So, you have assimilated Big Data into your organization by identifying and consolidating data sources, deciding the data to be distributed to departments, setting up the relevant infrastructure, testing the entire system and going live.

Congratulations! You have joined the elite tribe of companies that use Big Data technology to become more production efficient and profitable.

However, the game has just begun. What matters is not if you have adopted it, but how are you going to consume the data in your day-to-day process. Data is just plain junk and will occupy terabytes/petabytes of space in your servers unless you intelligently & consistently use it to improve your planning & operations.  

In this blog, we will highlight how you can do that.

Predictive Analysis & Preventive Maintenance

On a day-to-day production, a huge amount of data is collected from machines via monitoring sensors that track temperature, sound, vibrations, operating time, idle time, breakdown patterns, scrap generated, materials consumed etc.

This data can be used to identify patterns that point to imminent machine breakdowns. These breakdowns can be prevented before they occur, drastically reducing repair costs, breakdown time, production delay and ensuring stable production, profitability.

Quality tracking devices and sensors embedded in raw materials, semi-finished goods measure various quality parameters at each production phase. The data is tracked real-time, enabling you to detect anomalies in the product or production system based on measurements and rectify it immediately. This ensures optimum quality levels in the products manufactured.

Also in a post-sale scenario, you can track product usage and breakdown patterns to arrive at service maintenance schedule for your customers. You can send alerts to specific customers well in advance thereby increasing customer service quality and support efficiency.

We have covered this section in one of our previous blogs in much detail.  

Supply Chain Management

Your ERP system collects mounds of supplier data such as the quality of supplier’s products over a period of time, delivery schedule performances, supplier response, credit management, purchase history and much more. ERP’s integration with Big Data system paves way to analyzing a minefield of useful data.

By analyzing supplier’s response history to ad-hoc/scheduled procurement orders, you can tweak your strategy to face fluctuating or sudden market demands, time-sensitive orders in a better manner and manage production effectively.

Also, you can understand your suppliers better by creating their profiles containing historical information about their association with you, thereby improving vendor relationship. Also, their performance history helps you take a call on whether to continue with existing suppliers or move on with new ones.  

As a manufacturer, your primary objective is to carry out uninterrupted production with quality products produced, all at a minimal operating cost. Possible? Yes. With Big Data.

A shop-floor generates huge amount of data from machines, machine monitoring devices, tracking sensors and personnel inputs. These provide useful insights into production patterns, inventory availability, material consumption, scrap generated, personnel/machine availability, disruptions and much more.

Using this data, you get a comprehensive picture of how does your production capacity looks like and how much of it is being fully utilized. Based on demand patterns, you can utilize this capacity to scale up production and achieve just-in-time delivery.

Demand Forecast

Today, forecasting market demand doesn’t rely only on sales history, seasonal trends and historical market response. The influx of e-commerce and social media has brought in additional complexities into the demand game.

With Big Data, you can add a punch to your forecasting methods by considering data from your social media accounts, website and e-commerce portal into your analysis. These data tell a lot about your prospective customers, their buying perspectives, need for customization, buying pattern, mode of purchase etc. In combination with market factors, you can utilize this data to arrive at demand volume that will cater to prospects at a specific time/season. Based on this, you can align production schedules and deliver the products just in time to your customers.

Completely relying on traditional demand forecasting methods can have a negative effect as market dynamics have drastically changed with the arrival of customized SKUs for a single product version that might take a long time to manufacture in a traditional production setup.

Product Configuration

To handle varied product requirements of customers, it is important to arrive at the most profitable customized or make-to-order configurations that has a minimal impact on production. In a traditional setup, these range of products bring the maximum revenue to the manufacturer, however at an equally higher cost. Big Data analytics helps you analyze various product configurations and their sales history i.e. highest selling and most popular configurations. The analysis helps you to arrive at optimum product configuration along with a snapshot of the effect it will have on your shop-floor, production schedule, machine/personnel availability, inventory etc. Accurate configurations help you to accelerate production and meet fast delivery times to stay ahead of competition.

Sales & Marketing

Big Data has a huge role to play in the area of sales & marketing. Sales wars are now fought more on the digital front compared to market store-front. Data from unstructured sources such as social media accounts, e-commerce portals, website, feedback system etc. give a multi-dimensional view on customer buying sentiments, customer engagement, demographics, product preferences, testimonials, feedback etc.

Big Data analytics processes these to derive customer profiles and customer engagement levels that can be used to create effective marketing and advertising campaigns aimed at specific targeted customer segments. Also, customer feedback helps you to make subtle changes to the product, tweak your customer support mechanism, and improve customer interaction thereby driving high reputation levels of the organization.

Conclusion

These are just few manufacturing processes out of the complete list that can be improved by Big Data Analytics. But it surely gives you an idea of how it effectively impacts the way you run your manufacturing business right from planning, sales & marketing, production, monitoring to product delivery, customer service and even product innovation.