Optimize Capacity Planning with Predictive Analytics

Manufacturers face constant challenges to improve profit margins, adhere to regulatory compliance standards, and keep up with unpredictable supply and demand changes. They must do this all while introducing innovation to their products and maintaining their share of the market. In terms of keeping up with demand, however, a company's capacity planner needs to determine the ideal production capacity that will improve the on-time delivery of products without sacrificing quality. That can be a struggle since there must be a careful balance between having enough capacity to meet anticipated needs but not so much to have excess resources sitting on the shelf for an extended period of time. One solution that has proven to be effective in combating these challenges is the use of predictive analytics.

Improved Quality

In general, more data about the company's products and the manufacturing process gives planners more valuable information to analyze. By considering multiple factors, planners can better predict previously unpredictable issues that come up by analyzing variables that negatively impact manufacturing quality. Keeping an eye on order status, on-time delivery estimates, and order revenue projections can prevent mishaps and keep quality consistent. Additionally, analytics let planners compare scheduled performance against actual performance to determine what exactly is causing delays so that any issues can be resolved immediately.

Preventative Maintenance

Analytics also help determine patterns that indicate symptoms of machine failure. This type of actionable information prompts planners to schedule the necessary maintenance before an emergency occurs. Gone are the days where production had to be stopped for an undetermined amount of time. A statistically driven approach can utilize historical data to predict the likelihood of certain maintenance upkeep. These "what if" scenarios minimize risk and improve cost across all processes.

Resource Utilization

Without analytics, manufacturing capacity planners would have to devote long stretches of time to map out creative solutions in order to maximize the use of machines and other resources on the factory floor. Analytics removes much of the time-intensive labor through automation. Combining a precise demand forecast with the amount of resources on hand can help a planner develop a more profitable, optimized schedule.

Demand Forecast

Demand is not the same for every manufacturing company. Oftentimes, it can be seasonal as in the case of certain holiday-specific products. Other times it can be cyclical. Analytical tools aggregate all the data to show demand peaks and valleys for better resource allocation. Analytics removes all of the guesswork so planners do not have to rely on assumptions made by salespeople, for example. Certain software features include statistical models that account for seasonality and trends.

For many manufacturers, integrating an Advanced Planning and Scheduling (APS) software includes in all the analytics features they need. Furthermore, APS commonly features mobile accessibility so that the entire team is capable of monitoring real-time data from anywhere in the world. Real-time interactive dashboards also empower staff to come up with solutions for optimal capacity planning in a drastically shorter amount of time than without the information. These tools, if used properly, are helping manufactures to become more efficient, streamlined, and revenue oriented.