Manufacturing scheduling software is a detailed scheduling tool that enterprise manufacturers use to coordinate the raw materials, machinery, workforce, and production processes on their shop floor. This software enables manufacturers to run an efficient operation that delivers orders on time and reduces manufacturing costs.
Unfortunately, not all scheduling tools are created equal. There is an ever-widening chasm when it comes to the performance of traditional excel/heuristic algorithm-driven scheduling tools and modern optimization-driven Advanced Planning and Scheduling (APS) software.
Today's manufacturing environment is highly complex. Traditional scheduling tools like excel or low-powered heuristic scheduling systems lack the ability to:
The result is inaccurate schedules that cause a myriad of problems including:
Prioritizing orders by the due date to ensure on-time delivery, is a complex process because in the modern production ecosystem there are many different steps, machines, and workers involved in producing a single order. All these manufacturing resources need to be coordinate efficiently so that most of the sales orders can be produced on time. But if that is not possible, it is imperative that at least the priority orders be ready by the due date given to important customers.
Raw material inventories need to be managed, intermediate/work in process (WIP) products tracked, and machinery and worker availability monitored. To achieve this level of detail, a high-resolution model must be built inside the production scheduling software system and updated with data from connected production systems, to ensure that it remains accurate.
Such models are not supported by Excel or traditional APS software. These scheduling tools only allow manufacturers to create approximate, high-level maps of this production ecosystem and do not allow them to update production schedules with real-time data to ensure accuracy.
So what happens when a priority customer comes in with a last-minute order that must be fulfilled on time. If the production scheduling system cannot fully model the existing state of the production environment, then there is no way to know, with accuracy, how to adjust production to get these orders done on time. And even on a more basic level, the manufacturer will not know if it is possible to get these orders done on time. They have no idea when these orders will be ready because traditional scheduling systems do not have accurate Available-to-Promise (ATP) capability.
Excel and heuristic-based APS systems take shortcuts when it comes to modeling the production environment and as a result, a manufacturer relying on these types of manufacturing scheduling systems will be forced to give a very rough estimate to priority customers. In the long run, this leads to a reduction in customer satisfaction, followed by a reduction in orders and a reduction in revenue.
For most enterprise manufacturers, creating a production schedule takes anywhere from several hours to several days. Large teams of schedulers work full time creating schedules and then when something inevitably goes wrong, they correct them. The problem is that when it takes several hours to create a schedule manually in spreadsheets and the state of the shop floor changes in minutes, the schedule is already outdated the second it is complete.
Heuristic-based scheduling tools may seem like an improvement over excel because they are least fast and can create a schedule in seconds. Unfortunately, they use shortcuts to create schedules because they are unable to model or process the full complexity of the modern, enterprise manufacturing environment. The result of shortcuts being taken during the production scheduling process is inaccurate schedules that are not feasible and fall apart on the shop floor.
Most schedulers using heuristic tools, know this limitation and manually adjust the output of these systems to improve the schedules before they are launched into production. Unfortunately, this manipulation typically takes just as long as it would have taken to create a schedule from scratch.
Other schedulers have given up. They have accepted the false belief that there is no such thing as an accurate production schedule. They take the schedules spit out by the heuristic manufacturing scheduling systems and execute them as is. When inevitably a major bottleneck or stoppage occurs on the shop floor, they regenerate another schedule.
Whenever schedulers use under-powered tools, the result is always the same ... a perpetual scheduling emergency. Because manufacturing scheduling is so critical to the production process, operations managers are forced to divert more resources to this function. The scheduling teams seem to grow and grow, eating through operational resources and overtime at an alarming rate.
What is the impact of low-resolution schedules, generated in excel or with heuristic scheduling tools on machine utilization and throughput?
Such scheduling techniques tend to cause bottlenecks because they have not considered all the machinery and workforce deployment possibilities available to the manufacturer. They have not found the optimal solution.
The result is a schedule that will drive massive fluctuations in machine and staff utilization. Some machines will be run at maximum capacity and still not be able to keep up with the demand, generating a bottleneck. Other machines will sit idle. Other times, another machine will become the constraint and cause a bottleneck, while the rest of the machines sit idle.
Another common cause of low machine utilization and throughput is ineffective changeover management. In most operations, the assembly line and machinery must be cleaned or set up in different configurations when changing between the production of different classes of products. Last-generation scheduling systems do not have the modeling or optimization capabilities to group products by feature types and prevent changeovers. As a result, changeovers happen sporadically, dramatically reducing efficiency and productivity.
Although some machines will be running at maximum utilization, the overall utilization of equipment at a factory without an effective scheduling process will be quite low. Machine utilization does not factor directly into throughput, but the two are closely correlated.
