Artificial Intelligence in Production Software: Benefits, Limits, Costs

Artificial intelligence is no longer a topic for large factories only. Every manufacturer that keeps its orders, work orders, stock and machine records in software is asking how to get more out of that data. At the same time, because the subject is discussed so much, expectations easily run ahead of reality. This article explains in plain terms what artificial intelligence in production software is, what it is not, where it really helps and roughly which cost items it brings.

What is artificial intelligence in production software?

Artificial intelligence is the general name for components that work inside or next to production software and draw conclusions from recorded data. In practice two kinds stand out. The first are prediction models that work with numbers: they look at past order, production and downtime records to forecast the next period or to flag an unusual situation. The second are language models that work with text: they take a question in plain words and answer it, or read a document and summarise or extract the information in it.

Neither is a product on its own; both are capabilities added on top of existing production tracking software. The data is still produced and stored by the production software, and artificial intelligence helps to interpret it.

What artificial intelligence is not

Setting the right expectation matters as much for the success of a project as the features themselves.

• It does not replace production software. If orders, work orders and stock are not recorded, there is no data to interpret.
• It does not take over decisions. It makes suggestions; approving the plan, accepting the order and carrying the responsibility remain human tasks.
• It is not always right. A forecast can be wrong and a language model can produce a plausible but incorrect answer. Output must be checked.
• It is not something you set up and forget. As products, machines and customers change, the model needs to be reviewed.
• It does not fix a disorganised process. In a workshop where records are entered late or incompletely, artificial intelligence only speeds up the error.

Practical use cases in production

The following are typical examples where artificial intelligence can be considered by businesses that keep regular data.

• Demand and due date forecasting: Using past orders to foresee the workload of the coming period and a realistic delivery date.
• Planning suggestions: Sequencing suggestions that weigh mould changes, due dates and machine load together when assigning work orders to machines.
• Maintenance and downtime: Noticing the risk of a failure in advance from trends in downtime and maintenance records.
• Quality and scrap: Analysing the causes of scrap by product, machine and shift; with suitable infrastructure, catching defects from images.
• Stock and purchasing: Suggesting when and how much to order according to the rate of consumption.
• Questions in plain language: Answering a question such as "Which orders were delayed most last month?" without searching for a report screen.
• Document reading: Producing a draft order from an order received by e-mail or as a PDF and presenting it to the user for approval.
• Cost deviation: Warning when a product moves outside its expected cost.

Not all of these suit every business. Which one is useful depends on which data has been kept, for how long and how consistently.

Data first: the precondition

Most artificial intelligence projects get stuck on the data, not on the model. If the opening and closing times of work orders, production entries, reasons for downtime, scrap quantities and subcontracting movements are not recorded regularly, there is no foundation to forecast from. That is why the first investment is usually not artificial intelligence but production software that keeps records in order.

Our Production Software brings the process from order to shipment together in one place; order and due date tracking, machine planning, production tracking, subcontracted operations, stock and cost records accumulate in the same database. These records are also the raw material of any artificial intelligence application built later.

Approximate cost items

The cost of artificial intelligence is not a single price; it is the sum of several items. The figures below are meant to give a sense of scale and are not a quotation. Prices vary by provider and change often.

• Ready-made assistant subscriptions: General-purpose assistants are mostly sold for a monthly fee per user. Individual and team plans start roughly in the range of 20 to 30 US dollars per user per month and rise for enterprise plans.
• Usage-based fees: Language models embedded in software are charged by the amount of text processed. For a small or medium-sized business making a few hundred queries a day, the monthly amount usually stays between tens and a few hundred dollars. Long documents and heavy use increase it.
• Integration and development: Usually the largest item. A single use case, such as reporting in plain language or reading order documents, is a piece of work measured in weeks. Broader work such as forecasting and planning can take months.
• Data preparation: Cleaning old records, merging codes and filling gaps is often underestimated, yet it can take a significant part of the project time.
• Running on your own server: If data must not leave the company, the model can be run in-house. This requires a server with graphics processors; the hardware investment can reach tens of thousands of dollars, plus electricity and maintenance.
• Maintenance and monitoring: Regular checking of output and updating of the model is an ongoing expense, not a one-off.

In short, a small pilot can be tried on a low budget with a subscription and limited development. What really increases the cost is not the fee for the model but the effort spent adapting it to the processes and data of the business.

How is the return measured?

An investment in artificial intelligence should be judged by a measure chosen before starting. Time spent on planning, the number of late orders, unplanned downtime, scrap quantity or the time spent on order entry are examples. Unless before and after are compared with the same measure, whether there was a benefit remains open to debate.

Rather than allocating a large budget to a benefit that cannot be measured, a short pilot aimed at a single problem gives a healthier result.

Where to start

• Set up record keeping first: orders, work orders, production entries, downtime and scrap should be kept in software.
• Choose the single problem that costs the most time or money.
• Check whether you have enough reliable data for that problem.
• Start with a small pilot and evaluate the result with the measure you chose.
• Always have the output approved by a person.
• Decide at the outset which data may leave the company.

Conclusion

Artificial intelligence does not replace production software; it is a helper that makes use of the data the software collects. Set up well, it speeds up planning, tracking and reporting; started with the wrong expectation, it only produces cost. The first step is orderly data. To bring your production processes on record you can review our Production Software page, and for a solution specific to your business you can request a quote.