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by Diptimayee Pradhan
Management Information System is a key tool for the decision makers to take decisions effectively. In this article author has discussed about the Key Performance indicators (KPIs) commonly used in monitoring the performance of a apparel manufacturing facility.
It is rightly said “What can be measured can be monitored and what can be monitored can be improved”. That’s where MIS comes into picture playing a vital role in improvement of the factories/ organisations.
Management Information System is a key tool for the decision makers to take decisions effectively. In this article author has discussed about the Key Performance indicators (KPIs) commonly used in monitoring the performance of a apparel manufacturing facility.
It is rightly said “What can be measured can be monitored and what can be monitored can be improved”. That’s where MIS comes into picture playing a vital role in improvement of the factories/ organisations.
1. Why MIS?
Apparel operations have too many dependencies and it becomes much necessary to monitor each and every factor to be effective. MIS helps in consolidating a large pool of data into readable form and helps in quick decisions.
This paper is an approach towards identifying the key performance indicators and explaining each of them in brief for applying in apparel manufacturing scenario for monitoring, analysis, benchmark, and goal setting to improve manufacturing performance.
Performance measurement involves determining what to measure, identifying data collection methods, collecting the data, and analysis of data. The major expected benefits for measuring performance are:
However, the external mapping involves benchmarking with competitors within the same industry. This is when one organization pegs itself against the best in the industry within a geographical location or across geographies. Another form of external mapping involves specific processes or functions across industries and extending their best processes to one’s own paradigm.
A simple way of measuring productivity could be number of garments produced divided by number of machines in standard shift, although other variables such as type of fabric, construction method, machine type, extended working hours etc. provide distortion to the rather simple formula.
Productivity= (Total production / Resource Deployed)
Even in case of variance in no of working hour productivity does not give correct measurement. However, measuring efficiency could be one indicator which not only overcomes above variability but also provides a comparative analysis between two manufacturing facilities irrespective of their product profile, working hours etc.
In order to monitor, control and optimise non-operatives, Man to Machine Ratio (MMR) is an indicator which correlates number of operatives and number of operational machines and helps management to optimise labour costs without affecting the manufacturing capacity.
Therefore, to measure productivity, one needs to understand two factors: at what rate a manufacturer is producing (Efficiency) and how much manpower is being utilized (MMR).
For an apparel manufacturing process, efficiency can be defined as below:
= (Total SAM produced * 100/Total hours worked)
2. Defining Performance Indicator
Performance indicator is a type of performance measurement and is expressed as quantified amount, cost, or result of any process for indicating how much and how well the products or services are provided to customers during a given time.Performance measurement involves determining what to measure, identifying data collection methods, collecting the data, and analysis of data. The major expected benefits for measuring performance are:
- To learn and improve
- To control and monitor people & process
- To report externally and demonstrate compliance
3. Key Performance Indicators for Apparel Manufacturing
In a manufacturing supply chain, there are four key areas: Productivity, Quality, Delivery and Cost. Mapping each of these key areas is a continuous value-addition process, with different set of measures for different key area. Key KPIs across these four quadrants can be classified as: |
4. Applying KPIs
There are various ways to map the KPIs; one can start with introspection which is the simplest and the easiest way. Organisations have some key functions in their manufacturing chain, processes or methods which they have learnt to excel in, these are called core competencies. If there is a method to transfer or extend these competencies to some more functions or processes, or widen their scope; it can lead to a wider spread of internal excellence.However, the external mapping involves benchmarking with competitors within the same industry. This is when one organization pegs itself against the best in the industry within a geographical location or across geographies. Another form of external mapping involves specific processes or functions across industries and extending their best processes to one’s own paradigm.
KPI#1. Productivity
The rising costs and competitiveness has made it important for a manufacturer that he maximises his profits by not only optimising the productivity of operatives but by controlling total labour cost too.A simple way of measuring productivity could be number of garments produced divided by number of machines in standard shift, although other variables such as type of fabric, construction method, machine type, extended working hours etc. provide distortion to the rather simple formula.
