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How Manufacturing can Drive Efficiency in Volatile Times

In times of economic uncertainty, manufacturing needs a more agile and innovative approach. A recent report reveals the increase in energy costs, global competition and volatile exchange rates. On top of this, the ability to develop a workforce with the right skills, in order to enable local innovation, global connections, productivity improvement and future growth remains.

In order to combat these challenges, a shift in focus on big data is good news for Australian manufacturers who are looking for a competitive edge driven by innovative technology. Building an accurate predictive analytics model isn’t easy. It requires a skilled data team, advanced tools, and enormous amounts of clean data from the right combination of inputs.

It’s a difficult process, but an effective predictive analytics engine is an enormous asset for any organization.

Big challenges, big rewards

Manufacturing companies are in a unique position regarding data: they create and capture immense amounts of it every day. The process of producing goods is an enormous opportunity for data optimization. Raw materials need to be ordered, received, constructed, packaged, and shipped out for sale in the most efficient manner possible. Because the steps are repeated so many times through the process, a small edge created via predictive analytics in manufacturing will be magnified at every repetition to produce significant benefit.

Because of the cyclical nature of the manufacturing process, data-driven companies are building superior processes to create bigger and bigger advantages. Here are a few examples of global companies using manufacturing analytics to win the future:

Predicting return rate

Skullcandy’s dive into predictive analytics started with the challenge of understanding return rates on new products. The logic was that if the team could predict certain features or aspects of a product that would lead to a return, they could optimize their returns policy around those products.

They used BigSquid to blend and analyze historical data related to returns and added their learnings to features and products on their roadmap. From there, the team could ask new questions of that dataset to understand the way customers were interacting with their products and ultimately build a better warranty policy for products before they were even released. This data was also useful for product managers, giving them a clear picture of what was making customers adopt Skullcandy’s products (or not).

Once the return rate questions were answered, the team focused their efforts on uncovering insights around reviews and warranty claims to generate insights about positive and negative drivers. Data like this is ideal for making decisions for product roadmaps. All those customer insights can be used in a number of creative ways to better focus resources and improve products.

Improve forecasts and maximize revenue

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Gentex deployed Sisense to comb through millions of records after switching over from an outdated ERP system. They needed a platform that could churn through all that data and quickly deliver intelligence about both current and future revenues. Initially, Gentex created dashboards for their sales and operations teams that collected information about sales, quotes and orders across the company. Those dashboards answered immediate questions about the current state of the business.

To answer more forward-looking questions, Gentex can create a sales forecast for an entire year using just a few months of data. They use predictive models to forecast revenues based on spending. They even incorporate trend data to improve accuracy over time. Currently, Gentex builds visualizations of year-to-date revenue data to forecast up to 15 months into the future.

Operating with more accurate forecasts, Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by a massive 50%.

Improve inventory management with demand forecasting

Making a product that consumers want to buy is only useful if a company can find a way to get that product in front of the consumers who demand it. Several of today’s most cutting-edge manufacturers are blending historical customer data and external factors to predict demand for goods so they can increase production when demand will be high and decrease production when demand will be low. These companies aren’t just building for the future, they’re building the future.

The need to accurately forecast demand is crucial to these manufacturers.

Assessing demand in real-time is ineffective since companies need to make decisions about demand far enough in advance to complete an entire production cycle and get that product in front of customers. With a solid predictive analytics model in place, manufacturers can create exactly the right amount of products (and the right variety of those products) to satisfy future customers.

These forecasts optimize sales revenue, but it also avoids unnecessary costs associated with producing, shipping, and stocking items that won’t sell. Accurate predictions are a win-win for any manufacturer.

Focus is up on big data down under

Australia excels at the capture, collection, integration, analysis and application of big data, then using the data to solve complex problems. According to the report “Australian Disruptive Technologies” by Australia Unlimited and the Australian Government, Australia enjoys strong connectivity and a favourable federal and state government regulatory environment for the application of big data and analytics.

It’s interesting to see how Predictive analytics in manufacturing have gone from being science fiction to being a make-or-break addition to any company’s technology stack. Using a platform like Sisense for manufacturing analytics, combining internal and external information into a series of accurate forecasts is invaluable to any manufacturer. Improving any step of the manufacturing process is an advantage over the competition, but improving every step is a data-driven way to fast becoming more agile, responsive and able to weather the storm.

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