Blog about production optimization

Production Planning Integrated with SAP: APS and MES at Sanok Rubber Company

Wed, 28 Oct 2026 00:00:00 GMT

Sanok Rubber Company makes rubber components for the automotive and agricultural machinery industries across Central Europe. Its planners used to build the production plan across several separate Excel spreadsheets, copying data by hand from SAP and from paper records kept on the shop floor, so a finished schedule could already be out of date by the time it was released. VirtusLab implemented Nexelem APS and MES integrated with SAP, and the planner now works from a single schedule that updates continuously as orders actually progress.

Contents:

The company and the scale of the challenge
The problem
The solution
The results
FAQ

The company and the scale of the challenge

Company: Sanok Rubber Company, a manufacturer of rubber components for the automotive and agricultural machinery industries.
Scale of the plant in scope: 340 workstations, 1,200 part numbers, 2,000 routing operations, 5,000 planning tasks and 20,000 technological dependencies.
Scope: Nexelem APS and Nexelem MES integrated with SAP, plus a network of 120 production reporting stations on the shop floor.
Data sources before the project: SAP and paper production records.
Problem: the plan was assembled by hand across several spreadsheets and was out of date before it reached the shop floor.
Solution: APS pulling planning data directly from SAP and MES reporting progress from the shop floor in real time, combined into one schedule that refreshes on a regular cycle.

The Sanok Rubber Company plant runs hundreds of workstations and thousands of part numbers. At that scale the number of possible operation sequences, and the dependencies between them, grows faster than any spreadsheet can keep consistent. That combinatorial load, rather than a wish to “go digital”, was the real reason for the project.

The problem

Before APS went live, planners worked across several unsynchronised Excel files at once. Data had to be copied by hand from SAP (routings, BOMs, stock levels, sales orders) and from paper production records, because some information on order progress existed nowhere else. Keeping the files consistent took so much time that the response to a change in demand or a sudden swing in orders always came a step too late. A plan could be finished and already out of date at the same moment.

Behind that general picture sat specific questions that planners and management could not answer quickly:

Is the plant producing the items that are actually needed most right now, or the ones that happened to be easier to schedule?
Which production resources (machines, tools, work centres) are available right now, and does their load allow today's plan to be completed?
What skills do the people on the current shift hold, and do they match the operations scheduled for today?
Does the plan reflect the real constraints of rubber processing: changeovers and their duration, minimum batch sizes, availability of moulds and tooling?
Is communication between planning and production fast enough for the schedule to respond to deviations during the day, rather than the next morning?

The size of the data alone shows why Excel had reached its limit: 1,200 part numbers, 2,000 routing operations, 5,000 planning tasks and 20,000 technological dependencies between them, spread across 340 workstations.

When no spreadsheet can keep that many links consistent every time a single order changes, even the most experienced production planning specialists need a system to do it.

The solution

VirtusLab implemented Nexelem APS integrated with SAP as the source of planning data, and Nexelem MES as the layer that collects production progress directly from the shop floor. From SAP the system pulls planning data (routings, BOMs, production versions), stock levels, planned deliveries, lead times and material reservations for trial runs, sales orders together with safety stocks and orders from other plants, as well as data specific to rubber processing: alternative machines, simultaneous production in moulds, minimum batch sizes per workstation, machines running non-stop, finished-goods priorities, extrusion groups, and the requirements for and availability of production resources and tools (PRT).

This data passes through an intermediate layer, which can be either Nexelem's own scheduling algorithm or an external solver connected to Nexelem. In this project the client chose the external solver, which Nexelem's open architecture supports.

In the other direction, Nexelem supplies data that SAP does not hold and does not need to: MES production batches with their current status, a staff calendar with primary skills and planned detailed skills, a machine calendar with downtime, and new or modified production batches created during the day.

On that basis the planner works from a Gantt chart view that shows the current production plan and any gaps created during planning, built on a tree of dependencies between operations rather than flat orders. Scheduling rules keep changeovers to a minimum and extend runs wherever the process allows. The system tracks progress and anomalies on the shop floor in real time, compares the plan with actual output and re-optimises the production queue as it goes. The planner will also be able to run a simulation of an alternative version of the plan and compare it with the current one on hard KPIs before approving a change (what-if analysis is part of the target scope of the implementation).

On the shop floor, data flows back into the system through a network of 120 production reporting stations. Operators see their task list there and report progress as often as each workstation requires, and the system shows them how they are tracking against their target, so a question raised during a gemba walk can be answered without calling the shift leader.

“Thanks to the new planning system, each employee knows exactly what to work on and what their priorities are for the day, and the reporting system removes unnecessary paperwork and makes work more comfortable.” — Magdalena Bochnak, Production Department Manager, Final Operations, Sanok Rubber Company

The results

Sanok Rubber Company's production plan now refreshes on a regular cycle based on actual order progress, not only on assumptions made several days earlier. These are the effects observed by the client and the implementation team.

Planning moved from whole orders down to individual operations, updated online rather than once a day.
The plan takes stock levels and material requirements at each workstation into account, so the planner no longer has to check them manually in SAP.
The planner enters and validates changes to the plan in one tool, instead of negotiating priorities with the production shift over the phone.
Planners, supervisors, operators, logistics and maintenance all work to the same priorities, because they all look at the same schedule.
The system flags risks caused by bottlenecks up to 12 months ahead, before they turn into delays.
Yield, material consumption and operation times are reported directly from 120 stations on the shop floor, so the data reflects what actually happened in production, not what someone wrote down from memory at the end of the shift.

If you want to see how production planning integrated with an ERP system such as SAP works in practice, with a large number of part numbers and technological dependencies, read how MRP and production planning fit together, or book a demo of Nexelem or a free consultation using the contact form below.

From Weekly Spreadsheets to Automated Scheduling: Nexelem APS at Tricogen, Phase One

Wojciech Szywalski — Fri, 25 Sep 2026 00:00:00 GMT

Tricogen manufactures a wide range of peroxide, haircare, cosmetic, and personal care products for both professional and retail markets. It operates a manufacturing and production facility in Newton Aycliffe, County Durham, with the capacity to produce up to 6 million bottles per year.

Production planning runs on a cloud MRP system and a set of sophisticated Excel spreadsheets.

The planning process is a complex operation that must balance a number of factors, including material availability, tank and batch storage capacity, changeovers, labour availability, and customer due dates.

Given the complexity of the existing calculation system, ongoing management, review, and execution are required to maintain an optimal manufacturing schedule. To reflect the latest factory flows, stock levels, and demand, the Excel sheets are managed by the experienced production planning specialist and recalculated every week.

