Organisations identify the need to optimise and streamline their processes. They see the necessity for change at the core of how they work but their requirements are often multi-faceted and their improvement resources are limited or inexperienced, needing support and direction to make this happen.
Investing in Technology to Add Value was $3.8 Trillion globally (Source: Gartner 2014)
Unfortunately, 25% Spent on innovation and 75% Spent on maintenance, integration, and routine tasks
How can you get traction and deliver improved results as quickly and simply as possible?
How can you shift the ratio?
TQM Consultants use the following approaches:
(1) Business Process-as-a-Service (BPaaS)
Business Process as a Service (BPaaS) is a form of business process outsourcing(BPO) that employs a cloud computing service model. Whereas the aim of trade labour BPO is to reduce labour costs, BPaaS reduces labour count through increased automation, thereby cutting costs in the process.
Size of the public cloud Business Process as a Service (BPaaS) market worldwide from 2015 to 2020 (in billion U.S. dollars)
A TQM consultants can develop and customize the BPaaS for any service that can be automated, including managing e-mail, shipping a package, or managing customer credit.
(2) Develop the Business using Monte Carlo Simulation and Six Sigma Techniques
A TQM consultant will facilitate your operations to help improve performance by optimising and changing steps throughout the process flow. Following a Lean Six Sigma and Monte Carlo simulation approach, a client team identifies and make changes to what is done in the process and how it is done
What is Advantage of using Lean Six Sigma and Monte Carlo to optimize the process?
- Probabilistic Results. Results show not only what could happen, but how likely each outcome is.
- Graphical Results. Because of the data, a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.
- Sensitivity Analysis. With just a few cases, the deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results.
- Scenario Analysis. In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis.
- Correlation of Inputs. In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables. It’s important for accuracy to represent how, in reality, when some factors go up, others go up or down accordingly.
Optimisation can also include work on Change Management and takes place over an extended series of facilitated workshops. The objective is to enable results across all of the Quality, Cost, Delivery, Risk and Morale characters.
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