How Analytics improve Productivity and Profitability for Manufacturing sector
Manufacturers across the world have been constrained with a lot of pressure from various quarters with raw materials being expensive or difficult to source which in turn has affected the overall Productivity of these companies. Economic and political uncertainties have continued to loom over the world and the Covid-19 pandemic spreading all over the world has aggravated the situation further. Many manufacturing companies have resorted to the traditional time-consuming methods such as re-evaluating every single process, testing new ideas, implementing new changes, among others for gaining maximum productivity from their supply chain and plants but to achieve more in this ever-challenging environment, the manufacturers must scout for new ways to improve productivity and profitability.
Fortunately, there is one opportunity that the manufacturers have not considered properly which is the huge amount of data which they generate and is at their disposal and the intelligence which could be potentially derived from them. The availability of data has become more readily embraced than ever before due to the Industrial Internet of Things (IIoT). Sensors embedded in machines, cheaper computing power and advanced analytical opportunities can help manufacturing companies consolidate data from disparate sources and employ machine learning models and visualization platforms to unwrap new ways to optimize the end to end process right from sourcing of raw materials to the sale of finished goods.
Following are a few ways in which advanced Manufacturing data analytics can provide valuable insights, thereby helping the companies fine-tune their Production line and improve Operations.
1. Understanding Cost and Efficiency of Supply Chain
Manufacturing data analytics can help the company understand which supplier is charging too expensive per component and which component is failing regularly or not performing as per the desired standards thereby helping the company to proactively identify them before it becomes an issue. All such details would help the manufacturer identify the cost and efficiency of every component in the production lifecycle.
2.Preventive Maintenance of Equipment / Machines
With the help of big data analytics, companies can develop manufacturing systems that can automatically trigger alerts, for instance signalling the repair of a broken, torn belt, reducing product demand and load on this particular machine, or identifying how machines may be completely utilized in patterns, and others. This is a crucial step in ensuring that the machines are operating at maximum efficiency. In other instances, the application of predictive analytics could be used to provide insights into which components may fail most frequently and identify manufacturing defects in equipment/ machines much earlier before they go into production. This would help the factory save money and reduce the strain in meeting the needs of the business.
3. Machine Utilization and Effectiveness
The combination of the Internet of Things (IoT) systems and powerful predictive analytics in manufacturing can help companies gain real-time insights into how well their manufacturing lines are operating, both on a micro and macro scale. This would also help the manufacturer understand how the downtime for a single machine can affect the chain and which machines need to be brought online or shut off to prevent an issue. Generating data that is actionable help in understanding the improvements needed in the overall process. This is a great benefit of applying analytics to manufacturing.
4. Better Forecasting of Demand for Products
Demand forecasting guides every manufacturing company to meet the demands of strong sales or times when the demand is less and take decisions not to stock with too much inventory in the warehouse. Traditional forecasting of demand revolved around the historical sales data of previous years. However, predictive analytics utilize existing data which includes past sales, processes, how lines are operating to precisely project the purchasing trends. In addition to this, Analytics also helps in mitigating risks by considering a detailed view of the processes. This helps in identifying trends and events that seem to recur frequently and which can impact demand.
5. Improve Management of Storage Space
With more and more companies moving towards zero inventory, it is very important that the products that are ready to be delivered are stored in the warehouse only for the minimal/optimal time that is required. Advanced analytics helps manufacturers to improve management of inventory and warehouse by helping them to arrange efficiently, better inflow and outflow of products and in executing the most effective means of restocking thereby also improving operations and profit margins.
6. Quality Improvement
Quality improvement is one of the most common forms of predictive analytics. Databases are aggregated faster, data is cleansed quicker and the storage of data happens in smaller pieces. As a result of automatically performing these processes, predictive analytics requires less technical analysis. This results in enhancing the overall quality of the predictive analytics model thereby providing a robust plan of action for the manufacturer.
Transformation Program Requirement from Manufacturers
Apart from using advanced analytics, manufacturers need to also make several changes in other areas such as the people, process and technology to be able to deliver on the improvements gained through analytics and derive the most out of the findings. These are listed below:
1. Expertise in Data Management
Advanced analytics and modelling require the retrieval and cleansing of data structured appropriately for the platform being used and entails a lot of effort from the data scientists and engineers involved. This would involve expertise in aggregating data from sensors and in storing the data in the different platforms.
2. Complimenting Analytical Skills with Domain Expertise
Apart from data scientists and advanced platform specialists, companies would also require manufacturing subject matter experts in areas such as Supply Chain management, process technology and people who can liaise between these different areas to apply advanced analytics to Manufacturing Operations
3. Starting with Pilot Processes
Analytics transformation would require specific processes to be identified as Pilots which would help in resolving the problems that advanced analytics will be able to address and in demonstrating the value add and viability and serve as quick wins. This would also help in getting the buy-in of higher-ups in taking up more processes for transformation.
Some companies set up analytics labs within their Operational units comprising specialists across functions. The lab serves as a platform for new ideas and in cascading best practices to other units as the company scales its analytical program.
4. Transforming Business Processes
If a manufacturing company is planning to use advanced analytics to predict the breakdown of machines by sending alerts, this might not yield the desired results until the company reworks on some of its business processes, for instance, a supply chain which is quite old and may be obsolete. This would apply to many other similar processes as well. Advanced analytics can provide the maximum impact only when the solutions that are applied to the processes are standardized or harmonized in accordance with modern day operations.
5. Handling Change Management with Employees
Manufacturing companies must help the employees understand at a high level how analytics can assist them and add value in their roles. Only when the employees’ buy-in is obtained, will the company be able to help them adapt to using analytics effectively thereby helping the manufacturers move forward from data to insight and further gaining actual business benefits.
Analytics solutions have a huge potential to transform processes in the manufacturing sector, improving productivity, profitability, competitiveness and ultimately helping manufacturing companies shape business decisions. It is high time that the manufacturing companies start outlining a transformation pathway by building their analytics capabilities to leverage vital data, real-time by bringing together people, data, and systems.
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