We bring production, quality, maintenance and inventory operations together in a shared data model. The same stoppage, the same lot and the same shift meet in one record instead of four separate places; shift handover is put in writing, and management looks at a single set of indicators.
In a factory, production, quality, maintenance and the warehouse often look at the same event on the same day but write it down in different places. A line stops for three hours: on the production board it appears as a 'stoppage', in the maintenance logbook it is recorded as a 'bearing replacement', on the quality side the defective products from that shift are entered into a separate file, and the warehouse never sees the half-used material left at the line side. At the evening handover the information is passed on verbally, and more often than not incompletely. At the next morning's meeting everyone arrives with their own figure, and the first half hour goes on arguing about which figure is correct. The problem is not that nobody is doing their job; it is that the same event is recorded in four separate places without being linked.
The reason for this fragmentation is usually not that the systems are bad, but that they were bought at different times for different needs. Production tracking goes live one year, the quality form another year, the maintenance record a third. Each uses its own product code, its own machine name and its own shift definition. As a result the same machine is written three different ways in three systems, and the data only comes together when somebody matches it by hand. Manual matching only happens when somebody has the time; when they do not, management sees yesterday's factory a few days late and incompletely. In this picture nobody is lying, but nobody is looking at the same picture either.
Manufacturing operations management (MOM) is the approach that gathers these areas into a single management layer. As defined in the international ISA-95 model, MOM covers four headings under one roof: production operations, quality operations, maintenance operations and inventory operations. A production tracking system (MES) carries out execution on the shop floor; it sends the work order down to the machine, reads the counter, records stoppages and scrap. MOM sits above that execution: it links the quality record to the production record, the maintenance order to the stoppage reason and the material at the line side to the work order, and reads them all through shared definitions of product, lot, machine and shift. In short, MES executes and MOM manages. This distinction is not an academic preference but a practical boundary that determines where the scope ends.
It matters to draw that line honestly, because MOM produces no result on its own. If there is no measurement beneath it, MOM turns into an empty management dashboard; there is no data for it to bring together. That is why we do not recommend MOM directly to a plant with no measurement at all; we recommend collecting reliable data on the production side first. If you already have production tracking, quality or maintenance software, we do not put replacing it on the agenda from the outset either; the MOM layer sits on top of them and establishes a common language between them. The first thing we ask in discovery is which data is genuinely being collected today. The gain does not come from buying new software; it comes from linking four separate records to the same event, and from shift handover moving out of speech and into writing.
Plants where production tracking, quality records, the maintenance logbook and warehouse movements run in different programs or different files. In these businesses each system works on its own but none of them sees the others; where the losses accumulate only comes to light when somebody compares them by hand. Management usually learns which loss came from which department at the end of the period, and incompletely.
Factories working two or three shifts where the handover is done verbally. A problem that occurs on the night shift being passed on incompletely in the morning is a common and expensive situation. In these plants the first concrete gain is that shift handover becomes a standard, recorded procedure. Putting the handover in writing is not an extra burden; it is the recording of work that is already being done.
Managements that want to compare different factories in the same group, or different lines in the same factory. A comparison made without a shared set of definitions is misleading; which plant is genuinely performing better can only be stated with indicators calculated by the same rules. In these groups the work on shared definitions is the step that demands the most effort but is also the most decisive.
Manufacturers audited by overseas customers or large buyers. What is asked for in an audit is usually not a single report but the ability to trace an event: which lot, which machine, which shift, which quality record, which maintenance job. That trail can only be produced if the records are linked to one another.
Definitions for product, lot, machine, line, shift and personnel are gathered into a single dictionary; their equivalents in the different systems are mapped to it. The conceptual distinctions of ISA-95 are taken as the reference. This step looks like technical work on the surface, but it is the real backbone of the project; data combined without shared definitions is data that looks tidy but has been added up wrongly, and it makes the whole report unusable.
Work orders, production quantities, stoppage and scrap records coming from the shop floor are gathered at management level; plan against actual is compared by line and by shift. This layer does not replace the execution screens; it makes the data they produce meaningful and comparable, and gathers loss analysis in one place. Which loss is attributable to which cause thus becomes indisputable.
Quality control records, non-conformities and quarantine statuses are related to the production record through the same lot and machine. So when a defect appears, the shift it was produced on, the machine and the raw material batch are visible without a search. Detailed process management on the quality side is the subject of a separate module; what is built here is the link between those records and production.
Maintenance orders and the work carried out are matched with stoppage records in production. The same three-hour stop is not written twice, once as a loss in production and once as a job in maintenance; it is tracked as a single event. Once that link exists, the question of which failure type a machine's losses come from is answered with records rather than guesswork, and maintenance priorities are set on the basis of that data.
Semi-finished goods, line-side stock and work in process (WIP) are related to the work order; how much material is waiting in front of which job, and where it is piling up, becomes visible. Addressing, picking and counting processes on the warehouse side are the subject of a separate module; here MOM establishes the picture that production and the warehouse will look at together and aligns the figures of the two sides.
At the end of the shift, open issues on the production, quality, maintenance and material sides are collected in a single handover record; the incoming team sees in writing what has been finished, what is still open and what they need to watch out for. Information lost in a verbal handover is a real loss item that goes unmeasured in most plants. The log also produces a record that can be used in an audit.
