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Good Versus Great in Achieving Supply Chain Excellence

Supply chains have had a magnifying glass over them since the onset of COVID-19 at the start of the year.

Vikram Murthi, Vice President, Industry Strategy, LLamasoft
|Jun 27|magazine11 min read

Whether it is the automotive industry, pharmaceuticals or retail, supply chains across the world have been put to the test and have experienced huge disruptions over the last few months.

While COVID-19 remains the big current disruptor for supply chains, in this world of the never normal, it will one day be replaced by a new concern, such as Brexit or trade wars. It is during these geopolitical moments where great supply chains stand out, while simply good supply chains falter.

So, what exactly is the difference between a supply chain that is good and one that is great?

Is your supply chain ‘good’ or ‘great’?

A key underpinning to what makes a supply chain good or great is how companies use algorithms and AI. Algorithms are commonly used in areas such as network design, inventory optimisation, demand forecasting, pricing, capacity planning and sourcing. However, they are typically in one narrow area of the supply chain and do not take into account the end-to-end nature of the supply chain, which consist of connected processes.

While using algorithms in this way can provide companies with good results within their supply chain, its narrowness blocks them from achieving great results, which can prevent businesses from expanding on their capabilities. To improve this, companies need to change how they implement their algorithms across the supply chain. The techniques that they use must be holistic and available to all supply chain practitioners in the organisation, regardless of their skill level or the scope of their job function.

This, of course, is easier said than done but there are some principles that companies can adopt if they want to transition their supply chain from one which is simply ‘good’ to one that is ‘great’. These principles include modelling the entire end-to-end supply chain (with the digital twin), transitioning to prescriptive recommendations, augmenting the knowledge worker, scenario planning and leveraging external data to drive outside-in thinking.

Leveraging digital twin technology

Companies that want a holistic view of their supply chain should look no further than digital twin technology. Powered by analytics, this technology allows companies to plan end-to-end supply chain strategies in a virtual format, before putting it into practice in the real world. This allows them to uncover certain pain points in the supply chain and resolve these before they become both real and costly disruptions.

While good algorithms look at a specific portion of the supply chain (e.g. the factory only or the warehouse and its connections), great algorithms work off the digital twin and see the interconnected nature of supply chain decision making.

Using the digital twin, companies are provided with recommendations and solutions to their supply chain strategy, informed by data. However, more than this, they are provided with the underlying rationale behind why one alternative is better than another and clear trade-offs to make a business justification for a specific course of action.

However, despite the capabilities of the technology, companies must be wary of using it to replace human decision makers entirely. Its power lies in the technology’s ability to augment human decision-making, by giving them options and the trade-offs. As a result, there is an increasing demand for the multidisciplinary employee, who has an engineering or mathematical background but also has a deep understanding of the business, is creative and possesses great communication skills. Great algorithms support this symbiotic relationship between man and machine.

Achieving greatness

Using the digital twin, companies no longer need to exclusively think along the lines of ‘this or that’ when making decisions. Rather, they can start considering the ‘and’ option. For example, they can begin to ask, “how can I deliver faster and still reduce the amount we charge?”

However, this ability is heavily dependent on having access to reliable data both internal and external.

This data can then be used to feed the algorithms that powers the digital twin, ensuring that the insights being generated are as accurate and efficient as possible. When it comes to training algorithms, the more cleansed and harmonized data the system has to learn from, the better.

To move from good to great, companies will have to ask questions like (a) how do we get both internal and external data cleansed, harmonised and in a form to make holistic decisions? (b) how do we then leverage internal and external data to get greater value from our current assets and improve our products to grow market share profitably? (c) how can data drive more insightful decision making about future options and scenarios?

Achieving greatness in the supply chain is no easy task, but as recent events have illuminated, it is a journey worth making. By leveraging the best predictive technology, companies can prepare for whatever the ‘never normal’ world has to throw at them, giving them an edge over their competition. Supply chain leaders should set their expectations high and think not in terms of “good enough” but in terms of what will be needed to become “great” in their journey to supply chain excellence.

By Vikram Murthi, Vice President, Industry Strategy, LLamasoft

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