Innovating and adopting new technologies is a norm in this day and age. Whether is it adapting to digital transformation, changes in workforce skillsets or adopting a new supply chain business model, supply chain executives can be certain of this: The emergence of cognitive technologies and artificial intelligence is accelerating and a growing phenomenon in managing supply chains

It is safe to say that tomorrow’s supply chain will be intelligent, predictive and self-correcting. Businesses are moving towards a hybrid landscape with one foot in the traditional operations and the other in technology. 

A recent Gartner survey revealed that advanced analytics ranked among the top 2 emerging technology investments, with only 91% of supply chain organisations reporting plans to invest to developing this capability

Understanding Supply Chain Analytics

Supply Chain Analytics represents the ability to make data-driven decisions based on a summary of relevant, trusted data, often using general visualisation such as graphs and charts. According to IBM, there are four different types of Supply Chain Analytics: 

  1. Descriptive analytics. Analysis of past events and transactions. Provides visibility and a single source of truth across the supply chain for both internal and external systems and data.
  2. Predictive analytics. Helps an organisation understand the most likely outcome or future scenario and its business implications. For example, using predictive analytics can project and mitigate disruptions and risks.
  3. Prescriptive analytics. Helps organisations solve problems and collaborate for maximum business value. Helps businesses collaborate with logistic partners to reduce time and effort in mitigating disruptions.
  4. Cognitive analytics. Helps an organisation answer complex questions in natural language — in the way a person or team of people might respond to a question. It helps companies think through a complex problem or issue, such as “How might we improve or optimise X?”

Supply Chain Analytics of Tomorrow

Like most processes, supply chain analytics comes from a humble beginning. Before the advent of analytics software, supply chain analytics was limited to statistical analysis and quantifiable performance indicators for demand planning and forecasting. Data were often stored in spreadsheets or physical documents and aggregated from various participants in the supply chain. 

Business intelligence and predictive analytics software solutions only gained traction in the 2000s as more businesses saw the need to change their business models. These solutions have helped companies gain an in-depth knowledge of their supply chain performance, optimise their networks and make better decisions on the fly. 

Today, 9 out of 10 retailers have used big data and analytics to increase forecasting efficiency in new stores by 92% within 12 months of setting up data analytics. The data generated helps large retail supply chains monitor customer behaviour and make more accurate predictions of customer preferences.

Invest in Future-Ready Capabilities 

Investing into Supply Chain Analytics can bring major benefits in an organisation. IBM cited the following:

  •  Gain a significant return on investment. A recent Gartner survey revealed that 29 per cent of surveyed organisations said they achieved high levels of ROI by using analytics, compared with only four per cent that achieved no ROI.
  •  Better understand risks. Supply chain analytics can identify known risks and predict future risks by spotting patterns and trends throughout the supply chain.
  • Increase accuracy in planning. By analysing customer data, supply chain analytics can help a business better predict future demand. It helps an organisation decide what products can be minimised when they become less profitable or understand what customer needs will be after the initial order.
  • Achieve the lean supply chain. Companies can use supply chain analytics to monitor warehouse, partner responses and customer needs for better-informed decisions.
  • Prepare for the future. Companies are now offering advanced analytics for supply chain management. Advanced analytics can process structured and unstructured data to give organisations an edge to get alerts on time to make the optimal decisions. It can build correlations and patterns among various sources to provide alerts that minimise risks at little cost and less sustainability.

Round-Up: Supply Chain Analytics

Studies have pointed out that cognitive technologies and artificial intelligence will be the next frontiers in supply chain analytics. AI capabilities go beyond information retention and process automation. It can also evaluate, reason and learn in a human-like manner. 

The spread of digitisation and automation throughout the supply chain has undeniably transformed supply chain management’s fundamentals. Tomorrow’s successful organisations will need to reinvent and develop new capabilities utilising data to drive a more efficient operation. 

Businesses resistance to change upholding traditional capabilities risk losing out to competitors who have exploited digital technologies to predict better, react faster and maximise value across their channels and product lines.