data delusions: how poor product data compromises the
TRANSCRIPT
Manuscript for MIS Quarterly Executive, October 2013
Data Delusions: Is Poor Product
Data Killing Your Business?
Professor Joe Peppard*
European School of Management and Technology
Schloßplatz 1
10178 Berlin
Germany
Professor Richard Wilding
Professor Alan Braithwaite
Cranfield School of Management
Cranfield,
Bedford MK43 0AL
United Kingdom
Harshal Gore
GS1 UK
Staple Court
11 Staple Inn Buildings
London WC1V 7QH
ABSTRACT
It is difficult to argue against the importance of having accurate data in making
decisions and in optimizing the performance of the supply chain. Yet, when we
studied the product data held by retailers in the UK we found that they were
unaware of the poor quality of that data. They were deluded by the belief that
because it is “in the system” it must be accurate! In this article we identify the
sources, causes and effects of inaccurate product data and discuss the
implications this has for supply chain performance. To aid organizations in
assessing the quality of product data in supply chains and implications, we
present a supply chain cost levers framework that we have developed from our
research. Actions that can be taken to avoid “data delusions” are outlined.
Keywords: Supply Chain, Data, Data Quality, Information Technology, Competitiveness,
Performance, Grocery Industry, Retail Operations
*Corresponding author [email protected]
DATA DELUSIONS
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Data Delusions: Is Poor Product
Data Killing Your Business?
o make effective decisions a firm needs accurate data.1 Data errors can cost a company
millions of dollars, alienate customers, suppliers and business partners, and make
implementing new strategies difficult or even impossible.2 Indeed, the very existence of
an organization can be threatened by poor data quality. In supply chains and complex
ecosystems with multiple participants, significant performance implications can arise when data
used in making decisions is of poor quality. Yet assuring the quality of data is difficult and
consistently achieving high-quality data, particularly across supply networks, is a battle that is
never really won.3
In retail industries it has been recognized that competition is no longer between individual
retailers but between the supply chains they are part of.4 To gain competitive advantage within
the grocery industry, multiple relationships need to be developed between a retailer and their
network of stakeholders, including produce suppliers, haulers, merchandisers, regulators, and
even the wider community. Problems with data quality have the potential to impact the
efficiency of supply chains and the consequential implications for the management of operations,
logistics, merchandising, stores and ultimately revenue and margin.5
The impact of poor quality forecast and demand information on supply chains is widely
established6 and is demonstrated in the well known “bullwhip” effect where demand distortion as
a result of over ordering leads to unreliable deliveries, increases in safety stock and subsequent
stock shortages.7 ‘Glitches’ in the supply chain have been shown to result in a reduction of share
1 In this paper, ‘information’ and ‘data’ are used interchangeably
2 T. Redman, ‘Improve data quality for competitive advantage’, Sloan Management Review, Winter, 1995, pp. 99-
107. 3 D. Ballou, S. Madnick, and P. Wang, ‘Special section: Assuring information quality’, Journal of Management
Information Systems, Vol. 20, No. 3, 2004, pp. 9-11. 4 M. Christopher, Logistics and Supply Chain Management: Creating Value-added Networks, 4
rd Edition, Prentice
Hall/Financial Times, London, 2011. 5 This is in addition to the finding that companies collect and use much less detailed information than experience
shows is prudent in making astute supply chain decisions. See The Challenges Ahead for Supply Chains, McKinsey
& Company, 2010. 6 H. Forslund and P. Jonsson, ‘The impact of forecast information quality on supply chain performance’,
International Journal of Operations & Production Management, Vol. 27, No. 1, 2007, pp. 90-107. 7 H.L. Lee, V. Padmanabhan and S. Whang, ‘Information distortion in a supply chain: the bullwhip effect,
Management Science, Vol. 43, No. 4, 1997, pp. 1875-1886. See also H.L Lee and S. Whang, ‘Information sharing in
T
DATA DELUSIONS
3
price of up to 25%.8 For example, in 2005 number 2 UK retailer Sainsbury’s stock price suffered
a major slump when their newly instigated logistics fulfillment centre and IT outsourcing
arrangement with Accenture among others resulted in problems within their supply chain and in
ultimately getting stock onto store shelves.9 Inventory record inaccuracy and misplaced stock
keeping units (SKUs) has been shown to substantially decrease retail profits due to lost sales and
gross margin as well as additional labour and inventory carrying costs.10
However, the impact of
poor quality product data on supply chain performance is less known. What is the extent of this
problem and what can organization do to mitigate the risk of poor data?
To answer these questions, we first researched the UK’s grocery sector to assess the quality of
data within the industry’s supply chain, to consider the scale and implications of poor data
quality and identify ways to rectify the problem (see Appendix for further details of this
research). Our study reviewed product data held by retailers Tesco, Sainsbury, Asda (owned by
Walmart) and Morrison, who together account for 75.6% of the total industry, to identify data
errors and highlight the issues and obstacles to optimizing supply chain performance. Global
FMCG suppliers Unilever, Nestle, P&G and Mars also provided relevant information on the
products they traded with these retailers. We then undertook interviews with supply chain
executives in a number of different industries as well as engaged in a number of collaborative
projects to improve supply chain performance. Our findings make uncomfortable reading and
our analysis has led us to conclude that retailers are potentially leaving billions of dollars on the
table.
The supply chain concept – processes and data flows
The supply chain and logistics concept looks at the total end-to-end system of satisfying
customers’ requirements at a price they are prepared to pay and which generates a satisfactory
return to the corporation. The mechanism by which the complex network of entities, that together
comprise the supply chain, works is through shared information, closely aligned and integrated
processes and real time visibility.
The competitive advantage and effectiveness of the value chain is dramatically enhanced by
optimising across functions and entitities and through the whole chain compared with the
accumulation of what might be separate but individually optimised functions. This is achieved by
time compression, consistent and excellent processes, high levels of data accuracy and real time
a supply chain’, International Journal of Technology Management, Vol. 20, No. 3-4, 2000, pp. 373-387; and C.
Glatzel, S. Helmcke and J. Wine, ‘Building a flexible supply chain for uncertain times’, The McKinsey Quarterly,
March, 2009. 8 K. B. Hendricks and V. R Singhal, ‘Association between supply chain glitches and operating performance’,
Management Science, Vol. 51, 2005, pp. 695-711. 9 S. Ranger, ‘Sainsbury’s scraps outsourcing deal with Accenture,’ Silicon.com, 27
th October 2005,
http://www.silicon.com/technology/it-services/2005/10/27/sainsburys-scraps-outsourcing-deal-with-accenture-
39153723/; S. Butler, ‘Sainsbury drops Accenture to bring IT back in-house,’ The Times. And L. Clark, ‘Sainsbury
writes off £210m as supply chain IT trouble hits profits,’ Computerweekly, 24th
October, 2004. 10
A. Raman, N. DeHoratius and Z. Ton, ‘Execution: the missing link in retail operations’, California Management
Review, Vol. 43, No. 3, 2001, pp. 136-152.
