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Make-to-order vs. make-to-stock OEE is a subject that most manufacturers ignore. A make-to-stock plant and a make-to-order plant may use the same machines and the same ERP. But the way OEE impacts their performance, production planning and profitability is fundamentally different. <\/p>\n
Yet most manufacturers apply the same production objectives and improvement methods, whatever their model. This is a mistake. <\/p>\n
An OEE of 75% on a high-variety, low-volume make-to-order line does not mean the same thing as 75% on a dedicated line producing the same item 24 hours a day.<\/p>\n
This guide details what OEE actually measures in each context, where losses lie, and how to adapt your monitoring strategy to your production reality. Understanding this distinction is essential to any credible continuous improvement approach. <\/p>\n
In make-to-stock production, manufacturing is driven by forecasts and production schedules. Production runs are long, changeovers infrequent, and the main objective is to maximize throughput. Here, OEE functions as a pure efficiency indicator: each point gained translates into additional volume to feed warehouses and ensure inventory management. <\/p>\n
In make-to-order production, manufacturing responds to specific production orders. Production runs are short, changeovers are frequent, and flexibility takes precedence over gross volume. <\/p>\n
OEE is therefore an indicator of production capacity to meet deadlines, rather than an indicator of volume.<\/p>\n
As a direct consequence, the levers for improving OEE are not the same. Optimizing output on an order-driven line without reducing changeover times means improving a figure without improving actual performance. The way monitoring is organized must reflect this difference. <\/p>\n
The operational complexity of a make-to-order environment is structurally greater than that of a stock environment. More references, more settings, more risk of error. Ignoring this reality when setting up an OEE is like comparing apples and oranges. <\/p>\n
In make-to-stock production, loss of availability is enemy number one. Every minute of unplanned downtime represents a number of parts that should have been added to the stock. Machine breakdowns, raw material delays and preventive maintenance failures are the main causes. <\/p>\n
Actual operating time is often lower than what manual reports indicate. The gap between field perception and measured reality can reach 15 to 20 OEE points. This is a blind spot that distorts all downstream planning. <\/p>\n
Performance loss comes second. On long production runs, micro-stops of 3 to 5 seconds, repeated 80 times per shift, go unnoticed, but represent several hours lost per week. Actual cycle times drift away from theoretical ones, and no one notices without automated measurement. <\/p>\n
Quality losses have a multiplier effect: a batch of 10,000 parts with 3% rejects means 300 parts to reproduce and a delayed schedule. The overall yield rate suffers directly. The cost of non-quality is not limited to rejects: it includes machine time consumed, wasted material and the delay in subsequent production orders. <\/p>\n
The aim is to achieve a consistently high OEE. An average OEE of 78% that fluctuates between 60% and 90% is more problematic than a stable OEE of 72%. Why is this so? Because planning relies on predictability. <\/p>\n
If the logistics department can’t predict actual production to within plus or minus 5%, safety stocks explode. Inventory turnover plummets, and the cash tied up in inventory becomes a financial drain. <\/p>\n
Each percentage of OEE variability translates into euros of additional inventory.<\/p>\n
The key indicators to monitor are mean time between breakdowns, the difference between actual and theoretical cycle times, and the scrap rate per batch. Real-time monitoring is critical to detect deviations before they impact stock levels. <\/p>\n
A stable OEE also makes upstream supplies more reliable. When actual capacity is known and predictable, supplier orders are more accurate, emergencies are less frequent, and the additional costs of express transport disappear. <\/p>\n
In made-to-order production, the first OEE destroyer is the time it takes to change series. A line that changes reference 8 to 12 times per shift can lose 20 to 35% of its available time in set-ups. It’s a structural bottleneck on production lines that won’t disappear with the purchase of a faster machine. <\/p>\n
Start-up losses are the second critical source. After each changeover, the first parts are often non-conforming. On small production runs, these losses can represent 10 to 15% of total production. The actual manufacturing time is lengthened, and delays accumulate order after order. <\/p>\n
