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Operational Data Analytics - Three Ways to Unlock Value

Achieving the great promise of Industry 4.0 and the industrial internet of things (IIoT) requires changes to current methods of production, processing, and manufacturing. Many industries are already undergoing a reindustrialization to improve competitiveness. They recognize that Industry 4.0 will be mostly categorized using high-tech and big data. Yet, as the big data explosion continues unabated, research indicates that many executives are struggling to extract value and drive differentiation through data analytics and IIoT. At best, companies have captured just a third of the potential value. So, what's standing in their way?

There are currently two significant roadblocks to achieving a digital transformation: analytical talent and operationalization. Now, more than ever, it is imperative for companies to cultivate a team with an abundance of unique expertise, cultured analytic techniques and data richness.

Three ways to unlock value

Here we outline some of the key success factors to releasing value across your business, supported by real-world examples from Maine Pointe's experience.

Operational Data Analytics

1. Aggregate and visualize data across multiple business functions
Most organizations simply lack skilled resources and so struggle to leverage the real power of data analytics. Frequently, important market supplier, product, pricing, distribution, sales, marketing and customer data is incomplete or resides in functional silos. This is a serious impediment to executives' ability to unlock the power of their data to access the untapped potential that resides within procurement, logistics and operations functions.

Executives at a multibillion-dollar omnichannel mail order and electronic retail company faced exactly this problem. To resolve it, our data analytics team focused on over 100,000 seasonal SKUs. They implemented new data analytics processes and buy-planning tools that helped aggregate and normalize data from multiple siloed systems; reducing cost, enhancing control and optimizing inventory levels. They worked in conjunction with our logistics and operations experts, who installed a customized inventory management operating system with a heavy focus on seasonal/demand planning to mitigate excess inventory and obsolescence. As a result, our client was able to reduce working capital by 25% while improving management visibility and control.

2. Translate functional data into actionable business outcomes
As the big data tsunami continues, advanced data analytics and predictive modeling is rapidly emerging as a way for corporate differentiation to help drive increased EBITDA, growth and profitability. However, many companies face the challenge of attracting and retaining the right talent, not only data scientists but business translators who combine data savvy with industry, functional supply chain and operational expertise. This is fueling the need for a set of data analytics capabilities that combines deep functional knowledge with the data science expertise required to translate insights into actionable business outcomes.

We experienced this first-hand when executives from a global paper manufacturing company called us in. They were struggling to make business decisions due to poor integrity, visibility and interpretation of their data, which resided across four disparate systems. To overcome the problem, our data analysts, as part of a broader transportation engagement, cleaned and reduced duplicate records by 60%. We deployed fuzzy logic techniques to sustain data integrity and integrate it with the company's network optimization system for efficient transportation and enhanced performance. Interpretation of the insights delivered by both data scientist and functional experts directly improved the client's customer service and sales forecasting abilities. 

3. Implement effective demand forecasting and planning to reduce costs and create competitive advantage
Executives that succeed in achieving competitive advantage are those who have attained a market-leading position and insight into existing and emerging opportunities. They are able to make proactive decisions to achieve the desired business outcomes in real time. One of the chief reasons many organizations fail to reach this position is that they lack timely, accurate and consistent data. When combined with limitations in analytical capabilities, this leads to poor operationalization of analytical outputs.

This can have a significant impact on EBITDA, cash flow and growth, as we saw when we worked with a $50Bn energy company. Executives were struggling to optimize remote site service operations across three divisions. By deploying advanced data analytics capabilities, in collaboration with operations and logistics expertise, we were able to help save $45M on a sustainable basis and reduce carbon emission equivalent to 2,173 vehicles taken off the road. We achieved this by shifting the business to run dynamic, demand forecasting with predictive analytics to deliver advanced operations planning. This resulted in better control, greater accuracy and increased visibility of the client's aviation, ground transportation and lodgings divisions. 

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