Here, Frances Brunelle, founder of Accelerated Manufacturing Brokers, Inc., talks to Thomas R. Cutler about the importance of machine learning (ML) and big data when selling a manufacturing company.
TRC: How should manufacturers looking to be acquired prove they are best able to consider customer needs using data analytics?
FB: Every manufacturer who wishes to be acquired must demonstrate faster business value. As companies introduce aggressive business transformation initiatives, analytics are critical for extracting value from data.
Acquirers are facile and familiar with unlimited data potential. Massive unstructured data, increased computational power, and algorithmic innovation pave the way for sophisticated workloads.
Rather than guessing or predicting based on past-performance (especially during COVID-19) the ability to create real-time insights assures buyers when business accelerates, decision-making will keep-up, using streaming analytics.
As business models become more data-driven, the ways in which buyers are able to collate, manage, and evaluate data are more important than ever. The insights provided through advanced analytics are most powerful tools in driving innovation, engaging customers, and boosting efficiencies. They equip new manufacturing owners with the necessary tools to compete and succeed in entirely new ways.
TRC: How does a company looking to be acquired demonstrate they are able to bring products to market quickly using machine learning?
FB: Machine learning allows the buyer to automate intelligent decisions and deepen insights to enhance competitive positions. Machine learning algorithms, applications, and platforms help manufacturers find new business models, fine-tune product quality, and enhance manufacturing operations on the shop floor level. These techniques encourage a potential manufacturing company acquirer even in these uncertain times.
Finding new ways to grow and excelling at product quality, while still managing short lead-time production runs is all part of the machine learning value proposition. As new products are proliferating in manufacturing and delivery windows are tightening, manufacturers are turning to machine learning to improve the end-to-end performance of operations.
Supervised machine learning permits algorithms to parse incoming data, assigning labels to any datum that meets predefined criteria. Lean manufacturers could use such a technique to automate quality control for complex parts.
TRC: When selling a company how should manufacturers demonstrate their agility to overcome product failures?
FB: There are many manufacturers (including Fortune 500 industrial leaders) who have suffered from poor product designs and failures. The result is usually costly, both to budgets and reputations. Launching a new product is always a gamble but taking time to research customers and the market is essential. Acquisition requires a demonstration of “lessons learned.” Selling a company means celebrating the “wins” and admitting the “losses.” It can be as simple as acknowledging the combination of strategy and execution as reasons for successes and failures.
Culture is the primary challenge to becoming more agile, followed by the inflexibility of legacy technologies. Agility is becoming a priority during COVID-19. Old methodologies will no longer work. Agility is as easy as knowing what customers want and adjusting accordingly.
TRC: How are small and mid-sized manufacturers looking to be acquired using AI (Artificial Intelligence)?
FB: Focusing on machine learning, one can start to see how there are applications for artificial intelligence that are useful and accessible for most manufacturers, no matter the size. Often existing data are available in the form of data harvested by installed machine sensors, existing information technology (IT), and operational technology (OT) infrastructure. The capacity to share these data points with a prospective manufacturing buyers allows the kind of assurance that the operation is grounded in data and truth.
TRC: How does the use of big data accelerate the sale of a small or mid-sized manufacturer?
FB: Big data use cases in manufacturing, regardless of size, create metrics of confidence among business buyers.
Top uses of big data among small and mid-sized manufacturers looking to be acquired
TRC: Are manufacturing acquirers seeking improved eCommerce performance?
FB: The skillsets of new manufacturing business owners includes the experience and insistence of improving eCommerce performance across all platforms. Often this is the area in which the acquirer wants to impact the business model and may be more forgiving if the founder/owner has less familiarity.
Thomas R. Cutler is interviewing Frances Brunelle about the importance of machine learning (ML) and big data when selling a manufacturing company.
About the authors
Frances Brunelle is the founder of Accelerated Manufacturing Brokers, Inc., focused on the sale of lower middle market manufacturing companies nationally. Additionally, she hosts the WAM Podcast (Woman and Manufacturing), which highlights and profiles the opportunities for women in senior management and ownership positions. Brunelle helps manufacturing business owners prepare their companies for sale to ensure a positive financial result. She was recently named to 2020 Most Influential Women in Mid-Market M&A in Mergers & Acquisitions publication.
The author, Thomas R. Cutler is the president and CEO of Fort Lauderdale, Florida-based, TR Cutler, Inc., celebrating its 21st year. Cutler is the founder of the Manufacturing Media Consortium including more than 8,000 journalists, editors, and economists writing about trends in manufacturing, industry, material handling, and process improvement. Cutler authors more than 1,000 feature articles annually regarding the manufacturing sector. He can be followed on Twitter at @ThomasRCutler or email him at firstname.lastname@example.org.
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