Industry
In a manufacturing plant, the margin is often set by a quotation that only one process engineer knows how to prepare
The AI Business Center Foundation (AIBC) is a think tank and advisory partner that helps the boards of construction, industrial and technical-services companies adopt artificial intelligence so that it delivers measurable results and builds competitive advantage. In manufacturing plants we start with the path from enquiry to order.
The production line is no longer the bottleneck. The office in front of the line has become it
Many plants combine automated production with a manual office. An enquiry passes through sales, process engineering, planning and purchasing, and the decision on price and lead time depends on the knowledge of one or two process engineers. When they are busy, the quotation waits. When they get it wrong, the error surfaces on the shop floor or in a complaint.
How cost and risk arise
- quotations for non-standard enquiries that depend on the availability of a few people,
- orders passed to production with an incomplete specification,
- complaints whose cause lies in information, not in the technology,
- the know-how of process engineers and foremen, recorded in no system,
- the same parameter named differently in the sales system, the ERP and the technical documentation.
Enquiries increasingly arrive with a specification whose first version was drafted by an AI assistant on the customer’s side. Parameters, standards and references to specific manufacturers in such an enquiry are worth checking before they reach the quotation. We describe the phenomenon in the analysis Specifications drafted by language models.
The first step
The path of one type of enquiry to an order: from receipt of the enquiry, through quotation and confirmation, to handover to production preparation. We measure the time and the number of queries passed back, describe the process with its owner and select a process translator in process engineering or sales.
What the Foundation does
- 30-day readiness assessment: readiness scorecard, map of friction between departments, pilot specification.
- Semantic transformation, that is, organising a company’s names, processes and data so that people, language models and board reports understand them the same way: a shared vocabulary of product, order and parameter for sales, process engineering and production.
- GenAI competence: programmes for sales, process engineering, planning and quality.
- AI value report for the board: time from enquiry to quotation, rework, complaints with an information-related cause.
Frequently asked questions
Which process in a plant is worth organising first?
Usually the quotation of non-standard enquiries or the handover of an order from sales to production preparation. Both processes have an owner, data in the systems and a measurable lead time, and both determine the margin and the number of complaints.
Does this apply equally to heavy, process and light industry?
The mechanism is the same: knowledge of how the plant quotes, plans and solves problems sits in the heads of a few people. The processes and data differ, which is why we always base the assessment on the processes of the specific plant rather than on an industry template.
How can a plant measure the effect?
By the time from enquiry to quotation, the number of queries passed back between departments, the share of orders handed over with an incomplete specification and the number of complaints with an information-related cause, measured before and after on data from the plant's own systems.
Let’s start with an assessment
Let’s talk about the path from enquiry to order in your plant.