Abstract
Accurate manufacturing cost estimation is fundamental to pricing,
profitability analysis, production planning, and continuous
improvement. Many manufacturing organizations continue to rely on
standard costing or manually maintained cost sheets that
inadequately represent the actual cost incurred during production.
Such approaches often overlook process-level variations arising from
machine utilization, production speed, labour efficiency, and
equipment operating costs.
This paper presents a practical framework for
Process-Based Manufacturing Cost Calculation, where
the manufacturing cost of every operation is computed independently
using actual production parameters captured through shop-floor
execution.
The proposed framework calculates the cost of each manufacturing
process as the ratio of machine operating cost per hour to the
production rate achieved during that process. The production rate is
automatically derived from the actual cycle time recorded for every
operation within the Job Card.
The total manufacturing cost of a product is obtained by aggregating
the individual process costs across the complete routing. The
framework enables transparent cost computation, supports process
optimization, and provides a foundation for AI-driven manufacturing
cost analytics.
Keywords
Manufacturing Costing
Job Card
Machine Hour Rate
Cycle Time
Process Costing
Industry 4.0
ERP
Shop Floor Analytics
Introduction
Manufacturing cost is one of the most critical inputs for pricing,
quotation preparation, profitability analysis, and operational
decision-making. As products pass through multiple manufacturing
operations, each process consumes machine capacity, labour,
utilities, and factory resources.
Consequently, the true manufacturing cost of a product should
reflect the actual cost incurred at every process rather than
relying solely on predefined standard rates.
Modern ERP systems integrated with shop-floor execution provide an
opportunity to calculate manufacturing costs dynamically using
actual production data. By capturing cycle times, machine
utilization, and production quantities at each operation,
organizations can compute accurate process costs and continuously
improve operational efficiency.
This paper presents a practical process-based costing framework that
integrates machine operating cost with real-time production
performance captured through Job Cards.
Every manufacturing operation becomes an independent cost centre,
allowing organizations to calculate accurate process costs using
actual production performance instead of estimated standard costing.
Process-Based Costing Philosophy
The proposed framework treats every manufacturing operation as an
independent cost center. Each process contributes its own
manufacturing cost based on the resources consumed during execution.
Rather than assigning a single overall production cost to the
finished product, the framework computes the cost of every process
separately and subsequently aggregates these values to determine the
total manufacturing cost.
For a typical manufacturing sequence comprising laser cutting,
bending, welding, painting, and assembly, each operation generates
an individual process cost. This approach enables organizations to
identify high-cost operations, compare process efficiencies, and
perform detailed cost analysis at every stage of production.
Machine Hour Cost
Foundation of Process-Based Manufacturing Cost Calculation
Machine Hour Cost represents the cost of operating a manufacturing
resource for one hour under normal production conditions. It forms
the foundation of the proposed costing methodology and reflects
the total expenditure associated with making the machine available
for production.
The Machine Hour Cost is derived by considering all significant
operating expenses associated with the manufacturing asset. These
typically include machine depreciation or asset recovery, interest
on capital investment, electricity consumption, preventive and
corrective maintenance, factory rental allocation, labour assigned
to operate the equipment, production supervision, indirect factory
overheads, insurance, utilities, and other recurring operational
expenses.
Asset Cost Recovery
Labour Cost
Energy Cost
Maintenance Cost
Consumables Cost
Building Rental
Other Operating Expenses
Factory Overheads
Key Insight
The resulting hourly operating cost provides a realistic
measure of the financial resources consumed while the machine
is available for production and serves as the basis for
calculating process cost.
Determination of Production Rate
Using Actual Job Card Cycle Time
The second key component of the proposed framework is the
production rate, expressed as the number of parts manufactured per
hour. Instead of relying on theoretical machine capacity, the
framework derives production rate directly from the actual cycle
time recorded during shop-floor execution.
For every manufacturing operation, the Job Card records the actual
cycle time required to produce one component. This information
reflects real operating conditions, including machine performance,
operator efficiency, tooling condition, and process
characteristics.
Job Card
Actual Cycle Time
Calculation
3600 ÷ Cycle Time
Automatic Production Rate Calculation
Since cycle time is independently recorded for every
manufacturing operation, production rates are automatically
calculated for each process without manual intervention. This
approach ensures that manufacturing costs reflect actual
production performance rather than planned estimates.
Process Cost Calculation
Once the Machine Hour Cost and Production Rate have been determined,
the manufacturing cost associated with an individual process can be
calculated using a straightforward relationship.
This formulation establishes a direct relationship between machine
operating cost and production efficiency. As production rate
increases through process improvements, automation, or cycle time
reduction, the process cost decreases proportionally.
Conversely, longer cycle times or reduced machine productivity
increase the cost of the corresponding manufacturing operation.
Because every process possesses its own machine characteristics and
cycle time, each operation naturally generates a different
manufacturing cost.
Job Card Based Cost Aggregation
A typical manufactured product undergoes multiple production
operations before completion. Within the proposed framework, each
operation maintains its own cycle time, production rate, Machine
Hour Cost, and Process Cost.