Productivity= (Total production / Resource Deployed)
Even in case of variance in no of working hour productivity does not give correct measurement. However, measuring efficiency could be one indicator which not only overcomes above variability but also provides a comparative analysis between two manufacturing facilities irrespective of their product profile, working hours etc.
In order to monitor, control and optimise non-operatives, Man to Machine Ratio (MMR) is an indicator which correlates number of operatives and number of operational machines and helps management to optimise labour costs without affecting the manufacturing capacity.
Therefore, to measure productivity, one needs to understand two factors: at what rate a manufacturer is producing (Efficiency) and how much manpower is being utilized (MMR).
KPI #2. Efficiency
Efficiency is an indicator which helps in analysing unit’s functioning. Efficiency in its simplest form can be defined as ratio of output and input.For an apparel manufacturing process, efficiency can be defined as below:
= (Total SAM produced * 100/Total hours worked)
So while efficiency is calculated based on SAM of style / product, it helps to establish comparative performance measurement across various styles / products.
Business Case:
Considering an apparel factory with 500 operational machines and producing knits products such as Polo T-shirt of average fob price of USD 4.0 $ and work content of 12 SAM with operational variables of operational efficiency of 50%, DHU level of 12 and man to machine ratio (MMR) of 1: 2.2.
Calculating current state of efficiency (average monthly efficiency):
(a) Calculating SAM produced:
(b) Calculating total clock time:
So, the average monthly efficiency of the facility = 34, 65,000 * 100/69,30,000 = 50%
Impact assessment of increasing average efficiency of the factory:If we focus on improving value adding activities and speed of work and improve the current state of operational efficiency by 10%, i.e. from 50% to 55%, there should be an improvement in productivity, overall production capacity and sales by 10%, reduction in cost of manufacturing by 9.09%.
Man to machine ratio (MMR) can be defined as = (Total manpower / no. of total operational sewing machines)
MMR is a reflection of two factors – process design and use / extent of technology.
Optimised process design results in comparatively lower ratio whereas higher technological level helps reduce dependence on skill requirements along with manpower count. Labour cost can be reduced by improving on any or both the aspects.
Calculating current state of MMR:
Since, structure and requirements of the design, product development and marketing teams may vary from one organisation to other; the MMR calculated here is for operations of the organisation. Operations team includes management, production merchandising, commercial, accounts, raw material warehouse, production, quality, industrial engineering, maintenance etc..
Buyer never pays for the cost of repair or rework done by the manufacturer. So cost of repair and rework is an unwanted cost which can be minimised by controlling quality in the manufacturing process. Moreover poor quality further leads to rejections and delayed deliveries.
Quality performance may be measured as % Defective Rate, Defects per Hundred Units (DHU), Rejection Rate, and Inspection Pass Rate etc.
KPI#4.1 Defects per Hundred Units (DHU)
Any non-conformance to requirement is called a defect. Any garment containing one or more than one defect is called a defective garment. Defect per Hundred Units (DHU) can be defined as number of defects per hundred garments checked.
DHU are a universal measure of quality which provides a platform to perform root cause analysis to identify process abnormalities for improvements.
Rate of % defective can be defined as ratio of defective garments to garments checked.
Per cent Defective Rate and DHU can be calculated as follows:
Impact assessment of reducing DHU
An improvement in quality performance by reducing the DHU by 33% from current level of 12.0 to 8.0 will result improvements in productivity, production, production sales and reduction in cost of manufacturing simultaneously. The cost of manufacturing could be reduced by about 4.1% from USD 0.097 per minute to USD 0.093 per minute whereas production sales could be increased by 4.5%.
SAM or Standard Allowed Minute is the measure of the work content in a style, which has the following components.
SAM = Average Single Cycle Time X (1 + PF + MD) + BHT
Allowances:PF – Personal & fatigue, MD – Machine delay, BHT – Bundle Handling Time
 |
Standard minute calculation
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Business Case:
Considering an apparel factory with 500 operational machines and producing knits products such as Polo T-shirt of average fob price of USD 4.0 $ and work content of 12 SAM with operational variables of operational efficiency of 50%, DHU level of 12 and man to machine ratio (MMR) of 1: 2.2.
