Supply chain management requires continuous decision-making in five distinct areas: production, inventory, location, transportation and information by each of the supply chain members individually and collectively (Hugos 2006:4–6). The sum of these decisions will define the capabilities and effectiveness of the entire supply chain. Information is mainly required to coordinate daily activities, relating to the functioning and planning of the other four key areas. The information collected at certain points in the supply chain has to flow efficiently between the functions of an organisation and between the member firms of the supply chain (Hugos 2006:16). However, efficient information flow, accuracy, reliability and comprehensiveness are directly associated with effective interoperability between various supply chain partners handling and using this information. Achieving interoperability means faster information flow, and an effective decision-making process (Tyrinopoulos 2004:101).The inability of transferring information correctly, to where and when it is required, will have an impact on the decision making in the four key areas and therefore on the performance of the entire supply chain.

Information sharing in complex linkages, vital to the functioning of the supply chain, would be impossible without efficient information flows. Information sharing is actually required to occur on a real-time basis, to decrease uncertainty between the members of the supply chain and leading to a smoother and more efficient functioning and integration of the supply chain. The flow of information in business organisations and particularly in supply chains affects productivity and innovation because it determines the speed by which individuals can act and plan future activities (Wu et al. 2005:1). Therefore it is obvious that information flow needs to be managed in SCM. The measurement of efficient information flow should form an integral part of the management of supply chains. No evidence could be found of the measurement of information flow efficiency in supply chain management literature.

Earlier studies in supply chain management focussed on cost but later performance measures also included customer responsiveness and activity time (Arntzen et al. 1995:69–93; Lee & Billington 1993:835–847; Pyke & Cohen 1994:18–94). Beamon (1999) argues that these performance measures are of limited scope and might be inconsistent with the strategic goals of organisations. He developed a framework for the selection of performance measures for supply chain systems. The framework contained three types of measures: flexibility, resource measures and output measures. Sezen (2008:233–240) conducted a study in Turkey and found that there is a positive significant correlation between information sharing and supply chain performance in three categories of performance measures (resource, output and flexibility) in supply chain management. However, no evidence could be found of research done in the measurement of performance or assessment of information flow efficiency.

Other dimensions of information flow were researched. An example is the research done by Li & Lin (2006:1641–1656). They investigated the impact of environmental uncertainty, intra-organisational facilitators, and inter-organisational relationships on information sharing and information quality in supply chain management. They found that both information sharing and information quality were influenced positively by trust in supply chain partners and shared vision by supply chain partners, but negatively by supplier uncertainty. Furthermore they found that information sharing and information quality are not impacted by customer uncertainty, technology uncertainty, commitment of supply chain partners and IT enablers. These studies contribute to the focus on information flow in the supply chain.

The obvious importance of information flow in the performance of the supply chain necessitates formal management attention. The measurement of the efficiency of information flow should form an integral part of the management of supply chains. As indicated above no evidence could be found of the measurement of information flow efficiency in supply chain management literature. Hence, the aim of this article is to explore the measurement of the efficiency of information flow in SCM and taking the first steps to develop an instrument or framework with measures (indicators and metrics) to measure the efficiency of information flow.

Therefore the main focus points of this article will be information flow, performance measurement and the explorative development of indicators and metrics.

Ravichandran (2008:77) indicates that the bullwhip-effect has implications on efficiency on various levels. At the macro level, the bullwhip-effect induces poor service levels, inefficiencies in production, scheduling (capacity utilisation), sourcing, distribution, revenue generation and its realisation. At the operational level it generates more (additional) inventory and keeps it in the most inappropriate place to meet a specified service level. At a performance level it can reduce the velocity of cash, destroy potential revenue, and significantly erode revenue realisation through price discounts. It can potentially dilute competitive strategy and position and therefore can be a ‘strategy buster’.

The distortion of demand information implies that the manufacturers who only observe their immediate order data will be misled by the amplified demand patterns. This has serious cost implications. For example, manufacturers could incur excess raw materials cost due to unplanned purchases of supplies, additional manufacturing expenses created by excess capacity, inefficient utilisation and overtime, excess warehouse expenses and additional transportation costs due to inefficient scheduling and premium shipping rates (Lee, Padmanabhan & Whang 2004:1875). In some supply chains the bullwhip-effect can drive 13% – 25% of operating costs (Fawcett, Ellram & Ogden 2007:515). It is clear that a lack of real-time information can have a major impact on supply chain effectiveness and efficiency.

