ROI and measurement
Measuring an intervention whose counterfactual is invisible.
- ROI and measurement in logisticsLogistics managers use AsscherAi to measure shipment velocity and on-time delivery rates, focusing on targeted metrics to evaluate intervention...
- ROI and measurement in healthcare providersMeasuring ROI in healthcare providers is challenging due to invisible counterfactuals and data access constraints, but AsscherAi can help by...
- ROI and measurement in FMCGMeasuring ROI in FMCG requires a nuanced approach, considering factors like promotional distortion and distributor visibility, to accurately assess...
- ROI and measurement in energy and utilitiesEnergy and utilities companies can use AsscherAi to investigate the signals that precede an unplanned outage, allowing them to ask what deviated this...
- ROI and measurement in e-commerce and retailAsscherAi helps a category manager measure ROI by analysing sales data and customer behaviour, enabling informed decisions on promotions and product...
- ROI and measurement in chemicalsChemicals plants use AsscherAi to measure ROI by analysing production line data, identifying yield drift and feedstock variability, and comparing...
- ROI and measurement in automotiveAutomotive companies can measure ROI by tracking leading indicators such as reduction in downtime or cost savings from supplier changes, allowing...
- Common mistakes when applying AI to supply chainAI systems can perpetuate existing flaws in supply chain management if the underlying process is not well-mapped, leading to automated inefficiencies...
- Common mistakes when applying AI to customer serviceApplying AI to customer service data helps managers make informed decisions by querying data in real time, but mistakes like automating unmapped...
- Common mistakes when applying AI to salesA sales director can optimise their team's efforts by applying AI to answer questions about their own data, such as what products are most likely to...
- Common mistakes when applying AI to research and developmentApplying AI to research and development requires careful process mapping, otherwise automation efforts will fail to deliver expected results, as seen...
- Common mistakes when applying AI to qualityImplementing AI in quality control requires careful planning, as automating a poorly understood process can lead to errors, and treating inspection...
- Common mistakes when applying AI to productionImplementing AI in production requires careful planning to avoid common mistakes, such as automating unmapped processes or treating AI output as...
- Common mistakes when applying AI to procurementProcurement teams overlook data inconsistencies when applying AI, leading to flawed insights and poor decision-making, whereas a thorough data...
- Common mistakes when applying AI to operationsImplementing AI in operations requires careful consideration of process mapping, data quality, and user access to avoid common mistakes and ensure...
- Common mistakes when applying AI to maintenanceApplying AI to maintenance requires careful consideration of existing processes and systems, as seen in the example of a shift supervisor using AI to...
- Common mistakes when applying AI to executive leadershipApplying AI to executive leadership requires careful planning, starting with mapping out processes and identifying areas where AI can add value, to...
- Common mistakes when applying AI to human resourcesImplementing AI in human resources requires careful planning, otherwise it can lead to automating inefficiencies, inaccurate information, and poor...
- Common mistakes when applying AI to financeImplementing AI in finance requires careful consideration of existing processes and systems to avoid common mistakes, such as automating unmapped...
- How to measure AI in supply chainMeasuring AI impact on supply chain efficiency requires tracking lead time variance and fill rate, as a category manager would, to evaluate the...
- How to measure AI in customer serviceEffective AI metrics in customer service involve tracking first contact resolution and time to answer, enabling teams to assess the technology's...
- How to measure AI in salesMeasuring AI in sales requires isolating its impact on metrics like forecast accuracy and win rate, by establishing a baseline before rollout and...
- How to measure AI in research and developmentMeasuring AI in research and development requires tracking leading measures like failures found before validation and effort against stage, allowing...
- How to measure AI in qualityEvaluating AI in quality control requires tracking leading measures such as first pass yield and defects per unit, which provide early indications of...
- How to measure AI in productionGenerative AI optimises production by providing real-time insights into equipment performance, enabling proactive maintenance and reducing unplanned...
- How to measure AI in procurementMeasuring AI in procurement involves tracking leading indicators like spend under management and price variance to contract, which help evaluate the...
- How to measure AI in operationsAsscherAi helps operations teams measure AI effectiveness by tracking leading metrics such as on-time delivery and exception rate, allowing them to...
- How to measure AI in maintenanceMeasuring AI in maintenance involves tracking leading metrics such as planned versus reactive ratio and mean time between failures to identify areas...
- How to measure AI in executive leadershipMeasuring AI in executive leadership involves tracking leading measures like decision latency and data-driven insights, as seen when a category...
- How to measure AI in human resourcesEvaluating AI in human resources requires tracking leading measures such as repeat question volume and time to answer, which provide insights into...
- How to measure AI in financeMeasuring AI in finance requires a combination of leading and lagging measures, such as variance to budget and commitment coverage, to accurately...
- A readiness checklist for supply chain AIImplementing AI in the supply chain requires a thorough readiness checklist to ensure connected data, clear definitions, and proper permissions, as a...
- A readiness checklist for customer service AIImplementing AI in customer service requires a readiness checklist to ensure connected data, agreed-upon definitions, and decided permissions...
- A readiness checklist for sales AIImplementing a sales AI checklist ensures connected data, agreed definitions, and decided permissions, enabling the system to provide reliable...
- A readiness checklist for research and development AIImplementing AI in research and development requires careful preparation, including connecting relevant data sources, agreeing on definitions in...
