Push-based production planning makes greater benefit of advanced analytics compared to pull-based production planning and control. The reasons for this are manifold, but include the fact that push-based production planning involves forecasting of demand and subsequent production and capacity planning based on this demand. Advanced analytics techniques are used to improve the accuracy of demand forecasting. Advanced analytics also comprise prescriptive analytics. E.g. in the form of production plan optimization and optimized scheduling using mathematical programming.
LEAN is popular, and promotes pull – but push is not bad
Both push and pull production planning are widely used and effective methods in manufacturing industry. LEAN is a management philosophy that aims to improve efficiency and eliminate waste in all aspects of a company’s operations, including production planning and control. LEAN has become very known and has many prominent advocates. Pull-based production planning and control methods are often seen as more consistent with the principles of LEAN, as they focus on producing goods only in response to customer demand, thereby reducing inventory levels and minimizing waste.
However, this does not mean that push-based production planning and control methods are inherently bad. Push-based methods can be effective in certain situations, such as when there is high demand variability or when producing in large batches is more efficient. The choice between push and pull-based production planning and control methods should be based on a careful analysis of the specific production processes, market demand, and company objectives.
The benefits of push-based production planning
Push-based production was the standard in manufacturing industry for many years. It has become deeplz ingrained. It is, still, often a manager’s natural choice. The reason for this is, to some degree, historical. But, push-based methods are also affective at handling demand variability.
Push-based production can be better suited to handling demand variability than pull-based production. Push-based methods do not require the same high degree of communication and coordination between supply chain entities.
Push-based production can also help manufacturers achieve economies of scale by producing larger quantities of products. This can result in lower unit costs and improved profitability. In addition, push-based production may be better suited to complex products, where there are many components and a high degree of customization. In these situations, it can be challenging to coordinate production with demand, and push-based production is in such cases are more practical solution.
Another significant benefit of push-method is furthermore that they are much better at applying predictive and prescriptive analytics. Push-based production planning is all about planning. Demand is translated into requirements, and the requirements is translated into capacity plans and production schedules. Mathematical programming, predictive analytics, and machine learning can be used throughout these stages to improve planning even further. Pull-based methods, on the other hand, do not make use of advanced analytics to the same extent and, primarily, rely on effective coordination and communication.
PUSH production benefits from advanced analytics
The main reason for why push-based production planning is a better match for analytics is that it sets production plans and schedules in advance. These plans and schedules are defined based on forecasts and estimates of demand. This availbility of data makes it possible to apply mathematical optimization and simulation techniques to the planning and scheduling itself.
For example, predictive analytics techniques can analyze historical sales data to identify patterns and trends, and then use that information to forecast future demand. This can help manufacturers optimize production planning by identifying which products are likely to have high demand and allocating production resources accordingly.
Additionally, push-based methods often involve producing goods in large batches, which can create opportunities for optimization through advanced analytics. For example, optimization algorithms can be used to determine the most efficient way to allocate production resources across different products and production lines, or to minimize production costs while still meeting production targets.
In contrast, pull-based methods rely on real-time demand signals to trigger production, which can make it more difficult to optimize production planning using advanced analytics. However, there are still opportunities for optimization in pull-based methods, such as using machine learning algorithms to analyze real-time demand signals and adjust production planning accordingly.
Concluding remarks and related content
Pull-based methods for production planning and control are often associated with LEAN manufacturing philosophy. LEAN has become increasingly known and the trend in recent years has been towards increased use of pull-based methods. But, while some consultants might refer to push-based methods as being bad, they are in fact very popular and effective. They are more effective at achieving economies of scale, they are still preferred when there is a lot of variability or when the product is very complex. A significant advantage of push-based methods is furthermore that they are, generally, a better match for predictive and prescriptive analytics. That is because push-based methods are based on planning. And planning makes use of mathematical programming for optimization. Pull-based methods are based on reaction, coordination, and communication. They do not rely on a demand forecast, they do not derive production schedules and capacity plans from forecasts. And therefore they cannot to the same extend use analytics for improving their forecasts, plans or schedules.
Here are some applied analytics examples in the context of push-based production planning:
- Link: Optimization via master production scheduling
- Link: Price and inventory optimization
- Link: Job shop scheduling challenges
- Link: Optimized SCM capacity scheduling
- Link: Simulation-based capacity planning
- Link: Excellent job shop scheduling does not require fancy tools
Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python
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