Information can be defined in different ways, depending on the context. A general definition of information has been offered by the National Institute of Standards and Technology (National Institute of Standards and Technology [NIST] 2002) by stipulating information to mean ‘any communication or representation of knowledge such as facts or data, in any medium or form, including textual, numerical, graphical, cartographic, narrative or audio-visual forms’. Information is also defined as ‘an extraction from data that, by modifying the relevant probability distributions, has a capacity to perform useful work on an agent’s knowledge base’ (Boisot & Canals 2004:7). Stair and Reynolds (2011:5–7) argue that information is ‘a collection of facts organised in such a way that they have additional value beyond the value of facts themselves’. According to these authors information can be extracted from data by applying a transformation process or a set of logical related tasks performed to achieve a defined outcome to the data, which in itself applies knowledge by selecting, organising and manipulating data. Therefore information is data in a transformed usable state.

Reeker and Jones (2002:6–7) furthermore argue that information is transmitted in two different components. Firstly, there exists a physical component, the so-called potential information, capable of transmitting information; however, without attaching any meaning. The second component has been termed mediate information, through which the potential information becomes meaningful. These components are clearly shown in Figure 1.

FIGURE 1: The conveyance of knowledge process.

Figure 1 shows a simple transfer process of information from one person to the next person (Reeker & Jones, 2002:10), which is an extension of the historical communication model as described by Shannon (1948:379–423, 623–656). The transfer process is shown as an example of potential information being a spoken utterance, transferred by sound waves and the mediate information being some ideas on the relevant mediate information to make the potential information meaningful. The mediate information converting the potential information into meaningful information is partly a set of conventions commonly held by the speaker in the belief that the listener would interpret the information using the same conventions.

For the capture, storage, retrieval and access of current information and knowledge, information technology has widely been used which includes applications that require strong interaction and communication between humans and computer technology.

Grooms (2003:13) and Piltz (2001:1–12) describe human or electronic communication as computer-mediated communication (CMC), which they further define as ‘synchronous and asynchronous communication using text messages sent via the computer’ and ‘the interpersonal communication with the assistance of computers as a transfer medium, transferring spiritual or intellectual content via two spatially separated computers or terminals, connected to a central computer’. The communication between persons, in the case of CMC, is limited to a visual, textual channel, lacking the direct cognitive perception and observation of the communication partner and the situational, physical and social context. Electronic messages, e-mail, electronic discussion forums and computer conferences form part of CMC (Piltz 2001:3). As these forms of communication readily have become part of the communication within the supply chain, they are in use to transfer information which is the focus of this research study.

Person-to-person information transfer largely depends on the expertise and acquaintance of the persons in the relevant area, social and cultural barriers between the persons and trust, psychological safety and dependence factors between the persons involved (Borgatti 2005:1–2). Saariluoma (2006:1) indicates varying goals of interest and geographic differences as main factors, whilst shared interests of persons exchanging information and personal privacy policies of persons exchanging information form important parameters according to Wu et al. (2005:1).

Newcorn (2003:58) contends that additional variables must be considered in the case of human-to-computer interaction, such as errors in data collection processes, information systems not facilitating inputs, user-friendliness and training in using the system. Access to the computer system (Borgatti 2005:2), information behaviour (Johnstone, Tate & Bonner 2004:6) and the lack of formal procedures as well as the incompatibility between electronic information systems (Moser 2004:3) have been sited to influence human-to-computer interaction. Concerning machine-to-machine information transfer, incompatible systems (Moser 2004:3; Newcorn 2003:58), different interpretation of data at different network points or plants (Newcorn 2003:58) and adaptability and limited scalability of proprietary systems (Cisco Systems 2005:4) have to be considered.

From an organisational supply chain perspective, information flow may be differentiated from the viewpoint of organisational levels. On a strategic level, information flow between companies may be inhibited by a lack of trust between the supply chain members (Li & Lin 2006:1641). This phenomenon specifically occurs, the more independent the member companies are (Trkman & Groznik 2005:562). Determinants of information flow at operational level within the organisation approximate the decisive factors of information flow between humans and humans, and humans and computers as detailed above.

Despite the level of information flow in the supply chain, or the mode of information transfer, the speed with which information flows from one point to another point in a network of organisations or smaller entities of a supply chain, is also determined by the length of the path between the points or nodes of a network (Borgatti 2005:2). The speed of information flow thus has an influence on business and supply chain efficiency. In many cases companies employ information systems applicable to supply chains in order to improve information flow to its members. The nature of such information systems will be explored in the next paragraph.

