Proposing a Value-Added Indicators framework for the apparel and fashion sector: Design and empirical evaluation

Abstract

OBJECTIVE:

Automatic identification technologies have become omnipresent in the retail industry. The introduction of Radio Frequency Identification (RFID) in the fashion and apparel industry has led to massive data sets which are currently an untapped source for decision makers. We therefore aim to leverage the economic potential of RFID by proposing a framework for novel RFID-enabled indicators. We use the term Value-Added Indicators (VAIs), instead of the common acronym KPIs (Key Performance Indicators), because the proposed metrics aim to add additional value to the existing management reporting with regard to the fashion and apparel supply chain.

DESIGN, METHODOLOGY, APPROACH:

We combine two sources of knowledge in order to build a comprehensive framework. We (i) use the theoretical background from a review of the relevant literature and (ii) use the experience from an RFID-system integrator and two fashion companies that have already deployed RFID on a large scale. The framework is then validated in several iterations by academics and practitioners.

FINDINGS:

The study has produced a set of 60 different VAIs that use, or might use, RFID technology to produce, monitor and increase the overall value of the fashion and apparel supply chain. These VAIs are organised in a structured framework, built and validated by more than 10 experts from the field. The framework was then further validated by 24 experts, comprehending managers and researchers from outside the borders of the EU-Project SERAMIS.

PRACTICAL IMPLICATIONS:

Practitioners from the fashion and apparel industry benefit from our research by having available a validated framework with a set of 60 different VAIs that can be readily implemented into their own management reporting. The framework can thus serve as the foundation for building up an RFID-based measurement system or as a guideline for extending an existing measurement system.

ORIGINALITY/VALUE:

We are the first who structure RFID-related performance indicators in a validated framework. Our framework can serve as a foundation for further research that goes beyond the fashion and apparel retail industry. We encourage researcher to build upon our research and to transfer it to different industries.

Keywords

Radio frequency identification (RFID)fashion and apparel retailing Value-Added Indicators performance measurement framework

1 Introduction

Scientific literature and industrial practice agree on the central importance of measuring and evaluating performances of organisations. As a basic principle of management activities, metrics and performance measurement are the paramount elements in translating the strategy of an organization, that is its mission, into reality (Magretta & Stone, 2002; Melnyk, Stewart, & Swink, 2004). As the famous way of saying suggests: ‘You get what you inspect, not what you expect’: metrics are necessary to assess how work is done and how to direct the future activities (Beamon, 1999; Chatterji, Findley, Jensen, Meyer, & Nielson, 2016). In addition to the measuring and directing activities, metrics can also educate, because they quantify how we want to deliver value to our customers. As van der Vorst suggests (2006), an erroneous performance measurement systems can hamper the achievement of the objectives for every organization, be it a manufacturing business, a service business, or a public administration (Tyagi & Gupta, 2013). Metrics and strategy, in fact, are strongly intertwined: strategy without metrics is useless, whereas metrics without a strategy are meaningless (Melnyk et al., 2004). To maintain the strategy of an organisation in an established direction, in fact, its management can select the best fitting criteria of evaluation and then adjust its performance accordingly. For example, Mikušová & Janečková (2010) studied the prerequisites of business metrics so as to become important pieces of information of a performance measurement system; they mainly categorised indicators by subject of measurement (i.e. hard vs. soft); level of management (i.e. strategic, tactical, and operative); and area of measurement (i.e. efficiency, effectiveness, result and process).

Already 25 years ago, Kaplan and Norton introduced the balanced scorecard, to support organisations with a comprehensive view of their business model that did not only rely on already existing financial accounting measures (Kaplan & Norton, 2004). The goal of the balanced scorecard was that of translating the objectives of an organisation into a set of performance measures (Lueg, 2015). Since then, the number of studies on performance measurement has increased significantly in the last decades (Braz, Scavarda, & Martins, 2011; Gunasekaran, Patel, & McGaughey, 2004; Maestrini, Luzzini, Maccarrone, & Caniato, 2017). However, performance measurement continues to present a challenge to researchers and supply chain managers. Especially in these days, organisations are forced to adapt themselves to an environment that changes at an ever-quickening pace (Mikušová & Janečková, 2010). At the same time, the increasing adoption of new technologies that allow to collect, integrate and share information among different actors of the supply chain has revamped performance measurement systems, both in business practice and in research (Nudurupati, Bititci, Kumar, & Chan, 2011). Newly adopted technological solutions, in fact, have enabled quick and reliable data collection, robust data analysis, and easy management of performance communication: among these technologies we must list Radio Frequency Identification (RFID), the Internet of Things (IoT), big data, and web- or cloud-based platforms (Maestrini et al., 2017).

RFID, in particular, has proven to be a key technology that enables IoT-embedded networks (Abdulrahman, Kamalrudin, Sidek, & Hassan, 2016; Uckelmann & Romagnoli, 2016). By means of RFID, products can be tagged, and thus tracked and traced, throughout the entire supply chain (Bertolini, Bottani, Romagnoli, & Vignali, 2015; Feibert & Jacobsen, 2015), with typical benefits such as decreasing process times, increasing turnover/sales and reducing shrinkages (Bottani, Montanari, & Romagnoli, 2016). Only 10 years ago, Gruen & Corsten (2007) stated that ‘due to technological and financial reasons, most ... RFID applications [had] been limited to tags on pallets and cases and [had] not descended to the individual item level’. During these 10 years, the growing deployment of RFID technology at item-level has opened up a set of new processes and services, such as real-time location of items, enhanced shopping experiences (e.g. smart fitting rooms), and brand protection (e.g. RFID-enabled counterfeiting), amongst others. This shift is particularly clear in the fashion and apparel supply chain, where the higher marginality of the sector makes it less important to control the price of tags to guarantee a quick return on technology-related investments (Bertolini, Maggiali, Rizzi, Romagnoli, & Volpi, 2017; Rizzi, Romagnoli, & Thiesse, 2016).

