How we can help
You can’t forecast demand in a silo. Our systemic approach and analytics expertise span every business function from supply chains to marketing. We’ll help you focus your efforts, improve your approach, test your results, and scale a superior forecasting capability.
The days of simply creating a “set it and forget it” demand forecasting algorithm are gone. Today’s turbulent times demand adaptation and continuous adjustment. But it’s not enough to think about lower forecast error; you need to translate these adjustments into better business outcomes by thinking beyond sophistication to orchestration across your organization.
Our Demand Forecasting Center of Excellence believes that better data beats better algorithms. With this principle in mind, we use your company’s historical and near–real-time data to predict consumers’ behaviors and attitudes, while also bringing external data to the mix—think online searches, smartphone mobility data, and social media posts.
Demand forecasting is a change management effort as well as a technical one, so we also focus on the human element. By designing user-friendly interfaces and providing extensive staff training, we ensure that your teams have the skills they need to produce and interpret reliable forecasts, as well as to understand and apply them.
Since many of these forecasts are part of automated pipelines that produce thousands or even millions of individual predictions, forecasting is often as much a software engineering challenge as a statistical one. Our software engineering teams ensure that computational efficiency is fully addressed so that you don’t get surprised by your cloud computing bills.
Another key decision when considering demand forecasting is “build vs. buy.” In order to supplement the early prototyping efforts to improve your forecast pipeline, we maintain a database of requirements and relevant vendors to help you through this important choice.
Finally, consistency counts. We reconcile demand forecasting across every facet of your organization, encompassing operations as wide-ranging as research and development, consumer pricing, and network optimization. By doing this we ensure that all of your operations are as smart as any of your individual efforts, today and well into the future.
The most intelligent demand algorithms are only as smart as the data that goes into them and how well they are understood by their users. Our end-to-end approach considers all these factors, consistently generating more effective plans and valuable business outcomes.
Most companies build strategies on heavily-biased, “most likely” assessments of the future based on presynthesized analyses and reports. We uncover creative primary sources and model scenarios along with point forecasts. We then apply a disciplined process to add judgment while limiting bias. The result: greater alignment on key decisions with better hedging on important risks.
We identify the places where forecast improvement will produce the most value
We develop better forecasts with a combination of quality data, algorithms, and machine learning
We test improved forecasts with in-market trials to measure real-world business impact
We distill requirements, support vendor selection, and mentor in-house staff for a smooth roll-out
increase in group operating profit
reduced time to market
increase in stores ordering daily, increasing product freshness
Grupo Bimbo, a $15 billion packaged food company, wished to create a system that would reduce waste while still driving growth. We helped them achieve this goal by building a new front line tool and an algorithm to adjust behaviors and improve order accuracy. By taking the guesswork out of ordering, we helped exceed the organization’s initial waste reduction target by 30%. Time and energy previously spent on making and adjusting orders was now redirected to focus on growth.
Results:
CPGCo is an international market leader in consumer packaged goods with more than $30 billion in annual revenue. But the company’s potential was hampered by decentralized forecasting models and siloed data hubs that made it impossible to generate accurate demand forecasts or draw quality, enterprise-wide insights. Using our artificial intelligence and machine learning expertise, we helped develop an AI-fueled, top-line forecasting system that can deliver an 18-month outlook for 180+ markets, by month. We also automated data feeds, designed a user interface portal, and trained the team to create seamless data flows across the organization.
Results:
PetChem Co, a large petrochemical company in Latin America, relied on a labor-intensive demand forecasting process that used rudimentary statistics, leading to poor accuracy. In turn, this generated high inventory levels. We helped revise their process to include relevant input, such as pricing strategy, and other market dynamics, like macroeconomic changes, import prices and industry indicators. Incorporating user input, updating analytics techniques, and identifying improvement levers for implementation reduced over 80 drivers of forecasting errors.
Results:
Grocery Co, a regional APAC supermarket chain with over 100 outlets, struggled with maintaining consistent stock availability. We helped them reduce the number of out-of-stock scenarios by adopting a data-driven and scientific approach to demand forecasting. A data-driven model now forecasts the top 1000 SKUs each week, decreasing both labor costs and the number of out-of-stock SKUs. We also helped develop smaller grab-and-go stores with fewer SKUs, and used space optimization to examine under-performing stores to further lower costs.
Results:
A large consumer health company found that its typical demand forecasting process could not stand up to pandemic-fueled supply constraints and overinflated orders. To improve accuracy, our hybrid team of consultants and Advanced Analytics experts helped ConsumerHealthCo establish a single, stable source of truth. After identifying issues such as data governance gaps and manual adjustments, we worked with the client to redesign the data collection and modeling process, using machine learning to identify patterns and select better algorithms. We tested a prototype with sample SKUs and continually drew on demand planners’ feedback over time to refine the process.
Results:
Grupo Bimbo, a $15 billion packaged food company, wished to create a system that would reduce waste while still driving growth. We helped them achieve this goal by building a new front line tool and an algorithm to adjust behaviors and improve order accuracy. By taking the guesswork out of ordering, we helped exceed the organization’s initial waste reduction target by 30%. Time and energy previously spent on making and adjusting orders was now redirected to focus on growth.
Results:
CPGCo is an international market leader in consumer packaged goods with more than $30 billion in annual revenue. But the company’s potential was hampered by decentralized forecasting models and siloed data hubs that made it impossible to generate accurate demand forecasts or draw quality, enterprise-wide insights. Using our artificial intelligence and machine learning expertise, we helped develop an AI-fueled, top-line forecasting system that can deliver an 18-month outlook for 180+ markets, by month. We also automated data feeds, designed a user interface portal, and trained the team to create seamless data flows across the organization.
Results:
PetChem Co, a large petrochemical company in Latin America, relied on a labor-intensive demand forecasting process that used rudimentary statistics, leading to poor accuracy. In turn, this generated high inventory levels. We helped revise their process to include relevant input, such as pricing strategy, and other market dynamics, like macroeconomic changes, import prices and industry indicators. Incorporating user input, updating analytics techniques, and identifying improvement levers for implementation reduced over 80 drivers of forecasting errors.
Results:
Grocery Co, a regional APAC supermarket chain with over 100 outlets, struggled with maintaining consistent stock availability. We helped them reduce the number of out-of-stock scenarios by adopting a data-driven and scientific approach to demand forecasting. A data-driven model now forecasts the top 1000 SKUs each week, decreasing both labor costs and the number of out-of-stock SKUs. We also helped develop smaller grab-and-go stores with fewer SKUs, and used space optimization to examine under-performing stores to further lower costs.
Results:
A large consumer health company found that its typical demand forecasting process could not stand up to pandemic-fueled supply constraints and overinflated orders. To improve accuracy, our hybrid team of consultants and Advanced Analytics experts helped ConsumerHealthCo establish a single, stable source of truth. After identifying issues such as data governance gaps and manual adjustments, we worked with the client to redesign the data collection and modeling process, using machine learning to identify patterns and select better algorithms. We tested a prototype with sample SKUs and continually drew on demand planners’ feedback over time to refine the process.
Results:
