Businesses are constantly making operational decisions such as planning resources, re-organising
production plans, ordering inventory or analysing customer margins. These decisions have a direct
impact on profitability (and shareholder value), customer service and sustainability targets.
The common theme in most businesses we’ve helped, is that day to day operational decisions are
made without a clear understanding of the full impact of their decision, be it financial or operational.
We have found that many businesses have access to data in the form of scattered or fragmented
reports presenting outdated financial and operational metrics. This data generally lacks the insights
needed to make optimal operational decision
These “insights” range from identifying the optimal volume of stock to order considering demand,
costs and lead times, to predicting when we are going to breach a customer SLA or assessing if a
certain customer/job is profitable.
We must ask how often businesses meet their planned or quoted operating margins, per their pricing models?
In our experience – rarely! Let us unpack why this is the case:
- The quantum of data in today’s business environment is proprietary, significant in volume and spread across multiple systems (8-10 on an average)
- These systems are generally designed to store data and manage process workflows, but lack context that can assist the business in decision making
- Data is fragmented in collections like excel spreadsheets, e-mails, documents etc. as well as externally sourced data such as shipping status, weather, traffic etc.
- Events such as changes in customer orders, supply chain impacts, resource availability have an operational impact that is in desperate need of being pulled together contextually as it is created or received.
The combination of the above points has made it impossible for Operators to get a clear
understanding of most recent data, let alone make optimal decisions considering business goals..
This results in suboptimal decisions that diminish margins, impact customer service and ultimately
shareholder value.
What if you had an army of AI Analysts, working on your business 24/7 and guiding your operations to make optimal decisions
The common theme in most businesses we’ve helped, is that day to day operational decisions are
made without a clear understanding of the full impact of their decision, be it financial or operational.
Aggregate data from multiple sources in real-time
Run predictive or forecasting models, and optimisation models
Run recommendation engines that are trained in your business processes/SOP
Run what-if/scenario analysis
Use GenAI models to talk to you in business English to simplify human-system interaction
To put this into perspective in an inventory planning scenario, the AI models recommend the
optimal inventory orders by balancing demand, supplier costs, reliability and sustainability targets –
trained with a clear agenda of maintaining service levels whilst minimising working capital.
When new events occur, such as changes in customer ordering patterns, delay in shipping or build
up of excess inventory, the AI Analysts recommend an optimal action based on what it sees and
what it predicts. This could range from redirection of stock from other DCs, promotions or
emergency orders.
Every AI driven recommendation, takes into consideration the impact on profitability, customer service and sustainability
In order to successfully realise the targeted ROI on AI, there are a variety of micro-solutions that need to be collectively implemented, including:
- Business SME knowledge – experts who have been in operational roles and have a strong understanding of the operational decision-making process,
- Modern data engineering practices that fast-track and economise data aggregation and processing from siloed systems in real-time,
- Platform to train and deploy AI/ML models with business context and processes, and
- A strong desire to evolve and make an impact.
We have you covered – Get in touch for more.