Changing a production workflow can be the first step in allowing a company to stay updated and relevant. Making informed decisions can come from gathering the information that helps companies better understand every aspect of a workflow. Companies can take advantage of multiple processes of gathering and visualizing data in real-time by taking advantage of a concept known as manufacturing analytics. In this article, we define this industry term, review best practices, and list nine ways businesses use them in the real world.
What is manufacturing analytics?
Manufacturing analytics is the collection of real-time data on the various machines, operations, and systems that work during a business’s workflow. Companies organize, filter, and visualize these various statistics to modify aspects of their supply chain to improve and optimize it. This can include key functions such as product quality, machine maintenance, and workflow planning. Some of the goals of partaking in manufacturing analytics include:
Increasing production: Businesses can take advantage of manufacturing analytics to analyze an assembly workflow. This can help in decisions that can increase the ratio of output to input to increase the overall productivity of the production system.
Improving workflows: Businesses can use manufacturing analytics to deliver data on the efficiency of each production in a workflow. This can allow a business to make changes that reduce costs and raise productivity.
Increasing product quality: Businesses can use manufacturing analytics to generate data on the products created in a workflow. This data can then help decide changes that will lead to product quality increases.
Reducing downtime: Businesses can take advantage of manufacturing analytics to track every detail of a workflow nearly instantaneously. This can allow companies to reduce downtime that may result from direct changes that need to be made to the production process.
Best practices for manufacturing analytics
Manufacturing analytics comprises multiple processes that companies can use to enforce their data-driven decisions. Here are the best practices that companies can use to apply the concept of manufacturing analytics to their pipeline:
Data aggregation is the process of compiling large amounts of raw information from multiple sources and automating the organization into a single database. This can allow companies to analyze the data and extract insights without requiring the tedious workflow of manually reformatting, standardizing, and preparing the data. Companies taking advantage of manufacturing analytics can use tailored programs that directly extract data from the machines they use in their production pipelines, and then input the outflow into data aggregation systems to create its database.
Descriptive analytics is the specific investigation of historical data to understand any changes in the production pipeline. This process can involve drawing comparisons over a set period, including month-to-month or year-to-year changes. Descriptive analytics can help to identify the strengths and weaknesses in production. This can include the company creating the desired benchmark for a specific metric, like energy use, costs, growth or efficiency and then comparing it to a range of historical data to identify possible trends and use them to inform future decisions.
Predictive analytics is the use of predictive modeling, statistical algorithms, and machine learning to identify the likelihood of future trends and behaviors. Companies can take advantage of these predicted outcomes to create preventative measures and reduce possible future risks like fraud, defaults, and malfunctions. In the manufacturing process, predictive analytics can enable companies to minimize the downtime that may result from corrective actions. This can include concepts like changing the production process after identifying a possible quality failure or predicting machine wear to schedule maintenance before a mechanical failure can happen.
The prescriptive analysis is a combination of predictive and descriptive analytics, as it includes observing historical data and predicting outcomes. The major difference with a prescriptive analysis is that it emphasizes actionable insights over data monitoring. This creates a system that can empower companies through a more technological approach. Companies can take advantage of prescriptive analysis to examine highly complex problems that include hundreds of variables and make it easier for non-technical users to factor in challenges and objectives to change a pipeline.
Cognitive analytics takes the process of prescriptive analytics by applying intellectual technologies to it. This can include involving semantics, artificial intelligence, deep learning, and machine learning in the process of data analysis to process large volumes of information quickly and automatically make decisions in real-time. Another major benefit of cognitive analytics is that the process is self-learning, meaning that it can continually improve with the increase in data input and time. This creates a self-diagnosing system that can help improve productivity.
9 examples of manufacturing analytics
Here is a list of specific real-world processes that involve manufacturing analytics:
1. Demand forecasting
Demand forecasting is a form of predictive analytics that tries to understand and predict a customer’s interest in a product and optimizes supply chain decisions to compensate. Demand forecasting gives companies valuable insight into their current market and other potential markets so they can make informed decisions about the pricing of their products and the strategies they use for growth. It can also allow businesses to optimize inventory by increasing turnover rates and reducing holding costs. This works with companies understanding when to increase and decrease staff and supply chain resources to keep operations running efficiently.
2. Order management
Order management is the process of capturing, tracking, and fulfilling customer orders. Order management systems can automate the entire lifecycle of an order, including inventory tracking, work order creation, and even quality-of-life options like refunds and exchanges. These systems take advantage of manufacturing analytics by generating consumer trends so that companies can understand when to restock to prevent shortages. It also takes advantage of historical data to calculate the building and shipping time of each product in the pipeline.
3. Inventory optimization
Inventory optimization is the process of maintaining the correct amount of product stock to meet the demand of your customers. It takes advantage of manufacturing analytics by tracking fluctuations in customer demands, raw material shortages, and supply chain delays to prevent shortages and surpluses in inventory. This can allow companies to overcome the common challenges that may come from maintaining an inventory of varying products, including overstocking and back-ordering. Companies can use inventory optimization to reduce operational costs, boost customer satisfaction, and maintain a balance in inventory levels.
4. Supplier management
Supplier performance management is the process of measuring, analyzing, and managing the work of a company’s providers. This can include tracking various metrics to create a report of the relationship a supplier may have with the company it’s providing for. These common metrics include the quality of provided materials, delivery times, acknowledgment rates, and responsiveness. This can allow companies to predict a supplier’s performance by understanding how quickly it can acknowledge new orders and accept order changes and the efficiency and accuracy of deliveries.
Transportation analytics provides companies with real-time insight to create efficient traveling routes for supply chain providers. It includes getting data about the various streets in an area and uses various metrics like driver habits, traffic habits, and planned construction to provide accurate information about pre-trip, drive, and unloading times. Companies can take advantage of analyzing every aspect of the shipping life cycle to optimize order processing, fleet maintenance, and shipping route systems.
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6. Preventative maintenance
Preventative maintenance uses real-time data collection and analytics to prevent unplanned breakdowns in manufacturing technology. It can reduce costs by scheduling repairs at a machine’s optimal time, which leads to an increase in productivity through a reduction of overall downtime. Companies use the guidelines and standards created by manufacturers along with the metrics of the machine itself to create a schedule of proactive maintenance. This can help prevent reactive maintenance situations that may occur when the machinery has already started to fail.
7. Price optimization
Price optimization is the process of analyzing customer and market data to ensure the optimal cost of a product. The primary goal is to determine the right price that helps companies attract consumers, maximize sales, and increase profits. This can include the collection of demographic, psychographic, and historical sales data to better understand customer behavior and use it to determine starting, discounted, and promotional prices.
8. Warranty analysis
Warranty analysis can include the collection of data associated with the offer to repair or replace failures in a product that a company extends to its customers for a certain period. By understanding the distribution of historical warranty data, companies can create failure estimations over a determined period of time, such as the next day, month, or year. This can also allow companies to determine units that may require changes or optimization if customer behaviors include a high return rate.
9. Process measurement
Companies can use manufacturing analytics to provide estimations about the production process by analyzing historical data about similar products. This historical data can include the various details of the entire production process, including the materials, machines, and processes involved in the creation process. This can allow businesses to optimize a product’s launch by making data-driven decisions on launch dates, starting prices, and marketing strategies.
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