Best Practice: Calculating warehouse automation ROI
In a constantly evolving supply chain logistics landscape, the efficiency gains that can be realised by automating labour intensive tasks are extremely attractive. Calculating return on investment for warehouse automation is not a simple task, but it is certainly crucial for continual process improvement.
A holistic and transparent ROI calculation requires critical evaluation of both a business’ current (‘As Is) and its future (‘To Be’) scenarios. The physical and digital elements of automation at play bring multitudes of operational efficiencies to any warehouse operation and therefore, it is essential to capture the overall benefits of using right techniques to build strong business cases and accurately capture the return-on-investment horizons.
Automation can bring huge advantages. Work measurement or knowing the right amount of time needed to complete each of these warehouse processes by an average warehouse operative is hard to calculate, but when done correctly could become very handy in productivity benchmarking of operations staff, as well as in general labour costing and wage management.
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of the warehouse workload typically entails repetitive processes
Work measurement methods
There are several standard work measurement procedures used around the world that have evolved over time.
Broadly, these can be classified into:
- Estimation Studies
- Direct Observation
- Pre-Determined Time Systems
Estimation techniques typically rely on experience and historical data sets to base the standard times upon. The accuracy of the time output is dependent on the quality of data and personal judgement, and therefore this method is not generally recommended or used widely.
This is one of the most popular methods and generally requires an analyst to capture time taken by an operator to complete a task or task intervals (when task is broken down into subunits) using a stopwatch. Once the time study is complete, this method also accounts for operator performance levels, and therefore aims to generate a more standard time output.
Another approach which is often utilised under this category is the work sampling method, which is used to capture the utilisation of an operator or a machine over a period such as on a warehouse shift. A detailed classification of all possible states for a target subject be it human operator or machine is are listed and then samples are recorded randomly to understand the percentage distributions in these different states.
Pre-Determined Time Systems
The latest generation of techniques being utilised to measure work times are pre-determined time systems. Under these methods, the warehouse processes are divided into a series of predetermined motion elements that have a standard time value attached to it. There are number of frameworks such as MODAPTS, MOST and MTM that fall under this category and are being widely used in the industry.
For the purpose of this blog, we will be focussing on one of these frameworks – MODAPTS.
MODAPTS or Modular Arrangements of Pre-Determined Time Standards utilises MOD as a basic unit of work time measurement, which is based on standard movements of human body. Every activity in MODAPTS is defined in a two-part code, where the first half of the code represents the type of activity and the second half describes the time required in MOD units to complete the activity.
This includes all movements of finger, hand, forearm, arm, and trunk.
For example, M1 is used for Finger Action such as triggering a scan action and M4 is used for Full Arm Actions such as lifting an item to pack.
A terminal activity is done at the end of movement and consists of get and put activities.
For example, G1 corresponds to a grasp action such as getting a printed label off a printer and P2 corresponds to putting something at a specific location such as putting a label on the desired side of the box.
The auxiliary activities comprise of activities such as walking, decision making etc.
For example, W5 is assigned for each step when an operator lifts a footeet and places it down and R3 is assigned for reading three digits as part of a mental activity such as looking for a product code on the shelf.
Both MODAPTS and Time-Based studies generate comparable results but have below differences.
‘As Is’ Processes
Once the standard ‘As Is’ processes have been agreed and documented, a MODAPTS based work time study can be conducted to calculate the average time taken by an average warehouse worker to do a certain process such as packing a single item order. Often, packing and despatch functions coincide in a warehouse and requires a level of decision making in terms of carrier selection, right documentation etc., if done manually. A MODAPTS based study considers all these auxiliary requirements within a process and therefore is more appropriate.
Once the time taken to complete a certain process has been calculated, this can easily be extrapolated over a shift timing to understand the maximum orders an average warehouse operator can pack in one shift.
Dividing the number of average parcels over a normal day will help to understand what is the minimum number of operators that will be needed to run the operations smoothly. In a similar way, considering the peak parcel volumes that need to be despatched can easily help to account for additional workers that would be needed over peak.
A time-based work sampling study could also be conducted in conjunction to get a view of elemental state ratios for a target – be it machine or operator. For example, it can help to identify how often a warehouse operator sits idle in his shift and therefore helps account for productivity degradations.
Once the ‘As Is’ scenario has been baselined, an estimate of current costs can be generated and extrapolated over a timeline considering elements such as business growth, labour rates inflation etc.
‘To Be’ Scenarios
A similar study then can be performed for a ‘To Be’ scenario, where depending on the level of Automation planned, a manual assistance might or might not be considered. Reviewing the throughput levels claimed by the machine manufacturers, and considering the standard time needed by one person to feed the machines, we can easily calculate the number of pack benches that would be needed to fulfil both everyday orders and peak orders.
Using this, we would be able to articulate the savings generated on day-to-day operations. On top of this, other benefits such as error reduction rates due to increased accuracy and decrease in health and safety incidents can generate additional cost savings. These savings will then need to be extrapolated over the combined costs of the physical automation, consumables required, electricity consumption, license fees and support packages to generate a return-on-investment horizon.
Need further information?
If you are considering automation, but would like support to understand what results this will have on your ROI, we can help. An experienced team and careful planning will help you find the best warehouse automation solution for your operational needs. Get in touch to see how we can help improve your accuracy, lower labour costs and streamline your warehouse operations.