Analytics is Powerful For Production Scheduling



By and large, production planning still relies heavily on labor-intensive data aggregation and cleaning, manual analysis and personal judgment. However, advanced analytics is being rapidly implemented to meet the increasing variability in today's environment.


Production scheduling is the allocation of resources, events, and processes to create goods and services. It is important because it helps meet delivery commitment reliably and maximise capacity utilisation.


“Time and money are your scarcest resources. You want to make sure you’re allocating them in highest-impact areas. Data reveals impact, and with data, you can bring more science to your decisions.”

Therefore, managing variability in a sophisticated fashion has positive effects. It allows manufacturers to develop a competitive advantage that sets them apart from competitors by being able to better use resources.


 

Removes guesswork for sales forecast


Accurate production scheduling starts with an accurate sales forecast to plan what products to produce to meet demand. With accurate sales forecast, there can be accurate capacity planning to meet current and future needs.


To avoid bias and increase accuracy, analytics uses historical trends with other inputs to suggest future trends. The longer historical data, the more likely the model will be able to pick up on factors to enhance forecast. The factors are such as seasonality, cyclical events, and long-term trends.


This differs from the traditional method, where a salesperson comes up with a number for that quarter. How they arrive at the final number is up to their own discretion and expertise. Sales operations then collects and combines all the spreadsheets. They make the necessary adjustments, aggregate them and supply the forecast number.


Manual Sales Forecast, Sales Analytics, Sales Forecast
Sales Forecast Template (Source: HubSpot Blog)

For example, a toy manufacturer is able to use predictive analytics to determine seasonal demand. It can predict that a line of robotic action figures is expected to rise beginning in late October based on the anticipated holiday shopping demand, and seasonal demand for a line of dolls is expected to drop.


 

Provides transparency and visibility into capacity


Production planners can monitor machine usage in real time with the use of sensors. For example, if a machine seems to be behaving abnormally, or throughput seems to be slowing, planners can identify the issue as it unfolds and take steps to address it. It reduces idle times and downtime. This improves capacity management.


Also, it increases visibility of the entire production process. The production schedule, materials management, current production operations and quality are available to the organisation in real time.


An important and must-have metrics for manufacturing is the 'Overall equipment effectiveness' ("OEE") matrix. It is a measure of how well you utilise a manufacturing operation (facilities, time and material) compared to its full potential, during scheduled run periods.


Calculation formula for OEE matrix is 'Quality x Performance x Availability' for the entire plant. Calculation summary is provided in the diagram below:


Overall Equipment Effectiveness Calculation (Source: Trebing + Himstedt)

Diagram below shows how OEE matrix is used to analyse where plant inefficiencies are:

OEE as Starting Point for Machine Learning in Manufacturing (Source: SelectHub)

 

Enable data-driven decision making


When making predictions with data, algorithms tend to be superior to humans. You can quickly determine both small and large “what-if” outcomes from a particular set of inputs. You can analyse production scenarios which are too complex to be done manually.


Also, there is rapid development of information technologies such as radio frequency identification devices ("RFID"), enterprise resource planning ("ERP") and manufacturing execution system ("MES"). This enables low level data to be captured and recorded in database which can be used for analytics.


This low-level operation data can then be analysed for more accurate values for the parameters in production scheduling models and algorithms can be estimated, making the scheduling results fit the actual manufacturing implementation better.


 

Accurate production scheduling helps companies gain agility and maximise profit. Manufacturers must be able to respond to market demand with optimal production cost, speed and flexibility.


It is critical to reduce changeover time between production cycles, minimise wasted materials and run effective manufacturing practices. Also, it allows you to identify and take action against bottleneck and capacity issues. This can be achieved by using advanced analytics for production planning.



Do you use analytics for production planning? How useful do you find it? Share with us by leaving us a comment. If you require more information or assistance on analytics, contact us. Subscribe to our newsletter for regular feeds.


Did you find this blog post helpful? Share the post! Have feedback or other ideas? We'd love to hear from you.


 

References


Dzone, 5 Ways Manufacturing Analytics Will Change Your Business, https://dzone.com/articles/5-ways-manufacturing-analytics-will-change-your-bu, published 2 March 2018

Planet Together, Optimise Capacity Planning with Predictive Analytics, https://www.planettogether.com/blog/optimize-capacity-planning-with-predictive-analytics, published 25 July 2016

Tibco, Applying Analytics to Manufacturing Capacity Planning, https://www.tibco.com/blog/2013/02/14/applying-analytics-to-manufacturing-capacity-planning/, published 14 February 2013


14 views0 comments