Closing the loop: Integrating contract management and supply chain systems for future-ready manufacturing

Contract manufacturing involves outsourcing production to third-party manufacturers, which allows companies to focus on core competencies such as design, branding, and distribution.

The global contract manufacturing market was valued at USD 246.51 billion in 2022 and is expected to reach USD 512.74 billion by 2030, growing at a robust compound annual growth rate (CAGR) of 9.58%.¹

Each year, contract manufacturing launches over 1,000 new food and beverage products in the United States, accounting for more than 10% of all new introductions in the consumer packaged goods (CPG) category.² Meanwhile, the global market for electronics contract manufacturing and design services is set to expand from USD 546.4 billion in 2023 to USD 847.2 billion by 2028, at a CAGR of 9.2%.³ The global automotive contract manufacturing market is also on the rise, expected to grow from USD 65.58 billion in 2023 to USD 124.96 billion by 2030, marking a CAGR of 9.64%.⁴

The procurement-supply chain divide

Despite its prevalence in industries such as retail, electronics, and automotive, a disconnect between procurement and supply chain management often results in inefficiencies, increased costs, and missed opportunities. Misalignment between manufacturers and their contract partners can trigger a cascade of issues. In automotive, mismatches in quality standards might lead to critical safety issues and costly recalls. For CPG, inconsistent production schedules can result in stockouts or surpluses, disrupting the supply chain. In electronics, miscommunication about component specifications can delay product launches and inflate costs, as seen when last-minute changes in design lead to rework and waste. Right now, they work in silos, but what they really need is to come together as one team. 

Many companies that have identified and addressed this gap are now reaping significant financial rewards and operational improvements, including supplier-integrated contracts and streamlined processes. By fostering collaboration and improved interaction, they could reduce total inventory levels by at least 15% across the value chain, significantly cutting supply chain costs.⁵

Advances in data management and AI, including generative AI, are facilitating the integration of contract management systems (CMS) with supply chain management (SCM)—a fusion offering a technological breakthrough that streamlines operations, boosts agility, and sharpens decision-making. In this article, we look at the challenges of contract manufacturing and explore how the emerging technological capabilities of AI and Gen AI can provide solutions to these challenges.

Challenges that emerge from gap between CMS and SCM

Below are some key challenges faced by contract manufacturers due to misalignment between procurement teams and their supply chain colleagues.

Fragmented information

Procurement teams often handle crucial contract details like pricing, delivery schedules, quality standards, and penalties separately from supply chain data. This siloed approach leads to fragmented information, causing misaligned objectives and a tendency toward reactive management. Moreover, procurement specifics such as payment terms, discounts, and purchase order (PO) minimums often get overlooked by supply chain managers. They tend to focus more on inventory logistics like unit cost, capacity, shipment times, and landed costs. This oversight can create significant gaps in both planning and execution, hindering seamless integration and efficiency.

Operational inefficiencies

Operational inefficiencies stem from the disconnect between CMS and Supply Chain Planning Systems (SCPS). This gap forces manual data reconciliation between CMS and SCM, leading to errors, delays, and inefficiencies. Manual processes not only cause time delays but are also error-prone, disrupting the smooth flow of supply chain operations. Moreover, the lack of real-time integration means that any updates in contracts or supply chain plans aren’t communicated swiftly. This delay again forces companies into a reactive mode, dealing with issues only after they arise instead of proactively preventing them.

Visibility and control issues

When stakeholders don’t have a unified view, they miss out on key insights into supplier performance, contract compliance, and overall supply chain dynamics. This lack of visibility can stall strategic decision-making and complicate efforts to optimize operations. Inconsistent information flow can strain supplier relationships. When discrepancies arise between contract terms and actual supply chain requirements, it can lead to disputes and erode trust, disrupting operations and jeopardizing future collaborations.

Increased costs and risks

When procurement terms like payment days and discount terms are overlooked in supply chain planning, companies can end up with suboptimal purchasing practices, driving up costs and creating inefficiencies. If contract terms aren’t aligned with actual supply chain needs, it can lead to inventory imbalances—either stockouts or excess inventory. Both scenarios can be costly and disrupt business operations significantly.

Closing the loop: Bringing together CMS and SCM

By integrating CMS with SCM, we bring procurement and supply chain teams together under one umbrella of truth using a cloud-native data platform. This coordination aligns procurement contracts with supply chain plans, facilitating better planning and execution—affirming that supply chain resilience starts with data. Critical contract terms such as payment terms, minimum order quantities (MOQs), and purchase order (PO) specifics are incorporated into supply chain considerations resulting in:

• Real-time data synchronization: Real-time synchronization between contract terms and supply chain activities enables you to make proactive adjustments to production schedules, inventory levels, and logistics plans. This agility is vital for swiftly responding to market shifts, supplier issues, and changing customer demands, keeping you one step ahead at all times.

• Improved supplier relationship management: A unified system improves supplier collaboration by providing clear visibility into contract terms, performance metrics, and compliance requirements. This transparency fosters stronger partnerships and encourages suppliers to meet or exceed expectations, leading to better outcomes across the board.

Achieving a successful integration between CMS and SCM depends on robust data governance and bringing CMS under the control tower alongside SCM systems to proactively identify and mitigate emerging challenges. However, data alone does not solve the issues unless applied and analyzed. That’s where generative AI steps in, turning raw data into actionable insights and driving smarter decision-making.