DATA DELUSIONS
4
visibility through the chain. The experience of this process synchronisation along the chain is
substantial business benefits in the form of: improved customer service experience, reduced
inventories, lower operating costs and improved use of fixed assets.11
The consequence of less
than optimal processes is waste and lost opportunity.12
These observations have lead to a widespread focus on process excellence within the supply
chain, with many organizations adopting the Supply Chain Operations Reference (SCOR) model
from the Supply Chain Council.13
SCOR characterises the supply chain through five core
processes: plan, source, make, deliver and return. In the model, these core processes are
decomposed into several levels of detail. The model is also used by the big enterprise systems
vendors like SAP and Oracle who have process templates for specific industrial segments that
align with the SCOR model.
A limitation that we have observed in the application of the SCOR structure is that it is generic
and does not relate directly to the actual processes carried out by specific functions or entities
within their industries or firms. For example, the detail of Vendor Managed Inventory or
Consignment stock will require a firm specific systems representation that goes beyond the
standard but is critically important for the trading of that business. In addition, the templates
from the systems vendors tend to focus on the transactional dimension and do not provide strong
insights into supply chain best practices and variations around it.
Building on the strengths of SCOR, and to address these shortcomings, we have in our research
developed a framework that reflects the specific details of the retailing environment (see Figure 1
for the high level view). This framework captures the hierarchy of retail planning and execution
and how it interacts functionally across the supply chain in retail organisations. The model has
been used to define detailed process architecture at a range of retailers including electrical
retailer Dixons (DSGI), UK discount store Poundland, grocer Morrisons, Israeli retailer
Shufersal and Russian electrical retail chain M-Video.
Stepping through the model from the top of Figure 1, the starting and ultimate feedback point is
the business plan which is expressed in financial and volume outcomes and will generally be set
in the context of prior years, modifications in retail footprint, corporate plans for changes to
business positioning, competitor activities as well as the economic environment. The business
plan unpacks into the category plans which describe the category objectives in terms of range,
sourcing, price points, customer proposition and promotions and markdowns. The category plans
do not describe the specific products in the range; that is achieved by the range plan. In the range
plan the activities of planning and placing the stock keeping units (SKUs) in the range takes
place along with their pricing, sourcing, promotion and allocation to stores. This is the engine of
any retailer since it is the point at which the category plan is turned into reality and positioned for
physical execution.
11
M. Christopher, Logistics and Supply Chain Management: Creating Value-added Networks, 4th
Edition, Prentice
Hall/Financial Times, London, 2011; and A. Braithwaite and R.D. Wilding, ‘Laws of logistics and supply chain
management’, Financial Times Handbook of Management, Third Edition, Chapter 4, pp. 249-259, Pearson, 2004. 12
A recent McKinsey & Company survey highlighted the problem of getting different functions to collaborate. See
The Challenges Ahead for Supply Chains, McKinsey & Company, 2010. 13
Supply Chain Council. www.supply-chain.org
DATA DELUSIONS
5
FIGURE 1 Supply chain process model for retailers.
Alongside the range plan is the requirement for item management. This is the process of putting
products and relevant data “on the system” so that the product can be bought and subsequently
distributed. This is the point at which data accuracy is either achieved or compromised.
Following through the rest of the model comes supply chain planning which is the forecasting,
positioning and replenishment of stock in the chain to satisfy the planned and actual demand and
puts in place the resources to hold the stock and move it from sources through distribution to
outlets or customers. This in turn cascades into operations management, logistics execution and
retail store management.
The boxes to the right of the figure indicate that the plan must maintain requisite visibility at
each stage and the ability to model and adjust both up and down depending on the implications
of actual performance. To the left is the feedback loop to the higher levels of planning as well as
the process of capturing, reporting and managing to key performance indicators (KPIs) from
every level.
All of the processes downstream of range planning are dependent on data accuracy: cost, price,
packaging, volume (cases and cubic metres), lead times, order quantities and many other
attributes. The process of achieving data accuracy is therefore core to overall supply chain
performance and ultimately the performance of the business. The presence of a significant
number of errors will put cost on the business. This recognition of the importance of data
accuracy is carried through into the data architectures of the major IT systems providers; they
refer to it as Master Data Management and it is documented in their libraries in a way consistent
with our framework.
Supply chain operations management
Range planning
Category planning
Product category planning
Supplier strategy
Customer planning
Store & channel planning
Logistics Retail operations
Bu
siness p
lann
ing im
pact cascad
e
Business planning
Measu
remen
t versu
s KP
Is
Retail price mgt
Markdown mgt
Demand planning & forecasting
Intake planning
Replenishment & inventory planning
Freight & transport planning
Network capacity planning
SKU range planningSupplierplanning
Promotion planning
Store grade & space planning
Itemmgt
Promotion mgtMulti channel order
fulfilmentImport mgtOperational buying
Warehouse& DC mgt
Transport mgt
Stock control Returns
Store operations POS
Call centre & e-commerce
Customer service & loyalty
Sup
ply ch
ain visib
ility & trackin
g
Sup
ply ch
ain visib
ility & trackin
g
Supply chain planning
1.0.0
2.0.0
2.1.0 2.2.0 2.3.0 2.5.0 2.6.02.4.0
Supply chain operations managementSupply chain operations management
Range planning
Category planning
Product category planning
Supplier strategy
Customer planning
Store & channel planning
LogisticsLogistics Retail operations Retail operations
Bu
siness p
lann
ing im
pact cascad
eB
usin
ess plan
nin
g imp
act cascade
Business planning
Measu
remen
t versu
s KP
IsM
easurem
ent ve
rsus K
PIs
Retail price mgt
Markdown mgt
Demand planning & forecasting
Intake planning
Replenishment & inventory planning
Freight & transport planning
Network capacity planning
SKU range planningSupplierplanning
Promotion planning
Store grade & space planning
Itemmgt
Promotion mgtMulti channel order
fulfilmentImport mgtOperational buying
Warehouse& DC mgt
Transport mgt
Stock control Returns
Store operations POS
Call centre & e-commerce
Customer service & loyalty
Sup
ply ch
ain visib
ility & trackin
gSu
pp
ly chain
visibility &
tracking
Sup
ply ch
ain visib
ility & trackin
gSu
pp
ly chain
visibility &
tracking
Supply chain planning
1.0.0
2.0.0
2.1.0 2.2.0 2.3.0 2.5.0 2.6.02.4.0
DATA DELUSIONS
6
Data quality and supply chain management
Supply chain data can be explored under two main headings: planning and co-ordination flows
and operational requirement flows.14
The overall purpose of the planning and co-ordination flow
is to integrate the plans and activities within the individual organisations and, wherever possible,
across the complete supply chain. Data is vital to ensure the effective co-ordination of the supply
chain. The addition of information to support collaboration and coordination has become
increasingly important, highlighting the need for effective information sharing.