Classic performance losses also exist, but they are more difficult to isolate because the theoretical cycle time changes with each reference. Without an automated system, the performance component of the OEE is often wrong in production to order. <\/p>\n
The additional difficulty in made-to-order production is traceability. Each order has its own specifications, tolerances and requirements. OEE tracking must be able to link performance data to each production order to identify problem references. <\/p>\n
In make-to-order production, the aim is not to maximize gross OEE, but to maximize the time available to produce value. Reducing changeover times using the SMED method is the number one lever, and a direct factor in cost reduction. Every minute saved on a changeover is an extra minute of production. <\/p>\n
Key indicators are the average changeover time, the first-time rate to reduce start-up losses, and the ratio of value-added production time to total time.<\/p>\n
The overall OEE remains relevant as a trend indicator, but it is the detailed analysis of the causes of downtime that generates the gains.<\/p>\n
Rigorous control of the manufacturing process after each changeover reduces start-up rejects. Best results are achieved when operators have a validation checklist integrated into the monitoring system. <\/p>\n
A critical point: the measurement system must manage reference changes automatically. If every change requires manual intervention, operators will abandon the system. Technology must adapt to the field, not the other way round. <\/p>\n
Many manufacturers set a universal OEE target of 85%, inspired by excellence standards. This figure makes sense for in-stock production. It makes no sense at all in make-to-order production, where changes structurally consume 20-30% of available time. <\/p>\n
For example, an aerospace line with 10 changes per shift will never reach 85%, no matter how efficient its operators are. To set such a target is to discourage teams and discredit the indicator. <\/p>\n
As a result, order teams are perceived as underperforming. Operators lose motivation in the face of unattainable targets. <\/p>\n
The plant abandons OEE monitoring precisely where it would be most useful.<\/p>\n
The right approach: a production plan with OEE targets adapted to the model. In make-to-order production, an OEE of 60% with an improvement of 2 points per month is an excellent result. The important thing is the trajectory, not the absolute value. <\/p>\n
Management must also review the way in which the OEE is communicated to teams. An achievable, contextualized objective mobilizes people. An objective disconnected from the field demobilizes. Success depends on all stakeholders understanding the context. <\/p>\n
Production planning is based on an assumption of capacity. If this assumption is wrong, the whole production plan collapses. In make-to-stock production, an overestimated OEE generates shortages. In make-to-order production, it generates contractual delays. <\/p>\n
Integrating actual OEE into the planning process is a paradigm shift. Instead of planning on 85% theoretical capacity, we plan on 65% measured capacity. The result: fewer broken promises, less express transport, fewer penalties. <\/p>\n
Companies that connect their OEE tracking to their ERP or manufacturing execution system see a 15-25% improvement in their on-time delivery rate within the first three months. This is a rapid and measurable return on investment. <\/p>\n
Customer satisfaction in industrial B2B is mainly measured by the rate of on-time and complete delivery. This rate is directly conditioned by actual production capacity, and therefore by OEE. <\/p>\n
A plant that plans on a theoretical capacity of 85% when its OEE fluctuates between 55% and 65% is mechanically accumulating delays. Unmeasured production losses are the main cause of broken promises. <\/p>\n
Demonstrating to a customer that you’ve improved your OEE is more powerful than any sales pitch. It’s a measurable competitive advantage that builds trust and loyalty. <\/p>\n
Many plants operate in mixed mode. Some lines produce long runs for stock, while others respond to specific orders. Sometimes, the same line alternates between the two models, depending on the period and the project in hand. <\/p>\n
Comparing the OEE of a line on stock and a line on order on the same dashboard without contextualization leads to absurd decisions.<\/p>\n
The 62% made-to-order line, which delivers 98% of its orders on time, outperforms the 80% made-to-stock line, which generates overstocks.<\/p>\n
Customer satisfaction is the final judge, not the raw OEE figure. OEE is a diagnostic tool, not a contest between lines. <\/p>\n
