of analytics initiatives and identify areas for further exploration.
For Manufacturers, especially those within the Automated and Industrial sector, Big Data is more than just a buzzword. It’s a transformative force, driving innovation, efficiency, and growth. As we navigate the future of manufacturing, the integration of advanced analytics will be paramount for those aiming to lead the pack. Are you ready to harness the transformative power of Big Data in your manufacturing processes?
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