Noshin Kagalwalla writes about predictive analytics being a blend of tools and techniques that enables organisations to identify patterns in data that can be used for making predictions of future outcomes. It has applications across multiple business functions in the manufacturing industry.
The global and Indian manufacturing industry has seen many revolutions over the past decade. Technology has truly transformed areas such operations, supply chain, marketing, manufacturing, inventory management, etc. This has led to the digitisation of data – the way in which organisations collect, store, manage and access data has changed. Today, data comes from multiple sources – point of sale, sensors, social media, emails, branches, etc. Most organisations are overwhelmed with data which arises from every interaction, transaction, process, and channel and tend to look at big data as a challenge. However, organisations who drive a culture of data-driven decision making and leverage analytics, are well-poised to transform the big data challenge into big business opportunity. Predictive analytics can empower manufacturers to take fact-based decisions, enhance customer engagement, forecast sales, optimise inventory, streamline SCM, and much more.
Predictive analytics is a blend of tools and techniques that enables organisations to identify patterns in data that can be used for making predictions of future outcomes. Predictive analytics helps unveil and measure patterns to identify risks and opportunities using transactional, demographic, web-based, historical, text, sensor data, and unstructured data. Such level of insights and foresights helps business users to take forward-looking decisions, arrest potential threat and tap future opportunities. Analysts predict that, by 2016 nearly seventy percent of high-performing companies will manage their business processes using real-time predictive analytics.
Predictive analytics has its applications across multiple business functions in the manufacturing industry. Some of the key application areas are:
Predictive Asset Maintenance
For any manufacturing plant, it is important to reduce downtime and unscheduled maintenance. This helps in ensuring that the assets are running at peak performance to comply with profitability, safety and environmental goals. Predictive analytics plays a vital role in analysing data from multiple sources such as sensors, historical data, maintenance cycles, etc., to accurately predict events that could cause outages; further helping in boosting uptime, performance and productivity while lowering maintenance costs and the risk of revenue loss.
Supply Chain Intelligence
Supply Chain Intelligence can deliver a critical advantage to organisations by helping them turn data into knowledge and develop unique insights about their demand patterns, supply networks, operations and customer service requirements. With predictive capabilities, organisations can identify potential bottlenecks, prescribe best cost measures, streamline logistics, and much more. The importance of predictive analytics in supply chain is growing with the emergence of globalisation and fragmented points of purchase and distribution. A product might be designed at one part of the world, is being manufactured in a different continent and consumers from a separate geography might buy that product using a mobile app. The dynamics of SCM are changing at a rapid pace and it is important for organisations to be able to see future trends early and be proactive instead of being reactive. Predictive analytics has its role in every element of supply chain management and helps organisations in ensuring that the costs are lowered and at the same time best value is delivered to the customers.
With predictive analytics, organisations can manage inventory data on millions of SKUs, gather and consolidate huge data volumes throughout the distribution chain, then transform, standardise and cleanse the data for inventory optimisation. Manufacturers can also simultaneously optimise inventory levels for every SKU at every location in the organisation. This helps in reducing product inventory levels, inventory carrying and expediting costs and at the same time plan for replenishments, thus saving costs and increasing customer satisfaction.
One of the key areas where predictive analytics plays its role is demand-driven forecasting. Today, customers are undoubtedly the most important stakeholders and therefore it is important to streamline manufacturing operations based on the potential demand. For instance, if an automobile manufacturer can predict the variant and colour of a car model that will sell maximum in a particular region and a specific persona of customer; it will help the manufacturer in keeping the stocks ready, creating suitable offers and ensuring sales optimisation. With techniques such as in-memory analytics and data visualisation, organisations can improve forecast accuracy and ensure that the right decisions are taken to fully utilise future opportunities. Data visualisation techniques also help in providing an easy-to-use interface for non-technical users to slice and dice data and derive foresights and collaborate with peers across multiple departments to create synergies.
In this digital era, the balance of power has certainly shifted in favour of the customers. Brand loyalty is decreasing and customers are becoming much more aware and sensitive. They write, share tweet, interact and create content over digital and social networks. At the same time, they purchase products or interact with organisations through multiple channels such as kiosk, online, stores, mobile, call centre, etc. It is important to actively listen to customer transactions and interactions, create micro-segments and run targeted campaigns with specific offers. Predictive analytics helps a long way in doing so. Marketers can determine the likelihood of a customer to attrite, what kind of offers is a customer receptive to, a customer’s channel preference, and much more. This helps in creating a cross-channel integrated customer experience, increasing brand loyalty and enhancing opportunities to up-sell and cross-sell.
After-sales service has always been a critical factor for ensuring customer satisfaction and loyalty. Using data management and predictive analytics, organisations can integrate warranty and other data with key customer, product and geographic information so they can accelerate issue detection and reduce time to correction. It also allows business users and decision makers to identify areas of improvement and prepare a product development plan and at the same time keeps them prepared in terms of solving service-critical issues. It lowers costs and increases customer satisfaction, enabling organisations to improve product quality and brand reputation.
Predictive analytics finds its applications across the length and breadth of manufacturing business. Today, data is emerging as a strategic asset for businesses and it is important to leverage this goldmine in order to attain breakthrough business outcomes. With in-memory analytics and data visualisation techniques, organisations can uncover the true potential of their data and unleash meaningful insights and foresights. Analytics is a strategic enabler that can help optimise supply chain, manage inventory, forecast sales, and much more. Manufacturers constantly strive to provide flawless customer experiences, maintain manufacturing efficiency and brand consistency across all available touch points and ensure that the right goods are available at the right time and via the right channel. To do so, it is vital to understand demand and align supply chain & inventory management accordingly. Predictive analytics empowers business users to identify threats and opportunities, leading to proactive, accurate decisions that help in fuelling enterprise growth and business impact.
(The author is Managing Director, SAS India. It is a leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster.)