The current combined planning system involving an MRP and Excel, leaning on the planner’s experience and operational knowledge of the plant, has supported the business growth for years.

However, checking a schedule against material stock, capacity, and staffing is manual work rather than something the system does on its own, and cannot be automated at the current state.

The complexity of coordinating bulk manufacture, intermediate storage, filling operations, labour resources, raw material availability, and customer demand across multiple product categories is growing rapidly.

The company decided to improve the efficiency of the planning and scheduling process to support decision-making, growth, and production ramp-ups.

Working with VirtusLab, Tricogen has completed the first phase of a Nexelem APS implementation: a working prototype that proves the MRP integration and the scheduling logic before the full system gets built.

Contents:

Tricogen and the scale of the planning challenge
The planning process today: manual, experienced, and time-consuming
Phase one: proving the integration before building the whole system
What phase one delivered
Looking ahead: what phase two will bring

Tricogen and the scale of the planning challenge

Company: Tricogen (Tricogen Laboratories Ltd), a UK manufacturer of peroxide, haircare, cosmetic, and personal care products for professional and retail markets, based in Newton Aycliffe, County Durham, UK.
Scope: a Nexelem APS prototype and interface design connected to a working copy of Tricogen's cloud-based MRP system.
Problem: production schedules assembled by hand across several weekly spreadsheets, with material availability and capacity checked manually rather than in one live view.
Solution, phase one: a proof of concept plus a Figma-based interface design, confirming that work orders, recipes and due dates can move automatically between the MRP system and Nexelem.
Status: Integration feasibility confirmed; phase two, the full APS build, is planned as the next stage.

Tricogen runs a two-stage production model: bulk manufacture of a base or batch, followed by mixing, bottling and filling. Across all filling and finishing lines, overall manufacturing capacity runs to roughly 130,000 units a week, and each product family, peroxides, creams, lotions, styling products, needs its own tank or batch storage between the two stages.

Planning runs on a 12-week rolling schedule plus a further six-week "pipeline" view. None of that is unusual for a chemical and cosmetics manufacturer. What makes it demanding is complexity; how much has to be coordinated at once, and across several product categories: bulk manufacture and the intermediate storage it needs, filling operations, raw material availability, labour and the skills on shift, changeover time between products, and customer demand that has to be met whatever the rest of the picture looks like. Each of those moves independently of the others. Holding them in balance, week after week and at the level of detail a plant like this runs on, is exactly the problem APS technology is built to solve.

The planning process today: manual, experienced and time-consuming

No single constraint governs Tricogen's week. The lines can run around the clock, so labour availability and skills matter a great deal, but so do material availability, tank and batch storage between bulk manufacture and filling, changeover time, and the customer due dates every schedule has to hit. A sequence that suits one of those can create a problem in another, which is why so much of the plan rests on the planning specialist's experience and operational knowledge.

That knowledge is applied through spreadsheets: one per week for the schedule, plus a separate one tracking work order durations, with similar orders grouped by hand to keep changeovers down. The cloud MRP generates its own output, used as a cross-check rather than the base for planning, and demand is built from the schedule and then verified against the warehouse manually.

The approach has supported the business for many years, and orders ship. What it costs is time and visibility. Every cross-check is a manual one: whether materials will be on site when a batch is due, whether the people rostered for a shift hold the skills the job needs, whether resequencing to save a changeover creates a storage clash further down the line. Larger changeovers are generally scheduled for Fridays, which gives the week a natural buffer. The limit is not the quality of the decisions, it is how long they take to make and how much has to be redone by hand when something moves. That is the gap phase two is meant to close: the same expertise, applied to a schedule that arrives already checked against live data.

Phase one: proving the integration before building the whole system

Rather than committing straight to a full build, Tricogen and VirtusLab agreed to validate the riskiest part of the project first: whether Nexelem could reliably talk to Tricogen's cloud-based MRP system, one whose internals sit outside Nexelem's control. The integration method settled on during this phase is a data warehouse layer built from SQL queries against the MRP database. Those views refresh on demand, get exposed through a dedicated API secured with a key, and a small connector application calls that API to move data into an external schema, which Nexelem then imports. The API stays disabled outside of these refresh windows, keeping the exposure small.

Alongside the technical proof of concept, Tricogen's planner worked through a Figma board of the future Nexelem interface: the dual view of a detailed Gantt chart and a week-by-week summary that the finished system will use, before either was actually coded. That let change requests surface as sketches rather than as rework after launch, which matters more than it sounds like it should on a project where the planner's day-to-day workflow is the thing being redesigned.

What phase one delivered

The result of this first stage was not a live production schedule; it was proof that the harder assumptions underneath the project hold up.

Work orders, recipes (BOMs) and due dates moved from the MRP into a working Nexelem prototype without manual re-entry, end to end.
The chosen integration method, on-demand SQL views behind a secured API, matched Tricogen's existing IT setup and security posture rather than requiring a new one.
The Figma interface work gave Tricogen's planner a concrete look at the dual Gantt and week-by-week summary views before development started on the full system, and produced a specific customization list for phase two: per-workstation targets, a bill of materials view flagging warehouse shortages, and the ability to add manual comments directly on the Gantt chart.

Tricogen's Managing Director summed up the collaboration this way:

"Throughout our interactions, the VirtusLab team demonstrated a high level of professionalism, technical expertise, and responsiveness. They took the time to understand our business requirements and operational challenges, ensuring discussions remained focused on practical solutions and achievable outcomes. […] We found VirtusLab to be a knowledgeable, capable, and professional organisation that values long-term customer relationships and works constructively to deliver value." — Managing Director, Tricogen

Looking ahead: what phase two will bring

Phase one answered whether the integration would work. Phase two is where the planning itself becomes automated.

Once complete, Nexelem will pull work orders, recipes, due dates and material availability from the MRP and calculate an optimized schedule that weighs labour availability, material availability and production capacity before a plan reaches the planner's screen. Setup time starts with a simple default of thirty minutes per changeover, treated as a zero-quantity task with its own slot, with a full changeover matrix planned as a later refinement once enough real setup data has been collected.

The planner will still review and approve every schedule, on the same Gantt chart and week-by-week view validated during the Figma stage, but with material shortages and capacity conflicts flagged up front rather than found later in the week. Approved schedules will sync back to the MRP system automatically, closing the loop that today still runs through manual date updates. Tricogen will also get what-if scenario planning, so a rush order or a machine issue can be tested against the live schedule before anyone commits to it, rather than firefighting a disrupted plan after the fact.

