Measuring Efficiency of a Supply Chain pdf

Alistair Knox December 2007 The two part article is based on doctoral research by Prabir Jana at Nottingham Trent University, UK “An Investigation into Indian Apparel and Textile Supply

Measuring Efficiency of a Supply Chain -I

by Prabir Jana, Prof A.S Narag and Dr Alistair Knox

December 2007

The two part article is based on doctoral research by Prabir Jana at Nottingham Trent University, UK “An Investigation into Indian Apparel and Textile Supply Chain

Networks.” In first part we will discuss about efficiency measurement framework in Apparel Supply Chain and in second part we will discuss a case study of practically measuring supply chain efficiency of an apparel manufacturing organization and

associated complications and nuances.

Introduction:

What can’t be measured can’t be improved Even though Supply Chain Management is the most talked about topic today, currently no tool is available to measure any

manufacturing organizations’ supply chain efficiency Unlike productivity and or quality measurement, where the parameter can be measured objectively and expressed in unit or ratio, supply chain measurement is currently more of a qualitative statement Even though the word ‘performance’ or ‘efficiency’ is often used communicating the same meaning, measuring the performance or efficiency of an ‘enterprise’ or a ‘supply chain’ conveys different meaning altogether

Challenges of Measuring Efficiency of an Apparel Supply Chain

If we define ‘supply chain’ as an extended enterprise then efficiency measurement of a supply chain will mean efficiency measurement of multiple organizations in

synchronization One of the major strategic objectives of supply chain planning and management is to maximize total profit in the chain rather than maximizing profit of an organization in isolation The typical adversarial relationship between upstream and downstream players in the apparel supply chain is still prevalent making the job more difficult than saying Can you imagine if the buying organization you are dealing with, is sharing the profit with you or you have to share your profit and loss with your fabric supplier! Can you blindly trust your fabric supplier that the fabric developed for you will not be shown to another apparel manufacturer? Information that potentially influence the bottomline of an organization is kept so confidential, no trust, or partnership can

penetrate that It is not impossible, but difficult and not yet common in marketplace

What Are The Measurement Systems Available?

A variety of measurement approaches that have been developed and traditionally used for measuring supply chain performance (Lapide 1999) Apart from the wildly popularized Balanced Scorecard, there are other measurement approaches like Supply Chain

Council’s SCOR Model, the Logistics Scoreboard, Activity-Based Costing (ABC) and Economic Value Analysis (EVA)

Balanced Scorecards

Balanced Scorecard (BSC) was developed by Robert S Kaplan and David P Norton in

1992 (Kaplan et el 1992) BSC recommends use of executive information systems (EIS) that track a limited number of balanced metrics based on the following four perspectives: financial, customer, internal process, and learning and growth, which are closely aligned

to strategic objectives

Financial perspective (e.g., cost of manufacturing and cost of warehousing) Customer perspective (e.g., on-time delivery and order fill rate) Internal business perspective (e.g., manufacturing adherence-to-plan and forecast errors) Innovative and learning perspective (e.g., APICS-certified employees and new product development cycle time)

While BSC is popular among several industry segments and considered most balanced measurement of possible parameters, application of BSC in contract apparel

manufacturing is not suitable because organizations are secretive about financial data, customer perspective is out of bound and innovative and learning perspective is virtually missing in majority That leaves out only internal business perspective

The Supply Chain Council’s SCOR Model

The Supply Chain Council (SCC) was set up between 1996 and 1997, with members representing most industries and global geographies, including BASF, Bayer, Colgate-Palmolive, Lucent technologies, Procter & Gamble, Unilever and Siemens, as well as consulting organisations The SCC designed SCOR model, which is designed and

maintained to support supply chains of various complexities and across multiple

industries It spans all customer interactions (order entry through paid invoice), all physical material transactions (supplier’s supplier to customer’s customer, including equipment, supplies, spare parts, bulk product and software) and all market transactions (from understanding of aggregate demand to the fulfillment of each order)

This model is finally adopted to develop the measurement framework, and will be

discussed in detail in part II of this article

The Logistics Scoreboard

Another approach to measure supply chain performance was developed around logistical measures like

Logistics financial performance measures (e.g., expenses and return on assets) Logistics productivity measures (e.g., orders shipped per hour and transport container utilization) Logistics quality measures (e.g., inventory accuracy and shipment damage ) Logistics cycle time measures (e.g., in transit time and order entry time)

