The manufacturing industry has been using ERP systems for many decades, which generate their own data. However, they are lacking in agility which makes them ill-suited for the retail end of the supply chain. With the evolution of mobility, new tech vendors saw a gap in the market and developed agile software for retailers, including the informal main market, which generate a wealth of information as well. The challenge is that marrying these two sources of data has proven to be a difficult task. A layer of consolidation can help to bring the data together to facilitate optimisation across the supply chain, however this requires data sharing, which is a new concept for many parties.
The analytical advantage
Data analytics is nothing new in the supply chain, but parties have, to date, only been able to analyse the data they have, which limits insight. However, with a view across the value chain this can change. We are now beginning to see a lot more collaboration between ERP platforms and mobile vendors operating in the supply chain space, in an effort to develop that all-important single version of the truth.
Armed with this overarching view, both manufacturers and retailers can get a complete picture of actual sales versus manufacturing. This empowers the manufacturer to better predict demand, allowing them to align products in the distribution channel to maximise volume of delivery. This in turn plays a significant role in ensuring retailers always have the stock they need when they need it.
Applying AI and machine learning
Artificial Intelligence (AI) and machine learning is improving efficiencies in the supply chain. However, the effectiveness of these technologies once again relies on complete and accurate data, which can only be gained through a consolidated repository of information. In the supply chain space, there are huge volumes of data, much of which has no intrinsic value to the business analytic. Current AI capabilities can assist with separating the wheat from the chaff so to speak.
Using AI, all information that can be used to add value will be separated from the information that will not deliver value, based on rules and KPIs developed around relevance to the market. Historical data gives a solid platform to allow for benchmark comparisons to prior periods and align trend forecasting for future periods.
Consolidation is key to competitive advantage
There are many applications for data analytics and AI in the supply chain space, but they have limited value when faced with siloes of data. It is essential to be able to visualise the entire supply chain cycle from the manufacturing of a product to its sale. Only once a consolidated version of information is available can it be determined whether consumers are seeing the right products at the right time in the right place for them – and this is key to the sale. Collaboration is the future of data analytics in the supply chain, and we will see more and more of this in 2020 and beyond.