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DEMAND FORECAST ACCURACY

DEMAND FORECAST ACCURACY

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Demand forecasting is essential for businesses to plan effectively and maximize efficiency. Generating highly accurate demand forecasts is extremely challenging due to the many variables that can impact demand. While demand forecasts will never achieve 100% accuracy, forecasters can take steps to improve their forecast accuracy over time.

One of the most important factors that determines forecast accuracy is the choice of forecasting method. There are various quantitative and qualitative forecasting techniques that are more or less suited to different business contexts. Quantitative methods rely on historical data patterns and include simple exponential smoothing, regression analysis, and ARIMA time series analysis. Qualitative techniques incorporate expert opinions, consumer surveys, and indicator data. The appropriate method depends on attributes like a product’s life cycle stage, demand predictability, and data availability. It is usually best to test various methods on historical data to determine which produces the lowest errors for a given situation.

Equally important is having high quality demand history data to feed the forecasting models. Demand data needs to be cleansed of errors, adjusted for factors like price changes or promotions, and segmented appropriately – for example by product, region, or customer type. Missing, inaccurate, or aggregated data can significantly reduce a model’s ability to identify demand patterns. Continuous data quality management processes are required to ensure the inputs yield good forecasts.

Business changes like new product launches, market expansions, or supply constraints also impact demand forecast accuracy. Forecasting models may need to be re-developed when major changes occur since historical demand patterns are unlikely to continue unchanged. Temporary adjustments may help during transitions until new normal demand levels emerge with new historical data. Close collaboration between forecasters and product/supply chain teams ensures such changes are integrated into future forecasts.

Key external variables that are difficult to predict also introduce uncertainties. Economic indicators, competitor actions, new technologies, and weather can all cause demand to deviate from projections. While these macro factors cannot be controlled, forecasters should continuously monitor such variables as much as possible and build scenarios accounting for plausible outcomes. Qualitative inputs from sales, market research, and external data providers help augment quantitative analyses.

Continuous improvement practices help elevate forecast accuracy progressively. Recalibrating forecasting parameters and models based on evaluation of past forecast error patterns helps address known sources of errors. Automated validation and adjustments of prior forecasts based on incoming actual demand data ensures accuracy benefits carry forward. Leveraging advanced techniques like machine learning and partnering with specialist forecasting service providers helps optimize forecasts further. Regular audits reveal ongoing demand changes requiring new forecasting strategies.

Closely involving customers and end users ensures forecasts represent real demand levels and validate assumptions. Gathering timely feedback from customers on order patterns, influencing factors, and future demand indicators helps refine forecasts to anticipate demand shifts. This collaborative approach across functions delivers more demand transparency, allowing issues to be addressed proactively through supply chain readiness or promotion changes, rather than reactively through firefighting shortages or surpluses.

By implementing an integrated approach spanning data quality, forecasting methods, improvement processes and collaboration, businesses can gain significant benefits from higher demand forecast accuracy. While there will always be some unavoidable variation between projections and actual demand, continuous enhancements inch forecasts closer to ground realities over time. This supply chain predictability helps optimize inventory investments, production plans and delivery performance to meet customer expectations.

HOW DO YOU PLAN TO EVALUATE THE ACCURACY OF YOUR DEMAND FORECASTING MODEL?

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To properly evaluate the accuracy of a demand forecasting model, it is important to use reliable and standard evaluation metrics, incorporate multiple time horizons into the analysis, compare the model’s forecasts to naive benchmarks, test the model on both training and holdout validation datasets, and continuously refine the model based on accuracy results over time.

Some key evaluation metrics that should be calculated include mean absolute percentage error (MAPE), mean absolute deviation (MAD), and root mean squared error (RMSE). These metrics provide a sense of the average error and deviation between the model’s forecasts and actual observed demand values. MAPE in particular gives an easy to understand error percentage. Forecast accuracy should be calculated based on multiple time horizons, such as weekly, monthly, and quarterly, to ensure the model can accurately predict demand over different forecast windows.

It is also important to compare the model’s forecast accuracy to some simple benchmark or naive models as a way to establish whether the proposed model actually outperforms simple alternatives. Common benchmarks include seasonal naïve models that forecast based on historical seasonality, or drift models that assume demand will remain flat relative to the previous period. If the proposed model does not significantly outperform these basic approaches, it may not be sophisticated enough to truly improve demand forecasts.