According to Cuenca (2004:242–243) systems that achieve the afore-mentioned tasks have been developed since the realisation of the first computer. Individual material requirement planning (MRP) systems were soon developed into enterprise resource planning (ERP) systems, which are installed throughout the entire organisation and include all the fundamental activities as detailed above. In order to achieve real time updating of the ERP systems, so-called manufacturing execution systems (MES) were utilised. MES typically interface with manufacturing machinery to retrieve the occurrence of events and export the times and dates of such events to the ERP systems. Later developments in supply chain information systems include advanced planning and scheduling systems (APS), which enable companies to improve customer service dramatically and reduce costs (Van Eck 2003:1). Other system improvements emanate from the development of customer relationship and supplier relationship management (CRM & SRM) modules. Software vendors continuously upgrade and improve their products in an attempt to make the software user-friendlier, and to provide new tools for different industry sectors. Advances in computer technology and the desire for a tighter integrated supply chain in terms of information flows, material and financial flows are driving this development.

According to Swink et al. (2011:530–531) the Internet and other emerging technologies have improved communications, data management, visibility and coordination across global supply chain networks. For example, as sales are made in retail stores, the sales and inventory information is automatically captured by point-of-sale (POS) bar codes and radio frequency identification (RFID) scanners and conveyed through the Internet or other exchanges to a control point. An enterprise resource planning (ERP) or other system then shares this information with suppliers’ systems so that inventory records could be updated and replenishment orders are generated. Fast moving consumer goods supply chains are usually managed through efficient consumer response through ERP systems (ERP), for example in the grocery industry, and quick response (QR) in the apparel industry.

The decisions that are made with regard to the supply chain should be aligned to the overall corporate strategy of that organisation (Carter et al. 2009:6; Mohamed et al. 2008:153). To assess whether the strategic goals of a business have been achieved, Antić and Sekulić (2006:71) contend that companies use a variety of business performance metrics, which are being assessed periodically. Historically, business performance measurement systems were mainly and often solely financially oriented (Neely 2003:3). However, List and Machaczek (2004:1) as well as Rajamanoharan and Collier (2006:53) argue that financial figures are consequences of yesterday’s decisions and do not indicate tomorrow’s performance. Therefore businesses need to consider not only financial performance, but also performance in non-financial areas such as customer satisfaction, innovation and internal parameters such as process improvements. For this reason, business performance measurements increasingly include non-financial indicators (Paranjape, Rossiter & Pantano 2006:5) as used in the balanced scorecard (BSC). The key to business performance management remains the integration of financial, operational and strategic information (Antić & Sekulić 2006:74; Winkler 2004:1).

According to Coyle, Bardi and Langley (2003:482–485) ‘knowing what to measure’ influences the overall benefit derived from the measurement process. Business scorecards contain specific indicators and metrics to measure the organisation’s performance. A key performance indicator (KPI) may be defined as ‘financial or non-financial quantifiable measurements that reflect the critical success factors (key activity) of an organisation’ (Von Haaren & Malyshko 2007:9). According to Giannoccaro, Ludovico and Triantis (2007:91), a performance metric may be defined as quantifying ‘the effectiveness and/or efficiency of actions’. ‘Measurements are typically expressed in terms of time, quantity, quality or cost’ (Hugo, Badenhorst-Weiss & Van Biljon 2004:100–108). Key performance indicators provide a performance measure for a specific key performance area of an organisation, that is, customer satisfaction, whereas metrics offer performance criteria for activities within a key performance area, such as on-time delivery.

A variation of the business scorecard (BSC) is the corporate communications scorecard (CCSC) which includes communication metrics (Schuppener n.d.). According to this scorecard the added economic gains within the four perspectives of the balanced score-card are measured. Zerfaß (2004:1–7) who proposes the corporate communications score card (CCSC) for the strategic control and measurement of an enterprise, in terms of its communications strategy with its stakeholders, uses the social-political performance of a firm as an additional perspective for measuring communication success. These models are of particular interest to this study as they represent measurement frameworks which already comprise communications metrics.

The performance measurement of either a business or a supply chain is a complex activity (Coyle et al. 2003:482–483). If it is taken into account that a supply chain consists of various organisations, then clearly the measurement of a supply chain is even more complex than that of a single organisation (Hugo et al. 2004:101). There are several key performance indicators (KPIs) for key performance areas (KPAs) which are aimed at judging the performance of logistical and supply chain activities.