This method was developed by Logistics Resources International Inc (Atlanta, GA), a consulting firm specializing primarily in the logistical (i.e., warehousing and

transportation) aspects of a supply chain The company sells a spreadsheet-based,

educational tool called The Logistics Scoreboard that companies can use to pilot their supply chain performance measurement processes The Logistics Scoreboard is

prescriptive and actually recommends the use of a specific set of supply chain

performance measures These measures, however, are skewed toward logistics, having limited focus on measuring the production and procurement activities within a supply chain

This approach is more suitable for logistics service providers and none of the measures are in direct relevance to contract manufacturing

Activity Based Costing

Activity based costing (ABC) is an accounting methodology that assigns costs to

activities rather than products or services This was developed to overcome some of the shortcomings of traditional accounting methods in tying financial measures to operational performance The method involves breaking down activities into individual tasks or cost drivers, while estimating the resources (i.e., time and costs) needed for each one Costs are then allocated based on these cost drivers rather than on traditional cost-accounting methods, such as allocating overhead either equally or based on less-relevant cost drivers This approach allows one to better assess the true productivity and costs of a supply chain process From operational perspective ABC method highlights benefits through lower cost, improve quality and reduced manufacturing cycle time (Agarwal and Manjul 2005) For example, use of the ABC method can allow companies to more accurately assess the total cost of servicing a specific customer or the cost of marketing a specific product ABC analysis does not replace traditional financial accounting, but rather a post mortem

on past orders that provides a better understanding of supply chain performance by looking at the same numbers in a different way and helps better aligning the metrics closer to actual labor, material, and equipment usage

This method can be used for post mortem of cost incurred on different orders that are executed A case study of a garment manufacturer exporter (Agarwal and Manjul 2005) shows that cost calculated using ABC analysis was 27% to 31% higher compared to cost estimated traditionally using absorption costing While labour cost is the highest

component across all departments namely, sewing, cutting and sampling, it is as high as 90% in sewing and 50-53% in sampling As this method does not measure any other parameters related to time, quality and output oriented functions, so it is not a holistic approach to supply chain performance measure

Economic Value Analysis

One of the criticisms of traditional accounting is that it focuses on short-term financial results like profits and revenues, providing little insight into the success of an enterprise towards generating long term value to its shareholders – thus, relatively unrelated to the long-term prosperity of a company For example, a company can report many profitable quarters, while simultaneously disenfranchising its customer base by not applying

adequate resources towards product quality or new product innovation To correct this deficiency in traditional methods, some financial analysts advocate estimating a

company’s return on capital or economic value-added These are based on the premise that shareholder value is increased when a company earns more than its cost of capital One such measure, EVA, developed by Stern, Stewart & Co., attempts to quantify value created by an enterprise, basing it on operating profits in excess of capital employed (through debt and equity financing) These types of metrics can be used to measure an enterprise’s value added contributions within a supply chain However, while useful for assessing higher level executive contributions and long term shareholder value,

economic-value added metrics are less useful for measuring detailed supply chain

performance They can be used, however, as the supply chain metrics within an

executive-level performance scorecard, and can be included in the measures

recommended as part of The Logistics Scoreboard approach

This measurement method is long term financial health oriented While majority of the manufacturing organizations are self financed and balance sheets are not public,

Economic Value Analysis is not possible for such organizations

What measurement approach is right for apparel manufacturers?

In a platter full of so many options it is obviously difficult for apparel manufacturers to select the right approach While listing a comprehensive list of supply chain measures Lapide noted (lapide 2000) that most performance measurement systems are functionally focused For example SCOR model is a typical function based supply chain performance measure, often lead to functional silos and conflicting functional goals A balanced supply chain measurement system should cover function based, process based, cross enterprise and alignment of executives to management level measures Measuring

performance in a department as though it operates in a vacuum can have a negative effect

on other departments—and on the bottom line (Barnard 2000)

We have first highlighted the measurement parameters in the following table from a clothing manufacturer’s perspective While almost all manufacturing related measures are theoretically measurable by a manufacturer, only selected measures are possible in customer service, logistics and sales related parameters It is of pertinent importance to understand the secrecy and confidentiality issues perceived by every typical manufacturer working as CMT supplier or fully-factored clothing supplier to any high street retailer in