The Job Card acts as the central repository for collecting these
values throughout the manufacturing lifecycle.
Example Manufacturing Sequence
Process 10
Laser Cutting
Process 20
Bending
Process 30
Welding
Process 40
Powder Coating
Process 50
Assembly
This process-oriented aggregation provides complete transparency
regarding the contribution of each manufacturing stage to the
overall product cost.
Benefits of Process-Based Cost Calculation
The proposed framework offers several operational and managerial
advantages.
Accurate Product Costing
By utilizing actual machine operating costs and real production
cycle times, manufacturing organizations obtain significantly
more accurate product costing compared with conventional
standard costing approaches.
Process-Level Analysis
Individual process costs can be analysed independently, enabling
rapid identification of inefficient operations and high-cost
manufacturing stages.
Complete Visibility
Because every process cost is directly linked to a specific Job
Card, managers gain complete visibility into manufacturing
performance.
Continuous Improvement
Cycle time improvements, machine upgrades, automation
initiatives, and operator training immediately translate into
measurable cost reductions.
Reliable Decision Support
The framework supports quotation preparation, make-versus-buy
analysis, profitability assessment, production planning, and
investment justification by providing reliable process-level
cost information.
AI-Driven Cost Intelligence
The structured nature of process-level costing enables advanced
Artificial Intelligence applications.
Instead of analysing only the total product cost, AI systems can
evaluate every manufacturing operation individually and provide
meaningful recommendations for operational improvement.
Typical Analytical Queries
Which manufacturing process contributes the highest cost?
Which process experienced the largest increase in cost during the
previous month?
What is the financial impact of reducing welding cycle time by 15
percent?
Which machines exhibit the highest operating cost per component?
Which production orders achieved the lowest manufacturing cost?
Such capabilities transform manufacturing costing from a historical
accounting exercise into a real-time operational decision-support
system.
Conclusion
This paper presented a practical framework for process-based
manufacturing cost calculation using Job Card analytics. By
combining Machine Hour Cost with actual production rates derived
from cycle times captured during shop-floor execution, the framework
enables accurate calculation of manufacturing cost at every
production operation.
The aggregation of process costs across all manufacturing stages
provides transparent and reliable product costing while supporting
continuous improvement initiatives and informed operational
decision-making. Furthermore, the structured process-level cost data
establishes a strong foundation for future AI-driven manufacturing
analytics, predictive cost modelling, and intelligent production
optimization.
References
- ISA-95: Enterprise-Control System Integration.
-
ISO 22400: Automation Systems and Integration—Key Performance
Indicators for Manufacturing Operations Management.
-
Kaplan, R. S., and Cooper, R., Cost & Effect: Using Integrated
Cost Systems to Drive Profitability and Performance.
-
Porter, M. E., Competitive Advantage: Creating and Sustaining
Superior Performance.
-
Selected literature on Industry 4.0, Manufacturing Execution
Systems, and Smart Manufacturing Analytics.
Appendix
Business Value Progression Based on Process Cost Calculation
Maturity
| Level |
Level Name |
Primary Focus |
Business Outcome |
|
1
|
Traditional Standard Costing
|
Estimated product costing using predefined standards |
Basic product costing for quotations, budgeting, and
financial accounting
|
|
2
|
Machine Hour-Based Costing
|
Determination of machine operating cost based on asset and
operating expenses
|
Improved costing accuracy through realistic machine
operating costs
|
|
3
|
Process-Based Dynamic Costing
|
Calculation of manufacturing cost for every process using
actual cycle times captured through Job Cards
|
Accurate process-level costing, improved operational
visibility, and continuous process improvement
|
|
4
|
Integrated Process Cost Intelligence
|
Enterprise-wide integration of process costs with
production, quality, maintenance, inventory, and genealogy
data
|
Complete manufacturing cost visibility, advanced analytics,
benchmarking, and data-driven operational optimization
|
|
5
|
AI-Driven Process Cost Intelligence
|
Artificial Intelligence for predictive cost analysis,
optimization, and decision support
|
Predictive manufacturing cost intelligence, natural-language
analytics, autonomous recommendations, and continuous cost
optimization
|
Process-Based Cost Calculation transforms manufacturing costing from
historical financial reporting into an intelligent operational
decision support system, enabling accurate costing, process
optimization, predictive analytics, and AI-driven enterprise cost
intelligence.
Ajikumar T. N.
Director, iMAYAS
Ajikumar TN is the Founder Director of iMayas, a technology
company specializing in enterprise digital transformation
solutions. He is a technology leader with expertise in
Manufacturing ERP, Artificial Intelligence, Digital Product
Genealogy, and Smart Manufacturing. He has led the design and
deployment of innovative enterprise platforms that help
organizations improve efficiency, traceability, and operational
excellence. He holds three US patents in the Information
Technology domain, reflecting his commitment to innovation and
product development. Under his leadership, iMayas continues to
build next-generation AI-powered enterprise solutions that enable
intelligent and connected businesses.