Multi-site groups need to integrate the production model into their OEE repository using a common database.<\/p>\n
Internal comparisons only make sense if we are comparing comparable realities. Two sites in make-to-order production can be compared with each other. Comparing a make-to-order site to a stock site is misleading. <\/p>\n
Setting up a common reference system means standardizing definitions: what is a planned shutdown? How do you classify a series change? These seem like simple questions, but their answers vary from one site to another, distorting any comparison. <\/p>\n
In in-stock production, real-time monitoring enables performance drifts to be detected before they impact stock levels. A 5% drop in output undetected for a week on a high-speed line represents thousands of missing parts. <\/p>\n
A Pareto diagram of downtime causes enables you to prioritize corrective actions on the most costly operating losses. This simple tool transforms raw data into a concrete action plan. <\/p>\n
In make-to-order production, monitoring is even more critical, as lead times are contractual. If a series changeover takes 45 minutes instead of 20, the impact is immediate. The operator who sees the delay in real time can alert the planning department. <\/p>\n
Integration with a manufacturing execution-type system automates this information feedback and enables resource availability to be adjusted in real time. Planning no longer has to wait for the end-of-shift report to tell it there’s a problem. <\/p>\n
In both cases: operators improve performance when they see the truth in real time. The return on investment of such a system is measured in weeks, not months. The difference between manual tracking reconstructed a posteriori and automated real-time tracking is the difference between reacting and anticipating. <\/p>\n
Hutchinson, an automotive parts manufacturer producing from stock, increased the OEE of a site from 42% to 75% by identifying stoppages not detected by manual monitoring. The strategy was clearly geared towards long production runs: reduce stoppages, stabilize production rates and ensure reliable supply to automakers. <\/p>\n
The gains were achieved in a matter of weeks, thanks to the deployment of automated tracking. The teams discovered that micro-stops, invisible in manual reports, represented the primary source of losses. Without automated measurement, these losses would have remained hidden. <\/p>\n
The impact on the supply chain was immediate: fewer delays, fewer express deliveries, and greater confidence on the part of automakers.<\/p>\n
In the aerospace industry, subcontractors operate on a make-to-order basis, with high value-added parts. A gross OEE of 45% to 55% is normal. The challenge is to reduce changeover times and start-up scrap, not to chase an unrealistic figure. <\/p>\n
The most effective improvement projects in this sector focus on standardizing changeover procedures. Every minute saved on a changeover translates directly into additional capacity to fill orders. <\/p>\n
Nutriset illustrates the dual challenge of mixed environments: reliability in production from stock for humanitarian reserves, and responsiveness to orders for urgent needs in crisis zones. In the humanitarian context, every day of delay has direct consequences in the field. <\/p>\n
These examples show that the key is not the OEE figure itself, but the understanding of the losses it reveals, and the ability of teams to act on the right causes.<\/p>\n
Step 1: Identify the production model for each line. The same plant may have both stock and made-to-order lines. OEE tracking parameters must reflect this reality. This diagnostic stage is fundamental and must not be rushed. <\/p>\n
Step 2: Define theoretical cycle times by reference for order lines. Without this basis, the OEE performance component is meaningless. Modern IoT solutions automatically handle reference changes. <\/p>\n
Step 3: Separate changeover time from the causes of downtime. In make-to-order production, changeover time is not an anomaly, it’s an operational reality. Measuring it accurately is the prerequisite for improving it through SMED. <\/p>\n
Step 4: adapt dashboards. On-stock lines display overall OEE and rate trend. On-order lines display average changeover time and on-time performance. Each model has its own priority indicators. <\/p>\n
Step 5: Deploy ready-to-use automated tracking that captures data directly on the machines, in 2 hours, without infrastructure modifications. Simplicity of deployment is a key factor in adoption by field teams. <\/p>\n
Step 6: Train teams to read OEE dashboards. A monitoring tool is only of value if operators and line managers know how to interpret the data and trigger corrective actions. Training is an investment, not a cost. <\/p>\n
