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Data Analysis to Rule a New Manufacturing Era – Part 4

The manufacturing sector is poised on the cusp of a new era, one that will see the development of a new, large consuming class in developing economies that will bring with it many opportunities but also new, substantial market risks.

manufacturing4 Data Analysis to Rule a New Manufacturing Era – Part 4While manufacturing has always included more than production, over time the services like research and development, marketing and sales, and customer support have become larger parts of what manufacturing companies do, notes a report on the future of global manufacturing from McKinsey Global Institute.

Depending on the market segment, 30% to 55% of manufacturing jobs in advanced economies are service-type functions, and services make up 20% to 25% of manufacturing output, the research report notes.

This is the fourth article in a series that delves into how manufacturers can effectively compete domestically and globally in the new post-recession era by wielding big data as a weapon to drive innovation and growth.

Aftermarket services can offer manufacturers a number of benefits including smoothing cyclical sales, providing higher margin revenue streams and establishing new depths in customer operations that can lead to more sales opportunities, according to McKinsey.

One approach is helping customers improve their operations by using the manufacturer’s products more effectively. Based on its knowledge of customer needs, for example, John Deere has developed a service that uses sensor data from farm equipment to advise customers how to improve yields.

“To deliver such services, manufacturers need to understand customer business needs and invest in the ability to capture the data that enables the services,” the report notes. “Such high-value services often demand a broader and more intimate knowledge of customer needs than is needed to sell a product and may require manufacturers to engage different parts of the customer organization.”

In a blog post, IDC program manager Sheila Brennan notes that several factors are driving the increasing importance of data acquisition and data analysis to manufacturers providing services including:

  • Product complexity and the number of product configurations are growing
  • Optimizing service parts, technical information and resources is more difficult due to increasingly complex operations and expanded market reach
  • Product features and functions are not enough to sway discerning and demanding customers in an aggressive global market

Brennan suggests that manufacturers link analytics and mobility initiatives with efforts to select and optimize new maintenance service strategies.

“Advancements in technologies and connectivity have created new opportunities, and now is the time to rethink investments and strategies in service,” according to Brennan. “But, increased awareness of the importance of service at all levels of the organization, and across domains is critical to successfully harnessing these opportunities.”

The real return on investment from data analysis comes from the ability to increase the speed with which executives can make decisions, according to a report by NewVantage Partners.

For manufacturers looking to bolster service offerings, the management consulting firm offers these tips to squeeze the most value of out big data initiatives.

1. List the Five Most Pressing Business Questions to Answer. “By addressing a small subset of critical questions, executives can demonstrate an initial set of quick wins that provide business value and enable additional funding to ask additional business questions,” according to the report. “Starting small, and building from that foundation, is critical to ensuring successful business adoption.”

2. Create a Test Bed for Analytical Experiments.

3. Refine the Questions. Because many data analysis tools are cost effective and efficient, organizations can ask questions many times, refining future queries based upon answers to previous questions.

4. Validate Through Test-and-Learn Techniques. Test-and-learn is a way to test ideas in a small number of customer segments to predict impact and validate results before rolling out to a large number of users.

5. Embed Analytics into Operational Processes. “This enables companies to incorporate ‘new’ patterns and discoveries with ‘known’ algorithms that provide the background of their operational processes,” the report notes. “As a result, companies are realizing value from big data establishing a dynamic environment that merges the ‘new’ and the ‘known’ to create a more intelligent and sophisticated decision-making infrastructure.”

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One Comment


Nice information about new manufacturing era…..


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