Calculating current state of efficiency (average monthly efficiency):
(a) Calculating SAM produced:
Monthly production detail of the unit for the month of December, 2014:
Style
|
Pieces
Produced
|
Machine
SAM
|
Helpers
SAM
|
Total
SAM
|
SAM produced
in the Month
|
AF1234
|
15,000
|
9
|
3
|
12
|
1,80,000
|
AF1235
|
18,500
|
10
|
4
|
14
|
2,59,000
|
AF1236
|
24,000
|
8
|
3
|
11
|
2,64,000
|
AF1237
|
23,500
|
7
|
2
|
9
|
2,11,500
|
AF1238
|
33,400
|
11
|
4
|
15
|
5,01,000
|
AF1239
|
19,500
|
10
|
3
|
13
|
2,53,500
|
AF1240
|
24,500
|
9
|
4
|
13
|
3,18,500
|
AF1241
|
24,500
|
8
|
4
|
12
|
2,94,000
|
AF1242
|
26,500
|
9
|
3
|
12
|
3,18,000
|
AF1243
|
18,500
|
10
|
4
|
14
|
2,59,000
|
AF1244
|
25,600
|
8
|
2
|
10
|
2,56,000
|
AF1245
|
29,208
|
9
|
3
|
12
|
3,50,500
|
2,82,708
|
34,65,000
| ||||
SAM produced in the Month =Pieces produced X Total Garment SAM
| |||||
(b) Calculating total clock time:
- Average no of Direct Manpower per day: 550 People
- Working Hours per day (excluding breaks): 8 Hours
- No of working days: 25 Days
- Total OT hours worked in the month: 5,500 Hours
So, the average monthly efficiency of the facility = 34, 65,000 * 100/69,30,000 = 50%
Impact assessment of increasing average efficiency of the factory:If we focus on improving value adding activities and speed of work and improve the current state of operational efficiency by 10%, i.e. from 50% to 55%, there should be an improvement in productivity, overall production capacity and sales by 10%, reduction in cost of manufacturing by 9.09%.
 |
| Chart#1: Impact of increased efficiency |
KPI#3. Man to Machine Ratio (MMR)
Capacity of an apparel manufacturing facility is determined by no of operatives working on sewing machines, higher proportion of non-operatives increases labour cost without much impact on capacity. Man to Machine ratio is the indicator which help us to establish correlation in between the two types of operatives and provide information to optimise the manpower size and hence the cost.Man to machine ratio (MMR) can be defined as = (Total manpower / no. of total operational sewing machines)
MMR is a reflection of two factors – process design and use / extent of technology.
Optimised process design results in comparatively lower ratio whereas higher technological level helps reduce dependence on skill requirements along with manpower count. Labour cost can be reduced by improving on any or both the aspects.
Calculating current state of MMR:
Since, structure and requirements of the design, product development and marketing teams may vary from one organisation to other; the MMR calculated here is for operations of the organisation. Operations team includes management, production merchandising, commercial, accounts, raw material warehouse, production, quality, industrial engineering, maintenance etc..
- Design, production development and marketing team: 42 people
- Operations team: 1,187 people
- No of operational sewing machines: 500 Machines
- So, the current MMR of the facility is = 1187/500 = 1:2.4
 |
| Chart#2: Impact of MMR reduction |
KPI#4. Quality
Quality can be defined as conformance to the buyer requirements. Buyers accept the goods only if the required quality parameters are met. In case of failing to meet the quality requirements, rework has to be done on manufacturer’s cost.Buyer never pays for the cost of repair or rework done by the manufacturer. So cost of repair and rework is an unwanted cost which can be minimised by controlling quality in the manufacturing process. Moreover poor quality further leads to rejections and delayed deliveries.
Quality performance may be measured as % Defective Rate, Defects per Hundred Units (DHU), Rejection Rate, and Inspection Pass Rate etc.
