Vous optimisez l’efficacité de votre chaîne d’approvisionnement. Comment évaluer l’impact de l’intégration d’une nouvelle technologie ?

Forecast Error Benchmarking is based on understanding current Consensus Forecast Error against the Forecastability Band (Highest Tolerable Error vs. Lowest Achievable Error). It should enable apples-to-apples comparison of forecast health across the entire portfolio by reporting Baseline Relative Error (Consensus Forecast Error divided by Naive Forecast Error), which normalizes for different levels of inherent volatility in each time series. This enables a clear understanding of improvement potential (current relative error vs. relative error lower bound) to confidently set improvement targets. Tools like vyan.ai help baseline, set targets, and deliver against targets through optimal blending across a wide set of forecasts.

Best move = Model the integration of that new piece of tech and computer simulate the new operational environment. Proceed to full implementation if you are confident enough. Assess and make appropriate decisions

Focus on metrics like lead times, stockouts, and order cycle times. Be meticulous with data segmentation—separate by product lines, suppliers, or regions. This will help isolate variables and provide a clearer picture of where gains are achievable. Challenges? Data gaps and inconsistencies can skew results. Mitigate this by cross-referencing multiple data sources and running validation checks. Seasonal trends or external market shifts should also be considered to avoid misattributing fluctuations to the new technology. Solid baseline data is your control variable, enabling accurate impact analysis and guiding strategic adjustments.

Gartner has a good framework for establishing KPIs - It’s called Hierarchy of Supply Chain Metrics - balancing Costs and Perfect Order through Forecast Accuracy

Make sure you have the base scenario plus data, so that that you can model variants and answer some whatif questions that will come as you explore possibilities and alternatives.

When integrating new technology into your supply chain, gauge its impact through dynamic scenario modeling. Start by identifying the key metrics—like lead time, inventory levels, and cost variations—that will most influence your bottom line. Build models reflecting current operations and compare them against scenarios incorporating the new technology. Focus on potential disruptions, capacity shifts, and unexpected bottlenecks that could arise. Use sensitivity analysis to assess how changes in demand or supply fluctuations could amplify risks. This method ensures your decision is robust, balancing efficiency gains against operational resilience.

Forecast 'Value' Added remains an indirect metric of value to supply chain, as it refers to Accuracy Added, not the hard cost reduction or revenue enablement through better forecasts. Tools like vyan.ai can report Cost of Forecast Error directly in the context of cost to supply chain: taking all sorts of cost drivers into account: safety stock carrying cost, excess inventory liquidation cost, margin erosion, lost sales, expedite costs (production and/or transportation). This helps evaluate Return on Investment from an AI Optimal Forecast (much lower error) vs. pre-AI Consensus Forecast Baseline. Focus on extreme undersell and oversell error as supply chains are designed to handle average variations through safety stock.

When integrating new technology into your supply chain, conduct a thorough sensitivity analysis. Start by identifying critical cost drivers—implementation, training, and maintenance. Then, factor in variables like supply volatility, demand fluctuations, and operational disruptions. Don't just consider direct savings; evaluate impacts on lead times, inventory levels, and service quality. Monitor key metrics like throughput, downtime, and order accuracy. By running scenario-based simulations, you’ll better understand the potential ROI and avoid unforeseen operational risks, ensuring strategic alignment with long-term goals.

An important aspect to include in TCO is employee training and hiring costs. If the technology is very new, more likely than not the cost of hiring can be extremely high due to limited pool of talent available.

Technology integration hinges on adoption. While design, planning, and testing are crucial, the true test comes when users engage with the system. Readiness to adapt based on user feedback is essential. Modern ERP technology excels in this regard, offering flexibility to refine processes without extensive software re-engineering.

Stakeholder's feedback and action is always key things to include in process. They are the one who will be using the tech system. One should ensure that proper training and workshop must be provided to users after any new addition of features.