Although the number of publications on RFID in academic research has been significantly increasing in the last 15 years, as it is reported by several reviews on the topic (Musa & Dabo, 2016; Sarac, Absi, & Dauzre-Prs, 2010), the application of RFID in the fashion and apparel sector is still an under researched topic. Only few studies, in fact, have tried to review the value of RFID technology in this sector (Chen, 2014; Moon & Ngai, 2008; Wong, Chan, Hui, & Patel, 2006). In our paper, we examine to what extent large-scale RFID data sets allow for the generation of novel performance indicators and management reports for fashion and apparel supply chains, either in real-time or offline (Al-Kassab, Mahmoud, Thiesse, & Fleisch, 2009; Al-Kassab, Thiesse, & Buckel, 2013). The purpose of this research objective is to develop and validate a set of Value-Added Indicators (VAIs), which provide information to form a more agile, flexible, resilient and efficient supply chain. In our study, the term Value-Added Indicators is used, because our key metrics focus on increasing the value of the supply chain by means of RFID technology, in contrast to the traditional set of Key Performance Indicators. Our metrics, start from the point of sale to reflect the whole entrepreneurial performance, and all business processes can be assessed and monitored based on such indicators. According to Uckelmann (2012), in fact, ‘there is a need to respect information as an asset in its own right as well as a need to measure and quantify the value of information and separate it from the product and service values’.

To achieve our goal, and to complete the work of Bertolini, Romagnoli, and Weinhard (2016), where a preliminary framework for VAIs was proposed, we started from a literature review on business metrics, dealing with retail and/or RFID. This allowed us to produce a preliminary set of VAIs, that was enriched with existing metrics used by the industry (fashion and apparel retailers and RFID system integrators). Also, the list of VAIs was validated and organised in a structured framework, developed to examine how to leverage indicators to generate value, that is how the indicators can be used in practice. Afterwards, experts from different industries are requested to further validate the framework. The remainder of the paper is organised as follows: in Section 2 we report the methodology and the results of a literature review on performance indicators. Section 3 details the way we developed our framework for VAIs. The framework was then validated in Section 4, where we also discuss the results of the survey and re-evaluate the category of our use cases. Finally, Section 5 draws conclusions and suggest directions for future research.

2 Value-added indicators – a review of the literature

2.1 Methodology

We performed a structured search in order to identify which indicators have been presented and discussed in the scientific literature. We were furthermore interested to find out which RFID-based KPIs were in use at companies that already have introduced RFID-enabled processes. In order to accomplish the study, we started our research with the examination of various scientific publications, as well as practical results from the EU-Project SERAMIS. The SERAMIS project, which received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration, was created in order to find out how to push the boundaries of current RFID implementations in order to turn them into powerful tools for intelligent information management.

To generate our research corpus, we chose the Scopus database, which belongs to the Dutch publisher Elsevier and it claims to be the ‘largest abstract and citation database of peer-reviewed literature’ (Elsevier, 2017). We then constructed the search queries reported in Table 1, to find all relevant papers with regard to RFID, retail, Key Performance Indicators, business metrics and measurement systems which from our points of views outline the search area for Value-Added Indicators. The time frame for our research was from 1996 to present, as 1996 is a well-known threshold value for Scopus (Beatty, 2015). However, given that only 4 results from our search queries were published before 2008, and no paper was published before 2001 (see Fig. 1), we believe that this time frame is adequate.

Fig.1

Distribution of articles by year of publication.

Table 1

Overview of search queries

#	Title – Abstract		Keywords
	– Keyword
Query 1	retail	AND	“key performance indicator” OR “business metric” OR “business analytics” OR “balanced scorecard”
Query 2	RFID	AND	“key performance indicator” OR “business metric” OR “business analytics” OR “balanced scorecard”

In order to generate a comprehensive corpus, we combined the results of both queries resulting in 54 potentially relevant documents. We reviewed every retrieved article with regard to its relevance to our topic. Then, each article was categorized, analysing if it considered a horizontal or vertical supply chain perspective:

Sales/turnover point of view (horizontal/vertical).

Customer satisfaction/involvement point of view (horizontal/vertical).

Process cost/efficiency point of view (horizontal/vertical).

Process improvement/innovation point of view (horizontal/vertical).

Level of management involved with KPIs.

After we performed this procedure for every of the 54 found articles, we used the results as a solid foundation for the VAI-Framework.

2.2 Results

The 54 articles were published between the years 2001 and 2015 (see Fig. 1). As shown in this figure, the publication of articles slowly rose from 2001 on peaked in 2013 and declined afterwards. We assume this phenomenon could have been caused because of the RFID hype cycle.

Table 2 shows the distribution of articles by document type. There are 28 articles/articles in press, one book chapter, 23 conference papers and two reviews. Amongst the scientific journals our review covered the European Journal of Information Systems, the Journal of Theoretical and Applied Electronic Commerce Research, the Journal of Computers and Industrial Engineering and the International Journal of RF Technologies Research and Applications.

Table 2
Distribution of articles by document type

Document Type No. of articles

Article 26

Article in Press 2

Book Chapter 1

Conference Paper 23

Review 2

Total 54

Document Type	No. of articles
Article	26
Article in Press	2
Book Chapter	1
Conference Paper	23
Review	2
Total	54

We categorized the articles by their topics. The categorization resulted in 13 case studies of which ten have aspects of application, one comparison, 31 concept studies of which ten have some kind of application, one literature review, six simulation studies and two surveys/expert interviews. Figure 2 illustrates the results visually.

