Part 1 of a 3-part “Manufacturing Fitness” series
Any business that makes and delivers physical products can be likened to the human body. At the heart of the business is its manufacturing operations. The veins that connect to and flow from this manufacturing heart correspond to the digital supply chain, which carries the lifeblood of the organization throughout the business.
This “business body” wants to remain fit. In manufacturing and supply chain management, fitness means efficiency. Whether you’re operating on a traditional mass production model or something closer to “mass individualization” (where you serve the customer segment of one, profitably), efficiency is the name of the game.
Organizations can only increase efficiency (or fitness) with a concerted initiative focused on continuous improvement at scale. The “at scale” aspect is important – it means operational efficiency across your global manufacturing network. Just as building up your left arm to the exclusion of your right arm makes little sense, efficiency at one plant and not others (or across all facilities) is not enough. You want to improve the health of the entire body.
But let’s push the analogy a bit further: whereas manufacturing and supply chain management together make up the pulmonary system of the business, planning can be seen as the brain. The planning function needs to coordinate activities across all phases of the design to operate (D2O) lifecycle. This means understanding what’s happened in the past, what the requirements are in the here and now, and what’s coming next.
Traditional analytics and planning tools can provide visibility to the past and the present, but to see what’s coming, you need predictive analytics. One important source of predictive analytics has to do with all the other vital organs that make up your manufacturing operations – namely your manufacturing equipment. If continuous improvement at a global scale is the goal, then a key metric to look at is overall equipment effectiveness (OEE).
As the term implies, OEE is a measure of the effectiveness of all equipment in your manufacturing and supply chain network. To manage OEE across global manufacturing operations, equipment data needs to be moved to the cloud where it is accessible centrally to better serve the planning function. With OEE data in the cloud, it becomes possible to improve quality and yield, optimize resources, and increase asset reliability.
Let’s take a look.
Predictive quality and yield management
With a consolidated view of OEE across all areas of manufacturing and supply chain operations, your organization can correlate multi-tier data on machine health, material positions, process performance, and much more.
Using predictive analytics based on this data, you can then identify patterns to uncover quality and yield issues that might not be apparent to the naked eye. Perhaps machine performance is at the root of persistent quality issues. Maybe it has more to do with inferior materials as inputs into the process. Or maybe it’s the process itself. Insights from predictive analytics can help you continuously improve so that you can meet the quality expectations of today’s customers while still maximizing your yield to drive profits.
Classic material resource planning (MRP) involves forecasting based on traditional methods where past experience and foreseeable demand play the most important role. But in an increasingly volatile market made up of fickle customers empowered by information, the practical limits of traditional forecasting are all too evident.
This is why leading organizations are moving to demand-driven replenishment (DDR), where planners set strategic inventory decoupling points throughout the supply network to buffer against volatility and compress lead times. Based on real-time demand visibility rather than forecasts, planners can now auto-prioritize purchase orders and pull inventory from buffer to buffer as required. This gives you greater flexibility to respond to demand volatility.
Improved asset reliability
With predictive maintenance, your organization can get more out of your equipment investment while optimizing processes and minimizing asset downtime. With IoT sensors delivering asset operational status data back to mission central, operators or customers can assess asset health indicators and real-time performance.
Based on this data, you can then apply machine learning algorithms to detect anomalies, uncover leading failure indicators, and evaluate the effectiveness of maintenance strategies in light of this insight. The ultimate goal is to optimize maintenance by prioritizing work, planning resources, and doing service only when it’s necessary – down to the individual machine.
Predictive OEE is good for your business health
Analogies, especially those about business, are never fully consistent. Take, for instance, the fact that unlike the body, modern manufacturing never rests. When manufacturing operations are 24×7, the business can never rest.
OEE as a best practice for manufacturing and supply chain operations can help you track the equipment that production and productivity depend on in an always-on manufacturing environment. Based on this data – accessible in the cloud across global operations – you can apply the intelligence and analytics that enable you to continuously improve at scale. Stay healthy.
For more information, download the latest IDC report on Digital Manufacturing.