Transparent Inventory Turnover and Trade-Offs with SAP IBP

Recent Kearney research shows top companies in the high-tech manufacturing sector had increased total inventory by 53% to $250 billion between 2019-2022.

Between 2020-2022, S&P Global Ratings took negative rating actions on over 200 companies (30% of which were in the Consumer Products sector), due to their disrupted supply chains.

The major supply chain risks and disruptions of the last few years prompted most companies to excessively build and buffer inventory levels.

To encourage ‘inventory momentum’ the Kearney researchers recommended increasing inventory visibility, resetting planning parameters, and improving forecasting capabilities, among other factors.

Analysis of inventory levels seem to show even companies that referred to ‘inventory as fundamentally evil’ have recently increased inventories with lower inventory turnovers.

Such ‘resilience’ levels of inventory can challenge cashflow and even total shareholder returns, depending on industry and profitability.

In certain industries, excessive inventories are also occurring at a time of weakening demand and environment-profit trade-offs due to high production and waste levels.  

A recent McKinsey survey of supply chain leaders shows 68% are prioritizing inventory optimization in the next 3 years, while showing a mixed picture on inventory strategies.

How can companies use inventory transparency and optimization to improve inventory turnover and trade-offs in times of increased risk of disrupted supply lead times?

Raising Inventory Turnover Ratios

During the waves of disruption in recent years, industries that had established just-in-time (JIT) inventory principles over decades reverted to ‘just-in-case’.

Some companies bought in additional ‘resilience stock’ on top of safety stock to buffer for transportation or distribution disruptions and meet customer service levels.

With better visibility of risk, improved collaborative scenario planning and inventory lead time predictions, manufacturers can again right-size inventory levels across networks.

Supply lead time (SLT) variability and visibility are key factors for steering inventory replenishment to coincide more effectively with consumer demand sensing, or manufacturing schedules.

These lead time factors directly affect companies’ inventory turnover ratio, which is a key efficiency ratio to measure how effectively a company uses its assets.

Improved inventory turnover releases cash flow through faster cash conversion cycles. The cost of capital remains high, and the use of existing inventory helps procurement cost avoidance.

Efficient inventory management reduces inventory carrying costs such as storage and handling, obsolescence, or insurance. It also reduces seasonal risk and opportunity costs, and discounting caused by excessive inventories.             

Warehousing cost, and handling capacity, was at a premium during post-COVID-19 inventory replenishment and e-commerce expectations, which triggered a sharp increase in warehouse capacity builds and automation spend.

Data from the USA shows that new warehouse space is now increasingly remaining available, as companies draw down on existing inventory levels and move to leaner inventory strategies.  

These changing capacity impacts could also be related to the rise of new direct-to-consumer sales models and the increasing use of existing 3PL fulfilment.   

Rather than remaining exposed to demand and SLT variabilities, companies  are increasingly using probabilistic inventory planning and demand probability distributions to improve supply scenarios.

Since 2022, companies have been increasingly focused on improving their inventory positions and turnover across their network to improve working capital and profitability.

Investors monitor inventory turnover ratio as a key metric of financial health, and companies are progressively looking to inventory finance and consignment partnerships to keep inventory levels low and off balance sheet.  

These optimized inventory financial models enable faster inventory turns to support free cashflow and improved working capital, triggering shareholder value and returns.

Transforming Inventory Trade-offs

Optimizing inventory turnover involves trade-offs as it impacts various decision factors for companies, such as regional or customer service levels, production economies of scale, and efficiency.

Inventory turnover ratios for industries such as fashion and consumer-packaged goods (CPG) might require trade-offs on availability to avoid missed customer sales, or to simplify product variety and category management.

The Wall Street Journal recently highlighted how real-time connected demand signals are supporting retailers and CPG customers to drive more dynamic demand-driven forecasting to improve inventory-to-sales ratios.

Instead of over-ordering to deal with disruptions, companies such as IKEA are re-aligned on JIT inventory strategies.

However, US Retail and Food Trade data shows that inventory-to-sales ratios continue to fluctuate despite reduced inventory levels and positive economic indicators such as slowing inflation and NRF forecasts.

Many companies have spent the last few years extending n-tier supplier networks that support more flexible trade-offs and alternative supplier options to maintain inventory flows despite disruptions.

Reuse and circularity trade-offs can also be made on inventory across the network. The World Economic Forum cites research showing this market could be worth $4.5 trillion for manufacturers by 2030, despite the risk of bias and vested interests.

Using AI across these networks and product/location/SLT combinations can support less-biased trade-offs to support swift scenarios and decisions.

End-to-end data intelligence and integration of external variance data is critical to support new insights and accurate generative AI insights that planning teams can rely on.

Intelligent lead-time predictions derived from actual goods movement data combined with external factors, such as weather or transport disruptions, can help reduce deviations and deliver higher quality inventory planning.

Embedded multi-echelon inventory optimization helps planners and product managers with staging optimization trade-offs to determine where in the network to hold stock.

SLT machine learning recommendations can consider transportation, production, and supplier lead time trade-offs to recalculate target inventory components.

More agile and segmented inventory optimization across thousands of stock-keeping units (SKUs) and suppliers with collaborative planning processes and platforms can identify combinations to support transparent trade-offs.  

This inventory transparency can be extended with an end-to-end network platform enables agreed trade-offs for supplier collaboration and supplier-managed inventory.

Conclusion

Supply chain executives need to focus on improving collaboration and handoffs across functions in the forecasting process to improve alignment on inventory strategies, before focusing on technology for inventory optimization. 

Decision-making frameworks with finance and procurement involvement can also help steer and consistently improve turnover ratios.

Improved scenarios, trusted data and AI maintain transparency of inventory drivers and help executive teams make complex trade-offs across factors such as revenue, resilience, carbon impact, speed, and customer service.

SAP Integrated Business Planning (IBP) Inventory and Analytics can support these informed decisions at scale – find out more here.