If your own planning team is coordinating a schedule across separate spreadsheets and an MRP export, Tricogen's phase one shows a way to test the riskiest assumptions first. You can look at how Nexelem's APS handles constraint-based scheduling, or get in touch to talk through what a similar proof of concept would look like for your own MRP setup.

Just-in-Time (JIT) Manufacturing in 2026: Requires More Than Just Good Intentions

Wed, 23 Sep 2026 00:00:00 GMT

Manufacturers adopt just-in-time methods to free up cash, reclaim warehouse space, and surface hidden waste. The promise is clean: parts arrive as the line calls for them, and finished goods ship before they gather dust. A JIT manufacturing system rewards precision and punishes guesswork, though, so a policy pinned to the noticeboard rarely survives contact with a volatile market. Running JIT in 2026 takes real-time data and tight coordination between every moving part. Read on to find out what is needed.

Table of contents:

What JIT manufacturing means
The benefits of a JIT manufacturing system
Why good intentions are not enough
How APS software synchronizes a just-in-time production system
Making JIT resilient in 2026

Key Takeaways

JIT manufacturing means producing and receiving materials only when they are needed, never before.
The approach cuts inventory costs and waste, but it exposes every weakness in planning and supply.
A written policy cannot keep JIT running when demand and lead times shift by the hour.
Software synchronization through an APS system turns JIT from an ambition into a repeatable process.
Nexelem's Optimal Supply Chain feature orders raw materials against real demand, keeping stock low and lines fed.

What JIT manufacturing means

Just-in-time manufacturing coordinates production so that each component arrives at the moment it enters assembly, no sooner. Toyota shaped the model decades ago through its pull system, where real downstream demand signals trigger upstream supply instead of forecasts made weeks in advance. The core idea swaps large safety buffers for a steady, demand-driven flow of materials.

Under this model, inventory stops behaving like a comfort blanket and starts behaving like a cost. Every pallet in storage ties up capital, occupies floor space, and conceals problems that a leaner flow would surface within hours. The JIT manufacturing meaning that counts on the shop floor is discipline: make what the customer ordered, in the quantity ordered, at the time it is due.

Kanban cards, level scheduling, and short changeovers all serve the same goal: to keep material moving in small, frequent batches that track live demand. None of that works without accurate signals about what the next process needs and precisely when it needs it. A single stale number, and the whole chain drifts out of step.

The benefits of a JIT manufacturing system

The clearest JIT manufacturing benefits land straight on the balance sheet. Lower inventory releases working capital, shrinks warehousing bills, and cuts the write-offs that come from obsolete or damaged stock. A well-run JIT manufacturing system also lifts quality, because defects reveal themselves quickly when no buffer exists to hide them.

Speed is the second reward. Shorter cycle times let a plant absorb new orders without unwinding weeks of committed production, and they shorten the distance between a mistake and its correction. Teams spot problems sooner, chase root causes faster, and stop pouring effort into managing stock they never needed in the first place. Freed capital can then fund the machines, people, and capacity that grow output, rather than sitting frozen in racks of parts.

Why good intentions are not enough

Declaring a JIT policy is the easy part. The difficulty begins the moment a supplier slips a delivery, a machine trips offline, or a rush order lands, because each of those events ripples straight through a plan built on tight timing. Without live coordination between demand, capacity, and supply, a lean line turns into a fragile one that stalls as soon as reality parts ways with the schedule.

The disruption years taught this lesson at scale, when overstretched supply chains left lean factories standing idle for want of a single part. A just-in-time production system carries almost no slack by design, so it depends on information traveling as fast as the materials themselves. Weekly planning meetings and a wall of spreadsheets cannot keep pace with a shop floor that changes hour to hour. The answer is not to abandon lean thinking, but to give it a system that reacts quickly enough.

How APS software synchronizes a just-in-time production system

This is where advanced planning and scheduling software earns its keep. An APS platform builds a digital model of the plant, then reschedules production as orders, machine status, and material availability shift through the day, replacing static assumptions with a plan that mirrors the present. Synchronization, not policy, is what holds a just-in-time production system steady when conditions move.

Nexelem's Optimal Supply Chain feature connects planning and procurement. Our systems can order raw materials and components against actual production demand, so parts arrive in time for the line while stock stays close to the minimum the schedule requires. When demand eases, replenishment orders shrink on their own, and when it climbs, procurement moves early enough to protect the plan. Planners follow the whole schedule in a clear Gantt chart view and reschedule the instant a constraint appears, which keeps procurement and production reading from the same live picture.

Making JIT resilient in 2026

Modern JIT does not demand zero buffers everywhere. It means placing small, deliberate buffers where risk runs highest and holding the rest of the flow tight, guided by data rather than habit or memory. The aim is a plan that bends under pressure instead of snapping the moment a supplier or a machine lets you down. Software lets planners test a change against the entire schedule before they commit, so resilience becomes a decision.

The factories that thrive on JIT in 2026 treat their planning system as core infrastructure, not just a reporting afterthought. They connect suppliers, machines, and orders into one model and let that model, rather than a static policy document, decide what should happen next. That shift, from intention to live synchronization, is what separates a JIT slogan from a JIT operation.

Ready to make just-in-time work in practice?

See how Nexelem's APS software can turn your just-in-time goals into a synchronized, demand-driven operation. Book a demo, and watch the Optimal Supply Chain feature line procurement up with your real production plan instead of a forecast.

The Roadmap to a Paperless Factory: A Step-by-Step Implementation Guide

Tue, 04 Aug 2026 00:00:00 GMT

Paper still underpins a significant portion of manufacturing operations. Work orders are printed, completed by hand, and filed away, often remaining untouched until issues arise. Paperless manufacturing replaces this with digital data captured at the point of work, providing a live view of what is happening across the organization.

Table of contents:

What Paperless Manufacturing Means in Practice
Common Barriers That Slow the Transition
A Step-by-Step Paperless Manufacturing Process
Connecting the Paperless Layer to the Rest of Your Systems

What Paperless Manufacturing Means in Practice

Going paperless involves more than scanning existing documents. It requires redesigning production information flow so that data is created digitally at the source, rather than recorded on paper and entered later.

This change affects work orders, job routing, quality checklists, operator logs, material consumption records, and shift handover notes. Each becomes a structured digital entry instead of a handwritten form. As a result, production data is immediately available, searchable, and verifiable, rather than stored in filing cabinets or shared drives.

Common Barriers That Slow the Transition

Factories that struggle with paperless production typically encounter three issues: attempting to eliminate all paper at once and underestimating the need for change management; selecting tools that are difficult for operators to use under real shop-floor conditions; and lacking integration between the paperless layer and planning or ERP systems, which leads to manual data re-entry.

