Forecasting plays a pivotal role in most business decisions and few would disagree that that high-quality forecasting is a strong competitive advantage for businesses. Companies rightly invest significantly time and money in projects aimed at improving the quality of their forecasts. The approaches vary—be it processes, tools, data, or algorithms—but the singular goal is to have the most reliable forecast possible.

The practice of forecasting is built upon a simple foundational principle: the quality of a forecast is measured by its accuracy or, inversely, by its error rate. That businesses need a reliable forecast is not up for debate; a forecast with a low error rate is undoubtedly of better quality than one with a significant error rate. But let’s explore a more subtle question: Do our businesses truly need a reliable forecast in terms of ‘accuracy’?

But is forecast accuracy as important to corporate success as many people think? It’s quite a provocative question. It challenges a universal truth, one considered absolute in our field. Contemplating this questions requires taking a step back and reevaluating our preconceived notions of what really drives value and reassessing our daily practices.

In this and a following article, we’ll share the rather surprising—and perhaps concerning—results of a supply chain study within a Retail industry. This study is based on the analysis of a large dataset (more than 32,000 time series) to explore forecasting from the perspective of its added value and its economic contribution to the enterprise. It asks two key questions:

1. Are ‘accuracy’ and ‘value-added’ as strongly correlated as we think?
2. When should performance be deemed sufficient? When do further improvements to the forecast become irrelevant?

These two key questions are ones that no enterprise can afford to ignore. To start, let’s address the first question: Is better accuracy a guarantee of added value for the company? The answer is a simple no. On the contrary, as we will demonstrate, increased accuracy can even alter decision-making and lead to financial losses.

Setting Up the Experiment

The M5 competition, organized in 2020 by the Makridakis Open Forecasting Center (MOFC), is a global forecasting competition. It focused on forecasting demand for a subset of products and stores at Walmart, thus in a retail context. At the close of the competition, organizers made public around 130 distinct sets of forecasts: the top 50 deterministic forecasts, the top 50 probabilistic forecasts, and approximately 30 benchmark forecasts based on classical approaches.

This abundance of forecasts enables a deep analysis of the link between accuracy and added value. However, this M5 competition suffers from a significant limitation for our study. It was designed as a pure forecasting competition, completely ignoring the aspects of decision-making and impact evaluation. To conduct our study and explore business value successfully, we had to address this gap by defining our decision-making process and enriching associated data (packaging, supplier constraints, order frequency, cost structure, etc.). Our goal was to closely resemble real-world use cases in business. Thus, we relied on third-party data sources to define the most credible context possible (margin rates by product family, realistic packaging, target service rates, etc.).

Regarding the inventory policy, we established a weekly replenishment process with a 3-day lead time, following a classic “periodic review and dynamic order-up-to-levels” policy. Did it reflect any company’s exact replenishment policy? Clearly not, but it’s not a problem since the essential aspect is that they reflect a credible and coherent policy.

The results of our study do not claim to be universal. However, they demonstrate that there is a gap between the accuracy of a forecast and its economic value. Everyone is encouraged to replicate this analysis in their own context to evaluate if the results tie into their preconceived notions of forecast accuracy or conflict with them.

Once the procurement process was defined, and the data enriched, we developed a simulation tool and applied it for each time series of each forecast set. We employed a 4-step process:

1. Forecast ingestion
2. Evaluation of ‘accuracy’ (using various metrics)
3. Simulation of the procurement decision
4. Evaluation of economic performance (especially in terms of gains/costs).

This simulation applied to the M5 competition dataset provided us with numerous and varied data, totalling more than 9.4 million distinct cases.

From Universal Assumptions to Surprising Findings

Before detailing the main results, let’s recall the nearly universally accepted axiom: “If the ‘accuracy’ of forecast A is better than that of forecast B, then forecast A will enable better decision-making and present economic advantage.” To this, we can add a generally accepted limitation: “In some cases, a forecast, although more accurate than another, may not provide any additional added value.” We accept that the improvement might be too minor to have a real influence on replenishment decision making. For example, reducing an error from 4.4 to 4.3 units might have little impact on the replenishment of an item supplied in packages of 12 units.

There were three key findings from the comparison between forecasts, based on their accuracy (expressed here by MAPE) and their economic performance:

1. Finding #1: In 80% of cases, improving the forecast had no impact on the decision and thus on economic performance. This proportion exceeds expectations, implying a negative return on investment (ROI) in 4 out of 5 cases.
2. Finding #2: In 12.6% of cases, improving the forecast resulted in superior economic performance. This is our expected case. However, this proportion remains low, rewarding efforts to enhance the forecast in only 1 out of 8 cases.
3. Finding #3: In 7.3% of cases, improving the forecast altered economic performance. This case, initially considered impossible, occurred in 1 out of 3 cases when the forecast improvement influenced the decision.

Figure 1 | Breakdown of economic performance when forecast accuracy improves

These results, evaluated using the MAPE metric, are similar for other studied metrics (MAE, MSE, MSLE, RMSE, wMAPE). This observation is therefore not specific to the MAPE metric but rather associated with the notion of accuracy itself.

