Time is the ultimate capital. Other forms of capital can be created or availed for cheap, but time is limited and cannot ever be bought. It is hugely beneficial to conserve available time for productive activities. Some ways technologies involved with Industry 4.0 help are saving time, increasing production efficiency, improving automation, and cost reduction. This helps plant managers to reduce production times.
In addition, IIoT, cloud computing and Machine Learning play a vital role in reducing production time. Manufacturing analytics, combined with real-time data, predicts the number of goods for order, optimises the production process and plant layout. This helps with performing preventive and predictive maintenance. Also, manufacturing analytics helps save time by incorporating accurate demand data - meaning that only products that are in demand go into production.
Defining manufacturing analytics
Manufacturing analytics uses data on manufacturing processes to optimise the production process. This helps in bringing down costs, raw materials consumed, and time taken. Manufacturing analytics improves production planning, supply chain optimisation, production quality and maintenance.
With the prevalence of the Industrial Internet of Things (IIoT), data regarding every aspect of plant operations is available in real-time and becomes the raw material to perform analytics. Though manufacturers have always used data to improve production, today’s immense quantity of data is unprecedented. This is where modern analytics tools supported by Artificial Intelligence and Machine Learning play a role, thus, making it easy and actionable.
Manufacturing analytics is critical in performing management tasks in a modern plant and offers many benefits as below:
Increased production efficiency
Improved product quality
Better design for products and processes
Actionable insights into problems
Effective material utilisation
Though the benefits are innumerable, time-saving is one of the key benefits of performing manufacturing analytics – all production phases can be optimised due to insights.
Saving time with manufacturing analytics
Data analytics in the manufacturing sector offers many avenues to save time in the production process. Specific insights can be drawn from data concerning various phases of production and open ways to reduce the time taken in the production process. Some of the tasks that analytics has in saving time include supply optimisation, production optimisation and optimisation for demand.
The supply phase is where raw materials are aggregated for the production process. The major challenge is how to maintain the optimum level of inventory required for the production process. When there is a scarcity of raw materials in the inventory, the production process is halted. This wastes the time of labour and machines involved in the production process.
Keeping excess raw materials as inventory solves the problem, but it leads to high inventory holding costs, which again is not feasible. Manufacturing analytics helps plant managers know when & in what quantities raw materials are needed. The analytics factors are in the lead time required after creating purchase orders and other factors involved in delivering the raw materials.
The analytics suite can sift through large volumes of historical data and combine it with real-time data - this helps to accurately predict the time and amount of goods for order. It can also be optimised for any number of factors, including reducing lead time, which helps to continue the production process without halting and saves time.
The core part of time savings with manufacturing analytics happens in the production phase through process, layout and maintenance optimisation:
Process optimisation: Every factor of the production process and the steps involved are analysed. The massive data generated from these processes are analysed with Machine Learning algorithms to gain actionable insights about the processes and can be used to improve processes and reduce the time taken for each step of the process.
Layout optimisation: Analytics can be used as a tool to identify the most optimal plant layout, which is a huge determinant in the time taken for the production process. It helps reduce the distance between production stations. Idle time describes a situation where material travels between stations without any action performed on it, and implementing optimal layout helps with reducing it, as well as reducing production time.
Maintenance optimisation: Machine failures cause unscheduled downtime in the plant, which can be avoided by performing adequate maintenance at the right time. Analytics with CMMS tools can be used to perform preventive and predictive maintenance to eliminate the chances of machine failure. Maintenance intervention at the right time will help plant managers save time in the production process.
Optimisation for demand
The goods produced in a plant have to be sold immediately to reduce incurring inventory costs. Knowing the product demand and integrating it into the manufacturing process will help with making only the needed products. This saves time and reduces inventory costs that would have been incurred by manufacturing goods that aren’t in demand. Manufacturing analytics helps integrate live demand data with the production schedule to optimise production time.
Therefore, the above factors contribute heavily to saving time with the help of manufacturing analytics. This in turn helps plant managers to ensure effective productive readiness.