The total throughput of a manufacturing operation is calculated by multiplying the efficiencies of each individual machine and then multiplying that by the throughput of the slowest machine on the line.
Total Throughput = Total Efficiency x Slowest Machine on The Line
The total efficiency is calculated by multiplying the efficiencies of each machine, workstation, and worker. Machines that are not being utilized to full capacity are typically not going to contribute favorably to the overall efficiency of the factory.
The slowest machine on the line is also known as the constraint. This machine will often become the bottleneck, the step in the production line, that slows down the entire operation. If the manufacturing schedule does not skillfully utilize auxiliary resources and precise timing to bypass this limitation the impact on throughput will be significant.
Modern production scheduling software not only models the entire production environment in detail, but it also optimizes the schedules using powerful optimization algorithms. The result is accurate, feasible schedules that promote:
Constraint-driven manufacturing scheduling software is able to optimize a factory so that the majority of orders can be produced on or before the due date. This type of scheduling system can also guarantee that priority orders will be ready on time.
Sometimes focusing on priority orders can reduce overall efficiency. Fortunately, the optimization capabilities of a modern APS system mean that the software can insert last-minute priority orders without much of an impact on the overall production ecosystem. Likewise, the system can reduce the effect of unexpected disruptions, worker absences, and machine downtime on production efficiency.
How is this possible? By modeling the entire production ecosystem and breaking orders down by detailed, high-resolution mappings of routings, bill of materials, and similar production processes, an optimization-driven APS system finds the most efficient means of producing the orders.
Modern APS systems allow schedulers to create production schedules in four easy steps.
Schedulers model their production environment through constraints, not custom coding. This means that it is easy to set up a data model for even the most complex manufacturing ecosystem. Updating the model to account for even last-minute changes in the type of products being produced, plant setup, or unexpected disruptions takes just minutes.
The initial modeling and setup will take a couple of weeks at the time of implementation. But this is a one-off time investment and one where the implementation team of the APS software will do the majority of the heavy lifting. After the initial setup, the adjustments to the model will be very quick.
Orders, inventories, and other data are fed into the APS system from integrated enterprise resource planning (ERP) systems, manufacturing execution systems (MES), and other native production systems. In most factories, this data import and export process is fully autonomous.
Modern APS systems come integration-ready with most ERP systems like SAP and Oracle, and a wide variety of MES systems as well. Even if the production systems encountered in the integration process are new, the kind of data that APS systems require is standard and so setting up the integration is a smooth process.
Powerful algorithms in the APS software process the data and generate the best possible manufacturing schedule. The power of an optimization-driven scheduling solution is that the optimization engine performs the calculation in minutes and even seconds in real-time setups. This is a major time saving because on average it takes schedulers about 2 hours to produces schedules manually.
Step 3 can be run fully autonomously. The APS software can react to pre-determined triggers and reschedule in real-time. It can dispatch updated schedules directly to the production systems in response to changes in the production environment. In other types of setups, a scheduler runs the algorithm manually in seconds.
Many scheduling teams like to include an additional step, Step 4, and run what-if scenarios. They run and analyze different versions of the schedule with built-in dashboards to see which scenario satisfies their production objectives and KPIs the best. They then launched the best schedule into production, with a click of a button.
When you consider all the possible ways to combine raw materials, intermediate parts, production processes, machines, and workers in an enterprise manufacturing facility there are billions of different possibilities. True optimization considers them all.
And when you apply constraints to specify product requirements and business objectives, then the optimization engine figures out which schedule represents the best possible combination.
Modern APS systems have robust libraries of constraints that allow schedulers to easily create rules to reduce changeovers. These constraints are used to improve machine utilization or smooth production. They are used to track sales orders from key customers through the production process and make sure that these are completed on time, no matter what.
What makes constraints so powerful is that their priority can be adjusted relative to one another. Since the new generation production scheduling system is incredibly fast, it is possible to generate a wide variety of what-if scenarios. You can adjust the priority of the constraints and witness the impacts in near real-time. Modern APS systems even have built-in tools and dashboards to help you compare different scenarios against one another and look at how they satisfy your production KPIs/requirements.
True optimization seems like a no-brainer. So why isn't every manufacturer using this type of scheduling system? The reason is that in the past true optimization systems were extremely slow. Computer processors and legacy algorithms were just not powerful enough to process the massive quantities of data involved in true optimization to be useful in a production setting.
Today there is a new APS technology that is already running in hundreds of factories around the world. It is called optimization-driven scheduling, a patented scheduling technology by Optessa, that arrives at schedules many times more accurately than heuristic solutions in seconds.
If you are interested in learning how optimization-driven technology can help you improve your production scheduling, contact us for a free custom demo.