A paperless factory succeeds when it simplifies tasks for shop floor personnel, not just for the people analyzing the data afterward. Operators adopt the system when it benefits their daily work. If it feels like extra steps, adoption fails quietly.

A Step-by-Step Paperless Manufacturing Process

Begin with a process audit. Map each paper-based touchpoint in your production flow and rank them by the cost of failure. Prioritize digitizing areas where late or missing data has the highest impact.

First, digitize work orders and production confirmations, as these generate the most transactions and data entry errors, delivering immediate ROI. Next, move to quality recording with digital checklists, automated pass/fail triggers, and exception escalation. Finally, extend digitization to material tracking and operator time capture to support production management reporting and costing.

Connecting the Paperless Layer to the Rest of Your Systems

A paperless factory produces significant data, which is only valuable when integrated into the systems where decisions are made. Your MES, planning, and quality management systems need to receive production data in real time, not at the end of a shift. That integration is what turns digital records into data your managers can act on.

Nexelem connects shop-floor data capture to production planning and OEE monitoring, ensuring that operator entries are immediately reflected in management dashboards. This eliminates manual consolidation and end-of-day data entry, making the paperless layer the central data source.

Are you ready to see how a paperless production environment works in your industry? Explore Nexelem's platform and book a demo to walk through your current setup.

Get in touch for a free consultation with our experts to see how Nexelem works in your production environment.

High-Mix Low-Volume (HMLV) Production: Scheduling Strategies for Custom Orders

Fri, 10 Jul 2026 00:00:00 GMT

Key Takeaways

HMLV plants produce many different parts in small quantities, which makes every schedule a fresh puzzle rather than a refinement of yesterday's plan.
ERP modules and spreadsheets collapse under the variability, which is why custom shops miss deadlines even when they have spare capacity.
Finite-capacity scheduling, dynamic rescheduling, and visual shop-floor control are the three habits that separate plants in control of HMLV from plants drowning in it.
A purpose-built APS system cuts planner overtime and gives sales teams realistic delivery dates on the first call.
Kanban boards translate the schedule into something operators can follow, without constant supervisor intervention.

Custom orders rarely arrive the way they did last week. One day brings a batch of fifty assemblies, the next brings three prototypes in a new alloy, and Friday brings a rush change that flips the whole week's plan. This is high-mix low-volume manufacturing, the hardest environment in production scheduling, and it punishes any method built for steady, repeatable runs. The plants that thrive in HMLV are not the ones with the most capacity; they are the ones with scheduling discipline.

What Does High-Mix Low-Volume Manufacturing Mean?

High-mix low-volume, often shortened to HMLV, describes a production environment where many different products are made in small quantities. Job shops, contract manufacturers, custom machine builders, and aerospace component suppliers all live here. A single plant might run hundreds of unique part numbers in a quarter, and the volume per part stays low, sometimes a single piece.

The defining feature is variability. Setup times dominate, routings differ from order to order, and engineering changes land in the middle of a run. Every schedule starts as a fresh puzzle, not a refinement of yesterday's plan. That single fact rules out any planning approach built on the assumption of stable demand.

Why Traditional Scheduling Fails in HMLV

Most ERP scheduling modules were built for repetitive manufacturing. They rest on assumptions that break the moment product mix opens up:

When the planner runs MRP on Monday, by Tuesday afternoon, half the priorities have already shifted. Spreadsheets fill the gap badly. The senior planner becomes a bottleneck, holding the real schedule in their head and rebuilding it daily.

Sales teams quote delivery dates without seeing actual capacity, and the shop floor works from printouts that are obsolete the moment they are printed. Missed promises pile up, and customers learn to add a buffer to every order they place.

Scheduling Strategies That Work in High-Mix Low-Volume Planning

Three habits separate plants that handle HMLV work from plants that drown in it. None requires new hardware. All require a willingness to abandon planning logic that worked when the product mix was narrower.

The first habit is finite-capacity scheduling. Infinite-capacity logic, the default in most ERP systems, allows every order to claim resources without checking whether those resources are available. Finite-capacity scheduling matches operations to real machine and operator availability, so the schedule reflects what the plant can do, not what the system wishes it could do.

The second habit is dynamic rescheduling. Static weekly plans cannot survive the change orders, machine breakdowns, and material delays that a job shop sees every shift. The schedule has to respond when reality shifts, ideally in minutes rather than hours.

Our piece on how to reschedule production walks through the mechanics in detail.

The third habit is visual control on the shop floor. Operators need a current, simple view of what to run next, refreshed automatically. Without it, the best schedule on the planner's screen never reaches the people doing the work.

These three habits work together. Finite-capacity logic gives the planner a schedule they can defend, dynamic rescheduling keeps that schedule honest as the day progresses, and visual control puts the result in the hands of the people on the machines. Skip any one of them, and the other two start to leak value.

Giving Operators a Schedule They Can Follow

A perfect digital schedule is worthless if the shop floor cannot read it. In HMLV plants, supervisors waste hours each day translating the planner's spreadsheet into shouted instructions, sticky notes, and rushed handoffs. The information loop is slow, lossy, and dependent on a few key people.

Digital Kanban boards, like Nexelem's Kanban View, close that loop by giving each work center a live, ordered queue of jobs. When an operator finishes a task, the next priority appears on screen. When the schedule changes upstream, the queue updates without anyone having to reprint paperwork. Operators stop guessing, and supervisors stop running interference between the office and the floor.

Where APS Fits in the High-Mix Low-Volume Picture

Advanced Planning and Scheduling, or APS, is the tool category built for environments that defeat ERP. An APS engine balances several variables at once:

Finite machine and work-center capacity.
Alternative routings and material substitutions.
Set up time savings from grouping similar parts into families.
Operator skill requirements per operation.
Order priorities and promised delivery dates.

It produces a feasible schedule in minutes rather than days, and recalculates it the moment something changes. This is the territory Nexelem APS was built for. For high-mix low-volume planning, the value lies in three places. Planners stop rebuilding the schedule from scratch each morning, and customer service stops promising delivery dates the plant cannot meet. Quoting moves faster because real available-to-promise dates show up on the first inquiry. Senior planners free up their week for genuine problem-solving rather than firefighting.

The compounding effect matters more than any single number. Plants that get HMLV scheduling right tend to take on the work competitors quote and lose money on, because they know exactly what each new order will cost in machine time and labor before committing. Over the course of a year, that visibility quietly shifts the product mix toward the orders that pay the best.

