The rising costs of running a warehouse business, hiring and retaining employees, and margin pressure create great strain on wholesalers and their retailers. In the quest for warehouse efficiency, identifying the cost of downtime and the hidden costs it incurs should be just as important as traditional measures like stock control and pick rates.
How Small Issues Lead to Major Downtime
In increasingly automated warehouses, the cost of downtime grows significantly as the defective system or process has a knock-on effect across the business. In operational reality, many major aspects of downtime can be identified ahead of time through close management of every minor incident and the many issues that are never reported by operators.
By creating a total awareness of warehouse operations and issues, through the use of production monitoring software (PMS), management and operations leaders can identify those smaller problems before they lead to expensive wastage and downtime across the loading bays, packing systems, palletising, distribution and other areas.
Micro stoppages, manual system pauses and resets and other almost unnoticed breaks in the operational warehouse workflow are common, but often overlooked. Business leaders might focus on greater capital investment for automation, improving supply chains, installing faster-opening roller doors, and other improvements, but those short gaps in workflow can continue to grow along with their business impact.
Using PMS software, the overall equipment effectiveness (OEE) factors become visible to managers, helping identify poor processes, unsafe working practices, and identify machinery and plant creaking toward a more expensive breakdown.
When the business can identify these issues through a live dashboard or regular report, and the cost they create in terms of downtime, waste and overtime labour. It becomes more compelling to address them earlier before those costs mount and the downtimes from failures grow.
The Value of OEE to Warehouse Leaders
Having established OEE by factoring in working time, maximum theoretical output and the hours lost through downtime plus any wastage, the company can see the future costs of any major breakdown and use predictive analysis to see when a warehouse component or machine is likely to fail.
With that data visible in operational reports and meetings, the cost of early machine repair, replacement or upgrade to warehouse systems like the packing line becomes part of the planning budget and not an ad hoc and costly disaster.
The management can also focus on eliminating small stoppages through adjustments to machinery settings, revised processes for operators to reduce the strain and optimising labour patterns to prevent bottlenecks, reducing waste and the need for overtime.
Each step in these efforts can have an outsized benefit or fractionally reduce the cost to the business and improve productivity through the warehouse. Through predictive maintenance, managers will also know when to change assignments, shift patterns and zone adjustments or other operational changes to cater for the planned downtime. By integrating logistics scheduling software to better coordinate workflows and operational timing across the wider supply chain, teams can reduce delays and minimise overtime needs.
Most modern warehouses already have these systems in place, but for those upgrading or inheriting older facilities, starting with a deep dive into OEE metrics is a great place to start on planning to improve performance and set the benchmark return on investment (ROI) for future improvements, upgrades and wholesale replacement.
And any warehouse business that still relies on spreadsheets and paperwork spread across the floor will find itself at a major disadvantage to competitors when it comes to identifying losses through downtime and damaged machinery, overtime costs and wastage.
And when it comes to governance, health and safety, and similar aspects, a well-managed warehouse that tracks every datapoint will be better positioned to meet changing rules and regulations. To react to unexpected major outages, and to handle regulatory and insurance-led queries about operations.
Looking into the Future of the Digital Warehouse
The likes of Amazon, DHL and others already demonstrate the power of digital in their massive warehouse facilities. Starting with digital twins, a virtual version of a new warehouse can predict with high accuracy the operational efficiency of the warehouse.
The on-site reality can be measured against the twin to find weaknesses in the picking-and-packing processes and other aspects from the delivery bay to dispatch. Every aspect can be inspected from power usage to tolerances and flexibility, creating the most efficient operations where the need for overtime is eliminated, and waste becomes a micro-issue rather than widespread.
Predictive systems powered by AI will improve the efficiency of robotic warehouse systems, with total visibility into the supply chain and delivery schedules enabling the automated systems to operate at peak or most-effective efficiency, swap tasks and intelligently route goods as supply and demand shift.
While the current trend is for mega-warehouses, smaller automated local and regional warehouses will take over some of the effort to fit into the UK’s urban landscape. This will prevent the decimation of greenfield sites to cookie-cutter monolithic warehouse construction, and bring employment to areas for the engineers and managers needed to control automated sites.
Another aspect of future warehouses will be the focus on modular sites that can adapt to changing market needs. Through sustainability, including onsite renewables, they will be greener and help feedback into the local economy through power and water sharing.
Other adaptations will see the arrival of smaller automated distribution and delivery electric vehicles that are quieter and less of a hindrance to local traffic. All of which will make the warehouse of the future a more acceptable presence to local economies as customer and supplier needs change, across the socio-political, governance and operational environment.
Comments are closed.