According to an analysis, the global manufacturing analytics market will reach a whopping $28,443.7 million by 2026 at a CAGR of 21.9%. This is up from $5,950 million reported in 2018. Much of this growth can be attributed to the need for putting manufacturing facility and supply chain data to use and the rise of cloud analytics.
Cloud analytics is now gaining prominence as the way to perform complex statistical analyses on data stored in the cloud and deliver results when and where it matters most. The manufacturing industry can certainly benefit from this development — precisely what this article aims to explore.
What Is Cloud Analytics?
Cloud analytics refers to using analytic algorithms on the cloud to extract valuable insights from data. Today, organizations are moving their workloads to the cloud for better scalability, flexibility, and cost-efficiency. Running analytic algorithms against the data stored across the cloud environment serves enterprises in making proactive decisions right when it matters most.
Because everything happens in the cloud, cloud analytics is faster and easier to set up as compared to running analytics initiatives across traditional data centers. It enables users to access and process data from anywhere and at any time.
Benefits of Cloud Analytics for Manufacturing Companies
For manufacturers, embracing cloud analytics is no longer an option but a strategic imperative. Here’s why it makes sense for organizations seeking to thrive in today’s data-driven, competitive landscape.
Enhanced Decision-Making Capabilities
Cloud analytics equips manufacturing firms with powerful data-driven insights. It enables real-time monitoring of production lines, supply chains, and quality control. By harnessing historical and real-time data, manufacturers can gain a holistic view into their operations, allowing for more informed decisions.
Streamlined Operational Efficiency
Efficiency is the lifeblood of manufacturing. Cloud analytics contributes to the optimization of manufacturing processes by analyzing vast datasets to identify inefficiencies, streamline workflows, and improve resource allocation. Based on the insights delivered by a cloud analytics solution, manufacturers can enhance resource utilization, reduce downtime, and ensure smoother production flows, ultimately increasing output and profitability.
Cost Reduction and Optimization
Cloud analytics enables cost reduction by identifying areas where resources are underutilized or wasted. It also aids in predictive maintenance. This helps manufacturers prevent costly breakdowns by scheduling maintenance when it’s most needed rather than on a fixed calendar basis.
The cost reduction is also associated with the innate ability of a cloud analytics function to accrue lower operational expenses. For example, as compared to traditional on-premises analytics functions, cloud analytics:
- Has a lower total cost of ownership (TCO) because of the cost savings on hardware, software licenses, support, and maintenance
- Offers capabilities to analyze unstructured data. This saves costs associated with cleaning data and bringing it to a format that the analytics function can utilize.
- Handles substantial data volumes. For manufacturing facilities, this is of significant importance since data is collected across a variety of touchpoints.
Enhanced Regulatory Compliance
Manufacturing companies often face stringent regulatory requirements. There are also risks associated with worker safety. Cloud analytics helps ensure compliance by providing accurate and auditable data records. It tracks the adherence of processes and products to industry standards, thus reducing compliance-related risks and potential fines.
Heightened Operational Agility
In a rapidly changing market, manufacturing companies need to be agile and responsive. Cloud analytics empowers manufacturers to adapt to market fluctuations by providing real-time visibility into demand trends, supply chain disruptions, and customer preferences. This agility enables manufacturers to adjust production plans swiftly and stay competitive.
Challenges Faced by Manufacturing Companies
While cloud analytics offers numerous benefits, manufacturing companies also face significant challenges in its adoption. Here are some key hurdles they must address:
Security and Privacy Concerns
Manufacturing companies often deal with sensitive intellectual property, proprietary designs, and production processes. The transition to cloud analytics raises concerns about data security and privacy.
As such, companies must carefully select cloud providers with robust security measures and implement encryption, access controls, and regular audits to safeguard their data.
Manufacturing environments generate vast volumes of data from various sources, including production machines, sensors, and supply chain partners. This data is often siloed in different systems or formats, making it challenging to derive meaningful insights.
Integrating and centralizing data from these disparate sources is a sophisticated task that requires careful planning and investment in data integration solutions.
Lack of Skilled Resources
Effective cloud analytics requires skilled data scientists, analysts, and IT professionals who can manage, analyze, and interpret data. Many manufacturing companies lack in-house talent with expertise in data analytics and cloud technologies.
To address this challenge, companies can invest in training programs, hire external experts, or partner with analytics service providers to bridge the skills gap.
How To Overcome the Challenges of Cloud Analytics in the Manufacturing Industry
Here’s a step-by-step process to ensure the success of the cloud analytics function across a manufacturing setup:
- Choose a secure and reliable cloud provider. Consider the security of the platform and its ability to meet the specific requirements of the manufacturing facility. The provider should have a strong track record of security and compliance, and it should offer security features like encryption, access control, and auditing.
- Implement data governance practices. Data governance is the set of policies and procedures governing the collection, storage, use, and sharing of data. It is important to implement data governance practices to protect the confidentiality, integrity, and availability of data.
- Develop a cloud analytics strategy. A cloud analytics strategy should define the goals of the cloud analytics initiative, the data that will be analyzed, the analytics tools that will be used, and the people who will be involved in the project. The strategy should also take into account the challenges of cloud analytics specific to manufacturing as discussed above.
- Train employees on cloud analytics. Employees who will be using cloud analytics tools need to be trained on how to use them effectively.
- Monitor and improve the cloud analytics solution. Once the cloud analytics solution is in place, it is important to monitor its performance and make improvements as needed. This monitoring should include tracking the accuracy of the insights, the efficiency of the data processing, and the cost of the solution.
So, there you have it! At Ascentt, we help manufacturing companies leverage data at their disposal to make accurate forecasts and proactive decisions. Contact us today to learn more about how we can help you with your cloud analytics needs.