In an industry as competitive as fashion, margins can make or break brands. The Chinese fast fashion juggernaut Shein’s approach to the business is simply this – keep margins low, produce large quantities, and reinvest in the business.

Sounds like anyone can do it, right? Not really. Shein is valued at $100 billion – dwarfing Zara and H&M combined. One of the factors contributing to its smashing success has to be its ability to stick to its margins strategy.

Measuring business metrics – especially production performance metrics – is crucial to the protection of a garment factory’s margins. These metrics help understand where production, the most expensive aspect of the apparel value chain, succeeds and fails.

Based on this intelligence, a manufacturer can reorient their functioning to make production more profitable. In fact, the type, number, and frequency at which metrics are tracked - all affect how well a manufacturer knows their business.

Fast fashion is hurtling at lightspeed. So, garment manufacturers who do not digitalize all their workflows – from manufacturing to post-sales – stand to lose out. Migrating their production KPIs to digital channels can help manufacturers track their production in real-time and get ahead in the race.


Real-time, or no dice

The importance of speed in manufacturing isn’t lost on garment manufacturers. So, the speed of performance measurement must catch up with the speed of production. Here’s why –

• Increase in productivity
  

The productivity of an apparel factory depends largely on three factors – the production rate or efficiency, the amount of manpower used, and the time taken to fulfill orders. Timely assessment of these metrics helps manufacturers better allocate resources and boost the volume of production, help make decisions around elimination, retention, or reskilling of labor, and ultimately improve their throughput time.


• Optimize quality control processes
  

The need for checking for defects in real time before the product is inspected cannot be stressed enough. Early detection of unusual defect rates can help manufacturers course- correct in time and save high costs of repairs if the stock is rejected by the buyer.


• Improve stock management
  

Stock management can be daunting because it involves taking care of everything – from raw materials to the final product and more. Today, inventory management software has made this process much easier. It automates the recording of logistics and generates bills, and tracks sales, orders, and deliveries. Real-time stock management is non-negotiable where there is an omnichannel supply – selling clothes in brick-and-mortar stores, on the web, and even through e-commerce. Such prompt insights also aid quick decision-making regarding the composition of their inventory in an industry where trends are constantly changing so that manufacturers can meet the demand for popular styles and offload those that perform poorly.


• Resolve machine downtime and issues promptly
  

Machines can break down at any time. This results in time loss during production, and if left unattended, could lead to losses as well. A system that automates and measures the instance and frequency of equipment downtime could help save precious work hours.


Metrics that matter

A wide range of metrics and KPIs can be used to measure the productivity of a garment manufacturing factory. We have narrowed them down to a few essential ones:

Defect metrics
Defect metrics help gauge the number of garments that may fail quality checks due to stains, improper cutting, tears, or holes, among other reasons. There are a number of ways in which to measure defects and each of these can be used at various production junctures. The manufacturer can then take corrective steps and avoid the high costs of repairs.


• Defects per hundred units (DHU)
  

As the name suggests, this metric measures the number of defects in every hundred garments. It helps manufacturers rule out the reasons that may be causing consistent defects and take remedial measures. It can be calculated using the equation – DHU = Total no. of defects * 100 / total pieces checked.


• Defective percentage
  

Not to be confused with DHU, the defective percentage refers to the percentage of defective pieces from among a batch of hundred garments inspected. Here, defectives refer to garments that have at least one defect in them. This metric can help forecast future repair costs and estimate losses.
Here’s the formula to calculate the defective percentage – Defective percentage = (defective count / total pieces checked)*100


• Reject Percentage
  

Reject rate gauges the number of pieces that were rejected by a buyer out of a certain quantity of garments produced. It is important to have an estimate of this metric so as to inspect the causes behind subpar-quality garments and make the necessary amends for the next batch. It also helps manufacturers be consistent with product quality and ultimately retain customers.
Here’s how it is calculated—
Total rejected garments *100/ total pieces checked. Line efficiency metrics The production line lies at the heart of the garment manufacturing business. Its performance can be measured using these metrics –


• Overall equipment or resource effectiveness (OEE)
  

Overall equipment effectiveness (OEE) measures the difference between the ideal performance and the real performance of a piece of machinery. The OEE of a machine can be improved with timely servicing machines and a healthy ratio between the number of machines and operators. The metric can be applied to other production resources as well. The formula to measure OEE is this - OEE = performance * ability * quality.