From the model above, the co-ordination activity results in plans which specify the strategic
objectives, capacity constraints, logistical requirements, inventory deployment, manufacturing
requirements, purchasing and procurement requirements, pricing and margin management and
finally forecasting. The key driver for these plans should be the supply chain’s strategic
objectives focusing on customer service but related to the financial and marketing objectives.
Operational information’s overall purpose is to provide the detailed data required for integrating
the supply chains operation on a short-term basis. Importantly, it enables order management,
order processing, distribution operations, inventory management, transportation and
procurement. Order management refers to the transmission of resource requirement information
between the supply chain members. Key to this activity is the accuracy and timeliness of the
information. The transfer of information between supply chain members can be achieved by a
variety of methods including phone, fax, electronic data interchange or internet. Order
processing assigns inventory to customer orders. Distribution operations are responsible for co-
ordinating the information to provide the product assortments required by the customer, the key
emphasis is to store and handle inventory as little as possible while still meeting the customer
service requirements defined through the strategic objectives. The role of inventory management
information is to ensure the various supply chain operations have adequate inventory to perform
as planned. Transportation and shipping information directs the movement of inventory.
Change in ownership and the movement across national boundaries also requires supporting
documentation. The procurement information is necessary to complete purchase order
preparation, modification and release.
The critical importance of effective information management within the grocery supply chain
can be observed by viewing the transaction statistics of one of the U.K’s leading retailers. Using
Institute of Grocery Distribution15
data it is possible to estimate that in 2009, this organization
probably handled the following weekly transactions:
14
D.J. Bowersox and D.J. Closs, Logistics Management: The Integrated Supply Chain Process, McGraw-Hill, New
York, 1996. 15
Institute of Grocery Distribution data. See UK Grocery Retail Outlook 2009 - Repositioning for Growth, Institute
of Grocery Distribution, London UK. www.IGD.com. Also used data from K. Campo, E. Gijsbrechts, and P. Nisol,
‘Towards understanding consumer response to Stock-Outs,’ Journal of Retailing, Vol. 76, No. 2, 2000, pp. 219-242;
T. W. Gruen and D. Corsten, A Comprehensive Guide to Retail Out-of-Stock Reduction in the Fast-Moving
Consumer Goods Industry, Grocery Manufacturers of America, Washington DC, 2008,
http://www.globalscorecard.net/download/ecr_related.asp; T. W. Gruen, D. Corsten and S. Bharadwaj, Retail Out of
DATA DELUSIONS
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22 million sales
96,000 invoices
42,600 depot-to-store deliveries
29,300 store replenishment orders.
Managing the volume of data that results from these transactions, both internally and between
the organisation and its many suppliers, is impossible without the use of IT. Indeed, advances in
IT have had a huge impact on the evolution of supply chain management.16
As a result of such
technological progress, supply chain partners can now work in tighter coordination to optimize
the chain-wide performance, and the realized return is often shared among the partners.17
The
objectives of using IT in supply chain management include: providing information availability
and visibility, enabling a single point of contact for data, allowing decisions based on total
supply chain information, and facilitating collaboration between supply chain partners. Research
shows that effective information sharing significantly enhances effective supply chain practice.18
Yet while organizations have deployed IT to help them manage the increasing complexity of
their supply chains, many continue to struggle to exploit these investments.19
A key reason can
be found in the old IT adage “Rubbish in, Rubbish Out”! Unfortunately, most organizations often
just don’t know how bad their data is – in our study we had to discard the data of one retailer as
it was incomplete. Many managers are totally unaware of the quality of data they use, naively
assuming that because it is “in the system” it is accurate. And poor data quality can have a
Stocks: A Worldwide Examination of Extent, Causes and Consumer Responses, Grocery Manufacturers of America,
Washington DC, 2002, http://www.globalscorecard.net/download/ecr_related.asp. 16
For a literature review see A. Gunasekaran and W.W.T. Ngai, ‘Information systems in supply chain integration
and management’, European Journal of Operational Research, Vol. 159, 2004, pp. 269-295. 17
See M.J. Schnetzler and P. Schonsleben, ‘The contribution and role of information management in supply chains:
a decomposition-based approach’, Production Planning & Control, Vol. 18, No. 6, 2007, pp. 497-513; H.L. Lee and
S. Whang, ‘Information sharing in a supply chain,’ International Journal of Technology Management, Vol. 20, No.
3-4, 2000, pp. 373-387; J. Auromo, J. Kauremmaa and K. Tanskanen, K., ‘Benefits of IT in supply chain
management: an explorative study of progressive companies’, International Journal of Physical Distribution and
Logistics Management, Vol. 35, No. 2, 2005, pp. 82-100; and Gunasekaran and Ngai, 2004. 18
H. Zhou and W.C. Benton, ‘Supply chain practice and information sharing’, Journal of Operations Management,
Vol. 25, 2007, pp. 1348-1365. 19
This finding is not just confined to IT investments to improve supply chain performance but for all IT
investments. Surveys and reports continue to confirm that the majority of organizations do not realize expected
business value from IT-enabled business projects. One recent paper reported that 74% of IT projects from 1994-
2002 failed to deliver expected value. See D. Shpilberg, S. Berez, R. Puryear and S. Shah, “Avoiding the alignment
trap in information technology,” MIT Sloan Management Review, Vol. 49 No. 1, 2007. For additional evidence, see
The Challenge of Complex IT Projects, The Royal Academy of Engineering: London, 2004; National Audit Office
Delivering Successful IT-enabled Business Change, Report by the Comptroller and Auditor General, HC 33-1,
Session 2006-2007, London, November, 2006; R. Ryan Nelson, “IT project management: infamous failures, classic
mistakes and best practices,” MIS Quarterly Executive, Vol. 6, No. 2, 2007, pp. 67-78; and D.U. Himmelstein, A.
Wright, and S. Woolhandler, ‘Hospital computing and the costs and quality of care: a national study’, The
American Journal of Medicine, Volume 123, No. 1, 2010, pp. 40-46.