KPI#4.1 Defects per Hundred Units (DHU)
Any non-conformance to requirement is called a defect. Any garment containing one or more than one defect is called a defective garment. Defect per Hundred Units (DHU) can be defined as number of defects per hundred garments checked.
DHU are a universal measure of quality which provides a platform to perform root cause analysis to identify process abnormalities for improvements.
Rate of % defective can be defined as ratio of defective garments to garments checked.
Per cent Defective Rate and DHU can be calculated as follows:
- Per cent Defectives = No of defectives * 100 / No of garments checked
- DHU = Number of defects * 100 / No of garments checked
For example a broken stitch and a skip stitch on the same garments count to two defects, but the garment with these two defects is termed one defective garment.
In a typical shirt manufacturing factory, the amount of time required to repair any defect is about 7 – 8 times than its SAM.
 |
| Defect vs Defective |
Calculating current state of DHU and % defective
So the DHU for the facility = 33925* 100 / 282708 = 12
KPI#4.2 Buyer Inspection Pass Rate
Buyer inspection pass rate is the ratio of number of batches passing at the first inspection to the total number of batched inspected. This rate reflects the process capability aspect of manufacturing facility.
Buyer inspection pass rate= (No of audits passed on first inspection x 100 / total no. of audits)
The improvements can be achieved through eliminating non-value activities such as defects and rework in a manufacturing environment without changing pace of work (efficiency) or resource deployment design (MMR).
Calculating buyer inspection rate
- Total monthly production: 2,82,708 Pieces
- Total pieces checked: 2,82,708 Pieces
- Total defective pieces found: 26,85,726 Pieces
- Total defects found: 33,92,496 Defects
So the DHU for the facility = 33925* 100 / 282708 = 12
KPI#4.2 Buyer Inspection Pass Rate
Buyer inspection pass rate is the ratio of number of batches passing at the first inspection to the total number of batched inspected. This rate reflects the process capability aspect of manufacturing facility.
Buyer inspection pass rate= (No of audits passed on first inspection x 100 / total no. of audits)
The improvements can be achieved through eliminating non-value activities such as defects and rework in a manufacturing environment without changing pace of work (efficiency) or resource deployment design (MMR).
Calculating buyer inspection rate
- Total no of buyer audits in month: 24
- Audits passed in first time: 21
- Audits passed in re-audit: 2
- Audits failed: 1
Impact assessment of reducing DHU
An improvement in quality performance by reducing the DHU by 33% from current level of 12.0 to 8.0 will result improvements in productivity, production, production sales and reduction in cost of manufacturing simultaneously. The cost of manufacturing could be reduced by about 4.1% from USD 0.097 per minute to USD 0.093 per minute whereas production sales could be increased by 4.5%.
 |
| Chart#3: Impact of reduction of DHU |
KPI#5. Cut to ship ratio
The cut to ship percentage is the ratio of the total no of garments shipped and the total no of garments cut and indicates wastage in manufacturing process.
Cut to ship ratio can represented as a formula as shown below:
= (Total no of garments shipped *100 / Total no of garments cut )
= (Total no of garments shipped *100 / Total no of garments cut )
Calculating current cut to ship ratio
KPI#6.1 Cost of ManufacturingAny actions taken to improve any aforementioned KPIs will result in reduction of cost of manufacturing. Indicators used for measuring the improvement in the cost of manufacturing are:
Cost per Minute = (Total monthly operating cost/ Total SAM produced)
- Total no of garments shipped in month: 2,75,600
- Total no of garments cut in month: 2,91,304
- So the cut to ship ratio= (275600 / 291304) = 94.6%
KPI#6. Cost
Following are the two approaches to measure cost performance –- Cost of manufacturing
- Cost of quality
KPI#6.1 Cost of ManufacturingAny actions taken to improve any aforementioned KPIs will result in reduction of cost of manufacturing. Indicators used for measuring the improvement in the cost of manufacturing are:
- a. Cost per minute
- b. Cost per machine per month
Cost per Minute = (Total monthly operating cost/ Total SAM produced)
The cost of manufacturing is the function of not only labour wages, but efficiency, MMR and cost of quality also attribute to the cost of manufacturing.
