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Stop looking back to look forward: the role of data in supply chain planning

Witten by Adam Bimson, Director and Co-Founder, Vuealta


Read any business manual, and it’s likely that you’ll come across the concept that to look back is to invite future failure. Among the pearls of wisdom will almost certainly be the idea that to do something just because ‘that’s how it’s always been done’ is the most heinous of crimes.

So goes the theory; in reality, it actually happens all the time. Consider how most organisations draw upon historical performance and data in order to inform decision-making and plan for what comes next. Retailers will look at seasonal peaks to inform inventory purchasing and planning; their logistics providers will be conscious of their own peak seasons; pharmaceutical companies will ramp up flu and cold medicine production as winter comes around.

They do this because it has worked, to a greater or lesser degree, in the past. And it was true, when we lived in a more analogue world. Trends were slower to evolve, communication took longer, everything was less dynamic.

That’s all changed.


Continued disruption

What’s clear now is that we’re in a time of constant turmoil. From trade wars and Brexit, to pandemics and major natural disasters, to cyber-attacks that can cripple national health organisations and shipping companies at ease. It’s not an overstatement to say we are living in the era of uncertainty. There is significant disruption happening continually across the world. This means that, with global supply chains of increasing complexity and fragility, many businesses are exposed to significant risk.

In such a situation, how can last year’s January sales results help predict what 2021 will look like? How can a decade’s worth of sales figures support medicine production decision-making at a time when there is no guarantee manufacturers won’t be hit by new lockdowns?

That’s not say that historic data is worthless, but in a world of increasingly dynamic disruption, its value is limited.


Managing constant turmoil accelerated by the pandemic

This isn’t new – organisations have been aware of the issues around historic data for some time. What’s changed is the coronavirus pandemic. Senior executives that might not have given these issues their proper due have seen their supply chain fragilities exposed as lockdowns restricted production and movement of goods. It’s clear that existing infrastructures, no matter how lean or agile, are unsuited to delivering the level of service businesses, and more importantly their customers, require.

Businesses need to realise that they need to be able to manage constant turmoil in such a way that no matter what’s happening where, they can still operate effectively. In other words, if supply chains are going to be exposed to risk, there needs to be a way of accurately predicting the danger before it has a material impact on business operations.


Realising the value of data

While data may now be considered the world’s most valuable resource, it has always been possible to access a variety of external and internal data sources to provide hard evidence to back up insights and assessments when developing plans. The real challenge has been being able to bring all that together, across internal silos and in varying formats, to form a cohesive and clear direction.

What’s now therefore required is a way of identifying and understanding rapidly evolving trends, and combining it with new, accurate and reliable data sources, in real-time. That’s what’s critical – speed. It’s no good getting actionable insights a week or a month later – as we have seen with the pandemic, decisions that once took two committees and six months now need to be made in days, if not hours, so data needs to be able to keep up.


To be able to do all that is the pot of gold. To realise it needs the right technology in place. Technology will play a critical role in shaping the supply chain planning of the future and in a post-pandemic world, providing greater business intelligence and ultimately greater agility. In particular, emerging technologies, such as machine learning (ML), artificial intelligence (AI) and Internet of Things (IoT), offer potential solutions, but only when integrated and deployed properly. So, for instance, IoT-enabled sensors on shipments can capture real-time data on cargo location and state. AI platforms can then take these data and translate them into actionable insights for a business to use to make decisions accordingly. As another example, algorithms can identify increased social mentions of localised political unrest or health scares near manufacturing facilities, feeding those insights into an organisation’s planning systems and informing which suppliers are used to deliver products or to manage demand.


When we think about business processes there are three different types – strategic, tactical, and operational. Most start with strategic planning into which AI can provide powerful insights, improving efficiency through better decision making. At the more tactical and operational levels, businesses make hundreds of thousands of decisions every day, many of which could be automated. For instance, AI could detect that a certain product is doing well because the weather is cooler in the north at the same time another is selling out in the south due to a heatwave – then looking to realign the inventory. AI can also automate and improve the quality of decisions around pricing, procurement and replenishment to name just a few.

Armed with this information, companies can start to adapt much faster than before, enabled by digitised supply chains that are smart, responsive and resilient.


Living and planning in the here and now

In a world of constant disruption, looking back is not going to provide the insights organisations need in order to adapt. For businesses that hope to not only survive, but thrive, building supply chains that can adapt to the challenges of constant disruption is going to be a fundamental requirement. That can only be achieved through the use of emerging technologies, such as ML, AI and IoT, in order to create and harness new sources of data, deriving actionable insights that allow for real-time planning and, ultimately, accurate and fast decision-making.