DATA DELUSIONS
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substantial negative impact on an organization’s operating costs and efficiency and can
significantly compromise the execution of corporate strategy.20
Ensuring the quality of data is a crucial foundation for the effective management of a supply
chain. Data quality refers to the extent to which the data created and used by business operations
meets objective quality criteria and is regarded as fit for purpose by its different users. Data
quality itself is a multi-dimensional concept that can be defined by a number of objective and
subjective attributes. Some of the more common dimensions include accessibility, appropriate
amount, believability, completeness, concise reputation, consistent representation, ease of
manipulation, free-of-error, interpretability, objectivity, relevancy, reputation, security,
timeliness, understandability, and value-added.21
A more pragmatic view of data quality is
“fitness for use”; that is, if users of the data feel that its quality is sufficient for their needs, then,
from their perspective at least, the quality of the information available to them is fine. This, of
course, may be erroneous.
Achieving quality information is an inexact science. Methodologies for assessing information
quality have been proposed22
; usable data quality metrics have been suggested23
; while
information quality benchmarks have also been developed.24
Poor data quality can result from a
range of causes relating to information systems design, business processes and the behavior of
data subjects and employees.
For example, system design practices that impair data quality include making the recording of
data mandatory when there may be legitimate reasons for not providing it (e.g. requiring a fax
number for all orders) and restricting the range of data choices so that an inaccurate value must
be selected (e.g. forcing customers to select a number of employees greater than zero). By far the
most significant contributor to poor data quality, however, has been the failure of many system
designers to make provision for different meanings of missing values. Many systems, for
example, do not properly differentiate between a value of zero, “not applicable” and that the
correct value is unknown. Such ambiguities can have significant impact on data analysis, such as
making it impossible to distinguish between, for example, companies with no employees,
business that do not have employees (e.g. sole traders) and companies with an unknown number
of employees.
Efforts to improve data quality have mostly focused on accuracy and, even then, on the accuracy
of specific data types such as ‘names’ and ‘addresses.’ Just as product quality has been improved
through quality assurance and quality management processes, so data quality can be improved
20
G.L. Neilson, K.L. Martin and E. Power, ‘The secrets of successful strategy execution’, Harvard Business Review,
June, 2008, pp. 61-70. 21
R.Y. Wang and D.M. Strong, ‘Beyond accuracy: what data quality means to data consumers’, Journal of
Management Information Systems, Vol. 12, No. 4, 1996, pp. 5-34. 22
Y.W. Lee, D.M. Strong, B.K. Kahn, and R.Y. Wang, ‘AIMQ: A methodology for information quality
assessment’, Information & Management, Vol. 40, 2002, pp. 133-146. 23
L.L. Pipino, Y.W. Lee and R.Y. Wang, ‘Data quality assessment’, Communications of the ACM, Vol. 45, No. 4,
2002, pp. 211-218; and D.M. Strong, Y.W. Lee and R.Y. Wang, ‘Data quality in context’, Communications of the
ACM, Vol. 40, No. 5, 1997, pp. 103-110. 24
B.K. Kahn, D.M. Strong, and R.Y. Wang, ‘Information quality benchmarks: product and service performance’,
Communications of the ACM, Vol. 45, No. 4, 2002, pp. 184-192.
DATA DELUSIONS
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through the adoption of processes for assessing both objective and subjective data quality,
prioritizing quality issues, and acting on them through one-off improvement projects and
continuous improvement efforts. There has also been some limited research that has explored
incentive schemes with a supply chain for reliable and truthful information to be furnished.25
Dealing with quality issues usually requires a combination of both technical and organizational
changes. However, to begin, the nature and source of quality issue must be understood.
The extent of poor data in the retail supply chain
Within the grocery supply chain, every product – often referred to as a Consumer Unit (CU) – is
uniquely identified; each will have a number of attributes attached to it including dimensions,
weight, volume, sell by date, food allergy, etc. Supply chain parties assign a unique Global Trade
Item Number (GTIN) to every product. GTIN is an identifier for trade items defined by GS1
(comprising the former EAN International and Uniform Code Council) and used exclusively
with barcodes.26
GTINs can store up 66 items of data about a product. These identifiers are used
to look up product information in a database (often by inputting the number through a bar code
scanner pointed at an actual product, perhaps at a point-of-sale terminal or a data collection
device in a warehouse) which may belong to a retailer, manufacturer, collector, researcher, or
other entity. The uniqueness and universality of the identifier is useful in establishing which
product in one database corresponds to which product in another database, especially across
organizational boundaries. As each party in a supply chain will have its own systems and
databases, it is of crucial importance that any data held on products matches and is accurate
where performance is dependent on smooth data flows. If not, the performance of the supply
chain is compromised.
Surprisingly, when we reviewed the over 1 million product data records provided by the four
retailers we found a very significant amount of duplicate records (i.e. multiple records for the
same product). Indeed, after normalizing the data and removing these duplicates, the total
number of records available for analysis totaled 427,154 – less than half the original number.
Analysis of these records found that there was a high level of missing data from the retailers’
files. For example, 50% had dimension data missing, 70% had product net weight missing with
trading unit dimensions absent in 42% of files. Further investigation revealed that this was often
due to some of the retailers holding data in other bespoke systems rather than their core trading
systems. For example, some product data is collected and held by in-store merchandising system
rather than being held in a central product data master file. This can lead to further complications
as data held on ‘local’ systems can contradict that held in master data files.
25
M. Feldmann and S. Muller, ‘An incentive scheme for true information providing in supply chain’, Omega, Vol.
31, 2003, pp. 63-73. 26
The barcode on an individual product is generally a representation of the Global Trade Identification Number
(GTIN). This is analogous to a unique telephone number for a product. The first few digits identify the country of
the manufacturer, the next digits identify the company and the final digits identify the individual product.
DATA DELUSIONS
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The research also discovered a shocking level of inaccuracies in key product data, with
inconsistencies of up to 80% between the data held across the retailers. This was very often due
to ‘dummy entries’, for example 1x1x1 size dimensions, being entered to meet data entry
requirements. This in turn has resulted in costly “work arounds” being implemented within the
individual organizations. For example, we compared a total of 4,290 unique GTINs relating to
product cases across all four retailers. We found an extremely low correlation of case
dimensions, volumes and weights, with less than 50% data consistency even when comparing
data from two just retailers.
In particular, Ti/Hi data (number of cases stored on a layer and the number of layers stacked on a
pallet) is a critical piece of information for warehouse and distribution planning and
management. Our analysis found a high level of mis-matches because suppliers provide different
pallet configurations to their customers. The main reasons for this are commercial arrangements
and warehouse limitations, perhaps due to specific pallet height restrictions at a retailer’s
warehouse.