Put HMLV Scheduling on Solid Ground

If your plant runs custom orders and your planning still depends on heroics, see what an HMLV-grade scheduling stack looks like in practice. Explore the Nexelem APS platform for finite-capacity scheduling that matches the way your shop runs, then pair it with our Kanban View to put a live, automatically updated schedule in front of every operator.

The Future Factory on SuperAI 2026: Manufacturing AI Moves from Roadmaps to Running Systems

Wojciech Szywalski — Thu, 18 Jun 2026 00:00:00 GMT

The conference ran on June 10–11 at Marina Bay Sands, drawing over 10,000 attendees and 1,500 AI companies from 150 countries. Six pillars shaped the program: frontier models, AI infrastructure, robotics and embodied AI, biotech, enterprise AI, and safety and governance. Manufacturing sat across several of them, sometimes explicitly, sometimes as a recurring undercurrent.

I came back from SuperAI 2026 in Singapore with a different feeling than last year. Less excitement about what AI will eventually do. More curiosity about what it is already doing.

What struck me most was a simple observation: the companies talking about manufacturing AI in 2026 are not the same companies that were talking about it in 2025. Last year, it was concepts and roadmaps, things that might matter in five years. This year, presenters came with prototypes and products that already run on factory floors.

The gap between "this is possible" and "this is shipping" closed faster than most people expected.

Table of contents:

The biggest shift: from concepts to products
Digital twins finally get a brain
The AI agent that might know your floor better than your production manager
Humanoid robots: a product, not a promise
Appendix: The view from Birmingham: what manufacturers actually struggle with

The biggest shift: from concepts to products

A year ago, manufacturing AI sessions at conferences sounded like ambitious planning. This year, they sounded like operational reporting.

The change was not just in the vocabulary. It showed who was presenting. In 2025, the voices were often those of researchers and strategists. In 2026, engineers and operations leads showed dashboards, deployment timelines, and actual metrics. The level of specificity went up sharply.

Deloitte's Resilient By Design: The Agentic Supply Chain report, published in early 2026, describes agentic AI as ready to conduct always-on monitoring and autonomous decision-making at a scale beyond human limits — and notes that adoption is already accelerating. A separate IBM study found that more than half of supply chain executives surveyed are already deploying AI agents to automate workflows. The mood at SuperAI 2026 matched this. Robotics, digital twins, and AI agents were no longer the most speculative conversations in the room. They were the most concrete ones.

For a manufacturing audience, this matters: the technology discussion has moved from "is this feasible?" to "how do we deploy and scale it?" Those are fundamentally different conversations, requiring different people in the room and different decisions.

Digital twins finally get a brain

The most significant manufacturing trend at SuperAI 2026 had nothing to do with robots. It was the combination of generative AI with digital twins to build virtual factories that actually simulate production.

The core idea is straightforward, but the implications are large. Instead of spending 12 months manually configuring a digital twin of your factory, a narrowly trained generative model can build that virtual AI factory in days. Once built, the simulation can begin within a week. The smart factory does not yet exist in metal and concrete; it exists in data flows, running production scenarios at speed.

That speed has one precondition: the data has to already be there, structured, and live. In practice, this means a Manufacturing Execution System (MES) collecting real-time production data from machines and operators, and an IIoT aggregation layer that manages storage intelligently — full resolution for recent data, lower resolution for older records, like in our in-house solution, keeping storage costs in check without losing the historical signal. Without this data foundation, the generative model has nothing meaningful to build from. With it, constructing a virtual factory becomes a matter of days rather than an engineering project measured in quarters.

Siemens demonstrated this concretely with Digital Twin Composer, launched at CES 2026. PepsiCo used it to convert several US manufacturing and warehouse facilities into high-fidelity 3D digital twins, including every machine, conveyor, pallet route, and operator path, with physics-level accuracy. AI agents then simulated, tested, and refined system changes inside that virtual environment. The result: PepsiCo identified up to 90% of potential issues before any physical modifications, achieved a 20% increase in throughput during initial deployment, and reduced capital expenditure by 10 to 15%.

NVIDIA's Rev Lebaredian described the broader principle: "In an era where every physical object and process will have a digital twin, Siemens' Digital Twin Composer establishes a digital thread that connects the silos of design, engineering, and operations."

The digital twin is no longer a passive mirror of the factory. It is becoming a generative environment where decisions are validated before physical resources are committed.

For a plant director or production manager, the practical consequence is significant. Investment decisions, capacity changes, layout redesigns — these can now be validated virtually, at low cost, in a compressed timeline. The question shifts from "can we afford to experiment?" to "why would we not?"

This also reframes what capacity planning means. Instead of static models updated quarterly, the virtual factory becomes a living simulation that informs decisions continuously. The gap between planning and reality narrows not because people work harder, but because the model runs faster.

Nexelem MES and APS are structured for exactly this chain: MES captures execution data from the floor, the IIoT layer aggregates it at the right granularity, and APS uses that live picture to schedule production orders, allocate tasks to machines, and assign operators matched by their documented competencies. The architecture is already the right shape for a generative digital twin to sit on top of.

The AI agent that might know your floor better than your production manager

The second major theme at SuperAI 2026 was more conceptual, but it pointed at something that will matter enormously for manufacturing organizations: AI agents as a complement — and eventually in some cases a replacement — for the accumulated knowledge of experienced production managers.

This is not automation in the traditional sense. A production manager's core value is cognitive: knowing the floor, understanding the smell of the shopfloor, knowing the constraints, knowing what to do when things go wrong. That knowledge lives in their experience, not in any system.

What is emerging is a shift from "knowledge in heads" to "context in systems." An AI agent does not need to know everything in advance; it needs access to the right context: machine states, historical performance, current orders, and operator availability. Given that context, it can reason about what to do next.

So, the question is whether the role of the production manager in the era of AI and agentic factories would be an extension of HR. I see it here, but to me it's clear that the best production managers are not only the most skilled and experienced engineers, but also crew connectors, like captains on a ship who don't row or set sails themselves.

— The broader AI community calls this the shift from knowledge-based systems to context-driven agents. Applied to manufacturing, an agent does not need 15 years of floor experience to make useful production decisions. It needs good data, clear constraints, and the ability to act on them.

The underlying technology is maturing fast. Protocols like MCP (Model Context Protocol) are now enabling manufacturers to connect AI agents directly to MES and ERP systems without expensive custom integration work.

In concrete terms, this is the context a good APS system already holds: the current production schedule, machine and line availability, material constraints, and operator assignments matched by competence level. An AI agent that can read and act on this structured picture does not need to derive operational knowledge from scratch. It needs the right connection and a clear set of constraints to reason against.

