Post-COVID-19 challenges of operating supply chains, companies face the challenge of making their supply chains more cost-efficient and flexible. With supply chain operations accounting for about 70% of COGS, every serious action to reduce the costs can bring substantial savings to the bottom line. In this, the role of data analytics in cost reduction and sustainable operation is becoming increasingly important.
Data analytics is key in identifying operational baselines for the supply chains, enabling simulations and what-if scenarios, building collaboration within the business and with external partners, and much more. As the famous saying goes, if you can't measure it, you can't improve it. Data analytics provides insights into various aspects of the supply chain, helping identify cost-saving opportunities.
Developing a data analytics backbone is central to enhancing supply chain capabilities. It involves integrating master data such as material master, supplier master, and customer data and transactional data like the data from the ERP systems. This combination allows the company to build an ecosystem with a proactive approach to identify improvement areas across the supply chain.
Begin by picking areas within the supply chain that are most in need or offer the highest return on investment. This selection should be based on both qualitative and quantitative analyses, such as leveraging existing ERP data and conducting stakeholder interviews. Diagnostic toolsv from trusted advisors can also help identify potential areas for improvement.
While every company will have its own unique sets of needs and, hence, a possibly different combination of the items under areas of focus, here are 5 of the common ones:
As businesses start to integrate data analytics into their supply chain operations, they often encounter a set of challenges. These hurdles, while significant, can be managed with a strategic approach.
A primary challenge is ensuring the quality and consistency of data. Many businesses struggle with missing, siloed, outdated, or inaccurate data. Integrating data from various sources into a unified analytics platform is crucial yet complex. This involves aligning data from disparate systems, which can be technically challenging and time-consuming.
The lack of in-house analytics expertise is another common obstacle. Developing or acquiring the right talent to handle sophisticated data tools and interpret results is essential.
Continuous training and development are needed to keep the team updated with the latest analytics trends and technologies.
Investing in the right analytics tools and technologies requires significant capital. Despite this, in a recent Gartner survey, global CSCOs mentioned advanced analytics among the top 2 emerging technology investments, with only 9% having no plans to invest.
Alongside investment, adopting these technologies into existing systems and processes can see resistance from employees accustomed to traditional methods.
Data security and privacy concerns become more pronounced with the increase in data usage. Ensuring that sensitive supply chain data is protected against breaches is paramount. Compliance with data protection regulations adds another complexity to managing data analytics.
Moving towards a data-driven culture requires a shift in mindset at all levels of the organization. This involves fostering a culture where decisions are based on data insights rather than intuition.
Despite these challenges, a more flexible and cost-effective supply chain through data analytics is possible with a planned and phased approach. With commitment from the senior leadership and the right strategies, businesses can overcome these hurdles and unlock the full potential of data analytics in their supply chain operations.
Holocene is ready to support you in this transformation. Holocene offers cutting-edge analytics tools and expertise to help businesses unlock the full potential of their supply chain data. Embrace data analytics with Holocene and drive your supply chain towards greater efficiency and cost-effectiveness.