The one statistic which exhibited a higher degree of correlation was the number of consumer
units per traded unit (i.e. the number of items per pack/case or pack size). This data forms the
basis for calculating the volume of purchases placed by retailers on suppliers. It is clear that
more attention is paid to the accuracy of this data, rather than to other product attributes which
impact activities further down the supply chain. What this also does is illustrate the level of
accuracy that can potentially be achieved with focus and management attention.
It should be noted that even though this important purchasing data is of a higher quality, 10% of
the information relating to case and pack sizes remains inconsistent. These discrepancies in
traded unit data between suppliers and retailers will cause problems in invoice matching and
show up in apparent stock ‘shrinkage’, unexpected stock outs, and under- or over-payments to
suppliers.
As key data is also being shared with consumers, organizations’ data accuracy is under even
further scrutiny. A bar code, a unique product identifier, needs to be trusted with the data the
consumer accesses being accurate and timely. During our research, some humorous examples of
incorrect data were identified. For example, when scanning the bar code of a leading brand of
cornflakes, one mobile app provided information about dog bowls. When scanning another
barcode for thick sliced bread, information about a disposable latex gloves dispenser was
provided! While these are amusing, inaccurate health and wellness information can have
catastrophic consequences. Inaccurate product traceability data, enabling the origins of product
batches and their distribution through the supply chain to be tracked, can become problematic in
the event of any ingredient becoming the subject of a health alert. There’s a clear need here for
industry to collaborate so that product data is accurate, up-to-date and standardized across supply
chains, stores and online.
The impact of poor data quality cuts across all parts of the supply chain. For example an error in
data on case dimension results in inaccurate vehicle loading with underutilized capacity in the
vehicle or possibly items having to be left behind because they do not fit on the truck. The same
DATA DELUSIONS
11
error then means the commercial function risks lost sales due to left behind replenishment stock
and in the retail operation incorrect shelf face design leads to poor displays and double handling
of items.
The data provided by the suppliers were significantly more complete than those of the retailers.
This was primarily due to suppliers operating in-house product information management
solutions and having strong data quality governance processes. When we matched Consumer Unit
and traded unit data held by each of the four retailers with corresponding data held by the suppliers we
found less than 25% of the data held by retailers matched with product data from the supplier. We
discovered that the one exception where an improved correlation was apparent (a still low 43% match)
was an instance where one supplier had recently undertaken a particular data quality improvement project
concerning product weights.
Why data delusions propagate within the supply chain
With this level of inaccuracy and the importance of having accurate product data for the effective
performance of the supply chain, we sought to uncover why this situation exists. From our
analysis, the reasons for much of the inconsistency and inaccuracy of product data held by
retailers can be found within supply chain processes. In short, different functional areas have
different data needs; in the absence of an accurate and standardized source of data, each
department has created its own local repository of information.
Our study revealed a clear lack of clarity over who is responsible for data accuracy.
Investigations as part of our research suggest that a probable reason for this is that the
responsibility for master data management generally rests with the “commercial teams” in
retailing organisations, for whom such details are unexciting in relation to selecting new products
and doing deals with suppliers. The minutiae of adminstration of master product data is not the
most invigorating part of their jobs. As a result it and can easily be rushed or delayed – leading to
errors.
Figure 2 identifies key areas where these separate islands of data exist, and highlights the key
effects they have on the operational efficiency of the supply chain. Within the retail supply
chain it can be seen that poor data impacts on administration, depot and store resulting in
‘causes’ which then ‘effect’ the operational effectiveness of the supply chain. For example,
inaccurate product descriptions causing pricing and replenishment confusion in stores the effect
being additional manual effort required and delays in shelf replenishment.
For example, a multibillion dollar electrical retailer maintained a complete floor of its office
complex for a team that fixes queries and errors on purchases and inbound product flows. The
floor houses between 80 and 100 people and their work is about resolving purchase price
differences, invoice to purchase order discrepancies and delivery quantity differences. The
measured level of inaccuracy is more than 40% of all documents lead to a system query requiring
the intervention of this team. While suppliers in this sector are notoriously unreliable on both
delivery quantity and invoice pricing, the initial accuracy of the purchase orders is also a
DATA DELUSIONS
12
significant root cause of error chasing. The cost of this team is in excess of £4m per annum and
the consequential costs of quarantined deliveries and margin loss went un-quantified.
FIGURE 2 Critical areas that reduce supply chain performance.
A food equipment manufacturer and distributor we studied experienced a complaint rate of more
than 50% on all invoices issued by its service department. This was due to account details set up
errors and duplication issues where the price of the service contract was not accurately recorded.
The effect of this was to extend its accounts receivables to more than 150 days and an entire
department of more than 20 people spent their time fixing the problems that had been created
rather than fixing their origin. The company was considering an investment in systems to
DATA DELUSIONS
13
support faster query resolution until they realized that it would be cheaper and faster to fix the
problem at source.
Assessing the impact of data quality on cost and supply chain performance
From our work with retailers, we have developed a ‘supply chain cost levers’ framework that
can be applied so that the opportunities from improved accuracy can be estimated with this then
providing a platform for identifying improvement actions. Illustrated in Figure 3, this ‘levers’
framework can be used to identify cause-and-effect implications of poor quality data from
different performance levers along the supply chain. It is configured to ascertain the different
dimensions of data accuracy and how they impact on the functional performance in the areas
from suppliers to the shop floor.
On the left hand side of the diagram are the specific areas of data accuracy. At the physical level,
these include pack quantities, dimensions and distribution factors such as number of ‘cases per
pallet’ and ‘pallet height’. The commercial factors relate to shelf life, cost, pricing and
promotional activities as well as lead time and order quantities. Running along the top of this
figure are the supply chain touch points at which cost is created. These range from fulfilment
through invoice processing and distribution to the commercial teams, stock and retail operations
and waste.
The framework works by completing each cell where a possible interaction is identified; the
implication of this approach is that the effect of data accuracy does not touch every point in the
chain and the potential impact on value will also be different. That is why the framework has
apparant gaps where nothing is applicable. For example, the cell that describes the potential cost
of “pallet dimension” inaccuracy shows just one cell where it impacts. The comment in the box
says that it can lead to “Distortion of warehouse capacity planning – often leading to
unrecognised spare capacity”; this can result in excess cost due to having more capacity than is
actually needed. For example, if a warehouse operation is forced to use third-party outside
storage due to the erronous belief that additional capacity is required then the cost of both double
handling and the extra space will be incurred. These costs can be substantial.
