Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Manufacturers globally are facing fierce competition and rising costs, promoting a need to optimize costs and manage working capital effectively.
- Technologies like AI and generative artificial intelligence (GenAI) can provide the way forward by offering better real time insights through analyzing both internal and external data.
- By streamlining operational processes and being better prepared to respond to customer demands, AI and GenAI can help the manufacturers meet their revenue and cost targets.
On this page
On this pageinpage
AI is the game changer
Manufacturers are facing fierce competition and rising costs globally, prompting a need for real-time insights and strategic measures to enhance productivity and manage working capital effectively.
With the artificial intelligence (AI) revolution, the manufacturing industry is looking ahead for a competitive edge and revenue growth with enhanced efficiency and innovation.
Digitalization, including adoption of AI, has transformed the way organizations across industries operate and offer services to their customers. AI continues to revolutionize various industries, with an expected compound annual growth rate (CAGR) of 36.6% between 2024 and 2030, as per Grand View Research. This rapid growth emphasizes the increasing impact of AI technologies in the coming years.
AI can streamline tasks such as quality control, inventory management, supply chain optimization, and financial reconciliation. However, the efficacy of AI-enabled applications is heavily influenced by the quality and quantity of ingested data to analyze correlations, learn patterns, and make reliable predictions. By utilizing high-quality data sets, manufacturers can unlock the full potential of AI, gaining a competitive advantage in industry.
Order management
Streamlining the end-to-end order flow and making the supply chain more agile is essential to enhance operational efficiency.
By integrating existing IT applications with AI and maintaining a centralized data repository, all functions involved in order processing can access data at any given point in time. Manufacturers can identify the potential downtime of machinery and equipment through AI powered predictive maintenance and reduce the operational cost.
AI-driven algorithms can predict demand and optimize inventory levels by analyzing historical purchases, market trends, customer behavior, seasonal variations, weather predictions, upcoming holidays, and supply chain constraints.
One of the most prevalent uses of AI in manufacturing and distribution today is through AI-based chatbots. AI plays a pivotal role in enhancing inventory and warehouse management by analyzing inventory items, warehouse layouts, and orders and enhancing inventory utilization and minimizing waste production.
For instance, if you need to check inventory levels in a specific warehouse, you can simply ask the AI chatbot, and it will provide real-time visibility into the inventory data.
Another prominent use case of AI is optimization of labor deployment by analyzing projected demand and production schedules. This enables organizations to allocate resources more efficiently, minimizing unnecessary labor costs.
Manufacturers and distributors often deal with complex supply chains and logistics networks involving multiple modes of transportation. AI can optimize these networks by predicting the most efficient routes based on priority, destination and product dimensions, scheduling shipments, and reducing fuel costs.
Financial management
Companies are focused on customer retention and minimizing financial losses by reducing the days sales outstanding (DSO) cycle and bad debts.
AI enables businesses to configure credit risk scoring algorithms, generate accurate credit scores, and predict the likelihood of default or late payment. Real-time risk alerts and dashboards automate the decision-making process for low-risk customers.
AI can revolutionize the way collections and dispute resolution processes are handled by identifying patterns and presenting this information through digital dashboards, making the dispute process more transparent and efficient. A global industrial manufacturer implemented an AI-based solution that centralized collections, dispute management, and cash application, thereby automating accounts receivable across the enterprise. The implementation resulted in a 10% increase in free cash flow, operational efficiency, and an 80% improvement in customer payment compliance within credit limits.
Reconciliation
Accounts reconciliation and reporting are critical time-consuming tasks for the accounting and finance team.
To proactively anticipate reconciliation issues, AI techniques such as predictive analytics can be used. By analyzing historical reconciliation data and market trends, AI algorithms can identify potential discrepancies, reducing errors that occur in manual reconciliations, minimizing financial losses. A leading locomotive manufacturer faced challenges with numerous unreconciled accounts and duplicate/ late payments to suppliers, leading to penalties and cash flow issues. An AI-enabled solution was deployed to automate bank account reconciliation and real-time operational data like payables, receivables, payments, and customer feedback for analytics-driven dashboard, improving cash flow and financial visibility.
Reporting
AI-driven financial reporting aids swift informed decisions and drives significant performance enhancements.
CFOs and CEOs require accurate and timely financial reporting, to make informed strategic decisions. AI technology can provide real-time insights and financial reporting by analyzing data faster, reducing the reliance on human efforts. By using AI-powered tools, finance professionals can now concentrate on value-added and strategic activities rather than tedious data extraction and reporting. An aerospace company successfully implemented AI in cash flow improvement by implementing data lake and automating data acquisition. As a result, the company saw a $49 million increase in factoring, a 30% reduction in DSO, and access to financial data in just 15 seconds.
The way ahead
AI has the potential to transform and expand business operations.
AI can help speed up enterprise operations, which might otherwise be slow. By using AI, organizations can quickly analyze both internal and external data, adjust their forecasts in real-time, and be better prepared to respond to customer demands. The insights drawn from existing IT applications and inferred by AI technologies are invaluable for making informed decisions, minimizing disruptions in the manufacturing and supply chain processes, and improving cash position. Intelligent data can accelerate working capital, thereby improving operational efficiency. Besides, CFOs and finance leaders can leverage AI to make their O2C processes more efficient, reducing working capital requirements and costs.
