Production Planning
Assembly Line
🧠🗓️ Explainable production planning under partial observability in high-precision manufacturing
Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps require to use non-deterministic models to represent production and measurement tolerances. This paper demonstrates how to effectively represent these manufacturing processes as Partially Observable Markov Decision Processes (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find that a tradeoff between the quantitative performance of the solution and its explainability is required. In a nutshell, the paper elucidates the entire process of explainable production planning: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of machine learning research and as an illustration for high-precision manufacturing practitioners.
Using ML For Improved Fab Scheduling
The exact number of available tools for each step varies as tools are taken offline for maintenance or repairs. Some steps, like diffusion furnaces, consolidate multiple lots into large batches. Some sequences, like photoresist processing, must adhere to stringent time constraints. Lithography cells must match wafers with the appropriate reticles. Lot priorities change continuously. Even the time needed for an individual process step may change, as run-to-run control systems adjust recipe times for optimal results.
At the fab level, machine learning can support improved cycle time prediction and capacity planning. At the process cell or cluster tool level, it can inform WIP scheduling decisions. In between, it can facilitate better load balancing and order dispatching. As a first step, though, all of these applications need accurate models of the fab environment, which is a difficult problem.
The GlobalFoundries group demonstrated the effectiveness of neural network methods for time constraint tunnel dispatching. The relationship between input parameters and cycle time is complex and non-linear. As discussed above, machine learning methods are especially useful in situations like this, where statistical data is available but exact modeling is difficult.
The setup matrix for production optimization
A setup matrix is a powerful tool for detailed optimization of production for recurring lots of similar or identical products. The challenge is to determine the transition times from one product to the next with sufficient precision and to provide the data for a planning system.
The Benefits of Production Stabilization and the Sorcery of the Product Wheel
Volatile demand is everywhere, and companies facing it typically choose between two options: One, attempt to meet demand as it arises (chasing the volatility). Two, maintain a certain inventory level as a buffer from volatility. Of course, there are situations where option one is viable, but option two is the one that most companies take. Still, pursuing the benefits of production stabilization, even in this environment, is worth the effort.
The product wheel is a framework for consuming capacity by making specific products – on a particular asset, in fixed quantities – over a defined time horizon. Therefore, the ability to populate the wheels with products that can conform to smooth production is essential. Determining which products work with this strategy and which don’t is an analytical effort requiring product segmentation, statistical forecasting, replenishment policy selection, and inventory parameter development.
The Next Revolution: Industry 4.0 in the Intelligent Enterprise
Which companies benefit from being able to automatically control the entire supply chain through machines and sensors?
“Auto-control” management means saving effort in manual processes along the entire supply chain and realizing the full potential of intelligent machines and sensors. Businesses in Europe in particular are creating opportunities here – their strength is traditionally more in customer-centric manufacturing, rather than mass production. However, standard products also benefit from flexibility. The global crisis of supply and logistics poses challenges for every manufacturer. Only those who dynamically parameterize production to deploy alternative materials and processes at the push of a button will win the global race for capacity and resources.
Part Level Demand Forecasting at Scale
The challenges of demand forecasting include ensuring the right granularity, timeliness, and fidelity of forecasts. Due to limitations in computing capability and the lack of know-how, forecasting is often performed at an aggregated level, reducing fidelity.
In this blog, we demonstrate how our Solution Accelerator for Part Level Demand Forecasting helps your organization to forecast at the part level, rather than at the aggregate level using the Databricks Lakehouse Platform. Part-level demand forecasting is especially important in discrete manufacturing where manufacturers are at the mercy of their supply chain. This is due to the fact that constituent parts of a discrete manufactured product (e.g. cars) are dependent on components provided by third-party original equipment manufacturers (OEMs). The goal is to map the forecasted demand values for each SKU to quantities of the raw materials (the input of the production line) that are needed to produce the associated finished product (the output of the production line).