EU or US An organization of $ 25 million turnover is typically self financed and the operational efficiency horizon for such manufacturer spans between order receipts till goods trucked out of factory The objective was to develop easy and simple metrics to measure such organization’s supply chain efficiency After a thorough investigation of all measures SCORE model was selected for final adaptation Last, but not the least the measurement parameters are chosen based on the functional link between upstream and down stream players in the supply chain and not merely in house functions of an apparel manufacturer

Table: Lists of Possible Supply Chain Measures

Customer Service Measures Process, Cross-Functional

Measures

Purchasing Related Measures

Order Fill Rate

Line Item Fill Rate

Quantity Fill Rate

Backorders/stockouts

Customer satisfaction

% Resolution on first

customer call

Customer returns

Order track and trace

performance

Customer disputes

Order entry accuracy

Order entry times

Forecast accuracy Percent perfect orders New product time-to-market New product time-to-first make

Planning process cycle time Schedule changes

Material inventories Supplier delivery performance Material/component quality Material stockouts

Unit purchase costs Material acquisition costs Expediting activities

Extended Enterprise

Measures

Manufacturing Related Measures

Logistic Related Measures

Total landed cost

Point of consumption product

availability

Total supply chain inventory

Retail shelf display

Channel inventories

EDI transactions

Percent of demand/supply on

VMI/CRP

Percent of customers sharing

forecasts

Percent of suppliers getting

shared forecast

Supplier inventories

Internet activity to

suppliers/customers

Percent automated tendering

Product quality WIP inventories Adherence-to-schedule Yields

Cost per unit produced Setups/Changeovers Setup/Changeover costs Unplanned stockroom issues Bill-of-materials accuracy Routing accuracy

Plant space utilization Line breakdowns Plant utilization Warranty costs Source-to-make cycle time Percent scrap/rework Material usage variance Overtime usage

Production cycle time Manufacturing productivity Master schedule stability

Finished goods inventory turns

Finished goods inventory days of supply

On-time delivery Lines picked/hour Damaged shipments Inventory accuracy Pick accuracy Logistics cost Shipment accuracy On-time shipment Delivery times Warehouse space utilization End-of-life inventory

Obsolete inventory Inventory shrinkage Cost of carrying inventory Documentation accuracy Transportation costs Warehousing costs Container utilization Truck cube utilization In-transit inventories

Premium freight charges Warehouse receipts

Administration/Financial

Measures

Marketing Related Measures

Other Measures

Cash flow

Income

Revenues

Return on capital employed

Cash-to-cash cycle time

Return on investment

Revenue per employee

Invoice errors

Return on assets

Market share Percent of sales from new products

Time-to-market Percent of products representing 80% of sales Repeat versus new customer sales

APICS trained personnel Patents awarded

Employee turnover Number of employee suggestions

Source: Lapide 1999

Developing efficiency measurement framework in Apparel Supply Chain

Supply chain efficiency measurement framework is developed in terms of efficiency

shown by the chain with respect to key functional parameters spanning four different operation domains namely source, plan, make and deliver There are about five primary key performance indicators (KPI) identified in each operation domain and some primary KPI have multiple secondary KPIs to measure Each KPI is expressed in percentage Once all KPI are measured, weighted averages of all KPI would indicate the overall supply chain efficiency of the organization While a 100 percent supply chain efficiency index would mean perfect organization, there is a possibility of any organization having KPI value more than 100 percent

Operation domain KPI’s

Source

1) Inward Material Quality 2) Quantity and Timely Delivery 3) Procurement Unit Cost

4) Material Inventory Level

5) Vendor Development Capability

Plan

1) Adherence to Production Target 2) Sample Conversion Rate

3) Material Utilization 4) Cost Adherence 5) Planned T&A v/s Actual T&A

Make

1) Capacity Utilization 2) Production Cost Efficiency 3) Quality Capability

4) Change Over Time 5) Operator Training Effectiveness

Deliver

1) On Time Shipment 2) Order Fulfillment 3) Claims and Discounts 4) Quality at Delivery 5) Transit time

Conclusion

It is obvious from above parameters that all KPI neither have equal weight in final measurement nor all KPI are equally important for all organizations Organizations can decide priorities and weight at their will to finally arrive at the supply chain efficiency of

an organization as a whole In next part we will discuss how the above measurement parameters were used in a pilot case study

Measuring Efficiency of a Supply Chain -II

by Sharad Diwan, Prabir Jana, Prof A.S Narag and Dr Alistair Knox

December 2007

The two part article is based on doctoral research by Prabir Jana at Nottingham Trent University, UK “An Investigation into Indian Apparel and Textile Supply Chain

Networks.” In first part we have discussed about need and development of efficiency measurement framework in Apparel Supply Chain and in this second part we will discuss how to calculate each KPI and a case study of practically measuring supply chain efficiency of an apparel manufacturing organization and associated complications and nuances.