Fig.2

Distribution of articles by topic.

By analysing the KPIs found in the literature, we were able to identify two different kinds of RFID-related Value-Added Indicators. The first category of VAIs we found we denoted as RFID-based VAIs. With RFID-based, we mean VAIs that are novel and are now enabled by the tracking capabilities of RFID technology. For example, the number of misplaced merchandise in the sales floor. This VAI identifies how many items are not in their intended places. A company without an RFID-infrastructure, would have to perform excessive manual work in order to track the misplaced items and count them. But, an RFID-based Real Time Locating System or RFID-based Smart Shelves can perform such tasks automatically.

We furthermore identified a second category of RFID related KPIs which we denoted as RFID-influenced VAIs. This term refers to KPIs, which existed in companies that relied on the barcode but are now getting empowered through this RFID. As an example, we could name “the number of wrong delivery items” from a supplier. Certainly, the employees could measure this KPI with lots of manual labour. On the contrary, RFID capacitates the business to evaluate such KPIs much quicker, with less effort and more exact than before.

One result of our research was that only little of the metrics from the literature belonged to the category of RFID-based VAIs; the majority of VAIs were however RFID-Influenced. Although we considered contributions from several different industrial sectors in our literature review, the reported VAIs mainly come from the industry sector of fashion and apparel retail. This stems from the fact that most of the VAIs found were directly applicable in our framework. Furthermore, we only considered VAIs from the literature that were not described in a fuzzy way and for which the authors gave at least some direction on how to measure them.

3 Building the VAI framework

The starting point for our research is the recent study of (Rizzi et al., 2016), where use cases (UCs) of RFID in fashion and apparel retail have been appropriately reviewed and categorised. Briefly, in that paper the authors propose a frame of reference for UCs of RFID in fashion and apparel retailing: use cases are classified according to their scopes, or impact spaces, in six categories, namely:

Shop Floor Management;

Customer Relationship Management;

Marketing and Promotions Management;

Logistics;

Inventory and Supply Chain Management;

Brand Protection.

The paper identifies 18 proper UCs of RFID, and describes them at a greater level of detail, eventually grouping them in one of the above-mentioned categories. Also, the paper suggests which UC is mostly linked to costs reduction and/or increasing revenues. We decided to link our framework for Value-Added Indicators to the work of (Rizzi et al., 2016) and, to define and validate it, we followed the approach presented in Table 3, adapted from Bertolini et al. (2016). As a common approach, we used the two steps Delphi method. DELPHI is the name of a project conducted at “The RAND Corporation” where expert opinion has been used to obtain the most reliable consensus of a group of experts (Dalkey & Helmer, 1963). The Delphi method has proven a popular tool in information systems research (Schmidt, 1997), even in “a lack of a definitive method for conducting the research and a lack of statistical support for the conclusions drawn” (Okoli & Pawlowski, 2004).

Table 3
Steps to define and validate the VAI framework

Step Description Contributions

1 Generate a list of VAIs for fashion and apparel retail Academics and Industrials-SERAMIS

2 Validate the list of VAIs (generated at step 1) Academics and Industrials-SERAMIS

3 Define the VAI framework (validated at step 2) Academics only-SERAMIS

4 Validate the VAI framework (defined at step 3) Academics and Industrials

Step	Description	Contributions
1	Generate a list of VAIs for fashion and apparel retail	Academics and Industrials-SERAMIS
2	Validate the list of VAIs (generated at step 1)	Academics and Industrials-SERAMIS
3	Define the VAI framework (validated at step 2)	Academics only-SERAMIS
4	Validate the VAI framework (defined at step 3)	Academics and Industrials

At step 1, a list of VAIs is generated, taking into account the review of the literature and the experience of industrial partners. This list comprehends a wide set of VAIs for fashion and apparel retail, as reported in Table 4. At step 2, partners of the project validated the list with the first Delphi round: goal of this validation is to keep only VAIs with a sufficient potential for industrial and academic use. This step was performed again within the SERAMIS project, due to privacy reasons, because industrial partners did not want to make public the metrics they use or do not use for performance measurement purposes. At step 3, we define the category, nature and dimension of VAIs, as an outcome of the literature review process. Finally, at step 4, we aim to validate the VAI framework, as defined in step 3, taking into account the opinion of a wider community of researchers and practitioners, with different backgrounds, that exceeds the borders of SERAMIS project. This last step was conducted at UC level, rather than at VAIs level, because the evaluation outside SERAMIS consortium would have needed more than 2 hours to evaluate VAIs, against the 15–20 minutes for evaluating UCs. Thus, we kept it at UC level.

