The growing potential of big data is creating new capabilities in the manufacturing industry. Staying better informed by adopting technology is the minimum necessary these days. Predictive tools cannot tell manufacturers how a particular strategy will work, but it can predict how the market will behave in detail. That knowledge, about the future, helps manufacturers choose the strategies associated with minimum risk and maximum growth.

Prevailing conditions in the manufacturing industry include a financial crunch and massive amounts of data. With access to database systems, organizations can diagnose their business workflows across the globe and chalk out plans for overall remedial action efficiently. However, data alone is not enough. Applications are designed uniquely for each company and they can predict how markets will behave. Manufacturers want to stop guessing and start acting confidently.

Descriptive vs. Predictive Analytics

Descriptive analytics determines trends. It examines a company’s business environment holistically, while scanning data related to a host of activities including transactions and new implementations. The data covers geographies, time zones, products, suppliers, customer records, partners and other business dimensions to keep the ‘description’ of the business useful.

On the other hand, predictive analytics builds models at the individual level for suppliers, customers, products, campaigns, and services. It may look at descriptive data for behaviors and propensities, and uses mathematical models to predict the likelihood of specific actions. For example, companies can predict what customers might need even before they know it themselves.

Companies have adopted predictive analytics to identify customer attitudes and use the data to promote cross-sell opportunities. Such models help organizations to identify, attract, retain, and then multiply the ideal customer base. Expenditure on marketing activities can be optimized accordingly. But predictive may not be restricted to marketing only.

Doing More with Predictive Analytics

 Most organizations are familiar with predictive analytics, but can the technology go beyond predicting how markets will behave? It is important to know various other possibilities, and here are some additional functions to perform with predictive technology.

1. Fraud detection and security: Cybersecurity is a growing concern. Using behavioral analytics in a network helps detect abnormalities indicative of occupational fraud, zero-day vulnerabilities, and advanced persistent threats. With the help of methods such as mathematical analysis, anomaly detection, link retail analytics services, etc., companies can prevent losses and mitigate risks emerging from possible fraud.
2. Operations: Predictive models can help forecast inventory and manage factory resources accordingly. For example, manufacturers may predict higher sale of agricultural machinery in a particular season. Predictive analytics can be used to adjust the prices accurately, so that sales/revenue is maximized. Airlines use predictive tools to decide how many tickets to sell at what price for a flight specific to any particular season. Predictive technology can equip organizations to perform confidently, thus increasing efficiency and avoiding cumbersome situations.
3. Risk: Predictive analytics have behavioral capabilities and helps determine trends like delayed payments and supply failures. The information is vital for manufacturers to ensure a smooth supply chain and brings results from efficient workflow. Risks emerging from bottlenecks should be detected and resolved as early as possible.

Some manufacturers already use predictive technology for functions like risk management, operations, inventory planning, and fraud control, but are they performing better? Better than before, yes. But predictive technology has also helped decision making reach a new benchmark in the manufacturing industry.