Introduction:

In part I we have discussed about the genesis and development of a measurement

framework for measuring efficiency of apparel supply chains Here first we will define and discuss how to calculate each KPI and then we will discuss a case study where we have tried to measure the supply chain performance of ABC Enterprise ABC enterprise

is a $ 15 million enterprise from Northern Capital Region (NCR), India having an ERP system running

Source

Under this domain in the supply chain we shall consider the sourcing of raw material and consumables for manufacturing of the garment i.e fabric, trims and accessories and packing material All the parameters will be considered under three different heads: fabric, trims and accessories and packaging material

[1] Inward Material Quality:

This parameter shall evaluate the adherence of quality standards of material received from vendors to that specified i.e deviation from the quality levels agreed between the supplier and the company Also the material quantity accepted may be equal to the

ordered quantity or less If a lesser quantity is supplied then the penalty will be applied in the vendor lead times But if the material received is of required quantity but of inferior quality then good quality material is accepted after screening Also if there is some discrepancy in the quantity stated and actual it will be penalized as case three in this KPI

There can be three cases:

1)Quality of material supplied is as per desired standards and 100% material is accepted

KPI is 100 2)Quality of material supplied is not as per desired standards and 100%

material is rejected KPI is 0

3)Quality of material supplied is not as per desired standards and material is accepted

fully or partially KPI is calculated as under

: 99-75% accepted -70 points

: 74-50% accepted -50 points

: 49-25% accepted -30 points

Less than 25% accepted -10 point

Quality parameters shall be considered as a whole for a product and not individual parameters like fastness, weaving defect etc However if the company has no quality policy for sourcing, this KPI shall not be applicable

[2] Quantity and Timely Delivery:

This parameter shall evaluate whether the quantity ordered is delivered on time or not for all the materials mentioned earlier The time to be considered will be a percentage of the lead time of the raw material However in case of late delivery the penalty shall be according to the % lead time delay and quantity supplied as per matrix below A

percentage of the lead time is being taken as different materials have lead times varying from one to sixty days Only the quantity and time are considered as quality has been covered in earlier parameter

The points can be allotted as:

Qty Rcd

Time delay

100% 99-75% 74-50% 49-25% >25%

>50 % 0 0 0 0 0

Moreover if the material was ordered in bulk to be delivered in lots, then the quantity will

be taken as cumulative Higher the KPI, better the efficiency

[3] Procurement Unit Cost

This parameter shall evaluate the cost incurred to procure the material i.e the various costs such as correspondence (e-mail, fax, courier, telephone etc.), conveyance

(transportation cost of personnel involved in procurement), official’s salary, electricity bills, etc This can be measured as a ratio between the procurement costs per material to the cost of the material The transportation costs of material will also be included in the material costs and the material costs would thus be costs of material at site Also costs

incurred in testing of raw material will be added in material cost (Total procurement

cost / total cost of material procured) x 100 = procurement unit cost KPI is expressed

as 100 – procurement unit cost Higher the KPI better the procurement efficiency Data

is collected over minimum 6 representative months and averaged It may be noted that procurement cost incurred in the month of March may arrive at warehouse during April,

so data collected for more number of months will give correct measure of this KPI

[4] Material Inventory Level

This parameter shall evaluate the stocked inventory level of the company Higher

inventory level increases the capital investment and also acquires more physical space Lower inventory level indicates better sourcing efficiency The inventory level can be measured as a ratio of daily requirements in volume terms upon average daily inventory stock expressed in percentage

 Issued stock per day = (Monthly Closing stock- Monthly Opening Stock + Total Received Stock)/ working days

 Stock Held per day = Average daily Opening Stock of the month

This KPI is calculated as Inventory Level Stock Ratio i.e (Issued Stock per day) / (Stock Held per day) expressed as percentage Higher the KPI better the performance

Data is collected over minimum 6 representative months and averaged

[5] Vendor Development Capability

This parameter will determine the Sourcing Department’s potential and capability to assist vendor during the product development or in Order processing Three types of the parameters which need to be checked during the product development are Technological Assistance, Financial Assistance and Timeliness of information and the Extent of