Intro
It’s no doubt that COVID has ravaged the normalcy of the business world. Now, companies must process a vast amount of data to make decisions, especially in operationally challenged areas such as supply chain, procurement and contract pricing. Understanding such astonishing volumes of data is neigh impossible for even seasoned managers.
With the help of distributed computing and powerful machine learning (ML) and artificial intelligence (AI) systems and platforms, decision process automation (DPA) can be utilized to help companies analyze large volumes of data and automate decision making. Nevertheless, human involvement are still relevant as their experience and insight complement the decisions made by the DPA where understanding of context is needed which currently is too nuanced for AI and ML understand.
How DPA works
DPA is not a replacement for the existing automation technologies like RPA or IPA, but compliments them by using data as input that is generated from existing RPA. It is fundamentally a decision engine which exists in a virtual sandbox of company’s operations. DPA generally relies on evolutionary AI models and runs iterative sequences of scenarios, with hundreds of thousands of variables.
This sequential algorithmic analysis allows the DPA to rapidly refine to optimal outcomes, enabling automated decision-making at much faster rates. DPA probably can make the same choices as humans would, but with the ability to manage a large number of complex variables & parameters, taking inputs & data from evolving conditions in real time.
DPA Potential
Companies need copious amounts of data to compete especially in the post-COVID world. Using DPA companies can continuously model, simulate and select the best choices for decision-makers as DPA algorithms can process large volumes of data at much higher speeds.
DPA is considered the logical next step from robotic process automation (RPA) and intelligent process automation (IPA) in the of business process automation (BPA) arena.
RPA improves operational efficiency while reducing human error. IPA helps organizations automate complex tasks. But neither of these can understand & analyze multi-variable data; simulate and model the effects of different decisions either in the past or in the present; rank the optimal decisions based on desired outcomes, thereby allowing companies to then choose the best one.
On the otherhand, DPA leverages massive computing power made cheaper by cloud platforms and sophisticated algorithms to assist in business decision-making. DPA creates a digital twin model of the company, then examines historical data, trends and decisions; evaluates current conditions by analyzing incoming real-time data; models potential outcomes for each decision. Using DPA, companies can better examine evolving opportunities and formulate previously unseen solutions.
DPA will be able to suggest choices based on how the organization has worked in the past even as it incorporates real-time data from the present. Human involvement probably is required at this point where the human capacity for insight and foresight, and the ability to understand institutional context, offer the most value thereby reducing the mundane decision making processes for humans.
Ask yourself these questions.
- In what areas could DPA most benefit your managers ? In managing inventory? The supply chain? Freight management and costing, and ensuring on-time delivery? Adjusting contracts or procurement? Revenue collection?
- Which key performance indicators (KPIs) correlate to decisions? How could monitoring those KPIs help verify the benefits of DPA? What is the frequency and volume of decisions?
- Which teams in your company are ready to transform themselves? What processes might they redesign? Who can lead the project? Who will champion it? Who will benefit from it?
No matter how near or far down the path of evolving your company you are, IMSS Automation Services can guide you toward the future while learning from the past.
