Transport & Distribution

Continued trend of deteriorating working capital metrics due to higher DSO and lower DPO.

Transport & Distribution companies experienced increased levels of activity in 2018 on the back of favourable trading conditions for its customers across the Mining & Resources, Food & Beverage and Construction & Engineering sectors, with 63% of our sample reporting revenue growth and 75% reporting EBITDA growth.

However, this improved trading performance did not translate into better working capital outcomes, with the average DWC for our sample increasing by 6.4 days to 50.6 days in 2018 and all but one company in the sample reporting a deterioration in DWC. The main drivers were longer customer collection cycles (DSO up by 5.0 days to 69.2 days) and shorter supplier payment cycles (DPO down 3.8 days to 49.1 days). Inventory loads remained relatively stable.

Notably, 88% of the sample reported a structural “funding gap” in 2018 (meaning companies typically paid their suppliers on shorter terms than they collected from their customers). This “funding gap” widened for c. 75% of our sampled companies in 2018.

Given that key suppliers to this sector are often inflexible on payment terms (particularly for fuel, warehousing and subcontracted labour), Transport & Distribution companies must ensure tight controls around debtors and collections in order to manage cash flow and avoid liquidity pressures.

Brambles was the only company in our sample that managed to shorten its working capital cycle. The improvement was achieved through a combination of lower DSO (down 1.9 days) and higher DPO (up 5.8 days). This translates to unlocking c.$90 million in additional cash from working capital. At the other end of the sample, Steamships Trading Company and Bingo Industries reported DWC increases of 23.1 and 10.1 days, respectively. Both of these sector participants experienced material increases in DSO.

Note: airlines were excluded from our sample due to the contrasting nature of their working capital cycles (often negative) and the size and scale of their operations (which disproportionately skew the sample set).