Data analytics in manufacturing helps leverage advanced technologies to adopt the data-driven model for effective decision-making. Here, we’ll discuss manufacturing analytics solutions and why US manufacturers partner with these consulting firms to achieve their goals.
The US manufacturing industry is making the most of its legislation and the latest technology to boost the sector. The semiconductor and clean technology units have seen double the investments compared to 2021 and 20 times that of 2019. Despite the economic uncertainties, the shortage of skilled labor, and other supply chain concerns, the US manufacturing industry has a lot to look forward to.
According to a recent study by Deloitte, 86% of manufacturing executives believe that smart factory solutions will have a crucial role in handling competitiveness in the coming five years. Similarly, generative AI is expected to have a strong role in product design, supply chain management, and aftermarket services.
Smart factory solutions like manufacturing analytics, artificial intelligence, 5G networks, Internet of Things (IoT), cloud computing, etc., help create a cohesive factory unit where the systems, equipment, and human resources (employees) are connected using technology. Real-time insights, end-to-end visibility, agile manufacturing, flexibility, scalability, resilience, and efficiency are possible when the enterprise digitally transforms its processes by adopting smart factory technologies. Partnering with analytical service providers is a cost-effective manufacturing BI solutions for enterprises in the industry.
Let’s understand the top reasons for US manufacturers choose managed analytics consulting services to boost their overall efficiency.
Managed analytics is a service offered by third-party companies to help businesses collect, clean, store, process, and analyze large amounts of data to derive accurate, actionable insights for decision-making. Data is vital to make informed and quick decisions about various business aspects. Data scattered across the enterprise in different silos is easily outdated and of low quality.
Now, Manufacturing analytics solutions for enterprises solve this problem by streamlining data storage through data warehousing and data lakes. This allows the manufacturer to use historical and real-time data for analysis and get in-depth insights. Business intelligence platforms like Power BI also offer data visualization to convert insights into easy-to-understand graphical reports.
Manufacturing analytics managed services provided by companies like DataToBiz help manufacturers with the following:
Manufacturing analytics solutions help enterprises improve production efficiency, enhance product quality, reduce costs, and minimize the time-to-market by streamlining internal and external operations. US manufacturers are getting higher ROI after investing in data analytics and business intelligence. Here are a few reasons to choose managed analytics in the sector:
Compromising quality can lead to many problems for a manufacturer. However, too much emphasis on quality can increase costs and make production expensive. AI/ML based manufacturing analytics solutions help find the right balance between cost and quality. This is done at multiple levels, like early detection of faults, 24*7 anomaly detection, identifying areas of wastage, etc. This data and insights form the basis for making informed decisions quickly.
Data optimization in manufacturing includes the collection of raw data from several sources. This increases transparency and provides end-to-end visibility across the supply chain. The manufacturer can get real-time information about suppliers, materials, inventory in the warehouse, stock with distributors, market demand vs. supply ratio, etc. These reports allow enterprises to adjust production quantity, choose the right vendors and supply chain partners, streamline transportation, etc.
Equipment maintenance is a major issue in factories. Unexpected breakdowns can cause delays and losses as production takes a hit. However, this problem can be solved by deriving managed manufacturing data insights about the machinery and its health. IoT (Internet of Things) devices are used to collect data from machines and analyze it to detect signs of wear and tear or technical glitches. Then, the supervisors schedule predictive maintenance sessions during non-production hours. This prevents random breakdowns and increases equipment lifespan.
OEE can be improved through predictive maintenance, process optimization, reducing wastage, etc., achieved by using analytics at every stage of manufacturing. Investing in managed BI for smart factories is a sure way to ensure a greater OEE score and become a reputed manufacturer in the market. The service providers use tools like Power BI to set up customized OEE dashboards for supervisors, managers, and decision-makers.
In today’s world, enterprises have to focus on developing sustainable processes to become energy efficient and reduce carbon footprint. Manufacturing analytics helps with this aspect, too, by providing insights about how to optimize resources, where to conserve energy, how to choose alternate energy sources, which production methods to change, and so on. The manufacturer can reduce environmental impact, adhere to green regulations, and build a sustainable business for the future.
BI for manufacturing uses historical and real-time data to predict future trends in the market. The sales team can use the demand forecasting reports to plan their marketing strategies by finalizing the product launch time, target audiences, channels for communication, etc. It also helps streamline warehouse management and production to handle market demand effectively. The idea is to avoid being over-stocked or under-stocked.
