Production levelling helps in smoothing customer demand out over time, and aims at proposing a feasible and steady production schedule and plan. This helps production planning departments and production control in smoothing out peaks and reduces the risk of backlogs, as it creates an overall more realistic an manageable inventory build-up. When done effectively, production levelling takes place across the entire supply chain. Large orders are chopped into smaller batches of defined size, and produced on a daily level or shift level, instead of producing the full order. In general, production will be initiated earlier, and there will be more changeovers and setups in production, but the stress level on the manufacturing floor will be reduced – and so will backlogs. Customer satisfication and overall supply chain reliability will improve. This should have a positive effect on profitability.

In this article I point out the benefits of production levelling and explain which industries can benefit the most from it. I continue with a general step-by-step guide on how to implement production levelling in ERP systems commonly used by manufacturing companies for production planning. Lastly, I point out possible levers for improvement that can be utilized by deploying customized production levelling algorithms, applying mathematical programming and analytics.

Benefits of production levelling

Production leveling, also known as production smoothing or production scheduling, is a strategic approach employed in manufacturing to establish consistency and predictability within production processes. This practice involves minimizing fluctuations in production volumes while optimizing resource utilization to meet customer demand efficiently. While its principles can be applied broadly, certain industries reap the most significant benefits from production leveling due to their unique characteristics and operational challenges.

Industries that face substantial fluctuations in customer demand often find production leveling to be particularly advantageous. For instance, the automotive industry grapples with seasonal variations in demand and frequent model changes. By implementing production leveling, automotive manufacturers can ensure efficient resource utilization and minimize inventory holding costs. This approach helps them to maintain a more predictable production schedule and avoid costly overproduction or stockouts.

Production leveling is an essential component of just-in-time (JIT) production systems, which are prevalent in industries like electronics and aerospace. JIT systems prioritize minimizing inventory and producing items only as needed. Consequently, production leveling becomes crucial to maintain a smooth and uninterrupted workflow, ensuring that components and products are available precisely when required. This practice minimizes waste and enhances efficiency.

Certain industries, such as construction and segments of the furniture industry, thrive on custom and make-to-order products. Here, production leveling plays a pivotal role in accommodating unique customer requirements while maintaining a consistent production workflow. It helps strike a balance between customization and operational efficiency.

Industries that benefit the most from production levelling

Production levelling is especially meaningful in industries such as:

1. Pharmaceuticals and healthcare. These industries face strict quality control and traceability requirements. Production leveling helps these industries maintain a steady production pace. This reduces the risk of error-making as a result of rushed orders or frequent batch changes.
2. Food and beverage. Foods and beverages encounter fluctuating consumer demands, driven by changing tastes as well as seasonal factors. Moreover, these industries have perishable goods, such as e.g. meat processing industry. Effective production levelling flattens out the peaks in demand, and facilitates production in steady increments.
3. Consumer electronics. The consumer electronics sector is characterized by rapid changes in product designs and features. Production leveling is essential in this dynamic industry to adapt to these changes swiftly while maintaining manufacturing efficiency. This ensures that consumers have access to the latest technology without causing disruptions in production.
4. Textiles and apparel. Similary to foods and beverages, textiles and apparel, with their seasonal fashion trends, are prime candidates for production leveling. This is even more true due to the fact that these goods are less perishable, although customer demand itself follows a curve that at some point in time will flatten out and stagnate. Production levelling is especially critical in an industry driven by consumer preferences that evolve rapidly.
5. Semiconductor manufacturing. Semiconductor manufacturing processes utilize costly equipment. High equipment utilization is especially important in this case. Moreover, any interruptions can lead to substantial losses. Production leveling helps mitigating all of these risks.
6. Packaging and printing business. Industries involved in producing packaging materials or providing printing services for various products benefit from production leveling by efficiently managing diverse customer demands. This approach ensures that products reach consumers in well-designed packaging, contributing to a positive consumer experience.
7. Agriculture. Even in agriculture, production leveling is applied to manage crop planting and harvesting schedules. This optimization helps in efficient resource utilization and ensures a steady supply of products to the market. It aids in reducing waste and improving yield.

