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The soaring need to maintain a constant uptime and quickly adapt to the rapidly changing consumer needs is driving the manufacturing industry to focus on what can be called manufacturing intelligence.
Business intelligence concerns itself with measuring KPIs and revenues whilst manufacturing intelligence revolves around measuring the performances or productivity of both man and the machine on a factory floor. This manufacturing intelligence is the result of leveraging analytics and the resultant data points to improve the operational efficiency of the manufacturing processes.
One of the key ways in which the manufacturing industry leverages mission-critical data is by Predictive Modelling. Now, Predictive modeling is a forecasting tool that enables industries to identify unlimited possibilities for process improvement. Typically, in a manufacturing setup, predictive modeling has fourfold functions.
Four-fold Functions of Predictive Modelling in Manufacturing
1. Preventive or predictive maintenance of machinery- An unexpected machine breakdown can cost a manufacturing unit, an amount that can sink the ship. Traditional as well as routine inspection of machines can predict certain conditions in a given moment. This is not enough for ensuring constant uptime. Today manufacturing sector across the globe is aggregating real-time data from the sensors attached to critical machine parts. These real-time sensors predict potential threats and send notifications on machine maintenance and the best possible solutions to support the maintenance.
2. Augmenting existing MES- A Manufacturing Execution Systems is one of the most critical and irreplaceable tools used in manufacturing. It brings together all the critical data relating to the supply chain, raw materials, supply costs, and machinery. This system reacts to anomalies in any one of the given parameters just as an indication but cannot predict outcomes of the same. By implementing predictive tools on existing MES, a manufacturing unit can identify exact gaps in the process and drive operational efficiency.
3. Driving demand forecasting- Forecasting is a game-changer when it comes to manufacturing. It has the power to either take a manufacturing company to newer heights or bring it down in a single stroke. Predictive analytics brings in the added value of statistics that accurately predict the critical factors in manufacturing. The statistical algorithms very effectively establish connections between diverse factors of manufacturing painting the bigger picture clear and transparently.
4. Boosting KPIs for workforce management- Managing the workforce is one of the most critical aspects of maintaining constant uptime as well as productivity in the manufacturing process. Fluctuating market demands and machine breakdowns have been identified as two factors that impact the workforce in the manufacturing sector. When it comes to workforce management, predictive analytics can forecast the need for recruitment, industry hiring trends, employee productivity to name a few. Armed by the data points, initiatives can be launched to retain a skilled workforce and also close skill gaps through training.
From the above discussion, one can gauge how important it is for those working in the manufacturing sector to meticulously mine operational insights out of the humongous data generated by machines and MES. One of the best ways to develop a deep understanding of analytics and its potential to transform businesses is by enrolling in Executive Programme on Business Analytics,offered by IIM Calcutta in partnerships with Hughes Education. This course offers a thorough insight into the world of analytics and handholds the participants through advanced concepts that form the backbone of business intelligence.
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