How data-driven insights are improving supply chain decisions

The supply chain and logistics sector is one that’s defined by change… and yet is also highly dependent on consistency and stability. While, on a micro-scale, roadblocks, diversions, and traffic delays are known challenges on any route, success often comes down to the experience of the driver, the quality of the information they have available to make route adjustments, and the timeliness of just how quickly that information reaches them. Data-driven insights have been the lifeblood of the supply chain sector for decades, with hard-won knowledge used to make strategic decisions. Today, data is the navigator for this journey. Once gathered, processed, and analysed, it can get you to where you need to be while mitigating risk.

Supply chain activities directly impact each part of the customer journey – from anticipating and influencing demand to responding and fulfilling that demand and retaining and growing the business. Without the right information at the right time, that disruption can rapidly pile up. Commerce and customer loyalty is, naturally, experience-led. Broken supply chain processes, comparatively, also break the positive customer experience that businesses need to thrive. Aeroplanes are reportedly off course at least 90% of the time due to weather conditions, turbulence, and other factors. They are kept on track through hundreds of tiny data-informed adjustments – one degree at a time. But what happens when those micro-adjustments are no longer sufficient?

 

What’s over the horizon: the past, present, and future of data-driven supply chains

What’s changing in supply chains is that this data no longer resides in the head of the most experienced driver or warehouse leader. Now, that data has gone digital, with a massive shift in how that data is collected, stored, processed, and used. Route optimisations and disruption mitigations can be decentralised and distributed across the entire business.

The McLaren Racing Formula 1 team is a key example of an optimised supply chain underpinned by analytics  – with the need to secure materials for more than 80,000 unique and custom-built components. The car would not be built within operational budget caps without that reliability and consistency across inventory and part performance tracking. They use data science and analytics to help track and oversee the journey of each car part, from design and simulation to the complicated manufacturing and maintenance supply chains, to help drive that resilience. McKinsey research notes that of the supply chain executives surveyed in May of 2020, 93% intended to make their supply chains more flexible, agile, and resilient. For the organisations able to harvest, capture, and turn that data into insights, the potential competitive edge is significant.

In rising to meet these data challenges, supply chain specialists are transforming the ways in which they work and solve problems through data insights – providing more flexibility, agility, and resilience across their business. However, the true value of data increases when the responsibility for analysing data is democratised to the broader team.

With the advent of accessible low-code no-code tools, the responsibility for analytics no longer needs to be the domain of data scientists exclusively. Instead, those closest to the challenge can be empowered to solve it – using their expertise and experience to deliver insights that would have otherwise been missed and then easily and effectively automating that very same process. The missing ingredient is training – investing in human potential and not just layering technological solutions on top of each other.

 

Using data for intelligence

The pandemic effectively saw businesses take five years of digital transformation progression and condense it into six months. While vital to the continuation of service at short notice, any project concentrated to such a timeline is – by definition – going to run into roadblocks of its own without the necessary training to support it. While automation is a powerful tool, it is still a tool. Just as a hammer does not pick itself up and swing itself, automation technology only becomes a valuable resource when combined with human intelligence.

While an achievable and valuable goal, automation needs to come from a firm and robust foundation of practical data work. Instead, businesses need to start small by building intelligence and visibility across all teams and processes. By solving micro problems regularly and accurately, data teams can begin to expand their remit. According to a new Deloitte survey, 67% of business executives say they are “not [currently] comfortable” accessing or using data from advanced analytic systems.

A comparable study from KPMG explains that this phenomenon is due to reliability. With inconsistent results from data, many executives are confirmed to “lack a high level of trust in their organisation’s data”, with 67% of CEOs stating they “prefer to make decisions based on their own intuition.” In fully optimising supply chains and mitigating the risk of disruption, CEOs and business leaders must be provided the right information at the right time to make the right decisions. Providing these leaders with regular, accurate information is a core route to a data-driven supply chain. Still, that information must be accurate every time to build that trust.

A core premise within data science is reducing a challenge to its core components – to a point where reliability and accuracy are guaranteed – and building from there. While a micro solution can be scaled up to a macro one, a macro solution can rarely be condensed to work on a smaller scale. Materials shortages, delays from downstream facilities, and non-standard disruptions across any point of the chain are core examples of macro problems that can be addressed at the micro level.

Domain specialists achieve actual value when they can utilise the data to look for incremental gains as analytics helps optimise the entire supply chain journey. Analysing the most common delay routes or factors between the raw materials supplier and the initial warehouse by tracking late or ‘missing’ deliveries and factoring in other important data points like drivers or delivery service used. By building a grouping of data analyses across the numerous moving parts of the supply chain, we can begin to build a better picture of what is happening, what is causing specific disruptions, and what the critical points of failure are. We can create an impactful risk profile and mitigate macro supply chain risk beyond ‘gut feel’ decision making.

 

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