Custom analytics for custom processes and objectives

In conclusion, production leveling is a versatile strategy that offers valuable benefits across a range of industries. While its principles remain consistent, the specific challenges and advantages vary depending on the industry’s characteristics and demands. Industries with variable customer demand, just-in-time production systems, custom manufacturing requirements, and other operational complexities find production leveling to be an indispensable tool for achieving efficient resource utilization, minimizing waste, and meeting customer demands in a timely and cost-effective manner. By adopting production leveling principles, businesses can enhance their competitiveness, reduce costs, and ensure smooth and predictable production processes.

Customizing production planning algorithms allows organizations to adapt their planning processes to specific requirements, industry dynamics, and operational constraints. Here are some possible customizations of production planning algorithms:

1. Priority rules. Modify or create custom priority rules to determine the sequence in which production orders or jobs are scheduled. For example, you can prioritize orders based on customer due dates, order size, or profitability.
2. Resource constraints. Customize algorithms to consider specific resource constraints such as machine capacity, labor availability, and tooling limitations. You can set up rules to optimize resource allocation, taking into account downtime for maintenance or setup.
3. Alternate routings. Incorporate the ability to switch between alternate routings or work centers based on resource availability or breakdowns. This ensures that production can continue even when primary resources are unavailable.
4. Batch sizing. Customize algorithms to determine the optimal batch size for production orders. Batch sizing can be based on factors like order quantity, setup time, and economic order quantity (EOQ) calculations.
5. Setup time reduction. Integrate algorithms that reduce setup times by optimizing changeover procedures. Minimizing setup times enables more frequent changeovers between product runs, especially in industries with high product variability.
6. Lead time considerations. Adjust algorithms to account for supplier lead times and transportation lead times. This ensures that raw materials and components are available when needed in the production process.
7. Safety stock and buffer management. Customize algorithms to calculate safety stock levels dynamically based on demand variability, supplier performance, and production lead times. This ensures that buffer inventory is adequately sized to meet demand fluctuations.
8. Demand forecasting. Integrate demand forecasting algorithms or models to predict future demand patterns accurately. This information can help in adjusting production schedules proactively.
9. Min-max inventory policies. Implement min-max inventory policies, where production is triggered when inventory levels reach a predefined minimum threshold and stopped when they reach a maximum threshold. Customizing these thresholds is crucial for efficient inventory management.
10. Dynamic scheduling: Develop algorithms that can dynamically adjust production schedules in real-time based on changing demand, disruptions, or unexpected events. This allows for agile and responsive production planning.
11. Load balancing. Customize algorithms to balance workloads across different work centers or production lines. This prevents overloading certain resources while others remain underutilized.
12. Multi-objective optimization: Consider multi-objective optimization techniques that balance conflicting goals such as minimizing production costs, meeting delivery deadlines, and maximizing resource utilization. Custom algorithms can prioritize these objectives based on organizational priorities.
13. Cycle time reduction. Develop algorithms that aim to reduce overall cycle times in the production process. This might involve resequencing operations or optimizing work center layouts.
14. Constraint-based planning. Implement constraint-based planning algorithms that take into account various constraints simultaneously, including material constraints, capacity constraints, and time constraints.
15. Custom KPIs and reporting. Customize key performance indicators (KPIs) and reporting metrics to track the effectiveness of the production planning algorithms. These custom KPIs can provide insights into specific aspects of the production process that matter most to the organization.
16. Integration with external systems. Extend algorithms to integrate with external systems like suppliers’ systems for real-time inventory updates, order acknowledgments, and production status information.
17. Machine learning and AI. Incorporate machine learning and artificial intelligence techniques to make predictive decisions, such as predicting machine breakdowns or optimizing energy consumption.

Customizing production planning algorithms requires a deep understanding of the organization’s unique requirements and objectives. It often involves collaboration between production planners, data analysts, and software developers to tailor the algorithms to the specific needs of the business and industry.

Other SCDA articles related to production levelling

Here are some other SCDA articles that might also be of interest to you:

• Link: Implementing multi-stage production scheduling
• Link: Commercial production scheduling software list
• Link: Wire production batch / lot size optimization
• Link: Excel Solver job shop capacity plan
• Link: Job shop SimPy Python simulation
• Link: Poultry supply chain simulation in SimPy

Linnart Felkl

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python