Table 4

Validated list of VAIs, together with their source (IND or LIT), units of measurement, category, nature and dimension

Use Cases	Value-Added Indicators	UoM	Cat	Nat	Dim
1.1 - Locating items	1) average reduction of time spent to locate items post-RFID implementation, compared with pre-RFID implementation IND	%	I	B	V
	2) number of successful missions/total number of missions seeking for garments IND	%	E	B	V
	3) number of misplaced merchandise – either through smart shelf or RTLS LIT	#	E	B	V
	~~4) reduction of shelf Out Of Stock due to item location LIT~~	%
1.2 - Loss prevention	1) value of shrinkage due to thefts after the EAS RFID implementation IND	€	S	N	V
	2) number of stolen items read at the RFID EAS gates/total number of stolen items IND	%	E	B	V
	3) number of alarms at the EAS IND	#	E	N	V
1.3 - POS Transaction/Faster checkout	1) average time reduction for checkout transaction (comparing pre- and post-RFID implementation) IND	%	I	B	V
	2) average number of people in queue for a given store turnover IND	#/€	C	N	V
	~~3) additional turnover generated through automated checkout transactions IND~~	€
1.4 - Stock visibility/Replenishment from the backroom	1) average time spent on the shop floor area before sale (possibly at SKU level) IND	#/time	S	B	V
	2) number of sales per average inventory in the shop floor area (possibly at SKU level) IND	%	S	B	V
	3) sell through/sell in (possibly at SKU level) IND	%	S	N	V
	4) sell out/sell through (possibly at SKU level) IND	%	S	N	V
	5) number of SKUs out of shelf but available in the backroom – Never Out of Stock (NOS) articles LIT	#	E	N	V
	6) time needed for the replenishment of an Out of Shelf NOS-SKU IND	time	E	B	V
	7) additional turnover generated through the use of the replenishment proposal IND	€	S	N	V
	8) additional turnover generated through inventory correction for NOS-articles IND	€	S	B	V
	9) reduction of shelf space due to RFID LIT	%	I	B	V
	~~10) number of items rearranged between sales floor and backroom IND~~	#
	~~11) conversion - number of NOS garments sold after being replenished LIT~~	#
	~~12) value of conversion - value of NOS garments sold after being replenished LIT~~	€
2.1 - Social shopping	1) average number of posts on social networks LIT	#	C	N	V
	2) average number of answers (“comments”, “likes”, “re-tweets” and “shares”) per post LIT	#/# posts	C	N	V
	3) conversion – number of garments sold after being shared on socials/number of garments shared on socials LIT	%	S	N	V
	4) value of conversion – value of garments sold after being shared on socials LIT	€	S	N	V
2.2 - Store assoc. av./Customer knowledge	1) number of customers identified through RFID fidelity card LIT	#	C	B	V
	2) number of customised services provided to customers LIT	#	C	B	V
	~~3) additional turnover generated through the customised services provided to customers IND~~	€
3.1 - Customer experience	1) number of items identified by magic mirrors, smart shelves, smart kiosks LIT	#	C	B	V
	2) conversion - percentage of items sold after identification at magic mirrors, etc. IND	%	S	B	V
	3) value of conversion – value of items sold after identification at magic mirrors, etc., in a certain time period IND	€	S	B	V
	4) fitting room usage distribution - Number of try-ons on fitting room and cluster level (cluster = several fitting rooms in the same store area) LIT	%	C	B	V
	5) customer shopping time (with the help of a fidelity card) LIT	time	C	B	V
	6) average number of customers (with fidelity card) in the store for a given store turnover LIT	#/€	S	N	V
	~~7) customer distribution - Average number of customers per area of the store LIT~~	#
3.2 - Cross selling/Cross promotions	1) number of cross selling and cross marketing transaction proposed (e.g. from smart fitting rooms) LIT	#	E	B	V
	2) conversion - number of cross selling and cross marketing transaction completed LIT	%	S	B	V
	3) value of conversion – value of garments sold after cross selling/cross marketing transactions LIT	€	S	B	V
3.3 - Store associate empowerment	1) number of products suggested to associates for better customer service LIT	#	E	N	V
	2) conversion – number of products sold after being suggested LIT	%	S	N	V
	3) value of conversion – value of garments sold after being suggested LIT	€	S	N	V
4.1 - Process automation	1) time saved per item per process performed (comparing pre- and post-implementation) IND	%	I	B	V
	2) lead time reduction along the supply chain IND LIT	%	I	N	H
	3) number of scanned items per hour per shop LIT	#	E	B	V
4.2 - Process accuracy	1) average increase in process accuracies (comparing pre- and post-implementation) IND	%	I	B	V
	2) average decrease in process errors that generate inaccuracies (comparing pre- and post-implementation) IND	%	I	B	V
	3) recorded items/(expected amounts and additional recorded items) —- at receiving IND	%	E	B	V
	4) number of wrong delivery items IND	#	E	N	V
	5) decrease of markdowns due to RFID LIT	%	I	B	H
4.3 - After sales/Returns	1) percentage of products returned from customers and automatically managed through RFID information IND	%	E	B	V
	2) time reduction in managing after sales and returns (comparing pre- and post-implementation) IND	%	I	B	V
	3) value of defects the company can charge to suppliers IND	€	S	B	V
5.1 - Out of Stock/Inventory accuracy	1) inventory accuracy increase (comparing pre- and post-implementation) IND	%	I	B	V
	2) average increase in inventory accuracy along the supply chain (comparing pre- and post-implementation) IND	%	I	B	H
	3) number of stock outs (a certain SKU is completely out of stock) IND	#	E	N	V
5.2 - Omnichannel	1) value of sales through different channels IND	€ /channel	S	N	V
	2) increase in sales due to adoption of omnichannel IND	%	S	N	H
5.3 - Supply chain visibility	1) percentage of stock reduction due to inventory pooling amongst different stores IND	%	S	N	V
	2) number of different steps in the SC monitored for supply chain management LIT	#	E	B	H
	3) average number of reads for every product’s tag within the entire SC for supply chain management IND	#	E	B	H
6.1 - Grey market	1) number of bootleg products identified through RFID LIT	#	S	B	V
	2) value of potential sales identified in (and removed from) grey markets LIT	€	S	B	V
6.2 - Counterfeiting	1) number of counterfeited products identified through RFID LIT	#	S	B	V
	2) value of potential sales in counterfeited products identified through RFID LIT	€	S	B	V
6.3 - Traceability	1) number of different steps in the SC monitored for brand protection purposes LIT	#	E	B	H
	2) average number of reads for every product’s tag within the entire SC for brand protection purposes LIT	#	E	B	H

# indicates a number; € indicates monetary values; comparisons are always made between the post- and the pre-implementation values, respectively; Cat stands for category.

