Through the long centuries of man’s existence, man has always produced materials and products for specific uses. But at the turn of the 17th century, something interesting happened. Man had built industrial equipment for the first time which ushered in the age of industrialization. This age came with larger facilities dedicated to every aspect of the production lifecycle as we know it today. With these large facilities came the need to manage hundreds of workers, the transportation of materials, and the stages of production for a product. And as early as the 1800s, the need for production scheduling methodologies was apparent.
This need led to the development of scientific management processes by legendary figures such as Henry Gantt. In the 1800s, charts and manual data collection techniques were introduced to manage production scheduling challenges. Although these solutions worked perfectly with the industrial equipment and facilities of that age, advancements in production technology made them redundant by early 1900s.
Moving forward to the 80s, production scheduling was being defined as the process of planning to ensure the raw materials and production capacity within a facility are optimally allocated to meet demand. With time, this definition was updated to account for complex tradeoffs between competing priorities and the hundreds of varying relationships that occur on manufacturing shop floors.
To handle these complex trade-offs and production variables, advanced planning and production scheduling systems where developed. These systems or solutions were fondly called APS solutions and they accounted for the materials available for a production cycle, available labor and production capacity. APS systems successfully handled the scheduling of complex production processes by applying a constraint-based approach to scheduling. Thus, these tools created schedules for:
- Capital-intensive production process where constraints such as equipment and plant capacity where constraints to deal with
- Production processes where hundreds of components needed to be assembled when building the product.
- Production processes with changing schedules which were not predicted at the beginning of the process.
The success of production scheduling systems also led to the creation of hundreds of enterprises offering APS solutions and services to ease complex scheduling activities. Other spin-off solutions such as customer relationship management applications and enterprise resource planning solution were also developed due to the success of production scheduling systems.
As with most great technological advancements, the traditional product scheduling solutions began to meet more complex situations than it could handle due to the changing manufacturing landscape. These changes are both technological and conceptual in nature. In terms of technology, the advent of Industrial Internet of Things, smart manufacturing equipment, and automation were changes traditional scheduling software could not deal with. While the conceptual changes include the need to account for all data produced on the shop floor, make predictive analysis, manage disruptions in real-time, and cybersecurity challenges among others. These changes limited the efficiency of production scheduling software in diverse ways which will be further explored.
The Limitations of Production Scheduling Solutions
The limitations of production scheduling tools are all due to the increased complexity of today’s manufacturing and industrial facilities, as well as, the demand for more insight by enterprises. These limitations include:
The ever-changing processes in modern manufacturing facilities and the introduction of new equipment and process to the shop floor must be integrated into a functional scheduling system. The ability of traditional production scheduling tools to adapt to these changes is limited which means the schedule they produced will be skewed.
Challenges Integrating Real-Time Occurrences
The effects of downtime in manufacturing and industrial facilities have been highlighted in hundreds of reports. Downtime can be caused by a variety of issues but for the topic of production scheduling, a machine going down in a shop floor is the perfect scenario. Production scheduling tools will struggle to predict this event or even take it into account to reschedule events in real-time.
Although production scheduling tools can create schedules that take into consideration defective equipment, they make use of approximated data. This means the schedule they produce are static in nature and would not take into consideration real-time data such as the location of the machine, output at its workstation etc.
Requires Numerous Adjustments
This constraint is a follow up to the challenges production scheduling tools have with integrating real-time occurrences. To prevent trashing the systems integrator must create multiple custom algorithms for different scenarios. This means the product scheduling tool takes these algorithms and try to apply them to a new problem within a facility. To accomplish it multiple adjustments must be made to the initial adjustment which defeats the ability to create reschedule in real-time. According to Oracle, this challenge means the traditional product scheduling tools will struggle with finding good solutions to scheduling problems even when they exist.
With these limitations, a new process to accurately manage production scheduling tasks was needed. This led to the paradigm shift from traditional production scheduling solutions to simulation-based scheduling. Simulation-based scheduling involves the imitation of the operation of a real-world process over time using a digital model. The process involves building a simulation model of the physical process and populating the model with the detailed events and processes that occur in the real-world. The simulation model can then be run to produce an optimized production schedule.
