Scaling growth for manufacturing portfolio companies

Achieving scalable growth as a manufacturing portfolio company requires effective strategies. We’ve gathered, and detail, some of the top strategies and insights in our article.

For private equity firms and their manufacturing portfolio companies, improving forecasting capabilities, implementing real-time pricing, and harnessing the power of artificial intelligence are not merely buzzwords, they are essential steps in addressing the unique challenges manufacturers face today. This means the path to creating value is paved with strategic foresight and operational excellence. In this article, we cover some of the strategies that can support quick wins and sustainable value creation.  

Leveraging data for enhanced forecasting 

Modern manufacturers utilize various systems, from general ledgers and workforce management to HR and logistics systems. These organizations are data-rich but often can’t make sense of this data effectively; particularly in finance, back office, or demand planning. Here’s how private equity investors can help their manufacturing portfolio companies make the most of their data: 

  • Centralize data: Consolidate data into a central repository to create a single source of truth. This harmonizes data from different systems, product codes, vendor codes, and operational units. 
  • Implement robust data strategy: Develop a strategy to harmonize and aggregate data effectively. This step is crucial before diving into sophisticated technology solutions. 
  • Adopt advanced tools: Utilize integrated business planning tools such as Workday Adaptive, Anaplan, Oracle EPM, or OneStream. These tools streamline forecasting processes, reduce reliance on Excel, and improve accuracy and efficiency. 
  • Shorten planning cycles: Moving to automated platforms can significantly shorten planning windows, making forecasting more efficient and timelier. 
  • Incorporate external data: Enhance forecasts by integrating syndicated data, such as consumer price indices, export data, and commodity prices. This approach leads to more prosperous, more accurate predictions. 
  • Utilize AI and predictive analytics: Modern AI and machine learning technologies can build models that predict pricing trends and supply chain fluctuations. Tools that adopt these technologies can dynamically adjust pricing in real time, improving profitability and operational efficiency. 
  • Focus on continuous improvement: Integrating these improvements fully may take a year or two, but consistent progress is essential. Investors can help ensure sustained growth and success by focusing on people and processes first, and technology second. 

Overcoming challenges in implementing new technology

Implementing new technology comes with its set of challenges for manufacturing companies. The primary issue isn’t the technology itself but the change it represents. Moving away from familiar systems that, despite their flaws, are comfortable and known can meet significant resistance. Teams often show skepticism about the benefits of new systems, questioning how these changes will lead to better outcomes, especially in pricing. 

Successful implementation hinges on: 

  • Effective communication: Constantly communicate across the portfolio company the reasons for change, the expected benefits, and how it will improve processes and outcomes. 
  • Involvement and feedback: Involve team members at the manufacturer in the process, request feedback, and demonstrate flexibility in responding to their concerns. 
  • Tailored approach: Based on team feedback, adapt the implementation strategy, potentially adjusting timelines and methods to suit the organization’s needs better. 
  • Training and support: Provide comprehensive training and ongoing support to ease the transition, and help ensure employees are comfortable and proficient with the new systems. 

From a pricing perspective, integrating new technology into existing ERP systems allows for dynamic pricing strategies that reflect market fluctuations and improve margin management. Predictive analytics and real-time data enable companies to adjust prices quickly, helping ensure competitiveness and profitability. This holistic approach to implementing technology and leveraging data supports robust forecasting, efficient operations, and strategic pricing, driving growth and value creation for private equity portfolio companies. 

The role of AI in value creation and performance improvemenT

When we examine value creation and performance improvement, the triad of people, processes, and technology always comes into play. Private equity firms often believe their challenges are rooted in people issues, but often, the crux lies in processes, technology, or a combination of all three. AI stands at the intersection of these elements, offering robust solutions beyond mere automation. AI’s application in manufacturing is multifaceted. It’s not about replacing people with machines but rather enhancing decision-making by leveraging existing data more effectively. Depending on the organization's maturity, AI can be integrated in various ways: 

  • Back office and CFO support: AI can automate accounts payable (AP) processes, streamline analytics, and improve financial decision-making. This reduces manual workload, minimizes errors, and accelerates financial operations. 
  • Predictive maintenance: AI can predict machinery failures by analyzing data and telemetry output from production equipment. By anticipating when a machine might fail, companies can schedule maintenance during non-critical times, preventing unexpected downtime that could disrupt production, especially during peak periods like the holidays. 
  • Operational optimization: AI-driven insights can optimize production schedules, enhance supply chain management, and improve inventory control. By using predictive analytics, manufacturers can better align production with demand, reducing waste and increasing efficiency. 

Assessing readiness for AI integration

Assessing a company's readiness to adopt AI involves evaluating its current infrastructure and determining where AI can be integrated into its business processes. Several critical steps need to be taken to enable this technological transformation: 

  • Process evaluation: Companies must have well-defined and efficient processes before implementing AI. For instance, if a company wants to use AI-powered robots to audit its pick, pack, and ship processes, it must ensure its inventory system is meticulously organized. Every bin, rack, and column must be accurately labeled and optimized. 
  • Benchmarking and maturity assessment: Benchmark the company’s and its competitors’ maturity levels is key. This involves understanding whether the company’s systems can support the necessary data for AI model training and whether there are clear use cases for AI. 
  • Identifying use cases: Companies must identify specific use cases where AI can add value. This could stem from internal pressure to innovate or external pressure from competitors and suppliers. Clear use cases help focus efforts on areas with the highest potential impact. 
  • Readiness of people: It is crucial to ensure that the company’s workforce is prepared to adopt AI. This might involve upskilling current employees or hiring new talent to manage and leverage AI technologies. 
  • Technology and data infrastructure: Companies must evaluate whether their technology systems and data infrastructure can support AI initiatives. This includes making sure they have the necessary hardware, software, and data management practices. 

Scaling growth is a collective effort

Ultimately, the secret to enhancing value lies in this collective effort – a synergy between leveraging cutting-edge technology, optimizing processes, and maintaining a relentless focus on continuous improvement. It is through this united and strategic approach that private equity firms can drive exceptional value creation as well as robust, scalable growth for their manufacturing portfolio companies.  


Subject matter expertise

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jeremy swan

Jeremy Swan

Managing Principal - Financial Sponsors & Financial Services Industry
shawn gilronan

Shawn Gilronan

Principal, Digital Advisory Practice Leader

Henrietta Fuchs

CPA, Partner, Manufacturing and Distribution Industry – Co-Leader

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This has been prepared for information purposes and general guidance only and does not constitute legal or professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is made as to the accuracy or completeness of the information contained in this publication, and CohnReznick LLP, its partners, employees and agents accept no liability, and disclaim all responsibility, for the consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it.