There are also a multitude of metrics in use to gauge the performance of supply chains, including, outbound freight cost, order fill rate, finished goods inventory turns, returns and allowances, customer complaints, back orders, order cycle times, forecast accuracy, invoice accuracy to orders processed per time unit, cash-to-cash cycle time and inquiry response. Other metrics used include confirmed fill rate, response delay (in terms of confirming a delivery date), work-in-progress stock (WIP), sales or inventory ratios and sales (Kleijnen & Smits 2003:1–2).

Santos, Gouveia and Gomes (2006:2–4) propose a supply chain performance measurement framework based on the following four dimensions or perspectives of the BSC:

• customer perspective
• business process perspective
• financial perspective
• innovation and learning perspective.

Each of these BSC perspectives encompasses a number of KPI’s, which may be selected depending on the relative level of importance in each business and supply chain. Von Haaren and Malyshko (2007:11) propose a performance measurement framework for supply chain management that comprise five balanced scorecard perspectives, including the four perspectives mentioned above with the additional perspective of ‘cooperation’. Both Santos et al. (2006:2–4) and Von Haaren and Malyshko (2007:11) define specific KPIs and the metrics that may be used to evaluate supply chain performance, given certain key assumptions such as strategy and the network selected. According to Santos et al. (2006:2–4) metrics should be linked to business strategy, operational plans, goals of individual departments as well as global goals. It follows therefore that metrics may vary from industry to industry and company to company (Swink et al. 2011:41).

KPIs and metrics provided by the supply chain scorecard correspond to a large extent with the metrics proposed by the Supply Chain Council’s supply chain operations reference (SCOR) model (Simchi-Levi et al. 2009:381; Swink et al. 2011:42). The SCOR model was developed due to a need for finding new ways to communicate objectives and performance outcomes amongst supply chain partners. Besides providing metrics it also provides tools for charting and describing supply chain processes, best practices and technology. The performance measurement or metrics part is of particular use to this study. The SCOR model addresses the following five basic dimensions of performance on the different levels of operation (Swink et al. 2011:42): delivery reliability; responsiveness; flexibility; costs and asset management efficiency.

The measurement process is not merely concerned with the collection of data associated with a predefined standard, with performance measurement rather being perceived as an overall management system, involving the prevention and detection of non-satisfactory performance in a particular key performance area. It is the aim of the measurement process to improve the relevant underlying business process in order to bring about better customer service. In addition, the measurement process is also concerned with the optimisation of the relevant process based on the increased efficiency and effectiveness of the particular process under investigation (Kellen 2003:3–6).

The performance measurement paradigm may be applied in order to develop indicators and associated metrics of information flow efficiency in organisations and/or supply chains.

Real-time contextual data enable users to make better, faster decisions by providing information across both the plant and the supply chain in a single format. The efficiency of information sharing or of the information flow has a direct bearing on the speed with which decisions may be made about the future state of a supply chain. From a performance measurement point of view, the process concerning the information flow efficiency which must be investigated and measured; thus, also involves the methods of information transfer within an organisation and between the member organisations of a supply chain.

Based on the conveyance of the knowledge model, which is based on the traditional communication model (reflected in Figure 1), information that is transferred between two humans is subject to certain conditions. Based on these conditions it is clear that the efficiency of information transfer does not only depend on the speed of the transfer itself, but also on other factors. This interpretation may be derived from the basic definition of efficiency, which is described as ‘performing an activity correctly, referring to the economic utilisation of resources’ (Mahidhar 2005:58), where the term time refers to the physical time expended whilst the term effort refers to non-time based aspects such as mental and physical exertion. Therefore parameters other than solely time-based indicators and metrics must be considered.

In order to find a source for possible indicators and metrics for the measurement of information flow efficiency, the characteristics applicable to knowledge transfer: software quality and business performance measurement, were consulted as summarised in the previous sections.

A list of characteristics that are applicable to information systems and information quality is reflected below (Lin 2006:8; Stair & Reynolds 2011:7):

• information accuracy, provides error-free information
• complete information, contains all important facts
• flexible information can be used for a variety of purposes
• reliable information can be depended on
• relevant information is important to decision making
• timely information is delivered when it is needed
• information is verifiable
• information is accessible.

Twenty-six key characteristics of performance measurement system criteria were identified from: Abbadi (2007:1–19); Breyfogle (2008:1–3); De Haas and Kleingeld (1999:233–261); Eckerson (2004:1–5); Jensen and Sage (2000:33–61); Rosella Software (2008:1); Serrat (2010:1–8); Sullivan, McDaniel, Siegel, R&B Consulting and McDaniel Lambert Inc. (2004:2–4); Ying, Hong and Zhengchuan (2004:4–6). Upon closer examination of the 26 characteristics a narrowed down ‘duplicate-free’ list of characteristics were retained. These characteristics are reflected in Box 1.