For production managers, this is not necessarily a threat. It is a redistribution of what requires human judgment and what can be delegated. Experienced managers who understand this shift will likely use AI agents to multiply their effectiveness, freeing their attention for the decisions that genuinely require human judgment.

Humanoid robots: a product, not a promise

Humanoid robots were the headline at SuperAI 2025 when it comes to shipyards of the future. In 2026, they are still present at conferences, but they have moved from the main stage into the background. Not because progress has slowed, but because they are now products in production, not demos at a conference.

The numbers are concrete. Figure AI is now producing Figure 03 at one robot per hour at its BotQ factory, up from one per day just four months prior. Unitree shipped over 5,500 humanoid units in 2025, ranking first globally in shipments and targeting 20,000 units in 2026. Boston Dynamics' electric Atlas is shipping to customers including Hyundai and DeepMind. Agility Robotics has active deployments at Toyota Canada under a Robot-as-a-Service model.

One exhibit stood out as a deliberate counterpoint to all of this: Asimov by Menlo Research. They had a physical robot on the floor — 120 cm, 35 kg, 25 actuators — but were candid that most of the demonstrated behaviors were still running in simulation rather than on the hardware. What made them interesting was not the robot itself but the principle behind it: Asimov is fully open-source, available as a DIY kit with a public bill of materials and an open GitHub repository. In a field dominated by closed, heavily funded platforms competing on proprietary hardware and training data, an open-source humanoid is a genuinely different bet. Whether that bet pays off at an industrial scale is an open question, but the concept is worth watching, particularly for research teams and manufacturers who want to experiment without committing to a vendor ecosystem.

The question in 2025 was "Will these work?" The question in 2026 is "for which tasks do they make economic sense, and at what scale?"

The answer emerging from deployments: structured, repetitive tasks in consistent environments. Welding, assembly, and parts handling where conditions are predictable. Not general-purpose robots roaming a factory, solving whatever comes up. This mirrors what Persona AI was demonstrating at SuperAI 2025, configuring robots for specific roles — Builder, Welder, Fabricator, Assembler — rather than one-size-fits-all capability. That framing has proven correct.

From a production management perspective, humanoid robots introduce an interesting complexity: they become resources to plan around, like any other piece of equipment, but with different performance profiles and different failure modes. The integration question, how robotic workers connect to production scheduling systems, is one that the industry is only beginning to address seriously.

I had the pleasure of reconnecting with my colleague from QuikBot, whose robotic platform is being developed around a broader smart city vision. Their robots are already operating as autonomous couriers in office buildings and hospitals, where they help to transport medicines and supplies. We enjoy discussing factory use cases. It was a good reminder that many of the technologies discussed at AI conferences are now a reality. They are already being used in real environments to solve practical operational challenges every day.

The view from Birmingham: what manufacturers actually struggle with

SuperAI 2026 showed the cutting edge. A few days earlier, at Smart Manufacturing Week in Birmingham, the mood was different: grounded, sometimes frustrated, and very honest about what the middle market of manufacturing actually experiences.

Four themes came up repeatedly across panels and conversations:

Change management remains the primary obstacle to any technology implementation, including AI. The tools can be ready; the organization often is not.
Energy costs are rising fast enough to override other priorities. Production decisions are being made on energy grounds that no AI scheduling tool has yet solved cleanly.
Supply chain disruptions continue to require human adaptation that automated systems cannot fully absorb.
Pressure to use AI for problems that do not need AI is creating real waste. Manufacturers confirmed that the challenge is not tool availability; it is knowing which problems AI should actually touch.

One observation deserves separate attention. Middle-level production managers feel pressure from both directions: from above to digitalize and implement AI, from below to keep production running. This creates a specific kind of stress that no technology roadmap resolves. The best tools in the world do not help if the person responsible for deploying them is already overloaded.

The AI conversation at SuperAI 2026 and the operational reality on most factory floors are moving at very different speeds.

Bridging that gap is the actual work. Not selecting the next platform, but understanding where AI creates genuine value on a specific factory floor, with specific people, in a specific context. Manufacturers in Birmingham were clear: they are not looking for the vendor with the highest version number or best features. They are looking for partners with domain expertise who understand what production management actually involves.

This is consistent with what we hear at Nexelem from our own clients. The value is not in the tool alone; it is in the combination of the tool and the understanding of what needs to be solved. If you are thinking through where AI fits in your production management setup, we are happy to compare notes.

One more angle worth noting, particularly for manufacturing companies that also develop their own software. The AI transformation is not limited to production operations; it is reshaping how software is built. Organizations accustomed to traditional development cycles are finding that agentic AI changes the cadence, the tooling, and the review process at every step. VirtusLab addresses this through Visdom, an AI-native software development platform that helps organizations adapt their development processes to a world of AI agents, including a self-audit maturity matrix for teams that want an honest picture of where they actually stand.

Full Traceability: How to Prepare Your Production for Strict Audit Requirements

Thu, 11 Jun 2026 00:00:00 GMT

Regulators arrive without notice. When auditors request the origin of a raw material batch, its processing conditions, and the operators involved, you must provide that information immediately. In food, pharma, and automotive production, traceability ensures compliance, and the margin for error is nearly zero.

Table of Contents:

What Does Manufacturing Traceability Cover?
The Pressure Facing Regulated Industries
Why Paper Records Fail Under Audit Conditions
How an MES Supports Manufacturing Traceability
Building a Traceability Process That Scales

What Does Manufacturing Traceability Cover?

Manufacturing traceability is the ability to track and document a product's history across every stage of production, from incoming raw materials to finished goods leaving the warehouse. It captures who did what, when, with which materials, and under which conditions.

A complete traceability record connects batch numbers, supplier data, machine parameters, quality checks, and operator IDs into a single chain of evidence. Regulators, customers, and certification bodies require this chain. If any link is missing, the audit fails.

The Pressure Facing Regulated Industries

Food manufacturers operate under frameworks like FSMA and GFSI that require full chain-of-custody documentation at every production step. Pharmaceutical producers face FDA 21 CFR Part 11 and EU GMP requirements demanding electronic records and audit trails. Automotive suppliers must satisfy IATF 16949 and customer-specific quality standards that trace components back to raw material origin.

All these regulations require verified compliance, not self-reporting. Auditors expect documentary evidence, not assurances. Reviews are now more frequent and thorough, with increased penalties for missing information.

