RSMUS.comData driven production scheduling
In many manufacturing enterprises, effective utilization of installed capacity is of paramount importance. One of the key factors in determining the efficient fulfillment of production requirements is the precision and versatility of order scheduling. Accurate knowledge of product demand as well as having flexibility in order scheduling will minimize changeovers and will maintain a constant flow in the production process.
A high degree of integration among information systems such as production control and material requirement planning platforms can improve production efficiency by facilitating information flow from various sources and positively influence the decision-making processes. In such environments, forecasting algorithms can be leveraged to maintain correct inventory levels, schedule human resources, ensure equipment maintenance downtime, and improve production orders by making them more effective.
How to schedule production with an ERP system
Companies consume, analyze, and produce data; such data can be classified as internal and external. Internal information is the product of activities conducted inside the company; bills of materials, production routes, and performance metrics are some examples. External data, on the other hand, is provided by actors outside the company. Demand and supply information coming from customers and vendors are examples of such a source. Information including demand and supply forecasts can be used to process historic data to predict future market conditions. Job scheduling and capacity planning are processes manufacturers use to consume internal information to create an orderly and optimized production plan. Functions such as the ones mentioned here can be combined to consume internal and external data to produce a more efficient flow of materials and resources in production facilities.
When a company has a sufficient set of data, forecasting algorithms based on machine learning can produce predictions of future supply/demand data. Such information is key to being able to request material necessary for production and sale way ahead of when such materials are required. Additionally, having a better visualization of what is needed can assist purchase departments to obtain better prices by bulk purchasing products. Lastly, products can be scheduled to be delivered to specific production facilities to minimize transport and logistical costs when produced goods are ready to be delivered to customers.
Forecasting and properly scheduling production orders requires a great deal of time and computing power. In the modern environment, however, cloud computing can be leveraged to unleash massive computational power without the need to slow down end user interactions with ERP platforms. These two characteristics of cloud computing provide companies with the capability to use an elastic model in which companies only pay for the computational power they use. No extra hardware nor human resources need to be purchased to accommodate forecasting needs. In addition, processes run in the cloud do not slow down daily tasks from users. These computationally intensive processes can be run during working hours and be available as needed. Having the ability to conduct analysis at any time opens the possibility to perform data examination and decision making in real time.
Current ERP platforms can take advantage of forecasting information to automatically create production orders, purchase orders, and transfer orders to fulfill future customer needs. These platforms can also create relations between sales demands and orders to fulfil such demands. These relationships are used to accommodate a dynamic environment in which customer requests may change. In such cases, changes on customer requests trigger changes on orders or warnings to involved parties to take corrective actions. In addition, systems for order sequencing can be set in place to drive order processing in a way to minimize change overs and excessive clean up during production.
Future Data Points
Another data driven production scheduling technique relies on the use of industrial IoT (Internet of Things) sensor provided information. Information provided by these sensors can convey production process states. This information can be used to orchestrate an influx of raw materials or components as well as to alert of incoming actions that need to be taken for predictable and orderly production scheduling. In addition to assist with the orderly flow of materials, IoT can also provide equipment maintenance requirement information in a periodic fashion. Such information can be analyzed using artificial intelligence algorithms to determine potential maintenance times. Knowing when equipment will require maintenance can help plan equipment down time when it less impacts production. Also, it helps to avoid unplanned machine down time due to malfunctions.
Bringing it all together
As one can see, data driven production scheduling and production processing in general has many benefits. Control in operation flows, flexibility to accommodate changing needs, and guide production based on customer trends among others. Equally as critical as collecting and storing internal and external data is the ability for an organization to easily consume this information and make actionable decisions form it. Information produced and consumed by production facilities should be reviewed and analyzed periodically to accommodate changing needs. It is therefore required to have a visualization platform that will be able to store data and present such data in a dynamic and user-friendly fashion. Tabular models and data warehousing solutions can be put in place to provide near-real-time information for decision making.
We currently have a rich technical ecosystem of cloud-based technology to guide production in the most efficient way. This technical toolset paired with the breadth and depth of knowledge provided by RSM consulting teams can help companies to obtain a competitive advantage in the production arena for years to come.