[\/et_pb_text][et_pb_accordion _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_accordion_item title=”Can we compare the OEE of a line on order and a line on stock?” open=”on” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Not directly. A line to order incorporates changeover times that mechanically reduce the OEE. Comparing without contextualization leads to erroneous conclusions. We need to compare similar models and use complementary indicators adapted to each context. <\/p>\n
[\/et_pb_accordion_item][et_pb_accordion_item title=”What's a good OEE in make-to-order production?” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n
An OEE of 55% to 70% is realistic, depending on the frequency of changes. An OEE of 60% that is fully understood is better than an 85% target that is out of touch with reality. What matters is the improvement trend and the measurable reduction in changeover times. <\/p>\n
[\/et_pb_accordion_item][et_pb_accordion_item title=”Should series changes be included in the OEE calculation?” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n
Yes, to exclude them would be to mask the primary source of loss. Change must be measured to be improved by SMED. Transparent data is the key to continuous improvement. <\/p>\n
[\/et_pb_accordion_item][et_pb_accordion_item title=”How to manage OEE in a mixed plant?” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n
OEE is only of value in the right context. In make-to-stock production, it’s an indicator of throughput and regularity. In make-to-order production, it’s an indicator of flexibility and on-time delivery. Applying a single grid to both environments means missing the real levers. <\/p>\n
What counts: understanding where losses are, why they exist and how to reduce them. Real-time monitoring provides this visibility. <\/p>\n
Field teams who see the truth make the right decisions, whatever the production model.<\/p>\n
Properly interpreted, the OEE transforms the relationship between production, logistics and management. It replaces hunches with facts, accusations with diagnoses, and promises with commitments kept. <\/p>\n
TEEPTRAK deploys ready-to-use IoT solutions measuring OEE in real time, adapted to make-to-order, in-stock and mixed production environments. Installation in 2 hours, without infrastructure modification, with native management of reference changes. More than 400 plants in 30 countries rely on our solutions. Ask for a demonstration. <\/p>\n
[\/et_pb_accordion_item][et_pb_accordion_item title=”Does the OEE make sense for small production runs?” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n
Absolutely. Even in small production runs, OEE tracking brings the most value, as losses are more difficult to identify without automated measurement. OEE tracking reveals the hidden complexity of production processes, and helps to bring it under control. <\/p>\n
[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
OEE is only of value in the right context. In make-to-stock production, it’s an indicator of throughput and regularity. In make-to-order production, it’s an indicator of flexibility and on-time delivery. Applying a single grid to both environments means missing the real levers. <\/p>\n
What counts: understanding where losses are, why they exist and how to reduce them. Real-time monitoring provides this visibility. <\/p>\n
Field teams who see the truth make the right decisions, whatever the production model.<\/p>\n
Properly interpreted, the OEE transforms the relationship between production, logistics and management. It replaces hunches with facts, accusations with diagnoses, and promises with commitments kept. <\/p>\n
TEEPTRAK deploys ready-to-use IoT solutions measuring OEE in real time, adapted to make-to-order, in-stock and mixed production environments. Installation in 2 hours, without infrastructure modification, with native management of reference changes. More than 400 plants in 30 countries rely on our solutions. Ask for a demonstration. <\/p>\n
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Make-to-order vs. make-to-stock production is a subject that most manufacturers ignore. A make-to-stock plant and a make-to-order plant may use the same machines and the same ERP. But the way in which OEE impacts their performance, production planning and profitability […]<\/p>\n","protected":false},"author":10,"featured_media":76151,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","ai_seo_title":"","ai_meta_description":"","ai_focus_keyword":"","footnotes":""},"categories":[101,128],"tags":[],"class_list":["post-81320","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industrial-performance","category-process-optimization"],"yoast_head":"\n