Using Data to Optimise Supply Chains

Using data to optimise supply chains

The ripple effects of Brexit and pandemic restrictions continue to affect industries that are heavily reliant on supply chains.

Many manufacturers are still struggling to make up lost ground. For example, the Baltic Airfreight Index (BAI), which tracks prices for transporting cargo by air, is still down approximately 40 per cent from its peak, as its supply chain continues to heal. In this environment, ensuring that supply chains are running as optimally as possible, and are flexible enough to cope with evolving developments, has become paramount.

Data has long been the bedrock onto which these industries build their processes. Without an accurate, comprehensive view of the entire manufacturing operation such as product quantities, timescales, and other logistical detail, it’s impossible for executives to make effective decisions. In a 2022 Industry Pulse survey, manufacturing and distribution executives highlighted the criticality of real-time intelligence in managing their supply chains under volatile business conditions.

Various new technology powered by data allows businesses to continuously review their processes, and adjust to the ever-changing landscape. This, in turn, will have a wealth of positive implications such as diminished costs, reduced waste, and improved profit margins.

The amount of manufacturing supply chain data available today is staggering. While most manufacturers have now begun harnessing their data, many are still struggling to capture significant value from it. A 2021 study revealed that just 39 per cent of manufacturing executives had successfully scaled data-driven use cases beyond the production process of a single product.

So, how can organisations harness their data to improve their supply chains?

 Data can help increase transparency

One of the main challenges faced by supply chains is a lack of transparency. Supply chains often span across multiple manufacturing and logistics operators with several tiers of suppliers, and because of this, data is typically collected and stored in separate silos.

As a result, it’s difficult for supply chain managers to get a clear and holistic view of crucial KPIs, such as service levels and costs. This means information about the real-time performance of end-to-end supply chains is often unknown. Or, if it is known, it’s reported infrequently, which can impact business performance. For instance, the malfunction of remote equipment could remain undetected resulting in exploding supply chain costs and lead times.

Transparency can be increased by ensuring that more information is accessible and therefore usable. New technologies like the Industrial Internet of Things (IIoT), for example, can collect remote ‘process data’, which might include warehouse temperatures or transportation waiting times, via sensors and then forward this to the cloud in real-time to inform decision-making.

And with the cost of IIoT devices and sensors plummeting, and 5G connectivity expanding worldwide, manufacturers of all sizes have the chance to cash in on capabilities like tracking shipping containers on their journey. Thereby enabling them to set realistic customer expectations, schedule production activities dependent on the incoming shipments, and swap to alternate suppliers to overcome delays.

Data assists in strategic planning

The implementation of supply chain analytics, allows vital conclusions to be drawn from this real-time and supply chain data, allowing businesses to effectively plan ahead. This can be roughly categorised into four buckets:

  • Descriptive analytics uses historical manufacturing data gathered from suppliers, and customers data, to identify important trends or patterns.
  • Predictive analytics models out a range of ‘what-if’ scenarios by analysing a variety of macro-level data including consumer demand, weather events, and staff shortages to accurately predict how these may impact a manufacturer’s supply chain or production capabilities. All of which will ultimately inform the creation of a robust contingency plan.
  • Prescriptive analytics uses the results of predictive and descriptive analytics to suggest potential actions that a manufacturer could take to achieve predefined goals. For example, identifying weak links in the supply chain.
  • Augmented analytics harnesses Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyse huge, complex data sets from multiple sources to make highly accurate predictions. One new application of augmented analytics is the improvement of worker safety by using wearable sensors that collect data on worker health, stamina, and exposure to occupational hazards and alert managers when interventions are needed.

So, what can analytical techniques support a business with?

Demand planning and forecasting/resilience

Predictive analytics supplements historical data with data on current market trends and industry competition allowing for improved demand planning and forecasting. In a nutshell, this means that manufacturers can better align production with customer demand, improving efficiency and reducing waste, as warehouses will only stock what is needed.


The agility of a business’s operations relies on the amount of information it has and how accessible it is. Take data from manufacturing systems – it can inform decisions to accelerate production, adjust output parameters, or enable proactive equipment maintenance, as and when required. Similarly for managing vendors, ‘Dynamics 365 Supply Chain Management’ can connect to supplier catalogues and enable near real-time visibility of supplier processes. This helps businesses to understand and control costs through priority-based supply planning, make AI-supported inventory decisions, and automate warehouse operations.

Proactive risk management

Complex supply chains pose a significant risk for manufacturers, just one key supplier being out of action due to adverse weather can easily cripple production, resulting in costly delays. To overcome this, manufacturers and suppliers can opt to share data, allowing manufacturers to analyse supplier data to gain deeper insight into quality, on-time performance, and pricing. This knowledge gives manufacturers greater insight into each link of their supply chain, allowing them to renegotiate pricing, address quality concerns, or switch to a more reliable supply partner.

Whether you are looking for your next opportunity in the data industry, or need to build out a data team to optimise your supply chain? Get in touch with one of our team today who will be able to help.

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