Model evaluation should incorporate forecasts made on both the data used to train the model, as well as newly observed holdout test datasets not involved in the training process. Comparing performance on the initial training data versus later holdout periods helps indicate whether the model has overfit to past data patterns or can generalize to new time periods. Significant degradation in holdout accuracy may suggest the need for additional training data, different model specifications, or increased regularization.

Forecast accuracy tracking should be an ongoing process as new demand data becomes available over time. Regular re-evaluation allows refinement of the model based on accuracy results, helping to continually improve performance. Key areas that could be adapted based on ongoing accuracy reviews include variables included in the model, algorithm tuning parameters, data preprocessing techniques, and overall model design.

When conducting demand forecast evaluations, other useful metrics may include analysis of directional errors to determine whether the model tends to over or under forecast on average, tracking of accuracy over time to identify degrading performance, calculation of error descriptors like skew and kurtosis, and decomposition of total error into systemic versus irregular components. Graphical analysis through forecast error plots and scatter plots against actuals is also an insightful way to visually diagnose sources of inaccuracy.

Implementing a robust forecast accuracy monitoring process as described helps ensure the proposed demand model can reliably and systematically improve prediction quality over time. Only through detailed, ongoing model evaluations using multiple standard metrics, benchmark comparisons, and refinements informed by accuracy results can the true potential of a demand forecasting approach be determined. Proper evaluation also helps facilitate continuous improvements to support high-quality decision making dependent on these forecasts. With diligent accuracy tracking and refinement, data-driven demand modelling can empower organizations through more accurate demand visibility and insightful predictive analytics.

To adequately evaluate a demand forecasting model, reliability metrics should be used to capture average error rates over multiple time horizons against both training and holdout test data. The model should consistently outperform naive benchmarks and its accuracy should be consistently tracked and improved through ongoing refinements informed by performance reviews. A thoughtful, methodical evaluation approach as outlined here is required to appropriately determine a model’s real-world forecasting capabilities and ensure continuous progress towards high prediction accuracy.

HOW DOES BC HYDRO PLAN TO MANAGE THE INCREASED DEMAND FOR ELECTRICITY IN THE FUTURE?

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BC Hydro expects electricity demand in British Columbia to grow significantly in the coming decades as the population increases and transportation and building sectors transition away from fossil fuels towards more electricity-powered solutions like electric vehicles and electric heating. To adequately meet this rising demand while maintaining a reliable and affordable electricity system, BC Hydro has developed an Integrated Resource Plan (IRP) which outlines various strategies for managing increased demand.

One of the key focus areas in the IRP is on conservation and reducing energy usage. BC Hydro has very ambitious conservation targets, aiming to reduce energy use per capita by 1.5% annually over the next 20 years through various programs that encourage more efficient use of electricity. This includes rebates for efficient appliances and electronics, lighting upgrades, insulation retrofits for homes and buildings, and behavior change initiatives. Conservation is seen as the most cost-effective way to avoid or delay new infrastructure investments. BC Hydro expects conservation efforts could help offset up to 70% of expected load growth by 2040.

To supplement conservation, BC Hydro also has plans to develop significant new sources of renewable and clean electricity generation. This includes continuing to maximize the potential of large hydropower facilities like the Site C dam project underway in northeast BC. But BC Hydro is also turning to other renewable resources to add new capacity, such as substantial amounts of wind and solar power. The IRP envisions between 1,000-2,000 MW of new wind and solar capacity being brought online in the next 10-15 years.

Tapping more remote reservoirs for mini-hydro projects and pursuing geothermal energy are also part of BC Hydro’s diversification strategy. And a major initiative is pursuing electricity imports from independent power producers using run-of-river hydro, wind, and other renewables. BC Hydro has implemented a Standing Offer Program and Clean Power Call to attract private investments that align with their clean power objectives. By 2040 renewable energy could account for over 95% of BC Hydro’s total generating capacity.

Modernizing BC Hydro’s existing power grid infrastructure is another focus. Upgrades are planned across the province to enhance transmission capacity and distribution networks to deliver power more efficiently. This includes targeted reinforcement projects in fast growth regions as well as implementing more demand response and automated grid technologies to optimize capacity utilization. Microgrids and localized storage are also being piloted as strategies to defer expansion of centralized infrastructure into remote areas.

Advancing new clean electricity applications like electric vehicles, heat pumps and emerging technologies is identified as a key driver of future load. To support this transition BC Hydro’s strategy addresses accommodating charging infrastructure, time-varying rates, and flexible load and grid interaction opportunities. The utility is also piloting vehicle-to-grid capabilities and other virtual power plant demonstrations to leverage EV batteries as distributed energy resources.