• Standard allowed minutes (SAM)
  

Typically measured in minutes, SAM is a metric used to allot a particular time window to a certain task. Essentially, it means the number of minutes that it should take to produce one garment. This metric can help estimate accurate costs, be essential in planning factory capacities effectively, and make production timescales more precise.
It can be calculated using this formula – SAM = Basic minutes + bundle allowances + machine and personal allowances Garment manufacturers can either use synthetic data (estimated by presuming the motions performed by a line worker) or a time study (measured by watching the line worker actually perform the task).


• Standard allocated hours (SAH)
  

Just like SAM measures the number of minutes it takes to produce a garment, SAH measures the
number of garments produced by a line worker in their given number of working hours. It can be
measured using the formula – SAH = (SMV * garments produced) / 60

Production
The following metrics can be used to measure the number of garments produced across various steps of the production cycle.


• Planned targets
  

The production targets can be calculated for various time cycles. Planning these targets helps better allocate resources. Here are a few formulae garment manufacturers can use to measure their overall planned targets –

1. Hourly production target = (60 / operation SAM)
   
2. Daily line target = (shift hours * 60 * no of line operators in a line * line efficiency %)/ garment SAM)
   
3. Line efficiency % = (total minutes produced *100) / (total hours worked * 60)
   
4. Machine productivity = line output / no. of machines used
   
5. Line capacity per day = {(no. of machine * daily work hours * 60)*(1 – absenteeism%)} x Efficiency % (capacity in minutes)
   
6. Standard time = (Observed time * observed rating) + allowances
   



• Predicted trends
  

Regular quality checks and analysis of KPI metrics can help in finding out certain trends that you may
otherwise not notice. For example - if a certain defect is repeated in the production of a particular
style then stakeholders can assess the root cause and rectify it. If a line is doing multiple rounds of
re-works then either the machines need repairing or the workers need to be upskilled to reduce the defects.


• Work in progress (WIP)
  

All the inventory that is still being processed makes up a factory’s work in progress. This could include cut fabric, half-stitched garments, pieces waiting to be dyed, etc. Having a sense of this KPI ensures that worker pipelines are fed with an optimal number of pieces to be completed so that work can be divided fairly among several workstations. It can be calculated across cutting, sewing, and finishing departments using these formulae –

1. Cutting WIP = total cut garments - total garments sent to the sewing
   
2. Sewing WIP = total pieces put on the line - pieces completed
   
3. Finishing WIP = total garments received from sewing - total pieces packed.
   




Anatomy of a good KPI

The preceding list may seem like an arbitrary selection of some of the KPIs used in apparel manufacturing. However, they’ve been chosen based on the following criteria that define the effectiveness of a metric –

• Measurability
  

A good KPI is one that can be measured. Usually, this means it is quantitative and focused on a particular goal. By turning a garment factory’s performance into numbers, a manufacturer can better plan their workflows and meet their targets.


• Alignment
  

A metric worth measuring is one that is aligned with the overall organizational strategy and vision. These could refer to metrics of day-to-day performance in line with high-level goals.


• Achievability
  

Measuring KPIs that cannot be achieved can be demotivating for employees. Metrics that measure attainable goals are not only relevant but practical for employees and management alike.


• Timeliness
  

Useful KPIs are those that can be measured for uniform timeframes and analyzed frequently. This is imperative to draw accurate insights and identify trends. For instance, productivity metrics should be collected for and compared over two quarters.


To conclude

Measuring KPIs requires a thorough assessment of the company’s priorities and objectives, cherry-picking the right metrics, following through with data collection, and finally implementing changes based on these insights.

Untangling the complexity of this process is just the job of automation. It’s time that the apparel industry moves away from manual collection and calculation or even the use of spreadsheets. Fully digitized factory floors – apart from streamlining management – can also help in sourcing and computing performance metrics.

This blog is the first in our series on factory performance metrics. Watch this space for more in-depth content on KPIs across cutting, sewing, finishing, and packaging verticals.

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