Calculating cost per minuteCost per minute requires the total monthly operating cost which is the cost incurred to run the operations of the facility.
KPI#6.2 Cost per Machine per MonthWhile Cost per Minute indicates composite performance of operational performance and incurred operating cost on per unit (minute) basis, Cost per Machine is an indicator of actual expenses.
Cost per machine is the ratio of total cost incurred for time duration (most likely monthly basis) and total number of machines during the time period.
Cost per Machine per Month = (Total Monthly Operating Cost)/(Total number of Operating Machines)
Calculating cost per minuteCost per minute requires the total monthly operating cost which is the cost incurred to run the operations of the facility.
- Total monthly operating cost: 3,37,841 USD
- Total SAM produced: 34,82,900 minutes
- So the cost per minute= (337841/3465000) = 0.097USD per minute
KPI#6.2 Cost per Machine per MonthWhile Cost per Minute indicates composite performance of operational performance and incurred operating cost on per unit (minute) basis, Cost per Machine is an indicator of actual expenses.
Cost per machine is the ratio of total cost incurred for time duration (most likely monthly basis) and total number of machines during the time period.
Cost per Machine per Month = (Total Monthly Operating Cost)/(Total number of Operating Machines)
Calculating cost per machine
Cost of quality can be divided into two parts, cost of conformance and cost of non-conformance.
Cost of conformance is the cost incurred for maintaining good quality in the process. It includes appraisal cost and prevention cost.
Whereas the cost of non-conformance can be defined as the cost incurred due to poor quality in the process. It has two components – cost of internal failure and cost of external failure.
The cost of defects that occur before release to buyer or end user is considered as cost of internal failure whereas the cost of defect that occurs after release to buyer or end user is considered as cost of external failure.Any charge back / claim etc. should also be calculated as cost of external failures.
A typical estimation of cost of quality for an apparel factory may be about 18% to 22% of the operating cost. Elements of cost of quality may be as illustrated in chart.
- Total monthly operating cost: 337,841 USD
- Total no of operating machines: 500 Machines
- So the cost per machine= (337841 / 500) = 675 USD per machine per month
KPI#6.3 Cost of Quality
Cost of quality is the cost of not meeting quality requirement(s).It is proportionate to the amount of rework done and increases with an increase in rework.Cost of quality can be divided into two parts, cost of conformance and cost of non-conformance.
Cost of conformance is the cost incurred for maintaining good quality in the process. It includes appraisal cost and prevention cost.
Whereas the cost of non-conformance can be defined as the cost incurred due to poor quality in the process. It has two components – cost of internal failure and cost of external failure.
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| Chart#4: Impact of cost of quality |
A typical estimation of cost of quality for an apparel factory may be about 18% to 22% of the operating cost. Elements of cost of quality may be as illustrated in chart.
KPI#7 Delivery
In current business scenario, every buyer looks for opportunity to reduce Inventory and WIP across their supply chain. Order size and lead time are constantly being optimised to reduce business risks.
Delays in deliveries and short shipments from manufacturer’s end may result in to loss of sales at the point of sales. Buyers keep control over deliveries and quantities and often penalise manufacturers to compensate their losses due to any delay or short shipments.
Late deliveries and short shipments also affect the credibility of manufacturer from Buyer’s perspective.
So it important for a manufacturer to maintain the committed delivery dates and shipping of required quantities.
KPI#7.1 On Time in Full (OTIF)
On time in full is a composite measurement of deliveries with respect to the required delivery date and quantities. A better efficiency, lower DHU, higher inspection pass rate would help manufacturer to achieve higher OTIF percentage.
Any order which is shipped on time and meets the required quantity should be counted as qualified to OTIF. In case either there is delay in shipment or variance in shipped quantity, more than the tolerance provided by the buyer, the order should not qualify for OTIF.
OTIF can be calculated as ratio of orders qualified for OTIF to the total no of order processed.
