News

Information overload is hardly a new phenomenon. Indeed, one could argue out that its potential has existed since information became a critical element in human activities. For instance, as scientific disciplines took shape between the 17th and 19th centuries, the rapid expansion of knowledge led to an overwhelming volume of available information. Over time, it became increasingly difficult for individuals to stay abreast about all developments within what was once broadly known as ‘natural philosophy.’ Similarly, in highly specialised fields, the depth of expertise required has made it nearly impossible for professionals to remain updated on every aspect of their discipline. In some cases, even those within the same field may struggle to comprehend certain subareas. This challenge has been widely recognised across various domains, including accounting, information systems, communication research, and operations management, underscoring the growing need for effective information management strategies.

Building on this broader challenge, operations management faces a particularly complex issue—striking a balance between the benefits of information sharing and the risks associated with excessive data exchange. In connection to the former, information sharing is widely recognised as a crucial element of effective supply chain management, facilitating coordination and enhancing decision-making. Research highlights its critical role in gaining a competitive edge, particularly by improving supply chain visibility. With real-time insights into key variables such as production rates, inventory levels, and delivery schedules, supply chain stakeholders can track raw materials, components, and finished products across various points—including manufacturing facilities, port terminals, warehouses, and transit routes. This level of transparency enables businesses to detect disruptions promptly, minimise their impact, and enhance overall operational efficiency.

However, information sharing alone does not automatically translate into improved supply chain performance. Its effectiveness largely depends on the quality of the information exchanged. Studies by Saxena & Lamest highlight on this issue, noting that despite significant investments in ICT systems, many organisations fail to achieve the anticipated improvements in supply chain performance, a challenge merely suggesting that investing in data collection and dissemination does not automatically translate into better performance. Instead, it is essential to identify the specific types of information that should be shared and determine how they contribute to optimising supply chain design and operations. A well-structured approach to information management ensures that relevant, accurate, and timely data is utilised effectively, ultimately supporting better decision-making and operational efficiency across the supply chain.

Unfortunately, such a predicament befalls today’s supply chains, with data quality issues often manifesting as data overload. On one hand, businesses now have unprecedented access to detailed information about suppliers, customers, and internal operations, a capability that consequently increases visibility, and consequently enables companies to identify hidden patterns and trends, enhancing demand forecasting and supply planning. Research indicates that organisations leveraging big data analytics, a capability enabled through data sharing, can achieve notable efficiency gains, with studies highlighting benefits such as inventory level reductions of 15–20% and supply chain costs lowered by 5–10%. Leading companies such as Facebook, Google, Amazon, and Walmart have as well demonstrated the transformative power of data, leveraging consumer insights to refine marketing strategies and drive financial success. Mid-market enterprises are also recognising the value of data-driven decision-making and are now striving to establish best-practice models for filtering out irrelevant data while retaining critical insights for supply chain management. We are seen a plethora of integrated solutions that consolidate data from multiple systems, including enterprise resource planning (ERP), supply chain management, transportation management, warehouse operations, and financial reporting. Additionally, these solutions incorporate vendor data from suppliers, contract manufacturing organisations (CMOs), and third-party logistics providers (3PLs), as well as unstructured data extracted from emails, instant messaging platforms, and corporate intranet applications.

Nonetheless, the abundance of big data risks transforming an excess of information into a hindrance rather than an asset, potentially crippling supply chain operations. Recent surveys have highlighted that many companies are either hesitant to adopt advanced analytics tools or have struggled to fully leverage the technology, with the challenge hardly stemming from a lack of awareness of these tools or their potential impact—most end users are well-informed. Rather, the difficulty lies in effectively processing and integrating the data across the entire organisation, ensuring it is applied in a way that truly benefits supply chain management. An Accenture survey effectively highlights this challenge, with only one in five companies reporting that they are “very satisfied” with the returns from their analytics investments, a result that is rather disappointing. This issue is not due to a lack of effort, as two-thirds of companies have appointed a chief data officer in the past 18 months to oversee data management and analytics. Moreover, 71 percent of those without such a role plan to establish one in the near future, underlining the importance of data governance and strategic analysis for improved business outcomes. The question of how much information is too much thus remains a pressing concern for supply chain professionals, particularly in an era where big data continuously generates vast streams of readily available insights.

