Many companies have found that business process optimisation (BPO) and workflow redesign can deliver real benefits quickly. However, standard BPO rarely works for lab processes because of the variety, complexity and interdependency involved. In fact, classical BPO often results in poorly set targets, unspecific key performance indicators (KPI) and unsound investment decisions.
Accelrys multi moment analysis – the safe way forward
Sound investment decisions require hard facts and numbers, especially when investing in IT-systems.
Only with a clear view of the status quo, can you see any potential for process improvement and project the return on any investment (ROI). The best way to assess your organisation and discover the optimisation potential is to carry out a multi moment analysis with Accelrys.
From processes to quantitative results
Understanding and mapping your current lab processes is key to any improvement project.
The first step is a process and data-stream analysis, but that alone is not enough. You need to supplement your process data with quantitative results from a multi-moment analysis, which gives you a statistically sound basis for accurate time and resource measurements across all your processes.
Every participating employee receives a handheld device with a set of process tasks tailored to their specific job. Random prompts remind them to select the task they are currently performing – but they do not need to enter durations or any other sort of estimates. No laborious and inaccurate write-ups are required.
Quality data, quality decisions
A two-week study is normally enough to generate a statistically significant sample size. Based on the data pool collected, a statistical model calculates the duration and effort necessary for each individual process step.
The data pool provides detailed information on how effort is distributed – between processes, along with process steps, across organisations, seasonally or any other required criteria. Optional reference parameters can also be included to model and forecast the effects of various scenarios, such as an increase in sample volumes and workloads.