3.1 Generate a list of VAIs for fashion and apparel retail

We have generated a preliminary list of VAIs organised as and linked to the UCs of RFID. The table comprehends both the results of a broad literature review, as reported in Section 2, and information collected from a brainstorming session of academic and industrial partners of the SERAMIS project. It is important to notice that industrial partners of SERAMIS comprehend both users of RFID technology in the fashion and apparel retail sector, as well as an ICT-System Integrator. Thus, the proposed VAIs were generated not only taking into account the characteristics and needs of the fashion and apparel retail sector. The generated list of 67 VAIs is reported in Table 4. The VAIs are linked to RFID use cases with different cardinalities, ranging from 2 to 12.

Due to space constraints, we chose to report the table only once, thus the list of VAIs generated at step 1 also counts the indicators reported with strikethrough font, as it will be explained in the next section. Also, each VAI reports its source, i.e. weather it was first proposed from literature review or academics (LIT), or from the industry (IND). In Table 4, we used the definitions reported below. Note that most of these values can be calculated at any of the levels of granularity reported in Fig. 3.

Fig.3

Different levels of granularity we considered.

Unit of time: all VAIs are measured during a certain time unit. We define the standard unit of time of a week. VAIs, however, can also be measured with different units of time, such as days or months.

Sell in: number of items received into the store backroom.

Sell through: number of items replenished, i.e. taken from the backroom to the sales floor area of the store.

Sell out: number of items sold at final customers.

Out of Shelf (also called shelf out-of-stock): condition of items that, at a given granularity level, are not available on the shop floor area, but available in the backroom.

Smart shelf, smart kiosk, etc.: RFID-enabled tool that can read EPC values, and thus provide detailed information concerning the specific SKU/model, such as availability in other colours, sizes or stores; complementary products, and the like.

3.2 Validate the list of VAIs

Afterwards, the list of VAIs that we generated at step 1 was validated through the first Delphi round, performed in May 2016. A total of 12 experts took part to this step of validation, 6 from different companies and 6 from the academia, with another academic as a moderator. As indicated in Table 3, experts for steps 1–3 were all taken from the SERAMIS project. This is due to the fact that a great effort and time is needed to comprehend the framework of UCs and to generate, validate and evaluate around 60 VAIs. The outcome of this step is reported in Table 4, where the validated VAIs are reported with normal font (non-strikethrough). All the VAIs that were considered having sufficient potential, linked to the specific UCs and reported with possible Units of Measurement (UoM) are reported in the first three rows on the left-hand side of Table 4. The total number of validated VAIs is equal to 60.

3.3 Define the VAI framework

The second Delphi round comprehended 7 experts from 4 different universities. The experts assessed the category, nature, and dimension of all VAIs validated at step 2. The results of the second Delphi round are shown in Table 4, where VAIs have been categorised in terms of their category, nature, and dimension. Also, some simple statistics to describe the VAI framework are show in Table 5.

Table 5
Number and percentage of VAIs per category, nature and dimension

Category # % Nature # % Dimension # %

Sales/turnover 24 40% RFID-Based 39 65% Vertical 52 87%

Process Efficiency/cost 17 28% RFID-iNfluenced 21 35% Horizontal 8 13%

Process Improvement 11 18%

Customer satisfaction/involvement 8 14%

Total 60 100% Total 60 100% Total 60 100%

Category	#	%	Nature	#	%	Dimension	#	%
Sales/turnover	24	40%	RFID-Based	39	65%	Vertical	52	87%
Process Efficiency/cost	17	28%	RFID-iNfluenced	21	35%	Horizontal	8	13%
Process Improvement	11	18%
Customer satisfaction/involvement	8	14%
Total	60	100%	Total	60	100%	Total	60	100%

3.3.1 Category

The most important characteristic that we considered for VAIs is their category. As an outcome of the literature review process, in fact, and adapting what was proposed by Bertolini et al. (2016), we group indicators in the following categories:

Sales/turnover (S): indicators that provide direct assessment of an economic performance, with the aim of increasing sales/turnover of the company.

Customer satisfaction/involvement (C): customer-related indicators, that measure the level of customer satisfaction and/or involvement. This is typically done as a response to newly designed RFID-enabled processes, or it is measured with RFID technology.

Process efficiency/cost (E): indicators relating to the process efficiency, i.e. to the effectiveness achievable with a given process cost.

Process improvement/innovation (I): indicators that assess the improvements related to the implementation of RFID technology. Typical example is the comparison of post- and pre-implementation performance of a given process.

3.3.2 Nature

As mentioned in Section 2, an important difference obtained from the literature review is the nature of VAIs, i.e. the separation between RFID-based and RFID-influenced VAIs:

RFID-based (B) indicators can only be measured via RFID, because without RFID technology it would be either impossible or not feasible to measure and monitor it;

RFID-influenced (N) indicators can also be measured without RFID, and the use of RFID might allow to measure it in a quicker or more accurate way.

3.3.3 Dimension

Furthermore, VAIs can be differentiated by their dimension, which can be either horizontal (H) or vertical (V). To better explain differences between the two above-mentioned VAI categories, we refer to Rizzi et al. (2016). According to this study, indicators that focus on just one primary activity (e.g. operations, or service) are defined as vertical. On the other hand, indicators that focus either on more than one primary activity, or on secondary activities are defined as horizontal. Data in Table 5 are self-explanatory, and for more details on the nature and dimension of VAIs, we refer to Bertolini et al. (2016).

