Frequently Asked Questions

It depends on the complexity of the operation. In facilities that collect high-frequency signals from numerous machines and require monitoring with second-by-second resolution, a specialized solution might be worthwhile. However, for most medium-sized manufacturers, the main problem isn't the missing functionality, but the disconnect between the two systems : when field data and cost and planning data reside in separate locations, both remain incomplete. Having production execution work with planning and cost data on the same database is, in most cases, more valuable than the marginal benefit of separate specialized software.

Don't try to fix everything at once. The effective method is prioritization: products that generate the majority of turnover, those produced most frequently, and those with the highest costs are addressed first. Once this group is corrected, the system begins to produce meaningful results, and team confidence is built. The remaining products are then gradually corrected as they are produced, based on comparisons of actual consumption with recipe.

There are three sources: time studies, machine manufacturer data, and past actual reporting times. The most practical approach is to start with a reasonable estimate and establish a regular report comparing actual reporting times to the standard . The standard for operations showing chronic deviations is reviewed. Thus, standards approach reality over time. Trying to find a flawless standard from the outset means not starting the project at all.

Resistance is usually justified and stems from one of three reasons: input is too time-consuming, seems unnecessary, or is perceived as a control tool. The solution is, respectively: not requesting data that can be collected automatically from the operator, reducing manually entered data to selectable lists, and allowing the field team to see their own performance on the screen . When the team providing the data receives feedback in return, resistance gives way to ownership.

First, measure how much of your existing capacity you are using. When total equipment effectiveness is calculated, the picture that emerges in most facilities is that a significant portion of theoretical capacity is lost to downtime, loss of speed, and loss of quality. Setup optimization, reducing unplanned downtime, and focusing on bottlenecks often yield capacity gains at a much lower cost than new investment. The investment decision should be made after these losses have been measured.

It's partially applicable, but it leads to significant losses. In process production, the recipe also incorporates process parameters, output is measured through yield, by-products are generated, and batch traceability is essential. These are naturally not modeled in a batch production model. This choice made during setup is one of the most difficult decisions to correct later; the production type must be chosen correctly from the outset .

At its current level of maturity, artificial intelligence can generate schedule recommendations, detect anomalies, predict failures, and evaluate scenarios. However, its quality depends on the underlying data. A model built on uncoded downtime causes, outdated product trees, and incomplete reporting will produce confident but inaccurate recommendations. The correct order is: data discipline first, then artificial intelligence. The final decision, however, should remain with the individual who will bear the consequences.

It can be measured through four components: Capacity: production hours gained by reducing losses. Inventory: reduced raw material and semi-finished product inventory through better planning. Quality: reduced waste and rework costs. Delivery: improved delivery performance and the resulting reduction in customer and penalty costs. In addition, there are gains that are difficult to directly quantify but have a significant impact, such as shorter period closing times and pricing based on actual costs.