Tag Archives: Operations Management

The Responsible Supply Chain Management and its effect on Corporate Reputation

supply chain(image source: Carlson School of Management, University of Minnesota)

 

Corporate reputation has often been defined as “a set of attributes ascribed to a firm, that is inferred from the firm’s past actions” (Weigelt & Camerer, 1988, p. 443). Fombrun and Shanley (1990) argued that reputation “signals publics about how a firm’s products, jobs, strategies and prospects compare to those of competing firms” (p. 233). The value of reputation has been subject to extensive research, which has highlighted that reputation influences the stakeholders’ perceptions (Money, Hillenbrand & Downing, 2011), the customers’ choices and their purchase intentions (Keh & Xie, 2009; Siegel & Vitaliano, 2007; Mohr & Webb, 2005) Therefore, corporate reputation is related to corporate financial performance (Camilleri, 2012; Flanagan, O’Shaughnessy, & Palmer, 2011). Much of the work on corporate social–financial performance also implicitly assumes that this relationship is positive, because an improved reputation facilitates revenue and profit growth (Orlitzky et al., 2003; Surroca, Tribó & Waddock,. 2010).

Extant work suggests that reputation is important because it establishes credibility (Greyser, 1999; Herbig et al., 1994). The notion that reputation is related to credibility has also been noted in the wider corporate social (and environmental) responsibility literature. McWilliams and Siegel (2001) argued that building a reputation of ‘responsibility’ can signal an improved reputation (Husted & Allen, 2007; Brammer & Millington, 2005; McWilliams & Siegel, 2001; Fombrun & Shanley, 1990). Hence, responsible corporate behaviour “builds trust and enhances the firm’s reputation, which in turn attracts customers, employees, suppliers and distributors, not to mention earning the public’s goodwill” (Lantos, 2001, p. 606). In a similar vein, Lewis (2003) also held that responsible behaviours can establish trust and ultimately develop a company’s reputation. Social and environmental activities not only can enhance the reputation of the firm, but also enhance the goodwill trust of stakeholders (Carlisle and Faulkner, 2005; Siltaoja, 2006).
Therefore, corporate reputation is fundamentally a signal to stakeholders (Ponzi, Fombrun & Gardberg, 2011) and is particularly important in markets where there is imperfect information (Hoejmose et al., 2014.; Weigelt & Camerer, 1988). The market signals, including engagement in social and environmental issues could help to improve corporate image (McWilliams & Siegel, 2001; Bagnoli & Watts, 2003).

Markley and Davis (2007) also noted that responsible behaviours could send positive market signals to a range of stakeholders. Today’s firms are expected to implement responsible supply chain practices. If they won’t they run the risk of damaging their reputation and image among their stakeholders. Hence, there is scope for firms to implement socially and environmentally responsible practices in their supply chains (Ansett, 2007). Responsible supply chain management encapsulates social issues (e.g. child labour, working conditions, human rights et cetera) and / or environmental matters (e.g. environmental protection, waste management, recycling, reusing natural resources et cetera) (Hoejmose et al., 2013; Carter & Rogers, 2008; Seuring & Muller, 2008). Such responsible behaviours shield the firms from negative media attention and consumer boycotts (Hoejmose et al., 2013). The companies’ stronger engagement in socially responsible supply chain management enables them to manage exposure to risk (Tate et al, 2010; Van De Ven & Jeurissen, 2005). Thus, the businesses’ stakeholder engagement and their responsible procurement of materials and products is linked to corporate reputation, which in turn allows them to target discerning customer groups (Phillips & Caldwell, 2005; Roberts, 2003).

Kleindorfer, Singhal, and Wassenhove (2005) suggested that responsible supply chain practices can lead to increased profitability, as customer satisfaction and loyalty will improve as a result of a stronger reputation. Therefore, firms risk losing customers to rival companies over time, particularly if they fail to be responsible in their supply chain. In fact, Harwood & Humby (2008) findings suggested that suppliers were adhering to specific corporate social responsibility (CSR) requirements in order to reduce their exposure to risk. It may appear that the real value of social and environmental management is perhaps not from its role in enhancing reputation, but more about protecting it. This reflects Burke’s (2011) argumentation as he suggested that a firm’s corporate reputation is enhanced through positive actions, the programmes they implement and the other tangible things that they do.