4 Validating the VAI framework

In this section, we describe the results of the empirical validation of the Value-Added Indicators framework. It reflects the views of the management of two huge companies, one located in Germany and one in Italy, as well as points of views collected from different professionals and researchers outside the EU-Project SERAMIS.

4.1 Methodology

We developed an online survey for the validation of the importance of the use cases and the underlying Value-Added Indicators. We used the tool ‘Google survey’ because it allowed for an easy analysis of the survey results and an appealing design of the questionnaire. The questionnaire was delivered in three different languages: German, Italian, and English. It was then distributed to expert company managers and published online in selected LinkedIn Groups. For each of the 18 UCs, which we defined for the Value-Added Indicators framework, we asked the respondents on their opinions about the questions:

Overall importance of the use case? (General - G)

Importance of the use case for sales/turnover? (S)

Importance of the use case for customer satisfaction/involvement? (C)

Importance of the use case for process efficiency/cost? (E)

Importance of the use case for process improvement/innovation? (I)

We used a questionnaire with 90 items to which the respondents could answer on a Likert scale from 1 to 7. On the scale 1 meant not important at all, 4 meant neutral and 7 meant very important. The results show how the respondents perceive the importance of the use cases of the framework from their points of views.

4.2 Discussion of the survey results

The questionnaire was answered by 24 expert respondents. We are well aware that these results are not statistically significant, but believe nevertheless that they give us some interesting new insights in the perception of our framework. Their perceptions will help us to evaluate if the defined use cases are valuable for the fashion industry in practice and not only on a theoretical level. Table 6 shows the results of our survey.

Table 6
Aggregated results of the framework validation (averages)

Use Cases G S C E I

UC 1.1: Locating items 4.71 5.08 5.21 4.42 5.08

UC 1.2: Loss prevention 5.00 4.17 3.25 4.75 4.71

UC 1.3: POS transaction/Faster checkout 5.71 4.83 5.67 5.67 5.21

UC 1.4: Stock visibility/Replenishment from the backroom 6.13 6.14 5.57 4.86 5.86

UC 2.1: Social shopping 4.54 4.92 5.33 3.21 4.08

UC 2.2 Store associate availability/Customer knowledge 5.13 5.00 5.04 4.21 4.58

UC 3.1: Customer experience 4.92 5.21 5.58 3.88 4.58

UC 3.2: Cross selling/Cross promotion 5.04 5.50 5.50 3.92 4.75

UC 3.3: Store associates empowerment 5.00 5.17 4.96 4.33 4.75

UC 4.1: Process Automation 5.92 5.17 3.83 5.92 5.79

UC 4.2: Process accuracy 5.92 4.71 3.43 5.57 5.00

UC 4.3: After sales/return 5.17 4.38 4.96 4.92 5.21

UC 5.1: Out of stock/Inventory accuracy 6.00 5.71 5.25 5.58 5.92

UC 5.2: Omnichannel 5.58 5.79 5.92 5.13 5.38

UC 5.3: Supply chain visibility 4.33 4.29 4.08 4.25 4.58

UC 6.1: Grey market 3.88 3.71 3.17 3.75 3.96

UC 6.2: Counterfeiting 4.04 3.54 3.54 3.42 4.00

UC 6.3: Traceability 4.04 3.67 3.33 4.29 4.08

Use Cases	G	S	C	E	I
UC 1.1: Locating items	4.71	5.08	5.21	4.42	5.08
UC 1.2: Loss prevention	5.00	4.17	3.25	4.75	4.71
UC 1.3: POS transaction/Faster checkout	5.71	4.83	5.67	5.67	5.21
UC 1.4: Stock visibility/Replenishment from the backroom	6.13	6.14	5.57	4.86	5.86
UC 2.1: Social shopping	4.54	4.92	5.33	3.21	4.08
UC 2.2 Store associate availability/Customer knowledge	5.13	5.00	5.04	4.21	4.58
UC 3.1: Customer experience	4.92	5.21	5.58	3.88	4.58
UC 3.2: Cross selling/Cross promotion	5.04	5.50	5.50	3.92	4.75
UC 3.3: Store associates empowerment	5.00	5.17	4.96	4.33	4.75
UC 4.1: Process Automation	5.92	5.17	3.83	5.92	5.79
UC 4.2: Process accuracy	5.92	4.71	3.43	5.57	5.00
UC 4.3: After sales/return	5.17	4.38	4.96	4.92	5.21
UC 5.1: Out of stock/Inventory accuracy	6.00	5.71	5.25	5.58	5.92
UC 5.2: Omnichannel	5.58	5.79	5.92	5.13	5.38
UC 5.3: Supply chain visibility	4.33	4.29	4.08	4.25	4.58
UC 6.1: Grey market	3.88	3.71	3.17	3.75	3.96
UC 6.2: Counterfeiting	4.04	3.54	3.54	3.42	4.00
UC 6.3: Traceability	4.04	3.67	3.33	4.29	4.08

Overall 15 out of the 18 use cases were perceived as useful or very useful in general. The respondents perceived the Use Case 1.4 Stock Visibility/Replenishment from the Backroom as the most important of all use cases from our framework. But also, UC4.1 Process Automation, UC4.2 Process Accuracy and UC 5.1 Out of stock/Inventory accuracy were perceived as quite useful. The use cases which were perceived as the least important in general were UC6.1 Grey Market, UC6.2 Counterfeiting and UC6.3 Traceability. These use cases were also perceived as the least important in all of the categories we asked our respondents about.