The Impact of Simulation-Based Scheduling
It is important to note that simulation-based scheduling can be handled in two ways. These are through a discrete event simulation and a continuous simulation process. The discrete event simulation models the operation of a manufacturing or industrial facility as a discrete sequence of events that occur with time. In this model, events occur at a particular instant in time and record the change of state in the facility.
On the other hand, continuous simulation models continuously track the events and the changes they produce in the facility. Both the discrete event simulation and continuous simulation model take production scheduling to heights traditional production scheduling tools cannot reach. This paradigm shift has made real-time production scheduling more accurate and flexible enough to deal with the changes that occur in modern facilities.
As stated earlier, the introduction of production scheduling tools led to the development of other complementary technology solutions and this is also the case for simulation-based scheduling. One such concept is simulation-based Digital Twin solutions. The Digital Twin involves the mirroring of physical objects to create a virtual model through simulation-based engineering tools.
The ability to create Digital Twins of every facility and industrial process also takes simulation-based scheduling to new heights. Creating virtual mirrors of real-time systems or facilities and simulate the complex process that occurs in these facilities to create a far more accurate schedule than traditional production scheduling tools.
In the case off dealing with downtime, simulation-based digital twin environments can collect data from real-world sensors and use the data to predict asset –manufacturing equipment—behavior. This allows for the scheduling process to account for defective equipment and quickly reschedule the production process around the defective equipment. Also, simulation-based scheduling tools can manage what-if scenarios better than the alternative. Making it possible for operations teams to simulate possible challenges and create optimized schedules that take these constraints into consideration.
An example of how simulation-based scheduling alongside digital twin technology has been used to develop more efficient schedules. Is in the case of CKE Restaurants. Here, a Digital Twin of the restaurant facilities made it possible to create implementation schedules, supply and delivery schedules in its kitchen facilities. The end result was a more efficient production and service process driven by simulation-based scheduling and Digital Twin solutions.
How Simulation-Based Scheduling Transverses through Diverse Industries
Traditional production scheduling tools were designed and developed primarily for use in manufacturing settings and this still remains its key area of application. Unlike production scheduling, simulation-based scheduling can be integrated into any industrial process to produce accurate schedules.
Once again, its affinity with Digital Twin technology makes this possible. This is because, with digital twin technology, every process and asset in an industrial setting can be modeled and brought into a digital environment. The integration of simulation-based software in this digital environment can then simulate the industrial process and create schedules for them. Simulation-based scheduling can be used in the healthcare industry, pharmaceutical facilities, dockyards, ports, and in every facility where a process can be modeled and mapped.
The rise of Industry 4.0 manufacturing facilities and processes where data is king provides another avenue for simulation-based scheduling to prosper. Smart factories are being run by machines and devices with sensors, embedded systems, and system on modules solutions. This makes it possible to assess data from every asset and process in a facility.
Simulation-based scheduling software can leverage the data collected in an Industry 4.0 – compliant facilities to create real-time schedules. Computing simulations of schedules can also be achieved in real-time with increased accuracy due to the widespread availability of data in facilities that integrate Industry 4.0.
Simulation-Based Scheduling and the Road Ahead
The paradigm shifts from production scheduling solutions to simulation-based scheduling is still very much an on-going journey. This is due to emerging technologies which complement and enhance the use of simulation-based scheduling software. Examples include the rise of cloud-computing and high-performing computers (HPCs). These technologies make it possible to create models of very complex systems such as facilities or processes with thousands of variables while producing accurate scheduled for them.
The combination of these technological process will enhance real-time scheduling and rescheduling as we know it. As simulation-based schedule software leverage on the cloud and HPCs, complex simulations can be done in micro-seconds thereby delivering accurate real-time results that enhance productivity in industries. Thus completing the paradigm shift from manual and constraint-based scheduling to a responsive real-time scheduling era.