In order to remain competitive in the world of modern manufacturing, production planners are constantly searching for new ways to derive more value from their operations. This impulse takes many forms, but one of the most common is striving to improve operational capacities, usually by either reducing makespan or improving machine utilization. Though the obvious benefits of increasing your throughput may seem tantalizing, the process of actually doing so is not as simple as ratcheting up production speed or buying new machines. Rather, it is a complex process that requires a high degree of visibility into your value stream. To help you tackle these complexities, here are 5 key strategies for improving operational capacities.
If you’ve skimmed the contents of this list before digging in to each individual paragraph, you may be thinking that digitization seems a little out of place compared to some of the suggestions with more direct, obvious impacts on capacity management. But not only is there a reason that digitization is on the list, there’s also a reason that it’s the first item: you can’t improve what you don’t measure. In order to boost your capacity, you first have to measure what your current capacity level is. In order to measure your current capacity, you need a high degree of visibility into every aspect of your production stream.
As the Industry 4.0 revolution is increasingly making clear, digitization is one of the fastest and most reliable paths to the necessary level of transparency for measuring overall equipment efficiency (OEE), one of the more popular metrics for defining capacity usage. By leveraging a holistic view of machine usage, maintenance downtime and unplanned breakdowns, and the journey that each product takes through the production stream, you can determine your maximum capacity and see what percentage of that capacity you’re currently utilizing. In this way, you can determine how much room you have to improve and even some of the areas where improvements may be necessary.
It’s possible that the industrial internet of things (IIoT) could be considered a part of the digitization process, but it’s important to emphasize this particular component of Industry 4.0 on its own. Once your operations are digitized, and thus capable of managing mission critical information in a transparent way, your next hurdle is to ensure that you are well-positioned to collect that mission critical data in the first place. This means integrating smart sensors and other internet-connected devices into your factory floor operations, thereby creating a connected, digital environment that offers granular data about your production stream and promotes machine-to-machine communication. In this way, you can measure not just your OEE at the level of the factory itself, but also the efficiency of individual machines and processes.
Once you’ve determined your OEE and gotten a sense of machine uptime and downtime, you may find that one of the most significant drains on your capacity is unplanned downtime due to machine breakdowns. One crucial tactic for reducing breakdowns at unexpected times (which could potentially disrupt your entire production operation), is to schedule machine maintenance proactively, rather than waiting for a breakdown to occur. Increasingly, sophisticated machine learning algorithms are being trained to predict potential machine failures in advance and schedule minimally-disruptive maintenance before that happens. Even without sophisticated technology, however, it’s possible to take a proactive approach to maintenance scheduling, thereby boosting uptime and improving OEE.
One of the benefits of digitization is that it can help pave the way for huge improvements in the quality, speed, and availability of data—even facilitating the integration of real-time information into your value chain. If your organization has achieved this level of digital sophistication, new workflows suddenly become available to you. Sales & operations execution (S&OE) for instance, is a relatively new process that has emerged in recent years to fill in the gaps between day-to-day operations and longer-term sales & operations planning (S&OP). By monitoring real-time fluctuations in demand and transport conditions, S&OE keeps mid-term plans on track by responding to small disruptions and deviations from expectations on a daily and weekly basis. While this process requires a high level of digitization before it can be operationalized, it has the potential to align production processes with a more accurate picture of real demand than would have been possible otherwise, reducing the number of small-scale disruptions and thereby keeping production processes on track.
In addition to powering real-time supply chain information, robust levels of digitization (and therefore robust data collection) can also pave the way for advanced analytics integration. When it comes to increasing operational capacity, prescriptive analytics in particular can offer significant value to businesses that have the requisite levels of data integration. With prescriptive analytics, sophisticated algorithms can analyze your entire value stream, including production streams, and determine possible areas of improvement or processes that might be beneficially changed. Though many businesses have room to grow their capacity without analytics workflows, these new technological possibilities represent opportunities for optimization that would be virtually impossible to create manually.
If you want to learn more, download your guide to Transformation of Manufacturing Processes.
In this Guide you will learn:
Emerging Challenges in the Modern Truck/Automotive Industry
How Can Global Companies Adapt to These New Realities
How Decentralized Digital Systems Power Smarter Planning Processes
How flexis Can Support Flexible Supply Chain Transformation