We are not surprised by these results because the questionnaire was mainly answered by managers of two fashion companies who are most concerned about “Stock visibility”, “Inventory Accuracy” or “Process automation” because these are some of the pillars of their performance. We assume that their background also explains why they do not consider “Grey Markets” or “Counterfeiting” as important use cases in general and are rather neutral towards them.

When asked about cost reduction, the respondent perceived the use cases 1.3 POS transaction/Faster checkout, 4.1 Process Automation , 4.2 Process Accuracy and 5.1 Out of stock/Inventory accuracy as the most important of all. For the category customer satisfaction UC5.2 Omnichannel, UC1.3 POS transaction/Faster checkout and UC3.1 Customer Experience were perceived as the most important.

When asked about the importance of the use cases for an increase in sales and turnover, the respondents answered that the most important use cases were UC1.4 Stock visibility, UC5.2 Omnichannel and UC5.1 Inventory accuracy. Consequently, our respondents think that it is equally important that the inventory is accurate and that they know exactly how much there is in the stockroom as to be able to offer RFID-enabled omnichannel applications to the customers.

The most potential for process improvement was perceived for the use cases UC 5.1 Out of stock/Inventory accuracy, 1.4 Stock visibility and 4.1 Process automation.

Most of the use cases of our framework were perceived as useful or very useful. However, use cases 5.3 Supply Chain Visibility, 6.1 Grey Market, 6.2 Counterfeiting and 6.3 Traceability were perceived more neutral and consequently not so relevant from the points of views of the managers and the respondents from our LinkedIn Group.

Nevertheless, the results show the importance of the identified use cases and consequently also the importance for having RFID-enabled Value-Added Indicators to measure them. This survey was a further step in validating our theoretical framework from a more practical point of view.

4.3 Re-evaluating the categories

Even though we did not evaluate the framework on the level of Value-Added indicators (which would have needed a questionnaire with several hundreds of questions) we evaluated the general economic categories of the defined use cases. As we described in Subsection 3.4.1 each of the use cases was assigned certain Value-Added Indicators by the academics. The academics also assigned one of the following economic categories to the VAIs:

Sales/turnover.

Customer satisfaction/involvement.

Process cost/efficiency.

Process improvement/innovation.

Our main idea in this section is to compare the main categories of the use cases, given by the VAIs, to categories we obtained by our survey respondents. In order to identify the main category of a use case we (i) counted the categories of the VAIs that belonged to a specific use case and then looked which category was the most frequent one. (e.g. a use case with five indicators of the category Sales and two indicators of the category Cost will be attributed to the category Sales). Some use cases have, however, no specific main category because they have an equal amount of VAIs that belong to different categories (e.g. a use case with two indicators of the category Sales and two indicators of the category Cost has no specific main category). In this case a use case was assigned several main categories.

We then compared the main categories of the use cases to the economic categories that the respondents assigned to the UCs in our online survey.

Table 7 shows the results of this procedure. We obtained complete or partial matches between the categories of the online survey and the main categories that we defined before in 11 out of the 18 cases. This means that the survey respondents thought quite similar to the academics in about 61% of all cases. A further investigation reveals that the matching would be even better if the evaluation procedure would be less strict. This is due to the reason that there was often only a slight difference from the main category to second highest rated category in the online survey (see also Table 6). For example, for UC4.1 most Value-Added Indicators fell under the category Process Improvement. However, according to the survey results people rated highest for Customer Satisfaction with an average of 5.92 points on the Likert scale. Nevertheless, the category Process Improvement was very close by with an average of 5.79 points. Consequently, we can assume that the economic categories that were assigned to the use cases by the academics and the categories that were assigned to the use cases by the survey respondents match even more often than in 61% of all cases.

Table 7
Comparison of use case categories defined by scientific partners against the perceived categories according to survey results

Use Cases Academics Survey

UC 1.1: Locating items Cost Customer

UC 1.2: Loss prevention Cost Cost

UC 1.3: POS transaction/Faster checkout Cost, Customer Customer, Process

UC 1.4: Stock visibility/Replenishment from the backroom Sales Sales

UC 2.1: Social shopping Customer, Sales Sales, Process

UC 2.2 Store associate availability/Customer knowledge Customer Customer

UC 3.1: Customer experience Customer, Sales Customer, Sales

UC 3.2: Cross selling/Cross promotion Sales Sales

UC 3.3: Store associates empowerment Sales Sales

UC 4.1: Process Automation Process Cost

UC 4.2: Process accuracy Process Cost

UC 4.3: After sales/return Cost, Sales, Cost, Customer,

Process Process

UC 5.1: Out of stock/Inventory accuracy Process Process

UC 5.2: Omnichannel Sales Customer

UC 5.3: Supply chain visibility Cost Process

UC 6.1: Grey market Sales Process

UC 6.2: Counterfeiting Sales Process

UC 6.3: Traceability Cost Cost

Use Cases	Academics	Survey
UC 1.1: Locating items	Cost	Customer
UC 1.2: Loss prevention	Cost	Cost
UC 1.3: POS transaction/Faster checkout	Cost, Customer	Customer, Process
UC 1.4: Stock visibility/Replenishment from the backroom	Sales	Sales
UC 2.1: Social shopping	Customer, Sales	Sales, Process
UC 2.2 Store associate availability/Customer knowledge	Customer	Customer
UC 3.1: Customer experience	Customer, Sales	Customer, Sales
UC 3.2: Cross selling/Cross promotion	Sales	Sales
UC 3.3: Store associates empowerment	Sales	Sales
UC 4.1: Process Automation	Process	Cost
UC 4.2: Process accuracy	Process	Cost
UC 4.3: After sales/return	Cost, Sales,	Cost, Customer,
	Process	Process
UC 5.1: Out of stock/Inventory accuracy	Process	Process
UC 5.2: Omnichannel	Sales	Customer
UC 5.3: Supply chain visibility	Cost	Process
UC 6.1: Grey market	Sales	Process
UC 6.2: Counterfeiting	Sales	Process
UC 6.3: Traceability	Cost	Cost

This evaluation has helped us to further validate our framework. As the results have shown, most of the original categorizations are in accordance with our survey results. As a consequence, we assume that our initial categorization can indeed be seen as valid with regard to the needs of the apparel and fashion industry.