A hard discount retailer we studied was buying outside warehousing capacity and incurring
double handling because its systems were telling them that the warehouse was full. The cost of
this was around £400,000 per year. Detailed forensic evaluation of the warehouse occupancy
discovered that the standard pallet quantities in the systems were incorrect and their warehouse
location system was blocking slots. Far from being full, the site was actually 15% underutilised.
Accurate maintenance of the product master file and a systematic review of what was (or was
not) in each slot released space and bought this company a holiday in its outside storage
capacity.
DATA DELUSIONS
14
The bottom line of the framework shows the potential cost impacts at each touch point. These
costs can be measured or estimated so that the individual business has the opportunity to identify
its potential for improvement. Measuring data accuracy in each area provides the link to the
costs.
Completing the cells in this framework helps to quickly bring into focus the dynamics of the
problem and the following observations can be made:
Data elements impact the different functions and their costs unevenly when they are
not accurate; not every measure of accuracy from our study is relevant to each
function as shown by the blank cells.
Different data elements will impact in their accuracy; some will provide greater
financial potential such as sales and margin as against costs in warehousing and
administration, including making corrections to innacurate data.
Extending master data management into areas such a buying cost, pricing, order
quantities and replenishment parameters will have an even greater potential benefit.
DATA DELUSIONS
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FIGURE 3 A supply chain cost levers framework for analysing the impact of data quality on cost
across the supply chain.
Different ‘levers’ will have different impact in their accuracy; some will have greater financial
potential such as sales and margin as against costs in warehousing and administration, including
corrections. Extending master product data management into areas such as buying cost, pricing,
order quantities and replenishment parameters will have an even greater impact as price
inaccuracies destroy business margin more rapidly than any sales gain.
A business that carries out a focused analysis of its supply chain performance using both the
levers and master data points will identify real potential. On the basis of taking the wider
viewpoint on the impact of data accuracy on buying cost, pricing, order quantities and
replenishment parameters, we have brought together our experience of the true scale of the
potential. We have found in the retail sector that the following measures are a good starting point
for estimating benefits:
Supplier Fulfilment Invoicing
Logistics &
Distribution
Commercial &
Supply Chain
Store back room
Inventory Retail Operations Waste / Returns
Product pack
quantities
Lost sales through the
chain - but does not care
about any waste
Possible invoice disputes ---------
Risk of lost sales due to
under replenishment and
incorrect conversion of
forecast
Too high leads to excess
stock in back rooms, out
of life, damage
Too low leads to lost
sales due to under
replenishment calculation
Too high leads to waste
on shelf life products
due to over
replenishment
Case
dimensions--------- ---------
Inaccurate vehicle loading
for outbound delivery
underutilised capacity or
cases left behind
Risk of lost sales of stock
left behind on
replenishment orders
---------
Incorrect shelf face
design leading to poor
display and double
handling
---------
Pallet
dimensions
Ti-Hi
--------- ---------
Distorts warehouse
capacity planning - often
leads to unrecognised
spare capacity
--------- --------- --------- ---------
Cases per
pallet
Order quantities incorrect
so not maximising vehicle
fill
---------
Incorrect picking let down
leading to delays, lower
OTIF and extra work
--------- --------- --------- ---------
Shelf life
standard--------- --------- ---------
Waste and margin
erosion if stock and
availability planning is
wrong
---------Stock availability issues if
data on life is too short
Waste and margin loss
if data on life is too long
Product cost
Disputes on price at order
intake - delays to
promotions and on retail
invoice clearance
Invoice errors in payment
reconciliation with high
admin cost
---------
Extra effort in queries, risk
of margin and sales loss
to due incorrect retail
pricing
---------
Margin loss due to
incorrect pricing and loss
of sales if not competitive
---------
Pricing and
promotional
changes
Loss of volume for the
discount allowed if the
promo is not set up right
---------
Big volume surges must
be forecast and resourced
- risk of forecast being
incorrect
Waste of promotional
budgets if calendar and
master data change
---------Lost sales if promo is not
on shelf and at right price
Obsolete stock at end
of promotion
Conditions of
supply -
quantity and
lead time
Order quantities incorrect
so not maximising vehicle
fill
---------
Excess stock holding if
data for replen algorithm
has too long a lead time
Incorrect supplier OTIF
when lead time and Order
quantities are wrongly set -
extra admin time
---------
Low stock in distribution
may lead to low stock and
lost sales in retail
---------
Value
potential
Supplier data is most likely
to be right - value for
supplier is in better sales
through the chain
Administrative effort on
invoice difference
resolution
Capacity costs,
productivity costs and
throughput reductions
Sales and Margin loss
and extra work putting
things right
Excess stock in back
rooms with associated
shrinkage and damage
Sales loss and margin
erosion if product is not
available or wrongly
priced
Waste in form of
discounts and disposal
when excess / out of life
stock
Accuracy
Levers
across the
chain
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Sales gains – our work with retailers has lead to an empirical metric that a 0.6% increase
in sales is available from every 1% improvement in availability and applies for overall
availability into the mid 90%’s. Many retailers maintain availability levels on the shelf
of no better than 90% which means that a 3% sales gain is easily available and a
marginal contribution of as much as 1% of revenues.
Price matching and margin control – we have found errors of as much as 0.5% of sales
from incorrect pricing and purchase invoice matching combined. This is pure margin
erosion and in addition to the administration costs of putting things right. In fast moving
categories where price levels are changing all the time and new deals constantly being
done, this is a key skill with accuracy a fundamental part of it.
The administrative costs of corrections and adjustments – our experience in this area is
that considerable back office time and cost is spent resolving queries with a metric that
can be as much as 0.1% of sales.
The cost of warehousing and transportation – incorrect product data can lead to poor
racking and vehicle fill as well as productivity issues with double handling from outside
storage as well as when picking face replenishment is out of synchronisation. We have
observed cost penalties of as much as 5% of total logistics costs which translates into
0.2% of sales.
Overall these levers indicate a maximum potential of 1.8% of revenue. If we allow for not all the
numbers working together from the above benchmarks in a specific company it would appear
that the potential from supply chain accuracy could still easily be 1% of sales. We estimate
global retail grocery sales in 2009 were at least $4,200 billion27
; 1% can therefore be seen to be a
significant amount.
Avoiding data delusions
For any chief executive officer (CEO), chief financial officer (CFO) or supply chain executive,
the suggestion that there is 1% of margin to be won makes this an interesting prize and worth
addressing. Many organizations have already invested heavily in the backbone of identification
technology and systems; these are the foundations for the process excellence that drives these
additional and largely hidden benefits.
