Financing the future state of inventory
There are many simple factors that can affect inventory composition, including customer tastes, seasonal cycles or a new product entering the market. Third-party logistics providers (3PLs) — which look to match inventory with demand as closely as possible in order to best serve their customers — are well equipped to deal with changes of this variety.
There are also, however, many complex and far-reaching factors that affect what inventory composition will look like in the future. These could include the emergence and maturation of new technologies, new legislation or a big play from an industry behemoth.
For example, the complex sustainability issue will undoubtedly be at the heart of changes to future inventory compositions. According to the UN, 127 countries have placed limits on plastic bags. Meanwhile, the EU has banned a range of single-use plastic items by 2021 and the UK is also considering a law to tax packaging that does not include 30% recycled content.
In response, FMCG giant Unilever recently pledged to reduce the amount of plastic packaging it produces annually by circa 14% by 2025 across all its brands, from Dove soap to Lipton Tea. It also promised to halve its reliance on non-recycled plastics in a bid to improve the sustainability of its supply chain.
Changes of this magnitude have a significant ripple effect on manufacturing and supply chain processes and, in turn, on future inventory compositions. In the context of ever-shifting inventory needs, the perpetual challenge for 3PLs is knowing which stock keeping units (SKUs) to finance.
3PLs therefore stand to benefit greatly from the ability to anticipate the future state of inventory. The ability to predict demand for a particular SKU has the potential to both unlock considerable value and cut a significant amount of waste.
Predictive stocking
According to the 2020 Annual Third-Party Logistics Study, 95% of shippers and 99% of 3PLs agree that analytics are a necessary element of 3PL expertise. However, only 26% of shippers and 27% of 3PLs are satisfied with their current analytic capabilities. The debate about how best to organise and analyse the wealth of supply chain data rumbles on amongst 3PLs, under increasing pressure to provide value to their customers through new analytical techniques.
The greatest opportunity to capitalise on the glut of underutilised supply chain data lays in improving inventory management and optimising financing through predictive stocking. This process involves applying AI and ML algorithms to buyer preference data, purchasing patterns and other data produced at every juncture of the supply chain. Predictive stocking allows 3PLs to ascertain which items will be most in demand in the future and to finance accordingly.
For example, if a 3PL knows which SKUs are in demand at present and which will cater to future appetites, it can shift the prioritisation of the picking, transport, and inventory holding locations accordingly. In this scenario, the inventory has shifted from the previous state to a new future state, generating greater income for 3PLs by better meeting customer demands and generating less waste in the form of excess inventory.
The advent of AI-based recommendation engines changes the game in the context of residual default risk and other aspects of supply chain finance (SCF) risk. If a 3PL knows which suppliers produce the SKUs that align with future demand, those particular portions of the supply chain become a more desirable target for financing.
Optimising cash conversion cycles
Beyond predictive stocking, there is also an opportunity for supply chain stakeholders to utilise analytics in the context of improving cash conversion cycles.
For decades, SCF has been a successful mainstay of working capital finance, helping all parties in a transaction manage cash flow and risk with more certainty. A healthy and thriving supply chain, with technology-enabled working capital finance, is essential to the success of supply chain stakeholders across the board.
However, SCF is in need of a makeover, in line with the rapid expansion of supply chains in the digital age. The next generation of SCF will see AI, ML and the interrogation of big data employed as an even more efficient route to unlocking capital, drawing value from the troves of data that accumulate as part of supply chain processes.
Research from the Malaysia Institute of Supply Chain Innovation found that a machine learning approach to demand forecasting yields significant gains. It found that the cash conversion cycle was reduced by 21 days, representing an outstanding gain in working capital efficiency. Meanwhile, inventory turnover improved by 0.17 turns compared to the baseline figure.
Using these techniques, buyers can optimise their cash conversion cycle by focusing strategically on payment terms with their suppliers. Suppliers, meanwhile, can accelerate their own cash flow through access to early payment. In other words, the application of AI and ML is beneficial for stakeholders across the entire breadth of the supply chain.
Despite a level of uncertainty amongst 3PLs, suppliers and shipping providers about how specifically analytics should be deployed, the value of AI and ML in enabling predictive stocking and optimising cash conversion cycles is clear.
In order to make significant savings on waste and generate greater income, 3PLs must look to emerging AI and ML techniques. Doing so will give them the tools to realise the depth of value found in the data produced at every point in the supply chain and truly finance the future state of inventory.
