Artificial Intelligence: what contributions for industrial development? – Silicon

The inefficient search for resources resulting in a significant loss of time constitutes a first obstacle to the development of a project.

If the process of developing a project is, in theory, quite effective, practice shows the opposite. Respecting the specifications, in particular the budgetary aspect and the deadlines, can prove to be a difficult task. Companies can face two main difficulties.

The inefficient search for resources resulting in a significant waste of time constitutes a first obstacle to the development of a project

Large industrial companies driving complex projects need to build teams based on particular expertise and skills. However, finding the expertise you need can be tricky: it can be hidden in documentation, buried in SharePoint, or lost in a private conversation on Slack.

In addition, team leaders often recruit people they know, which sometimes goes hand in hand with insufficiencies in terms of knowledge and therefore efficiency.

Beyond knowledge gaps, cross-team collaboration is not always successful. Each has different information, uses disconnected systems to carry out the tasks entrusted to it, and employs a particular terminology to designate identical things, which inevitably creates confusion – not to mention the multiplicity of languages ​​spoken by employees scattered around the world. In summary, sharing knowledge and information is in many cases a real headache.

As a result of this complexity, teams spend a lot of time searching PLM, CRM software and other collaboration tools for the information they need to manage their project. Often, this quest leads to such a level of frustration that teams move forward without having the critical data that could support their hypotheses, or simply start from scratch. Such approaches result in even greater duplication of content, along with an increased risk of errors and delays.

The imprecision of the results and the interminable sorting of information related to it constitute a second obstacle to the realization of a project.

In manufacturing, a company can employ 30,000 people and sometimes more, manage dozens of operations around the world, and use hundreds of different applications. These companies produce tens of millions of documents which are then stored in countless systems, locations and formats.

Simply managing product information requires a host of lifecycle management (PLM) systems, enterprise resource planning (ERP) and product portfolio management (PPM) solutions. In fact, it’s not uncommon for one piece alone to generate up to three million documents.

In addition, many manufacturers store their documentation for several decades. But over time, part numbers change, processes evolve, and issues are resolved, which multiplies the number of versions of the same document and makes it significantly more difficult to find reference information.

Without using an intelligent search solution, it is practically impossible to bring out all the relevant information buried in the various silos. Conversely, employees recreate documents and solve the same problems over and over again, either because they do not know that a solution already exists, or because it is too difficult to find it.

It is thus possible that the same part is designed and built several times because it is too difficult to launch a search. What’s more, business proposals are often written from scratch because workable versions are inaccessible, and teams waste valuable hours solving a problem that has already been addressed.

In addition to a worrying accumulation of information, this type of behavior affects productivity and, even worse, risks causing delays that could result in legal action.

The use of Artificial Intelligence makes it possible to avoid the extension of deadlines and budgets

The question is how to exploit the huge amount of data and information generated by manufacturing companies. If manufacturers can connect silos of data and provide their employees with a single point of access to locate what they need, productivity will increase while errors and costs will decrease.

Smart search provides the solution to this problem. By using an intelligent search tool, manufacturers can unify their digital repositories, business applications, and communication platforms to provide development engineers with a panoramic view of available information that will help them make the best possible decisions.

By connecting all relevant sources of information to uncover useful documentation, past issues, and available expertise, these companies can reduce errors and production delays that cripple budgets.

A well-designed intelligent search solution should provide engineers with a familiar and intuitive experience. Instead of searching multiple repositories, a single, centralized place makes it easier to discover relevant information, even within unstructured data sources.

These engineers can not only quickly find the answer they need, but also locate experts and skills within the company, increasing efficiency and reducing the risk of duplication.

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Artificial Intelligence: what contributions for industrial development? – Silicon


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