5 Conclusions and future development

Measuring and evaluating performances are considered as tasks of the greatest importance for organisations of any kind because they allow to translate an organization’s strategy into reality. Despite the high numbers of articles published on this subject in recent times, the measurement and evaluation of performances is still challenging managers and researchers. The growing adoption of new technologies such as RFID, IoT, and big data, just to name a few of them, has renovated the interest in performance measurement systems. In particular, RFID has demonstrated how effectively and efficiently products can be tracked and traced from raw materials to the end customers. In this paper, we have produced and validated a set of 60 different Value-Added Indicators (VAIs) that use, or might use, RFID technology to produce, monitor and increase the overall value of the fashion and apparel supply chain. These VAIs are organised in a structured framework, that has been built and validated by more than 20 experts from the field, including researchers from the academia, managers from fashion and apparel retail and system integrator companies. VAIs have been classified into four different categories, and provided with a nature and a dimension. In detail, selected categories deal with increasing sales/turnover, involving/satisfying customers, increasing process efficiency and effectiveness. The nature of our VAIs explains their dependency from RFID or other auto-ID technologies, whereas their dimension describes how VAIs can affect one or more activities in the fashion and apparel supply chain.

We notice that, as we expected, most of the VAIs we proposed (65%) depend upon RFID technology for collecting, displaying and sharing data, and only 35% of VAIs can also be monitored without RFID. Also, the vast majority of indicators deal with just one primary activity, and thus most probably on just one actor in the fashion supply chain. Only 13% of VAIs, in fact, measure performances that are common to more than one activity of the value chain. Consequently, even with the introduction of RFID technology, and especially in the fashion sector, it is important to focus more on supply-chain-wide horizontal indicators, rather than on vertical and local ones. Lastly, indicators are spread amongst four different categories, with a clear importance of sales/turnover indicators, and lagging indicators that measure process improvements and customer satisfaction/involvement. Knowing “why” sales increase or decrease, by monitoring key metrics, is more important than only measuring “how much” they do so because this helps a company to react faster to changes in the business and to act accordingly. Thus, process improvement and customer satisfaction indicators should be at least as important as the ones which measure the trend of sales/turnover. The importance given to process efficiency is reflected by the results from the survey that we reported in our paper: the community of respondents, in fact, shifted the focus more on customer- and process-centred UCs, rather than on sales-related ones.

Our survey however also showed, that most of the use cases the respondents were asked about, were on average perceived as good or very good, but none of them as excellent. This mainly stems from the fact, that different types of managers with different backgrounds and different functions within their respective companies were involved. Furthermore, managers might perceive the benefits of use cases they knew from before (e.g. the ones that were already present in their company) more important than novel use cases that were collected from the literature and presented in our survey.

One of the main limitations of our study is the low numbers of respondents to our survey. Even by using the simplest possible questionnaire, and thus moving from the evaluation of VAIs to that of UCs, and not taking into account the nature and dimension of VAIs, still our questionnaire counted 90 items. If we also consider the need for respondents to know both how the fashion and apparel sector and how RFID works, it is clear why we did not receive a large enough sample in order to be able to prove the statistical significance of our results. These results, nonetheless, are promising for both practitioners and researchers. The former, in fact, could use the results of this study to assess and understand which use case of RFID could fit for their company and why, and also which VAI could be useful to assess their business processes and entrepreneurial performance, either based upon or influenced by RFID technology. Researchers, on the other hand, might use our VAI framework to set benchmarks for different market segments of the fashion and apparel retail sector.

There are several steps which could be performed within future work. First, the same questionnaire should be repeated with the same respondents within 6 months in order to be able to investigate if the general opinions towards several of the use cases will have changed. Second, the questionnaires from the managers should be compared to the questionnaires of the staff in order to gain novel insights about the perception of the respective use cases. Third, our results should be compared to other research about the same subject in order to further validate and refine the results. Finally, it could be very interesting to develop models capable of assessing the quality and quantity of results achievable by pursuing different use cases of RFID, according to the reference market segment.

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Proposing a Value-Added Indicators framework for the apparel and fashion sector: Design and empirical evaluation

Abstract

OBJECTIVE:

DESIGN, METHODOLOGY, APPROACH:

FINDINGS:

PRACTICAL IMPLICATIONS:

ORIGINALITY/VALUE:

Keywords

1 Introduction

2 Value-added indicators – a review of the literature

2.1 Methodology

Table 2 Distribution of articles by document type Document Type No. of articles Article 26 Article in Press 2 Book Chapter 1 Conference Paper 23 Review 2 Total 54

3.3 Define the VAI framework

3.3.2 Nature

3.3.3 Dimension

4 Validating the VAI framework

4.1 Methodology

4.2 Discussion of the survey results

References

Table 2
Distribution of articles by document type

Document Type No. of articles

Article 26

Article in Press 2

Book Chapter 1

Conference Paper 23

Review 2

Total 54