Root cause analytics provides in-depth insights into the reasons for the various issues affecting an enterprise. By using operational analytics for manufacturing, the business can get to the actual cause of the problem, understand why it occurs, and find a comprehensive solution to get rid of it forever. It helps implement corrective steps in different ways to achieve the desired results. Instead of finding short-term solutions, the focus is on solving the problem completely and preventing it from recurring.
No manufacturer can ignore their customers. However, understanding what customers want can be tricky and complicated. Fortunately, customer sentiment analysis, feedback analysis, and behavior analytics can provide reliable insights into what the target audiences expect from the business. This helps in attracting more customers and increasing brand loyalty.
Adopting customized BI for manufacturing processes allows the enterprise to get insights about each stage of the product lifecycle, be it design, research, production, quality control, warehousing, shipping, etc. It helps have better control over the process to eliminate unwanted steps, shorten the cycle, overcome bottlenecks, and enhance overall quality.
Suppliers and vendors are vital for a manufacturing business. Choosing the wrong supplier can ruin the enterprise’s reputation and result in losses. Data analytics can be used to analyze each supplier’s role in the business and choose the most suitable, aligned, and reliable partners to work with. It also helps maintain healthy competition and get the best deal for raw materials.
While any enterprise can build its data analytics model, it should also ensure to adhere to the data security and privacy regulations. Violating the regulations can compromise public data and attract lawsuits. Managed manufacturing data governance service providers know how to use customer and market data to derive insights while respecting data privacy laws.
Integrated analytics for manufacturing operations is a necessary part of smart factory optimization. It brings together various tools and technologies like IoT devices, real-time analytics, AI/ ML data models, data warehouses, cloud warehouse management software, CRM systems, and many others. It helps set up a comprehensive data-driven model in the enterprise and gain a competitive advantage.
Power BI for manufacturing helps stay up to date about the inventory the enterprise holds at any given point. The tool is integrated with warehousing software to get real-time updates about how much stock is left in the units, how much is being produced, etc. By streamlining inventory management, the manufacturer can make proactive decisions about production, storage, and distribution.
An enterprise has many assets, like factory units, equipment, inventory, etc. How to manage the various assets, how to calculate depreciation, when to replace the machinery, etc., are tricky questions as they lead to greater expenses. With data analytics, the manufacturer can handle these complexities and make fast decisions by relying on data-driven reports.
Cloud-based analytics for the manufacturing industry are useful for enterprises to effectively manage operations, supply chain, inventory, production, and sales. It helps manufacturers optimize business data and use it to derive meaningful insights. Instead of building an analytical model from scratch, enterprises can rely on managed analytical providers to customize, integrate, and maintain the tools necessary to adopt the data-driven decision-making model.
But how do you analyze production data?
A business generates large amounts of data every day. Managed data warehousing for manufacturing helps collect and store this data in a centralized database to make it easily accessible in all departments. The analytical tools are integrated with the data warehouse to run queries and derive insights in real-time.
Production data can be analyzed in many ways. For example, analyzing machinery data gives information about equipment’s health and maintenance requirements. Analyzing inventory data provides insights into the stock, production speed, sales, etc. Manufacturing analytics solutions are set up for various parameters and KPIs (key performance indicators) to get versatile reports from production data and have a clear picture of the enterprise’s market position and performance.
A manufacturing data analyst has a plethora of responsibilities. They work with engineers, designers, data scientists, and other professionals. The analyst has to process and analyze large amounts of manufacturing data to detect patterns, identify opportunities and threats, find cost-effective alternatives, etc. From conducting market research to developing AI/ ML models, establishing benchmarks, and delivering real-time reports for business intelligence in manufacturing, the analyst has to multitask and handle many roles together. That’s one reason manufacturers partner with third-party service providers and build an effective team to tackle the responsibilities.
Many US manufacturers choose managed analytics to save time, money, and resources when deriving real-time insights from historical and current data. The consulting companies offer end-to-end services and take care of manufacturing BI platform management in the long term to help the enterprise increase ROI, improve production quality, enhance customer service, and become an industry leader.
As new AI technologies enter the market, advanced analytics will become a deciding factor for survival in competitive markets. Stay one step ahead of others and grab opportunities to boost the business and achieve 360-degree success.
Talk to us to learn more about managed analytics for manufacturing.
Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer
Sunaina Meena ~ Digital Marketing Specialist
Data Engineering
AI & Machine Learning
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