The analytical advantage of sharing

14 October 2018

Stephen Ludlow, principal technology consultant at SAS UK & Ireland, believes that to get the most from predictive analytics it is important to share data and insights throughout an organisation.

Too many manufacturers are becoming prisoners of their own highly complex systems and supply chains. They sit on treasure troves of data generated by their machinery, employees and customers, but lack the ability to turn this into actionable insight.
Business intelligence (BI) has long been a manufacturing mainstay. This form of descriptive analysis allows companies to use their huge data sets to see what has happened and what is happening in their organisations. Yet, with access to the right tools, data is able to tell us so much more.
BI is essential to manufacturers for assessing performance, planning strategy and reporting. However, to use an analogy, you can’t drive a car with only a rear-view mirror. To stay competitive, manufactures need to complement their business intelligence with predictive analytics. 
From insight to foresight
Predictive analytics is changing manufacturing environment. Leveraging the growing volume, speed and complexity of data coming from connected devices as part of the Internet of Things (IoT), it can apply analytics in real-time as data is being streamed to find patterns that companies can use to predict future outcomes. This is also referred to as ‘analytics at the edge’.
Analytics allows organisations to go beyond insight provided by BI. While BI can tell you what product was most popular with customers last week, analytics tells you how many of these products you are likely to sell next month. One describes what has happened already, but it’s always more valuable to a business knowing what will happen next

Operating under difficult economic conditions in an increasingly competitive sector, manufacturers need all the help they can get. Being able to predict the future more effectively than competitors can provide a powerful advantage.
Contrary to the popular saying, what you don’t know can hurt you. The typical unknowns of manufacturing – system failures, supply issues, and so on – can be very costly. Being able to predict when a piece of equipment will fail, for example, is a game-changer. It allows you to schedule maintenance at just the right time to ensure the component doesn’t break, saving you the cost and disruption of unnecessary repairs and avoiding downtime.
Predictive analytics is an effective driver of improvements and profits in the manufacturing industry. It can identify product quality issues sooner during the manufacturing process, resulting in fewer recalls, increased sales and happier customers. Analytics also provides the best approach to supply and demand forecasting, ensuring that customers do not have to wait a long time for order fulfilment due to a lack of stock.
When deploying analytics across an organisation, it is important not to restrict implementation. Analytics is not just for solving a single business challenge. Instead, the ambition should be to make analytics pervasive across the operation. It should become active at all business levels, from a sensor embedded in a conveyor belt on the factory floor to a visual representation of next quarters profits in a board meeting. This will help to prevent data and analytical insights being kept in silos – only by sharing data and insights across the organisation can you better understand your business to make more informed decisions.

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