Manufacturing cycle time is the total time taken to convert raw materials into finished goods. This includes loading time, machining and assembly time, inspection time, material movement, idle waiting time, and the time taken for all other actions during the manufacture of finished goods. In short, it is the time between receiving the purchase order and/or “Start Work” notification from the customer and rolling the product out of the manufacturing line.

Calculating Manufacturing Cycle Time

Manufacturing cycle time can be calculated by adding the value-added time (productive hours) with non-value-added time (non-productive hours)

Theory

Manufacturing cycle time = process time + material movement time + inspection time + idle waiting time

Value added hours = The process time + inspection time

Non-value-added hours = movement time + queue time constitute

Reality

Tracking all the time constituents accurately is not possible. Hence, cycle time is calculated by dividing the number of parts by the run time required to produce them.

Cycle Time = Total Parts Produced / Run Time

Reducing Manufacturing Cycle Time

Prioritizing actions while optimizing the manufacturing cycle time is important. Starting with non-value-adding activities like waiting time and material movement time will minimize intervention in value-adding activities. Acting on value-adding activities can tend to have an impact on quality or continuity and hence should only be optimized after exhausting actions in the non-value adding activities.

Reducing Manufacturing cycle time plays a vital role in enhancing customer satisfaction and increasing the revenue of an organization.

1. Reduce Idle And Waiting time
   The availability of material and resources to minimize idle time is one of the first areas to impact while optimizing cycle time. Effective material requirements planning (MRP) that is integrated with the ERP and supply chain can ensure resource availability. Digitalization of MRP can make availability and accessibility of requirements related data almost instantaneous, resulting in faster actions.
2. Reduce Movement of material
   In-line and end-of-line inspections are key to ensuring quality in manufacturing and reduced waste. However, they consume time and do not directly add value to the product. Human intervention that is subject to fatigue and delays can be supported or replaced through automation. Guided inspection through visual stimuli to simplify decision making can accelerate inspection time. Ensuring the availability of the right gauges and instruments can also reduce inspection time significantly.
3. Reduce Inspection time
   MES is the first layer in the digital manufacturing environment that can address cost savings from two perspectives. The first perspective is aimed at reducing certain wastes due to quality issues, unplanned production, unnecessary material movement, or inefficiencies. The second perspective is avoiding waste that could have occurred due to human intervention, over-processing, or waiting. MES can address cost saving from a more comprehensive perspective than disparate systems at a lower level of industrial automation.
4. Analyzing order profile
   Cycle time optimization impacts customer satisfaction significantly. Hence initiatives to optimize it should also ensure order fulfillment to meet customer’s expectations. This is especially applicable while manufacturing non-standard or high variance products. Understanding the key components of the order and sequencing activities in parallel or series while ensuring minimum material movement is key to reducing cycle time.
5. Predictive analytics in Cycle Time Optimization
   Building scenarios using predictive analytics can help plan the most optimum manufacturing cycle time. Advanced analytics can also help identify the impact of optimization on outlier products or components and hence make informed decisions about cycle time optimization initiatives.

Achieve Minimal Cycle Time With Big Data Analytics

Guided analytics that leverages big data and machine learning have improved the ability to optimize cycle time. Using data, it is now possible to identify non-value-adding activities and minimize their impact on cycle time. Understanding the impact of such initiatives by running simulations using predictive analytics can help avoid expensive mistakes and select the right set of initiatives. Prescriptive analytics can also accelerate actions by operators and foremen in the form of real-time line instructions and alert mechanisms. Together, all these elements of big data and analytics come to deliver optimum cycle time in manufacturing today.