From our work with retailers, simple actions can be taken, on short, medium and longer term
horizons, to help avoid problems created by delusions of data accuracy. While these might seem
like common sense, our research suggests they are far from common practice.
Appoint a Data Tsar – make data quality a specific responsibility. Overall there is a need to
institutionalise and value the process of maintaining accurate data. It is of critical importance to
27
Grossing up from the UK where the market is worth $210 billion, on the basis that the UK is approximately 5% of
global business.
DATA DELUSIONS
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ensure all stakeholders understand the impact of poor data. Start small by having a ‘data
accuracy’ workshop with key stakeholders within the business and understand who are the
‘victims’ of these data delusions. Expand this communication initiative to the whole
organisation and supply chain partners. In a time when “cash is king” ensure data accuracy is
presented in a “language of profit” not as an information system initiative. Remember, poor data
can cost you millions and your customers and/or consumers need it to be right. The use of an
integrated framework, as presented in this article, where accuracy issues can be traced back to
the cost impacts by teams provides a way of understanding benefit potential for the firm and
prioritising actions to improve.
Our research has identified the ambiguity of this issue within organisations. We recently invited
Supply Chain Directors to an event on Data Quality; however, the majority responding “this is
not our responsibility but that of our CIO”. When inviting the CIO’s they also said it was not
there responsibility claiming that executives in the supply chain and procurement areas should
take responsibility and needed to attend the event. This illustrates the that within the majority of
organisations no responsibility is taken for such data and it subsequently falls between the
functions.
Audit and measure your data accuracy. It is important to gain transparency of the problem and
start to measure, don’t continue to be deluded! As Lord Kelvin stated: “If you can not measure it,
you can not improve it!” In the short term, managers should undertake a simple audit to
understand the accuracy of their master product data. A small audit on a number of items will
quickly highlight any problems. When we reported the findings of our study, the leadership
teams of the retailers could not believe the extent of the problem.
In the medium to long term implement a continuous performance measurement system and
perpetual audit process. It is important to use data capture and validations methods to search for
incomplete and inconsistent records – look particularly for gross error such as where decimal
points are misplaced. Reporting against six sigma standards is a good way to raise the profile of
the issue and, when improvements are made, to celebrate success. Publishing key performance
indicators (KPIs) and peformance attainment across functions helps to focus the whole business
on the reality that ‘my accuracy affects your performance’. This does not have to be a high
profile initiative but steady application will raise the game of the staff. It is important to
institutionalise and value the process inside the business.
These two short term actions will tune the business into the issue and help to understand its
scale:
Assign a specific responsibility (whether an individual or team) within the commercial
function for data quality and build goals into everyones’ KPIs. This assigns resource that
is aligned to the core organization and makes a big statement that this is an area of focus.
DATA DELUSIONS
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Charter that person (or team) with measuring actual data quality, starting with duplicated
records, missing fields and then specific product details such as dimensions and pricing.
A good way to track data quality is to get a system record of all changes and corrections
and classify them as that will identify root causes of poor quality.
Implement a process to clean up the issues identified and define a process to reduce new
errors entering the systems.
Make quality part of everyone’s KPIs on the basis that ‘what gets measured gets
managed.’
Establish a data quality improvement programme. With the organisational focus established
and measurement in place, the next step is to monitor and analyze data adjustments as this will
give a track and trace on underlying performance and the nature of errors. This is a great way to
understand how data is being managed by simply recording all the adjustments that are being
made and by whom. This gives a ‘track and trace’ on underlying performance, the data areas that
are most at risk and the nature of corrections. This approach is most commonly used in stock
auditing but is equally applicable on pricing, physical measurements and supplier data. By taking
an overview on what is going on, the experience is that situations can be identified where people
may be putting ‘right things wrong’. This approach enables an understanding of which processes
are present to “correct data” and which processes are disrupted by “poor data”.
Data corrections processes are non-value adding and this is the key to valuing the problem. If
the data has already been created by a supply chain partner or an other function in your
organisation it should be right! Identify any data correction processes and then inquire as to why
they exist. In addition, identify those processes which are continuously being distrupted by poor
data. Identify the source of this poor data and treat the cause rather than the symptom. Master
data management is a process and through definition and measurement can be improved with
process improvement methodologise. For example, Six Sigma. Process improvement may
involve short term re-assignment of resources but should always focus on putting in processes
that subsequently run at no ‘direct cost’ to the business.
These medium term actions will give the business the focus for change by valuing the
opportunity for improvement:
Through the costs of adjustments, identify the consequential costs to the business of
workarounds and corrections, double handling and margin loss.
This can be assisted using the ‘levers’ framework and approach described earlier which
highlights cause and effect and estimates scale of the problem.
Using those insights, redesign the key processes on ‘lean’ principles – right first time on
time to six sigma standards and eliminate the consequential work of correction.
Implement the changes with control in order to be able to demonstrate the value of the
change to the business.
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Investigate, justify and implementation automation of data management including
introducing synchronisation tools. There is really no excuse for data inaccuracies between
entities in a supply chain. Technologies such as Global Data Synchronisation can automate the
alignment of data between suppliers and retailers. Lobby your sector: suppliers, customers and
competitors to implement a system in you industrial environment. Already this type of
technology has been used in sectors in USA, Australia, Canada, the Netherlands and Germany.
This is not a universal panacea to the removal of data delusions but does help manage some of
the issues.
Conclusions
Industry evolution is driving the demand for more product data. Retailers and suppliers are
constantly seeking innovative supply chain initiatives to speed products to market faster and
utilize warehouses, delivery vehicles and shelf space more effectively. Key performance
indicators funnel down to the two imperatives – increasing sales and reducing costs. While the
objectives are clear, there is little understanding of just how far collaboration between suppliers
and retailers, and the implementation of fresh supply chain initiatives that deliver sales growth
and cost economies, depend on a solid foundation of accurate, clean and consistent product data.
The research reported in this article suggests that parties in a retail supply chain can be seriously
deluded regarding the quality of product data that they are using to make supply chain decisions.
This deficiency must be addressed.
An additional compelling reason for retailers and suppliers to take action to improve efficiency
and manage the quality of product data more effectively, is the increasing demand for better
information coming from consumers, governments, regulators and pressure groups. Meeting this
demand with an increased volume and diversity of stores, consumer outlets, products and
suppliers places a heavy toll on the quantity, quality and availability of product information.
Couple with this the financial imperatives to move products faster through the supply chain,
reduce stock and maintain high shelf-availability for consumers, and an irresistible momentum
builds behind initiatives to improve the quality and performance of product data management.