Democratizing CMS and SCM data with generative AI

Conversational AI assistants help democratize the data emerging from the integration of CMS and SCPS. These AI-powered assistants consolidate various data sources across procurement and supply chain systems, enabling meaningful questions to be asked and answered through chat interfaces powered by semantic vector search and Large Language Models (LLM).

Semantic vector search ensures information retrieval from vast datasets by converting text into vectors and finding contextually relevant results based on semantic similarity, surpassing the constraints of exact keyword searches. LLMs understand user intent, maintain dialogue context, and generate accurate responses by organizing information found through semantic search. They transform user queries into SQL (Text2SQL) for effective data extraction. Together, they enhance operational efficiency and effectiveness for both manufacturers and their suppliers by tapping into multiple systems, including CMS, SCPS, Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM), to ensure comprehensive data supports every decision.

Consider a knowledge assistant in a large retail chain predicting a product shortage before the holiday rush. By analyzing sales trends, inventory levels, and upcoming promotions in real time, the supply chain manager is alerted to restock well in advance, averting potential stockouts and guaranteeing shelves stay full.

At the same time, generative AI excels in intelligent document processing, quickly extracting insights, validating compliance, automating form filling, and refining RFP responses. 

This capability extends to providing contextual insights, such as how procurement terms like payment days and MOQs impact overall supply chain performance, and offering actionable recommendations. LLMs empower supply chain workers to pose complex queries in natural language, simplifying the exploration of intricate data on contract terms, supplier performance, inventory levels, and more—eliminating the need to manually navigate multiple systems.

Generative AI also empowers supply chain leaders with business intelligence capabilities that continuously monitor data to provide proactive alerts about potential issues like contract breaches, supply chain disruptions, or inventory shortages. This early warning system enables swift actions to mitigate risks effectively. The AI simplifies the understanding of complex databases, querying data, and interpreting results by automatically designing dashboard layouts, identifying relevant data tables and columns, generating SQL queries, determining optimal visualizations, and providing clear summaries. This streamlined process helps decision-makers optimize procurement strategies and supply chain plans, leading to significant cost savings, improved supplier relationships, and enhanced operational efficiency.

The true power of generative AI lies in its ability to continuously learn from new data and user interactions, improving its accuracy and relevance over time. This ongoing refinement ensures that the insights remain current and valuable, perpetually enhancing decision-making and operational efficiencies across the board.

Ensure accuracy and compliance with LLMops and RAG

As generative AI continues to evolve, it’s important to tackle potential risks like inaccuracies or hallucinations. To securely leverage generative AI and gather insights from SCM and CMS data, Large Language Model Operations (LLMOps) and Retrieval-Augmented Generation (RAG) are vital. LLMOps fine-tunes and manages closed-source models like GPT-4 and PaLM2, ensuring compliance while boosting performance and cost-efficiency. RAG enhances this by enabling real-time data retrieval and delivering timely and contextually accurate AI responses. Together, they automate compliance checks, streamline processes, and safeguard sensitive data with advanced security measures.

Key skills required to bridge the gap between CMS and SCM

To successfully integrate contract management systems with supply chain management, professionals need to develop the following key skills:

• Cloud integration: Mastery of cloud-based data integration tools like AWS Glue, Microsoft Azure Data Factory, and Google Cloud Dataflow is essential for merging data from various sources. Proficiency in managing scalable cloud infrastructures ensures these systems handle large data volumes and expand as needed.
• Machine Learning expertise: Skills in Machine Learning algorithms and frameworks (e.g. TensorFlow, PyTorch) are crucial for developing intelligent systems that analyze data and provide actionable insights. Expertise in NLP techniques and tools is needed to create knowledge assistants capable of understanding and responding to natural language queries.
• Data security: Knowledge of encryption, access control, and other cybersecurity measures is vital for ensuring data integrity and security in integrated systems. Familiarity with data protection regulations and compliance standards ensures adherence to legal and ethical guidelines.
• Agile project management: Proficiency in Agile project management methods helps in the iterative development and continuous delivery of integrated systems, ensuring flexibility and responsiveness to changing requirements. Effective communication skills are crucial for managing expectations and aligning all stakeholders with integration goals.

Looking ahead

As supply chains are reconfigured to adapt to new and emerging global dynamics, integrating CMS with SCM becomes essential for any business. Geopolitical shifts, trade policy changes, and evolving market demands are driving companies to rethink their supply chain strategies. The integration of direct procurement and supply chain functions, enhanced by generative AI, offers a strategic advantage. By bridging the gap between procurement and supply chain, and leveraging AI-powered knowledge assistants, intelligent document processing, and business intelligence tools, manufacturers can unlock significant operational efficiencies, cost savings, and competitive advantages.
Ready to integrate this intelligent approach to ensure that your business can respond swiftly and effectively to dynamic market conditions, fostering resilience and agility? Get in touch with us to begin your journey of optimizing operations as a real-time response to changing global dynamics.

Sources

1. https://finance.yahoo.com/news/global-contract-manufacturing-market-report-12430050.html
2. https://www.linkedin.com/pulse/hidden-powerhouse-us-cpg-contract-manufacturing-raphael-traticoski-uk8yf/ 
3. https://www.databridgemarketresearch.com/reports/global-contract-manufacturing-market 
4. https://www.openpr.com/news/3393591/automotive-contract-manufacturing-market-worth-124-96 
5. https://www.mckinsey.com/capabilities/operations/our-insights/bridging-the-procurement-supply-chain-divide