Therefore, the distinction between reputation protection and enhancement is subtle, but important. Corporate reputation protection is concerned with evidencing the firms’ efforts to meeting the stakeholders’ expectations, whilst reputation enhancement goes beyond a purely evidential basis to encompass embedded practice. Corporate reputation protection occurs when firms can prove to stakeholders that they took reasonable steps to prevent an incident from happening (Coombs, 2014). In fact, corporate reputations could be easily jeopardised by irresponsible supply chain practices which may “directly harm business contracts, marketing, and sub-sourcing, and damage the corporation’s brands and the trust they have established with their business customers” (Lee & Kim, 2009, p. 144). These companies’ failure to manage their supply chain in a responsible manner could result in negative repercussions for their organisational performance. Conversely, the corporations’ reputation and credentials in socially responsible supply chain management could lead them to achieve a competitive advantage (Ansett, 2007; McWilliams et al., 2006).

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Filed under Business, Corporate Social Responsibility, Corporate Sustainability and Responsibility, Shared Value, Stakeholder Engagement

Crunching Big Data for Operations Management

Big data

For decades businesses have been using data in some way or another to improve their operations. For instance, an IT software could support small enterprises in their customer-facing processes. Alternatively, large corporations may possess complex systems that monitor and detect any changes in consumer sentiment towards brands.

Recently, many industry leaders, including McKinsey, IBM and SAS among others have released relevant studies on big data. It transpires that they are using similar terminology to describe big data as a “situation where the volume, velocity and variety of data exceed an organisation’s ability to use that data for accurate and timely decision-making” (SAS). These providers of business intelligence solutions have developed technical approaches to storing and managing enormous volumes of new data.

The handling and untangling of such data requires advanced and unique storage, management, analysis and visualisation technologies. The terms of “big data” and “analytics” are increasingly being used to describe data sets and analytical techniques in applications ranging from sensor to social media. Usually, big data analytics are dependent on extensive storage capacity and quick processing power requiring a flexible grid that can be reconfigured for different needs. For instance, streaming analytics process big data in real time during events to improve their outcome.

Insightful data could easily be retrieved from the Web, social media content and video data among other content. Notwithstanding, such data could be presented in different forms; ranging from recorded vocal content (e.g. call centre voice data) or it can even be genomic and proteomic data that is derived from biological research and medicine.
Big data is often used to describe the latest advances in technologies and architectures. Nowadays, big data and marketing information systems predict customer purchase decisions. This data could indicate which products or services customers buy, where and what they eat, where and when they go on vacation, how much they buy, and the like.

Giant retailers such as Tesco or Sainsbury every single day receive long-range weather forecasts to work 8-10 days ahead. Evidently, the weather affects the shopping behaviour of customers. For example, hot and cold weather can lead to the sales of certain products. It may appear that weather forecasting dictates store placement, ordering and supply (and demand) logistics for supermarket chains. Other retailers like Walmart and Kohl’s also use big data to tailor product selections and determine the timing of price markdowns.

Shipping companies, like U.P.S. are mining data on truck delivery times and traffic patterns in order to fine-tune their routing. This way the business will become more efficient and incur less operational costs. Therefore, big data extracts value by capturing, discovering and analysing very large volumes of data in an economic and expeditious way. This has inevitably led to a significant reduction in the cost of keeping data.

Big data can also be linked with production applications and timely operational processes that enable continuous improvements. Credit card companies are a good illustration of this dynamic as direct marketing groups at credit card companies create models to select the most likely customer prospects from a large data warehouse. Previously, the process of data extraction, preparation and analysis took weeks to prepare and organise. Eventually, these companies realised that there was a quicker way to carry out the same task. In fact, they created a “ready-to-market” database and system that allowed their marketers to analyse, select and issue offers in a single day. Therefore, this case indicates that businesses became much more effective (and efficient) in their processes through iterations and monitoring of websites and call-centre activities. They could also make personalised offers to customers in milliseconds as they kept tracking responses over time.

Organisations are increasingly realising the utility of data that could bring value through continuous improvements in their operations. This contribution indicated that relevant data needs to be captured, filtered and analysed. Big data is already swamping traditional networks, storage arrays and relational database platforms. The increased pervasiveness of digital and mobile activity, particularly from e-commerce and social media is leading to the dissemination of meaningful data – that is being created each and every second. Successful, online businesses can gain a competitive advantage if they are capable of gathering and crunching data.

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Filed under Analytics, Big Data, ICT