• Are there obstacles to overcome in accessing information?
• Are the access screens for information retrieval user-friendly?
• Is information access personalised?
• Is the user able to choose his or her own information content?
• What is the relative cost of accessing information or data?

• Are the variables in the presentation of the information or data clearly defined?
• Is the classification or division of the information or data understandable?
• Are help functions available to explain how the information or data is derived?
• Is multilingualism taken into account?

• Is it possible to compare information or data of the same nature between different sources?
• Are changes to the presentation of information or data communicated globally?
• Is the process of information or data provision or derivation clearly defined?

• How long does it take to retrieve information or data?
• Is the information or data always produced on time?
• What is the time difference between the receipt and reading of e-mails or faxes?
• What is the time difference between the receipt and responding to e-mails or faxes?
• Is late or missing information or data communicated?

• Does the information or data presentation meet the needs (too much or too little)?
• Can the information or data presentation and content be adjusted to future needs?
• Is the information or data regularly reviewed (are feedback programmes in place)?
• Are the information or data priorities considered?

• Is the information or data always correct?
• What is the number of corrections required to amend the information or data produced?
• Does a quality standard for information or data collection or presentation exist?
• How is both internal and external data accuracy checked?
• How is faulty or erroneous data treated?
• Are accuracy levels for the information or data specified?

• Is detailed information or data protected against unauthorised access?
• Is detailed information or data protected against unauthorised change?
• Is detailed information or data backed up regularly?

• Do all the users of the information or data agree with the presentation or layout of content?
• Is it possible to customise or personalise the information or data layout easily?
• Do specific users of the information or data use the information or data presentation?

• Is all the information or data contained in the presentation useful to the user?
• Does the use of specific information or data dissipate over time?
• Is the information or data produced useful to all the users, from the strategic to the operational level?

• Is the information or data perceived to be always correct?
• How often has incorrect information or data surfaced in reports or presentations?
• Do multiple versions of information or data exist on personal computers, networks and in the paper archives?
• Are older versions of the information or data mixed with newer versions?

• Is it possible to reproduce this information or data within days?
• For which period is the information or data valid compared to the time required to produce it?
• Would it be possible to repeat the information or data if it was reproduced from different report tools?

• What is the time difference between the occurrence of activities and reporting of those activities?
• Are reports or information updated as soon as the information or data changes?
• Is transactional data updated real-time?

• Does the information or data produced cover all required aspects or variables?
• Do reports or information cover all requirements at all organisational levels?
• Is the information or data cascaded upwards into the next level indicators?

The selection of the indicators of information flow efficiency was explored by utilising statistical hierarchical clustering procedures. The assessed importance of the indicators was statistically evaluated by applying a cluster analysis, which seeks to identify homogenous subgroups of cases or variables. Ward’s method was used to evaluate distances between variables in a multidimensional space and to agglomerate variables into clusters (Dolnicar 2003:5–12).The results of the cluster analysis and the median importance ranking determined the selection of the master set of indicators.

The metrics were each rated on a scale from ‘01’ to ‘05’, with ‘01’ being the most important and ‘05’ the least important. Based on the results above, a bias towards more important ratings could be detected, as none of the metrics were judged as least important and only one metric, ‘multilinguism’, as having little importance.

The associations computed by the clustering algorithm also depend on the number of clusters chosen in order to achieve a sensible cluster schedule. By using the cluster analysis technique together with the box plot diagram, the appropriate cluster membership schedule was identified. The important characteristics could be segregated from the less important ones in order to limit the amount of data to be handled and to focus on what was thought to be important by the respondents. After eliminating the least important ones the remaining characteristics where used as indicators of information flow efficiency. The indicators retained were: ‘Accessibility’, ‘Consistency’, ‘Timeliness’, ‘Relevance’, ‘Accuracy’ and ‘Usefulness’, including their associated metrics.

Because of the small sample size and the fact that a purposive sample was used, the results of this research are exploratory in nature and further research is required to validate the set of proposed indicators and metrics. Scales need to be developed for each metric to actually measure performance. The shortcomings of this study pose opportunities for further research.

In conclusion, the development of indicators and metrics from characteristics of related concepts and existing performance evaluation instruments in this study, makes a contribution to supply chain management’s understanding of the enterprises’ and supply chains’ performance attributable to information flow efficiency.

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