While BC Hydro expects conservation, renewables and grid improvements can supply 80-90% of expected demand growth through 2040, some gas-fired generation may still be needed to ensure reliability during periods of peak demand or renewable intermittency. The IRP contemplates using existing gas plants more strategically and potentially adding limited incremental gas capacity in the long-term if cost effective compared to other options. The preference is for any new resources to be as clean, renewable and consistent with BC’s climate goals as possible.

Through diligent implementation of its IRP, BC Hydro aims to remain a world leader in clean electricity while successfully managing the challenges and opportunities posed by increasing demand into the future. Ongoing monitoring, review and adjustments to priorities and programs will be key to optimally balancing environmental, social and economic factors during this important transition period for BC’s electricity system over the coming decades.

CAN YOU PROVIDE AN EXAMPLE OF HOW THE PREDICTED DEMAND HEATMAPS WOULD LOOK LIKE

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Predicted demand heatmaps are visualizations that ride-hailing companies like Uber and Lyft generate to forecast where and when passenger demand for rides will be highest. These heatmaps are produced using machine learning algorithms that analyze vast amounts of past ride data to identify patterns and trends. They are intended to help the companies optimize driver supply to meet fluctuations in rider demand across cities over time.

Some key factors that are typically used to generate these predictive heatmaps include: date, day of week, time of day, holidays/events, weather patterns, traffic conditions, densities of points of interest like restaurants/bars, public transportation schedules, demographic data on populations and their commuting/travel habits. The machine learning models are constantly being retrained as new ride data becomes available, improving their forecasting accuracy over time.

For example, let’s look at what a predictive demand heatmap for a major city like New York City may look like on a typical Friday evening. We’ll focus on the 5pm to 8pm time period. At 5pm, the model would predict moderate demand across much of Manhattan as people finish work and start to head home or to happy hour spots. Demand would be somewhat concentrated around transit hubs like Grand Central and Penn Station as commuters enter the city.

Moving to 6pm, demand increases notably in the midtown and downtown areas as after-work socializing and dining out picks up steam. Popular entertainment and nightlife zones like the East and West villages would show strong demand hotspots. Commuter-centric pockets near transit become less prominent as rush hour disperses. Outlying boroughs like Brooklyn and Queens would exhibit growing but still modest demand levels.

By 7pm, Manhattan demand swells considerably, with very high-intensity hotspots dotting the map around prime dinner and bar neighborhoods. Moneyed areas like the Upper East Side, Chelsea and SoHo glow bright red. Streets surrounding Madison Square Garden or Broadway theaters flare up on event nights. Uptown zones near Central Park see less dramatic but steadier increases. Brooklyn Heights, Williamsburg and LIC emerge as outer-borough hotspots too.

At the 8pm mark, Manhattan demand reaches its peak intensity for the evening across a wide geography. Only the far Upper West and Upper East sides remain more tempered. Public transit stations show intense “bulges” as evening commuter flows build up again. Downtown Brooklyn and parts of western Queens pick up substantially as well. By contrast, outer areas like Staten Island or The Bronx exhibit only pockets of light demand at this hour on a typical Friday.

Of course, this is just one example using generic patterns – the actual predictive heatmaps factor in real-time adjustments for live events, construction, weather extremes or other unplanned variations that can influence travel behaviors. But it illustrates the type of spatial and temporal demand evolution ridesharing platforms aim to model across cities worldwide. These forecasting tools empower companies to strategically position available drivers and proactively handle surges, improving both efficiency and customer satisfaction over time.

While predictive analytics continue advancing, uncertainties will always exist when projecting human mobility behaviors. But democratizing urban transportation requires understanding fluctuating demand at a hyperlocal scale. Machine learning-enabled heatmaps represent an innovative approach towards optimally matching dynamic rider needs with dynamic driver supplies. As more ride data flows in, these predictive mapping technologies should grow ever more precise – helping riders easily get a ride, while helping drivers easily find their next fare.

Predictive demand heatmaps leverage powerful analytics to visualize expected usage hotspots for ride-hailing networks across cities and moments in time. They aim to optimize the passenger experience and driver utilization through data-driven operations. As an emerging application of artificial intelligence in transportation, their full potential to efficiently connect urban mobility supply and demand has yet to be fully realized. But with ongoing enhancement, these forecasting tools could meaningfully impact how people navigate and experience metropolitan regions worldwide every day.