Our iii ensures that your information is focused on identifying, monitoring and improving the drivers within your organisation.
The capacity to gain an accurate and deep understanding of someone or something.
Deriving knowledge from data that was never considered or wrongly accepted as the status quo.
What-ifs | Analysis | Investigations | Reporting Packs | Presentations
The main risk of the national restaurant chain is related to forecasting of weekly sales, that drives labour and stock requirements. An inflated sales forecast causes wastage with staff and stock, but if understated it affects performance and quality of the restaurant.
The challenge is to understand the relationship between the sales profile, the labour and stock required to ensure they are aligned optimally.
By analysing the restaurant's historical sales profile and understanding its key drivers (peak times, promotions and local events) sales can be better predicted.
Restaurants have operating profiles where footfall is driven by various drivers including location, proximity to events, seasonality etc.
Utilising Insights to understand averaged historical hourly sales for its respective restaurant profile and its drivers allows for labour rotas and stock to be optimised.
Understanding the drivers within a restaurant improves the accuracy of the sales revenue forecast allowing better control of labour and stock leading to increased profits.
A device providing specific information on the state or condition of something.
Leveraging data to provide the foundations for performance management metrics to monitor what is happening within the organisation.
KPIs | Scorecards | Responsibility Alignment | Performance Management
Forecasted sales drive procurement, production, inventory and supply chain requirements within manufacturing. Additionally, promotional trade spend also affects profit i.e. £1m sales is not viable with £0.9m promotional trade spend with all other associated costs.
If sales teams are incentivised for sales revenue instead of profit, the revenue is likely to be overstated and operational costs not considered.
The challenge is to ensure that the Sales & Operation Planning process incentives both sales and operation functions to work in the best interests of overall profit, not sales revenue.
Alignment of an organisational hierarchy, sales account responsibility and profit allow for incentivisation schemes to be designed rewarding positive performance, for both sales and operational teams.
To design S&OP processes to reflect how an organisation is structured, aligned with individual's responsibility and to shift incentivisation scheme from revenue to profit.
To implement a performance management framework built from account manager's profit predictions measured by their individual indicators that roll up to overall organisational targets.
A performance management framework, aligned with an organisation's responsibility structure, built from individuals' predictions ensures ownership of numbers where positive performance can be rewarded without risking profits.
Indicators can be used to identify performance trends from account managers, their customers and/or products which can then be addressed.
The ability to acquire and apply knowledge and skills.
Building upon data by adding sophisticated layers of third party data to enhance information and understanding of organisational drivers.
Sector Benchmarking | Predictive Analytics | 3rd Party Data | Automation
An ice cream manufacture's sales increase with sunny weather. In order to take advantage of the favourable market conditions, they need to ensure they have the capacity (manufacture, inventory and distribution) to fulfill the upwards spike in demand.
The challenge is to ensure that there is sufficient product available throughout the supply chain. If their retailers are understocked, there is a loss of sale opportunity for both retailers and manufacturer.
Knowledge of how sunny weather patterns (temperature change, length and geographic) uplift demand, creates opportunities for maximising sales for both manufacture and retailer.
By using analytics to combine historical sales and respective geographical weather patterns, the manufacturer can better predict expected uplift and plan for increased demand.
The manufacturer will understand whether they need to produce, store or distribute more product and influence their retailers to increase the stock levels thus maximising sales opportunities.