Production, quality, maintenance and stock indicators are calculated with the same definitions and gathered on a single dashboard; the meeting format in which every department arrives with its own figure comes to an end. How the indicators are calculated is defined in writing, can be seen from the dashboard, and can be drilled down to the raw record when needed. Meetings then discuss not whether the figure is correct but why it is what it is and what action to take; the debate runs on decisions rather than on data.
We map out which record is kept where today on the production, quality, maintenance and warehouse sides. Which data arrives automatically, which is entered by hand, which information is never recorded at all. A consolidation exercise carried out without this map hides incomplete data behind a tidy-looking dashboard and lays the ground for wrong decisions.
A single dictionary is set up for product, machine, line, shift and lot definitions; the equivalents in the existing systems are mapped to it. Conflicting and duplicate definitions are cleaned up at this step and an owner is assigned. The step is tedious, but the accuracy of every later report comes from it; when it is skipped, the problem surfaces months later.
A single line or plant is chosen as the pilot; connections to the existing systems are built and data starts to flow. The figures produced in the pilot area are compared with the figures the departments keep by hand today, and verified. The scope is not widened until every difference has been explained one by one, because the teams' trust in the system is won or lost at exactly this stage.
The shift handover flow goes live, indicator definitions are put in writing and verified together with the teams. During this period the figures are questioned, calculation methods are corrected and objections are dealt with one by one. Teams usually need a few weeks of this tuning period before they begin to trust the dashboard; if it is skipped, the dashboard remains a screen nobody opens.
The verified structure is extended gradually to the other lines or plants; user and manager training is given on site. How indicator definitions are to be updated, and how a new line or system is connected to the layer, is left in writing. After go-live, monitoring, maintenance and support for adding new areas continue.
No. A production tracking system (MES) carries out execution on the shop floor: it sends the work order down to the machine, reads counters from the equipment, captures stoppage and scrap records, and runs the operator screen. Manufacturing operations management (MOM) is the management layer above that, and it places quality, maintenance and inventory operations alongside production. MES runs a single area in depth; MOM manages four areas through shared definitions. A healthy set-up is the two working together; MOM does not replace MES.
No, that is not our starting assumption. Replacing a working system is both expensive and risky. The MOM layer sits on top of your existing systems; it reads data via API, database or file and maps it to shared definitions. If we conclude that a system really is inadequate, we say so plainly as a discovery finding, but the decision to replace it is yours and it is not a precondition of the project.
We do not recommend it, and we say so openly. MOM is a consolidating layer; if there is no data to consolidate, all that emerges is a dashboard filled in by hand, and such a dashboard soon stops being updated. In that situation we recommend collecting reliable data on the production side first, and then digitising quality and maintenance records. MOM is usually built on top of those steps, at the next stage.
It will not. ERP manages orders, stock, cost and the commercial process; MOM is the management layer for operations on the shop floor. The boundary between them is clear: ERP knows what is to be sold and what has been planned, MOM knows what is happening on the floor and feeds that back to ERP. We do not replace your existing ERP (Logo, Mikro, Netsis, Canias or another system — the ERP packages widely used in Türkiye); we build a layer that works alongside it.
We do not give a firm timetable before discovery. In practice, getting the first pilot area to the point where data flows is possible within a few months on most projects; but what really determines this is whether the existing systems give up their data and how scattered the definitions are. If there are many duplicate product and machine records to be matched, the timeline gets longer. After discovery we share the scope and the phases in writing.
A shared data dictionary for product, lot, machine, shift and personnel; production, quality, maintenance and inventory records linked to one another; a single set of indicators calculated from written definitions, and a management dashboard; a shift handover that is put on record; an audit trail with which you can follow an event end to end. The source code, the data model and the operational data that accumulates are yours.
In a 30-minute discovery call we listen to what you need and tell you honestly whether custom development or an off-the-shelf product is the better answer.
We keep machines, plant, vehicles and equipment on a single record from the moment they are bought to the moment they are disposed of. Warranty, criticality, spare parts, maintenance cost and depreciation accumulate on the same asset card; the decision to replace stops being a guess and starts resting on the record that has built up.
Details →In companies that sell machinery and equipment, service work usually starts on the telephone and ends in a notebook. We build the whole chain in a single system — from the service request to technician assignment, from the mobile work order to the parts used and the customer's signature — and track warranty cover and SLA times automatically.
Details →We establish from one place which version of a technical drawing, CAD file, recipe or sample record is the valid one. PDM manages the file and its revisions; PLM extends that across the product's whole life, from idea to serial production, together with change approvals. Whether the drawing that reaches the shop floor is the right drawing stops being a matter for debate.
Details →We take buildings, floors, rooms and desks into a single inventory, and run lease and subscription contract dates, planned building maintenance and the inspection schedule for fire and safety equipment in the same system. The cost of running the facility thus becomes visible by building and by area.
Details →We build a human resources layer that brings employee records, documents, leave and absence, the organisation chart and employee self-service together in a single record. It does not replace payroll; it prepares the data that goes to payroll accurately and traceably, and it connects to the timekeeping and PDKS (time and attendance) side through the same employee record.
Details →We build a system that runs the whole candidate process in a single flow, from drafting the job ad through to the offer and the handover to onboarding. Whichever channel applications arrive through, they collect in a searchable pool, assessments are written on a shared form, and retention periods and data destruction are put on record.
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