OTIF =(No of Orders or Lots Delivered On Time & In Full x 100)/(Total Orders or Lots Delivered)
Calculating current state of OTIF %
From this we can say 5 shipments out of 12 were on time and in full quantity so, the OTIF% will be
OTIF = (5/12) X 100 = 41.67%
Apart from appropriate selection and usages of KPIs, management commitment plays important role in bringing changes into the working environment and to achieve the defined goals.
Late deliveries and short shipments also affect the credibility of manufacturer from Buyer’s perspective.
So it important for a manufacturer to maintain the committed delivery dates and shipping of required quantities.
KPI#7.1 On Time in Full (OTIF)
On time in full is a composite measurement of deliveries with respect to the required delivery date and quantities. A better efficiency, lower DHU, higher inspection pass rate would help manufacturer to achieve higher OTIF percentage.
Any order which is shipped on time and meets the required quantity should be counted as qualified to OTIF. In case either there is delay in shipment or variance in shipped quantity, more than the tolerance provided by the buyer, the order should not qualify for OTIF.
OTIF can be calculated as ratio of orders qualified for OTIF to the total no of order processed.
OTIF =(No of Orders or Lots Delivered On Time & In Full x 100)/(Total Orders or Lots Delivered)
Calculating current state of OTIF %
S. No.
|
PO Number
|
Delivery
Date
|
PO
Qty.
|
Shipment
date
|
Shipment
qty.
|
On
time
|
In
Full
|
On time
– In full
|
1
|
AF1200
|
04-Dec
|
15,000
|
04-Dec
|
15,000
|
YES
|
YES
|
YES
|
2
|
AF1201
|
07-Dec
|
16,000
|
08-Dec
|
16,000
|
NO
|
YES
|
NO
|
3
|
AF1202
|
09-Dec
|
24,000
|
09-Dec
|
23,500
|
YES
|
NO
|
NO
|
4
|
AF1203
|
12-Dec
|
14,500
|
11-Dec
|
14,000
|
YES
|
NO
|
NO
|
5
|
AF1204
|
14-Dec
|
19,600
|
14-Dec
|
19,700
|
YES
|
YES
|
YES
|
6
|
AF1205
|
15-Dec
|
19,500
|
16-Dec
|
20,100
|
NO
|
YES
|
NO
|
7
|
AF1206
|
17-Dec
|
24,500
|
17-Dec
|
24,500
|
YES
|
YES
|
YES
|
8
|
AF1207
|
21-Dec
|
24,500
|
21-Dec
|
24,000
|
YES
|
NO
|
NO
|
9
|
AF1208
|
25-Dec
|
26,500
|
27-Dec
|
26,500
|
NO
|
YES
|
NO
|
10
|
AF1209
|
26-Dec
|
12,300
|
26-Dec
|
12,200
|
YES
|
NO
|
NO
|
11
|
AF1210
|
29-Dec
|
25,600
|
29-Dec
|
25,600
|
YES
|
YES
|
YES
|
12
|
AF1211
|
30-Dec
|
30,700
|
30-Dec
|
30,700
|
YES
|
YES
|
YES
|
252,700
|
251,800
|
9
|
8
|
5
|
OTIF = (5/12) X 100 = 41.67%
Conclusion
KPIs are an excellent method of performance evaluation, monitoring as well as benchmarking. KPIs act as communicators in any organisation to identify variations and exceptions in performance and enable the management for initiating corrective actions.Apart from appropriate selection and usages of KPIs, management commitment plays important role in bringing changes into the working environment and to achieve the defined goals.
For a complex manufacturing such as apparel manufacturing, using KPIs will not only help to sustain, but also will provide opportunities for improvement and growth.
 | About Mrs. Diptimayee Pradhan The author has more than 10 years of experience in working different profiles in Apparel industry starting form merchandiser till category manager to consultant. The author has been engaged in different key activities including MIS implementation, minimising start-up loss in garment industry, implement lean tools etc. She holds a bachelor’s degree from Utkal University, Odisha, (1998-2002), India and master’s degree from Veermata Jeejabai technological Institute (VJTI), Mumbai, India (2002-2004). |