Minimising supply chain disruption with advanced analytics

Supply chains can be considered the lifeblood of the global economy. When it falters, the knock-on effects can be tremendous. This is none truer than in the last 18 months, where supply chain issues have made news headlines worldwide. From the Suez Canal blockage costing traders more than five billion euros in lost revenue and the Colonial Pipeline ransomware attack that resulted in massive spikes in oil prices, to the desperate rush by retailers to get shelves adequately stocked ahead of christmas. The scale of supply disruptions has reached new heights in recent months.

Covid-19 has also put significant strain on the supply chain as it created fundamental changes to consumer behaviours. In fact, e-commerce acceleration due to the pandemic saw retailers’ digital sales penetration realise ten years of growth in just the first three months of 2020. It has become apparent  that predicting future supply chain demand using outdated data is no longer feasible.

Instead, strong data and analytics capabilities are crucial in understanding the complexities of the modern consumer, anticipating possible disruption and developing a quick response to resolve any interruption that arises. This can ultimately help businesses be prepared for any eventuality and avoid hiccups in the supply chain that lead to profits being impacted.

 

Improved visibility in the supply chain

Advanced analytics and enterprise data empower companies to have a completely transparent view of movement of materials and products within their line of sight. What’s more, these tools can leverage data from their suppliers to have a holistic view two to three tiers deep in the supply chain. Both of which ultimately help businesses to reduce the risks to supply chain.

Big data also enables predictive analytics to transform demand planning into demand forecasting by analysing data from tens to hundreds of data sources. This includes both internal sources such as enterprise resource planning (ERP), supply chain management (SCM) or manufacturing execution systems (MES) systems and external sources such as markets trends, weather and consumer pricing indexes. Ultimately, this provides real-time insights into inventory, replenishment, and a distribution plan fit for today’s modern consumer.

This capability is leading many companies to rethink moment in time practices. The traditional approach to just-in-time delivery, distributing raw material or products at the moment of need, with zero inventory based on pre-pandemic planning models is outdated. If the last eighteen months have taught us anything, it is that incorporating risk into the planning process should be the norm. Failure to do so can lead to delays, resulting in unhappy customers and missed sales.

Moreover, risk modelling was previously a qualitative guess, now modelling can leverage enterprise data to deliver quantitative assessments from a larger and more diverse data set.

Demand assessments, once singular insights driven by singular events, can now be presented as probabilities predicting a number of scenarios that can offer a range of solutions through targeted discussions, including upside potential and downside risks in sales and operations planning. This level of insights is fortunately possible through the availability of advanced data analytics and leveraging it will be key to achieving optimal efficiency.

 

Mitigating digital transformation risk

Optimising the supply chain does, however, come with risk factors. While enterprise data from external sources such as IoT devices and location devices at the edge provide unique insight, it is also well acknowledged that data from the edge is not risk-free.

Many factors including increased digital communication caused by remote working and breakdown of sensor devices can lead to data corruption brought about by shared supply chain data.

Yet there are ways to reduce supply chain risk, with a key solution being to leverage data where it lies;

  • Hybrid Cloud is key to getting the best out of data. In recent times, the cloud has been seen as the solution for many companies digital transformation strategies and hybrid cloud has grown significantly in popularity. Predicted to be worth $145 billion by 2026, hybrid cloud is enabling businesses to unleash the full capabilities of multiple on-premises apps simultaneously. For this reason, having an enterprise data partner that delivers a hybrid workload solution is vital. It not only reduces risk by processing data where it lies, which in tail mitigates data transfer risk, but it also reduces redundant data being incorporated into analysis. This in turn optimises both architecture use and spend.

  • Keeping data linage secured and governed. A business only stands to benefit from multi-functional data analytics if the data is protected, secured and governed throughout. Analytics and machine learning (ML) can become a risk if data security, governance, metadata management, and automation are not holistically applied across the entire data lifecycle. Without these elements gaps will start to appear in audit logs and this in turn can result in a compliance nightmare. Not only can each gap be a potential lawsuit or regulatory fine, but it can also lead to significant reputational damage. Gaps also lead to inconsistent insight and, with that, decisions that impact the business’ ability to manage its supply chain successfully. Fortunately, a hybrid cloud architecture can address all these issues with one common user interface, regardless of where the data is sourced, migrated, or replicated.

 

Building a robust supply chain framework grounded in data

As evidence from the disruptions supply chains have faced, there is a pressing need for businesses to build long-term resilience in their value chains to best manage any future challenges. This essentially requires a holistic approach to managing the supply chain. Businesses must incorporate sufficient flexibility to protect against future disruptions.

Business leaders should therefore consider developing a robust framework that includes a responsive and resilient risk management operations capability. That capability should be rooted in technology, utilising platforms that support apply analytics, AI, and ML. This will not only ensure full transparency across the supply chain but also set organisations up for business-as-usual, even if the worst were to happen.

 

 

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