BI#1 Manufacturing Analytics Solutions in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA use cases of machine learning and predictive analytics are as varied as the industries within manufacturing. However, there are a few common use cases that apply to most manufacturing verticals, typically grouped under terms such as smart manufacturing, industry 4.0 or industrial internet of things (IIoT).

BI#1 Manufacturing Analytics Solutions in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA

• Predictive Maintenance

Predictive maintenance is the most well-understood and varied use case in most manufacturing industries. Here, data from process monitoring sensors such as temperatures, pressures, flows, vibrations and more are captured in real-time and used in pattern recognition software to detect the earliest symptoms of wear and tear predictive of eventual functional failures. Early detection and prediction can help prevent failures or at least plan for eventual corrective actions leading to minimize downtime. Downtime, especially unplanned downtime can be a very expensive event, possibly leading to millions of dollars in losses. Some analysts estimate unplanned downtime in certain industries is worth $20 billion.

• Process Optimization

Process optimization, in which existing processes are updated and optimized based on historical data, is a critical use case, especially in industries such as power generation, oil and gas refining, petrochemicals and chemicals. Insurance Analytics Solutions in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA sensor data feed machine learning algorithms for yield and quality optimization of output components for different combinations and quality of input raw material feed stocks. This also helps with energy efficiency, thus improving sustainability and profitability for these process manufacturers. In the global airline fleet, for example, a 1% fuel savings would save $30 billion over the next 15 years.

• Supply Chain and Inventory Management

High levels of raw material, work-in-process and finished goods (e.g., replacement service parts) inventory are one of the highest contributors to inefficient capital utilization for discrete manufacturing industries. Using machine learning to improve raw material and demand forecasts while meeting dynamically changing production goals helps improve capital utilization and supports lean and just-in-time manufacturing production goals.

In today’s customer-driven world, manufacturers can no longer rely on selling expensive spare parts and services, since these become costs of supporting a single-price subscription. This shift will require manufacturers to completely rethink how they operate new organization structures and skilled resources, new incentive models, new KPIs to measure success and new processes replacing ones developed over decades and centuries. Healthcare business intelligence in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA will have to become data-driven organizations, investing in technologies to connect and track products, collect data and efficiently analyze these massive amounts of operational and service data, using technologies such as IoT, machine learning and predictive analytics. 

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Manufacturing Analytics Sooftware in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA
Manufacturing Analytics Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA
Manufacturing Analytics System in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA
Manufacturing Analytics Software in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA