What if you could...
Identify and sustain > $35M of the benefits by tackling ‘bad’ complexity
Food client had expanded through acquisition but not been able to address complexity. As their head of R&D said, ‘I have all the tools and data, but no insight or action.’
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GM was 5ppt below best in class
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Difficulty valuing impact of proliferation
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Actual unit costs 5-8% more than standard costs
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$20m packaging savings (5% of spend)
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$15M raw material savings (3%)
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Basis for designing cross brand ‘chassis’ to sustain and extend changes
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Scenario modeller to enable business to self serve and test opportunities
Improve how you price your products and service to create a revenue uplift of 3+%
Distribution business with 500,000 SKUs with complex discount structures. Increasing competition from OEMs direct and eCommerce meant pricing had to be more agile and fact based
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Significant leakage from gut feel pricing decisions
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Multiple Information systems with poor inconsistent data
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Pressure to implement from impatient PE owner
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Initial diagnostic highlighted oppurtunities including quick wins of >3% revenue uptick
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Support in implementing generated 4% revenue increase
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Segmentation by customer/SKU combination enabled lower risk dynamic price changes
Create and operate a new digital complexity tool that connects the financial and transactional data
Our client was thinking about embarking on a pricing project, but hadn’t segmented customer/products to refine pricing strategy. The opportunity lay not in pricing but in truly understanding profitability and drivers of waste
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Indirect costs lost (and growing) as business focused on absorption costs
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Actual unit costs diverging from standard costs by 9%
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Difficulty in drilling down into the BOM to unlock the true drivers of portfolio complexity and inefficiency
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Better segment customers and products into different service strategies
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Drill down into the supply chain to identify and quantify ‘bad’ complexity that should be managed to eradication
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Understand the drivers of direct ‘waste’ costs in operations
A rapid response & insight capability to actively manage performance gaps between forecast/plan & actual
Despite investing heavily in finance planning & supply chain tools such as TM1 & Kinaxis, there was a significant disconnect between the financial budget and operational reality:
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Lack of visibility of the true financial impact of decisions available to Sales & Operations
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Lack of forward-looking clarity of impact from changes
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Poor ability to take proactive action (too late or too general)
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Identifying the root cause of specific misalignment between financial & operational plans
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Detailed understanding of forecast/budget gaps vs actuals – where & what
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Rapid calibration of the financial impact from operational disruptions
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Have forward looking views of performance blockages with our driver based variance analysis engine
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Create a prioritized list of actions required by sales, finance, operations and procurement to better mitigate budget gaps and
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Identify, log and derive strategy how to mitigate root cause challenges in the future
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Align effort, become more effective in integrated business planning
Deliver carbon footprint reduction through detailed measurement across the end-to-end value chain
Operational environmental emissions targets (Scope 1 & 2) are already challenging. But, the end-to-end supply chain is a bigger concern for CO2 reduction & its complexity makes it harder to achieve. Measuring Scope 3 emissions across the value chain is a big data challenge.
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Need detailed visibility of CO2 footprint (product & process level)
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Data intensive work to map suppliers, operations, transport
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Internal systems not designed to support big data modelling
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Enriched analytics asset stitching multiple data sources for what-if analyses & footprint insights
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Move 25% + of all SKUs to different plants to improve CO2 + remove bottlenecks
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Reduce transport milage (km) & reactive airfreight journeys
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Improve service levels & identify other quick win opportunities
Effectively use AI/ML techniques to explore fundamentally complex problems
Clients are keen to better understand what might happen based on experience; large amounts of data has been captured over the years but not used to improve how the business solves core problems
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Lack of awareness among clients of the art of the possible (and limitations) of advanced analytics techniques
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Poor quality and unstructured data, often dumped into a repository
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Inability to optimise or forecast better than tossing a coin
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Supply forecasting. Improved significantly predictions on production yield and harvesting within 2 growing cycles, compared to decades of grower ‘experience’
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Predictive maintenance. Increased critical asset availability by 5% and reduced labour hours and total costs by 18%-24%
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Credit risk model. 85% accuracy and > 95% recall
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Customer churn. Reduction of bad churn by 17-21%