ADDRESSING THE CHALLENGE

Having a Clear Understanding of Your True Objectives

Acknowledging that improved service – reflected in innovative and more efficient processes – necessitates an expansion in data inputs, the ability to manage this data effectively becomes a fundamental priority. A critical first step is to focus on key performance indicators (KPIs) that align with the core objectives of all stakeholders within the supply chain. By concentrating on what truly matters, organisations can filter out unnecessary data, transforming information into a tool for insight rather than a source of complexity. However, selecting the most relevant KPIs is not always a straightforward task. The inclination to measure everything often arises, a tendency that can result in tracking an excessive number of metrics, ultimately diverting attention from those that truly drive performance. Parties should therefore seek to ensure established KPIs are directly linked to overarching supply chain objectives. To achieve this, stakeholders should ask themselves: What are the critical outcomes the supply chain, as a whole, seeks to achieve? For instance, for a pharmaceutical supply chain where the primary objective is ensuring the timely and safe delivery of medications, key metrics such as order fulfilment rates, regulatory compliance, supply chain resilience, and inventory accuracy become indispensable.

Another essential aspect is ensuring that established KPIs are both actionable and measurable. An effective KPI is more than just a figure—it should drive decisions and guide actions. If a metric fails to provide clear insights into the necessary next steps for supply chain stakeholders, its relevance should be reconsidered. Additionally, maintaining a focused set of KPIs is crucial. Streamlining metrics enhances clarity, allowing organisations to concentrate on the most impactful factors that drive performance and efficiency.

Establishing an Effective Reporting Framework

Once KPIs are established, the next critical step is to develop a streamlined reporting framework that meets stakeholders’ needs. Rather than becoming overwhelmed by the volume of metrics, organisations should adopt a structured approach that prioritises the most essential indicators. One widely recognised framework that facilitates this process is the Balanced Scorecard (BSC).

The BSC enables organisations to align performance metrics with strategic objectives across four key perspectives: financial, customer, internal processes, and organisational capacity. This comprehensive approach ensures that reporting extends beyond traditional financial metrics to encompass other critical factors that drive long-term success. By broadening the focus, supply chain parties can assess not only immediate financial outcomes but also the underlying drivers of sustained performance and overall supply chain resilience.

Effectively, the BSC encourages supply chain entities to maintain a well-rounded evaluation of KPIs across various areas, fostering a balanced approach to performance measurement. However, it is important to recognise that each of the four BSC perspectives encompasses multiple metrics. To prevent dilution of focus, organisations should agree on a select few key KPIs within each category, ensuring alignment with overall supply chain goals and strategic objectives. This targeted approach enhances clarity, supports informed decision-making, and ultimately strengthens supply chain performance.

Leveraging Tools, Techniques and Technologies

One final approach, though not exhaustive of the available solutions, involves leveraging a combination of tools, techniques, and technologies to manage the increasing volume of data effectively. This includes deploying intelligent agents for information filtering and implementing personalised recommendation systems. The use of intelligent agents for data filtering is not a new concept and is widely recognised in discussions on digital transformation. These agents, often powered by artificial intelligence (AI) and machine learning algorithms, help streamline vast amounts of data by identifying relevant patterns and extracting meaningful insights, thereby reducing the risk of information overload.

However, greater emphasis should be placed on the role of personalised recommendation systems, which have proven instrumental in enhancing decision-making processes within supply chain operations. These systems analyse historical data, user preferences, and contextual factors to suggest the most relevant insights or actions. For instance, AI-driven recommendation engines can support procurement teams by identifying the most cost-effective suppliers or flagging potential disruptions in supply routes. Similarly, predictive analytics tools, which rely on machine learning techniques, can assist in demand forecasting by tailoring recommendations based on real-time market trends and past purchasing behaviours.

By integrating these advanced technologies, organisations can refine their data management strategies, ensuring that the vast influx of information is transformed into actionable insights rather than becoming an operational burden. Ultimately, the goal is to create a structured framework that aligns technological capabilities with business objectives, enabling more efficient and informed decision-making.

Building on the above solutions, it becomes evident that the true power of data lies not in the sheer volume of information but in the ability to extract meaningful insights that drive action. Attempting to track every available data point is not a prudent approach, as it risks diverting attention from the key metrics that enhance performance and foster growth. Rather than achieving clarity, organisations may find themselves overwhelmed by complexity and uncertainty. In addition, effectively managing these challenges requires acknowledging that each supply chain operates within a unique context, meaning data utilisation strategies must be tailored accordingly. A well-structured, strategic approach that aligns with parties’ specific needs and long-term objectives is essential. By proactively addressing these complexities, supply chains can unlock the full potential of big data, enhancing decision-making and operational efficiency. Those that succeed in this transition will not only gain a competitive advantage but also establish themselves as frontrunners in an increasingly data-driven landscape.

References

Saxena, D., & Lamest, M. (2018). Information overload and coping strategies in the big data context: Evidence from the hospitality sector. Journal of Information Science, https://dx.doi.org/10.1177/0165551517693712.

Shahrzadi, L., Mansouri, A., Alavi, M., & Shabani, A. (2024). Causes, consequences, and strategies to deal with information overload: a scoping review. International Journal of Information Management Data Insights.