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Appendix: Research design
The UK grocery industry is a £146.3 billion industry, with food and grocery expenditure
accounting for 51% of total consumer spend. Product data was collected from Tesco, Sainsbury,
Asda and Morrison. Global suppliers Unilever, Nestle, P&G and Mars also provided relevant
information on the products they traded with these retailers. Each product is uniquely identified
within the supply chain by a GTIN (Global Trade Item Number). This is an identifier for trade
items defined by GS1 (comprising the former EAN International and Uniform Code Council).
GTINs employ 14 digits and can be encoded into various types of data carriers. Currently, GTIN
is used exclusively within bar codes. Such identifiers are used to look up or access product
information in a database (often by inputting the number through a bar code scanner pointed at
an actual product) which may belong to a retailer, manufacturer, collector, researcher, or other
entity. The uniqueness and universality of the identifier is useful in establishing which product in
one system corresponds to which product in another, especially across organizational boundaries.
GTINs can store up 60 items of information about a product, including description, weight,
volume, sell by date.
The data gathering and analysis process was as follows:
1. The retailers were asked to provide master data for their primary grocery products (all
live branded products) excluding own label and non-food items. To provide this, retailers
were able to make a full data “dump” from their internal systems requiring little initial
effort on their part. For each Consumer Unit or “Each” which is the product available for
purchase by the consumer, the following items of data were to be provided:
Consumer Unit Dimensions
Consumer Unit Volume
Consumer Unit Net Weight
Additional data was also collected relating to case and pallet including Consumer Units
per Traded Unit, Traded units per layer, Layers per Pallet and Total Life days; for a full
list, see table below. For Traded Units (what the supplier sends to the retailer), the
following attributes were measured: Traded unit Dimensions, Traded Unit Volume and
Traded Unit Weight.
2. The total number of product data records provided by the retailers was 1,039, 118. This
data was then cleaned to remove duplicates, i.e. the same product appearing twice in the
product data master file. For example, the product description had been put into the
system twice with differing name and description, despite been an identical product with
the same GTIN. The process for identifying mismatches and conducting validations were
agreed prior to implementation. The researchers often requested additional information
from the retailers to identify the primary GTIN. This process was aided by the use of a
software package to map the data attributes held on each product from the retailers
product catalogues and identify mismatches. After normalizing the data and removing
duplicate records, the total number of live records available for analysis totaled 427, 154;
less than half the original data set.
3. The GTIN was then used to match data across the four retailers. This matching occurred
at Consumer Unit and Traded Unit level. 11,378 items were common and sold in all four
retailers. This analysis was reported back to the retailers for validation.
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4. The suppliers were asked what products they traded with each retailer. No supplier was
found to have had duplicate GTINs. The retailer data was then matched against supplier
data.
5. Follow up interviews were undertaken with each participating organization to look
behind the reasons for our findings and to discuss their implications for the management
of supply chains.
6. Interviews were undertaken with supply chain executives in other industries to identify
ways to tackle the problems and challenges identified in this research.
Attribute name Definition Data quality rules
GTIN of Each
A particular global trade item number, a
numerical value used to uniquely identify a
trade item. A trade item is any trade item
(trade item or service) upon which there is a
need to retrieve pre-defined information and
that may be planned, priced, ordered,
delivered and/or invoiced at any point in any
supply chain.
Must have check digit. Max
14 digits.
GTIN of Inner Pack
A particular global trade item number, a
numerical value used to uniquely identify a
trade item. A trade item is any trade item
(trade item or service) upon which there is a
need to retrieve pre-defined information and
that may be planned, priced, ordered,
delivered and/or invoiced at any point in any
supply chain.
Must have check digit. Max
14 digits.
GTIN of Case
A particular global trade item number, a
numerical value used to uniquely identify a
trade item. A trade item is any trade item
(trade item or service) upon which there is a
need to retrieve pre-defined information and
that may be planned, priced, ordered,
delivered and/or invoiced at any point in any
supply chain.
Must have check digit. Max
14 digits.
Depth of Each (mm)
The measurement from front to back of the
trade item.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Height of Each (mm)
The measurement of the height of the trade
item. The vertical dimensions from the
lowest extremity to the highest extremity,
including packaging.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Width of Each (mm)
The measurement from left to right of the
trade item.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Depth of Inner (mm)
The measurement from front to back of the
trade item.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Height of Inner (mm)
The measurement of the height of the trade
item. The vertical dimensions from the
Must use packaging
measuring rules. Rules can be
DATA DELUSIONS
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lowest extremity to the highest extremity,
including packaging.
downloaded from GS1
website www.gs1.org
Width of Inner (mm)
The measurement from left to right of the
trade item.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Depth of Case (mm)
The measurement from front to back of the
trade item.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Height of Case (mm)
The measurement of the height of the trade
item. The vertical dimension from the lowest
extremity to the highest extremity, including
packaging.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Width of Case (mm)
The measurement from left to right of the
trade item.
Must use packaging
measuring rules. Rules can be
downloaded from GS1
website www.gs1.org
Trade Item Description
The concatenated product description of a
product or service.
Gross Weight of Each (G)
The gross weight of the trade item. The
gross weight includes all packaging
materials. At pallet level the trade item gross
weight includes the weight of the pallet
itself.
Gross Weight of Inner (KG) The gross weight of the trade item. The
gross weight includes all packaging
materials. At pallet level the trade item gross
weight includes the weight of the pallet
itself.
Gross Weight of Case (KG) The gross weight of the trade item. The
gross weight includes all packaging
materials. At pallet level the trade item gross
weight includes the weight of the pallet
itself.
Net Content of Each
Quantified by UOM
The amount of trade item contained by a
package, as claimed on the label.
Declared Net Weight of Each The net weight of the trade item. Net weight
excludes any packaging materials and
applies to all levels but consumer unit level.
Cases Per pallet
The number of trade items contained in a
pallet. Only used if the pallet has no GTIN.
It indicates the number of trade items placed
on a pallet according to supplier or retailer
preferences.
Must be equal to or less than
the number of eaches.
Layers Per Pallet
The number of layers that a pallet contains.
Only used if the pallet has no GTIN. It
indicates the number of layers that a pallet
contains, according to supplier or retailer
preferences.
Eaches Per Inner
The number of next lower level trade item
that this trade item contains.
Eaches Per Case
The total quantity of next lower level trade
items that this trade item contains.
Country of origin The country code (or codes) in which the Must use ISO code list (plus
DATA DELUSIONS
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goods have been produced, according to
criteria established for the purposes of
application of the value may or may not be
present on the trade item label.
EU)
Total life (days) The period in days for which the item can be
kept in total, from the date of production to
the shelf life expiration date.
Table Data requested from retailers and suppliers.