Why Paper Records Fail Under Audit Conditions

Production environments that rely on paper or spreadsheets face recurring issues under audit pressure: incomplete records, illegible handwriting, retroactive timestamps, and files scattered across departments, with no central view.

Paper records are physically fragile and can be lost, damaged, or misfiled. Reconstructing batch history from old paperwork during a recall or investigation is slow, error-prone, and often inconclusive, leading to compliance failures.

How an MES Supports Manufacturing Traceability

A Manufacturing Execution System captures production data automatically as operations happen, eliminating the manual logging step where most errors and omissions occur. Every scan, every parameter reading, and every quality check is recorded in real time with a timestamp and operator attribution.

Nexelem MES links material tracking with process data, allowing you to generate a complete traceability report for any batch in moments. The report includes inbound material lots, machine conditions, quality decisions, and final output, providing the audit trail regulators require, ready for immediate review.

Building a Traceability Process That Scales

Begin with processes that present the highest regulatory or quality risk. Map your current data capture points, identify the gaps, then implement digital tracking at those points first and expand across the production flow from there.

Consistency matters more than completeness at the start. A system that reliably captures 80% of critical data is easier to defend than a perfect plan that never gets implemented.

To see how Nexelem's Manufacturing Execution Software supports full traceability in regulated production, request a demo, and we'll walk through your specific audit requirements together.

Nexelem at Smart Factory Week 2026: Recap

Tue, 26 May 2026 00:00:00 GMT

Two days in Birmingham. You will leave with answers to questions you didn't know you had, valuable contacts, and at least one idea to implement immediately. Smart Factory Week 2026 is returning to the NEC Birmingham on 3–4 June, and the Nexelem team will be there.

This year's edition is set to be the biggest yet. Thousands of manufacturers, engineers, and technology specialists will descend on Hall 5 for two days of hands-on content, live demonstrations, and peer-to-peer conversations that simply cannot be replicated in a webinar.

What we found on the floor (EDIT)

Smart Manufacturing Week delivered. The program was genuinely varied, talks that held the room, exhibitors ranging from component and material suppliers through to integrators and software vendors, and a crowd that was clearly there to work rather than collect tote bags. What stood out most was how willing people were to talk: manufacturers, engineers, and technology providers in the same space, and none of the usual wariness you get at trade events where everyone suspects everyone else is trying to sell them something.

The session that stayed with us longest was a panel at the Marketing to Manufacturers Breakfast Club, focused on the challenges facing production managers and middle-level leaders in the age of AI.

Four themes kept surfacing across the conversation: change management, energy costs, supply chain disruption, and AI — specifically, the pressure on manufacturers to deploy it, whether or not it actually fits the problem.

The room was clear on this: the issue is not a shortage of tools. It is the gap between the expectation that AI will solve operational problems and the reality of limited implementation, poor scalability, and managers caught between pressure from the boardroom and resistance from the shop floor.

The other thing manufacturers were consistent about — "we are not looking for the newest software version, and new features, we want a partner who understands their industry and can work with them over time".

That is a distinction worth taking seriously.

Let's meet at Smart Factory Week

If you are heading to Birmingham, let's find half an hour.

It doesn't matter where you are in the process — whether you're seeking an early evaluation, trying to resolve a scheduling issue, or simply curious about what others are doing. Get in touch before the event and we'll sort something out.

Connect with us on LinkedIn and drop us a message: Nexelem on LinkedIn

The more specific your challenge, the more useful the conversation tends to be. We are not there to pitch — we are there to think through problems with people who have real ones.

What Smart Factory Week actually is (and why it matters)

Smart Factory Week is the UK's largest festival of advanced manufacturing and engineering. It brings together six co-located events under one roof: Smart Factory Expo, MAINTEC, Design + Engineering Expo, Drives + Controls, the Manufacturing Digitalization Summit, and the new Factory of the Future showcase. Researched and produced by The Manufacturer, the event is not a generic trade show. Every content session is selected for practical relevance, not promotional value.

If you work in production management, scheduling, or operational improvement, it is two of the most concentrated days on the UK manufacturing calendar.

What's on the agenda in 2026

The 2026 program covers robotics and automation, maintenance strategy, engineering design, and the question most manufacturers are still working through: how to actually build data-driven operations, not just talk about them.

A few things worth knowing before you go:

Smart Factory Expo is the heartbeat of the week — dozens of software and hardware vendors demonstrating live integrations, real-time production data, and the kind of shop-floor connectivity that turns an MES into a genuine decision-support tool rather than a glorified spreadsheet.

MAINTEC tackles predictive maintenance, asset management, and reliability engineering. Given that unplanned downtime still costs European manufacturers an estimated €864 billion per year (according to Siemens research), the numbers make it hard to treat as secondary.

The Manufacturing Digitalization Summit runs as a closed-door event alongside the main floor — senior leaders only, focused on strategic questions rather than vendor demos.

The exhibition opens at 09:30 each day (VIP entry from 09:00), with a drinks reception on the Wednesday evening from 16:30 — which, if past years are anything to go by, is where some of the most useful conversations actually happen.

Why the Nexelem team is attending

We build production management software — specifically, an integrated suite covering Advanced Planning and Scheduling (APS), Manufacturing Execution (MES), and Industrial IoT (IIoT) — and events like Smart Factory Week are where we stay honest.

— What does it mean?

Staying honest means listening.

It means walking the floor, sitting in the content sessions, and paying close attention to what production managers and planners are actually struggling with in 2026 in the UK, not what we assume they are struggling with from behind our monitors. The gap between vendor assumptions and factory realities is one of the most predictable sources of software that nobody uses.

It also means connecting with people who are thinking hard about the same problems we are: how to make production schedules that survive contact with reality, how to close the loop between planning and execution, and how to turn shop-floor data into something genuinely actionable rather than just a dashboard that someone glances at once a week.

Let's connect during the upcoming production festival.

Modular vs. Monolithic Software: Why Scalability Wins

Tue, 12 May 2026 00:00:00 GMT

Many factory managers hesitate to upgrade their technology because they picture a massive, expensive overhaul that disrupts the entire plant for months. Buying a massive all-in-one system often forces you to pay for dozens of features you will never touch.

A modular manufacturing system offers a much smarter path forward. Instead of ripping out everything at once and forcing your team to learn a completely foreign interface overnight, you add specific tools exactly when you need them.

The Hidden Costs of Monolithic Systems
Solving One Bottleneck at a Time
Scaling Without the Growing Pains
Taking Control of Your Technology Budget

The Hidden Costs of Monolithic Systems

The traditional approach to factory software usually involves a monolithic structure. You buy a giant package, hoping it fixes all your problems at once. In reality, this strategy creates chaos on the production floor, slowing down output while operators struggle to understand the new screens and processes.

The IT department spends weeks trying to force the new software to talk to your existing legacy machines. You end up adapting your perfectly good factory processes to fit the rigid rules of the new software, rather than the other way around. This causes frustration across the shop floor and quickly depletes your capital budget.

Solving One Bottleneck at a Time

Nexelem takes a completely different route. We designed our software as a comprehensive family of components tailored to fit your specific floor operations.

If your current biggest bottleneck is scheduling, you start by fixing that exact problem with advanced planning and scheduling software. You get your schedules in order, train your planners thoroughly, and see immediate results in your delivery times. Later, when you want to connect your machines for real-time data or to manage your warehouse more effectively, you simply plug in new modules.

This step-by-step approach defines the true meaning of modular manufacturing. You build your system piece by piece around your actual operational needs.

You avoid the massive upfront costs and steep learning curves associated with monolithic software deployments. Your operators learn one new tool at a time, making the transition smooth and manageable for everyone involved. Resistance to change drops dramatically when the new software actually solves an immediate headache for the user.

Targeted Problem-Solving Without Disruption

If your materials are constantly arriving late or you hold too much inventory, you might add an MRP system to your existing setup. You fix your purchasing and inventory flow without disrupting the scheduling software you already installed.

This targeted problem-solving keeps your operations stable while you upgrade your capabilities.

Scaling Without the Growing Pains

Growing your operations requires serious flexibility from your software vendor. With modular manufacturing software, your technology scales alongside your business at a pace that makes sense for your cash flow. You integrate new modules with your existing ERP, keeping your data flowing without skipping a beat. Every factory runs a unique modular manufacturing process, and your software should reflect that reality on the ground. You might run high-volume mass production on one line and custom, low-volume orders on another.

A modular setup lets you apply the right digital tools to specific areas of your plant without forcing a single solution on your team.

Smooth Upgrades and Maintenance

Maintaining a large monolithic system also requires constant attention and expensive consultants every time you need to update a minor feature. Modular systems bypass this headache completely.

Upgrading one specific component does not break the rest of your factory's digital ecosystem. You update your warehouse tools without taking your production scheduling offline. This keeps your factory running smoothly and reduces the hidden long-term costs that usually drag down large IT projects as you move toward Industry 4.0.

Taking Control of Your Technology Budget

You stay in complete control of your technology budget and your implementation timeline. You start small, solve your most urgent problems first, and expand your system as your operations mature and your team gets comfortable with the digital tools. You only pay for the features that actually provide value to your daily operations, keeping your overhead low.

Ready to build your system around your actual operations? Start at Nexelem.

Accurate Cost Calculation: Tracking Actuals vs. Standard Costs

Thu, 07 May 2026 00:00:00 GMT

For many CFOs and financial controllers in manufacturing, the end-of-month close is a moment of reckoning. It is the moment when the "Standard Cost", the estimated cost used for pricing and budgeting, meets the "Actual Cost", the reality of what happened on the shop floor.

When these two numbers drift too far apart, margins erode silently. In a volatile market, relying on annual standard cost estimates is a financial risk; you need the agility of real-time actuals.

To protect your bottom line, you must move beyond spreadsheets and adopt dynamic manufacturing cost calculation.

Table of Contents:

The Trap of Standard Costs
The Manufacturing Cost Calculation Formula: Theory vs. Reality
Beyond Direct Costs: Handling Overhead and Allocation
A Manufacturing Cost Calculation Example
How Nexelem Bridges the Gap

The Trap of Standard Costs

Standard costing is a proper accounting convention. It assumes a stable environment: material prices are fixed, machine cycle times are constant, and energy costs are predictable.

However, the factory floor is rarely stable. A machine breakdown might force you to switch to a less efficient legacy machine. A rush order might trigger overtime pay. A supplier issue might force you to buy premium raw materials.

If your manufacturing cost calculation software only tracks the standard, you are blind to these variances until the P&L statement arrives. You might be selling a product at a loss, believing it is profitable, simply because the actual labor and overhead consumed were higher than the standard model predicted.

The Manufacturing Cost Calculation Formula: Theory vs. Reality

At its simplest, the manufacturing cost calculation formula is:

Total Cost = Direct Materials + Direct Labor + Manufacturing Overhead

While the formula is simple, capturing the data to feed it is complex.

Materials: Did you account for the 5% scrap rate on Tuesday?
Labor: Did the operator spend 3 hours or 4 hours on the batch?
Overhead: How do you allocate energy costs when machine utilization fluctuates?

Without real-time data, this formula is populated by guesses. To get accurate actuals, you need to integrate your financial view with your operational data.

Beyond Direct Costs: Handling Overhead and Allocation

One of the most significant "blind spots" in traditional costing is the allocation of overhead. Many companies apply a "peanut butter spread" approach, assigning a flat overhead rate across all products. This approach distorts profitability.

For instance, Product A might require a high-energy furnace and constant supervision, while Product B requires simple manual assembly. If you apply the same overhead rate to both, Product B effectively subsidizes Product A. Accurate manufacturing cost calculation software prevents this cross-subsidization.

By tracking specific machine run-times and energy consumption per work order, you can allocate indirect costs with surgical precision. This allows you to identify which products are truly driving your utility bills and maintenance costs, giving you the data needed to adjust pricing or re-engineer the process.

A Manufacturing Cost Calculation Example

Consider a manufacturer producing an automotive component.

Standard: The ERP says it takes 10 minutes of machine time and $50 in steel.
Actual: The steel had a defect, requiring rework. The machine ran more slowly due to a worn tool, taking 15 minutes.

In a standard model, the cost remains $X. In a dynamic model, the cost spikes to $Y. If you have visibility into this variance immediately, you can investigate the root cause, the worn tool, rather than just absorbing the loss at the end of the quarter.

This is where precise tracking of OEE (Overall Equipment Effectiveness) becomes a financial tool, not just an operational one. It explains why the costs were higher.

How Nexelem Bridges the Gap

Nexelem moves cost calculation from "post-mortem analysis" to "live monitoring." By tracking production in real time, Nexelem captures the exact labor hours, machine time, and material usage for each work order.

This creates a feedback loop. You can compare the theoretical cost against the actual cost instantly. This granular visibility empowers CFOs to make data-driven decisions on pricing, sourcing, and capital investment. It transforms cost accounting from a compliance task into a strategic advantage for reducing production costs.

Stop guessing your margins. Gain complete control over your financials with Nexelem’s comprehensive manufacturing platform.