- A readiness checklist for quality AIA quality manager uses AsscherAi to identify trends in production data, by asking what factors contribute to high defect rates, and how they compare...
- A readiness checklist for production AIImplementing a production AI checklist helps organisations avoid common pitfalls by ensuring connected data sources, agreed definitions, and clear...
- A readiness checklist for procurement AIProcurement teams can use AI to identify patterns in supplier performance by connecting relevant data sources, agreeing definitions in writing, and...
- A readiness checklist for operations AIOperations teams can use AI to investigate signals preceding unplanned downtime by asking what deviated this shift, on which line, and how it...
- A readiness checklist for maintenance AIImplementing AI in maintenance requires a readiness checklist to ensure connected data, agreed-upon definitions, and decided permissions, allowing a...
- A readiness checklist for executive leadership AIA chief operating officer creates an AI readiness checklist to ensure executive leadership has connected data, agreed upon definitions, and decided...
- A readiness checklist for human resources AIImplementing AI in human resources requires a thorough readiness checklist, including connecting relevant data sources, agreeing on definitions and...
- A readiness checklist for finance AIFinance teams can use AI to answer questions about cash flow and budget variance by following a readiness checklist, ensuring connected data...
- Can AI reduce variance explanations that arrive after the period?AsscherAi reduces variance explanations that arrive after the period by analysing general ledgers and budgets in real time, enabling finance teams to...
- Can AI reduce unplanned downtime?Generative AI reduces unplanned downtime by making the signals that precede a stoppage cheap to investigate, a production team can ask what deviated...
- Can AI reduce the same policy questions every week?AI reduces repetitive policy questions by providing instant access to organisational policies and procedures, allowing employees to quickly find the...
- Can AI reduce the same issue answered from scratch each time?AsscherAi reduces the same issue being answered from scratch each time by learning from an organisation's existing data, providing quick access to...
- Can AI reduce spend fragmented across suppliers?AI spend analysis helps procurement teams identify fragmented spend by examining purchase orders and supplier contracts, enabling them to renegotiate...
- Can AI reduce reactive work crowding out planned work?AI reduces reactive work crowding out planned work by analyzing maintenance data to identify patterns and trends, enabling maintenance managers to...
- Can AI reduce lead time variance hidden behind a stable average?AI reduces lead time variance by analyzing purchase and delivery history to identify patterns, enabling decision-makers to take corrective action...
- Can AI reduce failures rediscovered across programmes?AI reduces failures rediscovered across programmes by analysing historical data and identifying patterns, enabling researchers to quickly identify...
- Can AI reduce defects traced late to a batch or supplier?AI reduces defects traced late to a batch or supplier by analyzing inspection records and non-conformance reports to identify patterns and anomalies...
- Can AI reduce cross-functional questions nobody can answer in the room?AI reduces cross-functional questions by providing rapid access to consolidated financials and operational KPIs, enabling teams to investigate issues...
- Can AI reduce anomalies only visible in combination?AI identifies anomalies in supply chains by analysing data from logistics, supplier performance, and software systems, enabling organisations to...
- Can AI reduce a forecast nobody can interrogate?AI reduces a forecast nobody can interrogate by analysing historical sales data and order book information to generate a forecast based on actual...
- ROI and measurement for supply chainAsscherAi helps measure ROI for supply chain interventions by tracking leading measures, such as equipment maintenance and supplier performance, to...
- ROI and measurement for customer serviceMeasuring ROI for AI-powered customer service involves tracking changes in first contact resolution rates and time to answer, using data from systems...
- ROI and measurement for salesMeasuring AI ROI in sales requires tracking leading measures like meetings held and proposals submitted, as a sales manager checks the team's...
- ROI and measurement for research and developmentResearch teams use AsscherAi to measure ROI by tracking leading indicators such as experiment velocity and prototype iterations, enabling data-driven...
- ROI and measurement for qualityA quality manager uses AsscherAi to investigate defect rates by asking what changed this month, on which production line, and how it compares with...
- ROI and measurement for productionPredictive maintenance systems integrated with production data enable managers to identify potential machine failures, reducing unplanned downtime...
- ROI and measurement for procurementProcurement teams use AsscherAi to measure ROI by tracking changes in spend patterns and supplier performance, allowing them to identify areas for...
- ROI and measurement for operationsOperations managers use AsscherAi to measure ROI by analyzing leading measures such as task completion rates and exception rates, enabling timely...
- ROI and measurement for maintenanceA maintenance manager uses AsscherAi to investigate the impact of a new predictive maintenance system, tracking leading measures such as planned...
- ROI and measurement for executive leadershipExecutive leadership measures AI ROI by tracking leading indicators such as employee usage and insight frequency, rather than relying on lagging...
- ROI and measurement for human resourcesHuman resources interventions can be measured by tracking leading indicators such as employee engagement and training participation, which can be...
- ROI and measurement for financeMeasuring ROI for finance interventions requires tracking leading measures, such as invoices processed on time, to identify potential issues before...
- How to start with roi and measurementMeasuring AI ROI requires choosing a first case with a known answer, such as a category manager comparing AI recommendations to manual supplier...
- ROI and measurement in the enterprise: a practical guideEffective AI ROI measurement involves tracking leading indicators like user adoption and data quality, rather than relying solely on lagging...