Improving the accuracy of a forecast does not guarantee better economic performance. Yet, this doesn’t mean that we should stop improving our forecasts. In fact, shifting the focus from the frequency of cases to the economic performance of the forecast shows that the value created by a better forecast (here $8,376) significantly surpasses the value lost (here $-3,251). The balance remains strongly positive. This is of course quite reassuring. This implies that improving the forecast indeed holds an economic advantage. However, this gain is significantly reduced (~-28%) by the recorded underperformance, which leaves room for further improvements in the forecasting practice.

Advocating for an Economic Approach

These conclusions do not claim to be universal. Transferring them from one context to another would be inappropriate. Indeed, a simple change in decision-making, cost structure, or constraints could produce radically different results.

However, the general conclusion is worrisome. The importance given to the accuracy of a forecast might not be as fundamental as assumed. The belief that improving the accuracy of a forecast is necessarily advantageous, is a myth. In the business realm, we are therefore wrong to be so obsessed with accuracy. Perhaps we’ve become so focused on improving our forecasts that we’ve lost sight of the fact that we’re not in an accuracy competition. Our sole goal should be to generate value. And in business, value—although it can take various forms—is primarily economic.

The future of forecasting lies in better integration of decision-making and its impacts into our assessments. The challenge is commensurate with the opportunity it represents! In an upcoming article, we will explore how to improve both efficiency and performance in forecast generation, detailing an approach to target areas where effort expended has a tangible impact on economic performance while identifying those where investment would be economically irrational.

This article first appeared in the spring 2024 issue of the Journal of Business Forecasting. To get the Journal delivered to your door every quarter, become an IBF member. Member benefits include discounted entry to all IBF training events and conferences, access to the entire IBF knowledge library, and exclusive members workshops. 

Forecasting is part of everyday life. We watch the weather channel before we make weekend plans and text our family our ETA when we leave work. However, forecasting in the business context does not happen naturally. It is the responsibility of the demand planning team and as far as the rest of the company is concerned, how we arrive at these forecasts is a mystery. However, it doesn’t have to be this way.

For companies just starting out in demand forecasting, I would like to offer some very practical advice.

Forecasting anything is about determining mathematical dependencies between variables. The dependencies can be linear or nonlinear. Typically, having more data means you have a better chance at figuring out those dependencies and, as a result, developing a decent forecasting model.

This doesn’t mean that Neural Network or any other advanced algorithm. In fact, a simpler model is often better.

Simple Models Mean Buy-in From Stakeholders

First of all, if you have a choice between a linear and nonlinear model, choose linear even if it means losing a few percentage points on accuracy. The main reason is that a linear model (e.g. a regression)  is easy to translate into a formula and can be easily understood by stakeholders.

If your team can’t explain the predictions, they have no value. There will be no user buy-in, no follow-up questions, no discussions. The wise consumer of a forecast is not a trusting bystander but a participant and, above all, a critic. It is almost impossible to offer any meaningful criticism to something that is difficult to understand.

As your team develops a forecasting model their ultimate goal is to come up with something that stakeholders will digest and retain. Those should be simple things like “if X goes up 10% our sales are likely to go up 5%”.

Secondly, while having external drivers as components is a potential game-changer in the forecasting world, it is absolutely not required to get started. The best foundational model can be achieved by creating drivers (features) that are rooted in the time series data itself. Some examples of those features can be month number, quarter number, and a rolling average value for a number of previous periods.

Source data (price by month) and the three features engineered for the simplest forecasting model

Having 100 external drivers (oil prices, labor market statistics, search word frequency, etc.) might look good on paper and result in a higher accuracy, but the business stakeholders will likely be bewildered and close the deck, never to open it again. The optimal number of causal relationships is between three and five; this way stakeholders can actually remember what they are.

Aim to Be Directionally Correct, Not Perfectly Accurate

The third and final point is that being directionally correct is the most important forecast characteristic. It is a very intuitive one, but it is often overlooked in the data science world. To illustrate this, let’s evaluate three different forecasts, and we’ll use Root Mean Square Error (RMSE) – one of the most common forecast accuracy metrics – as a way to compare them.

Comparing three forecasts

In the world of data science, the lowest RMSE wins. So would we pick forecast 3 as  the best one in this case? Not so fast. Out of the three models here only one correctly indicates a downward trend for the month of February. There are lots of use cases where being directionally correct is far more valuable than landing on a value that’s closest to the actual one. For example, in a Demand Review, the knowledge about a downturn in the market is a powerful weapon to wield. With that being said, in this case we would choose Forecast 1 as the best one.

An actionable forecasting model has stakeholder buy-in, is explainable and directionally correct.

 To sum up: an actionable, practical forecasting model is not the one that uses the highest number of external drivers, has the most advanced mathematical algorithm or even the highest accuracy metric. It is the one that has stakeholder buy-in, is explainable and directionally correct. This way your team can be sure that it will drive meaningful discussions and result in actions that bring value to the business.

“What can be measured, can be managed.” This statement is certainly accurate with respect to the real value and purpose of using KPIs, or key performance indicators, as essential tools for measuring, monitoring, and managing process performance.

KPIs serve as benchmarks for identifying opportunities for optimization and innovation. They are of great use in decision-making, and are good instruments for creating accountability by setting clear expectations for execution. These indicators are mostly used to measure and evaluate performance against specific objectives or goals. For example, it is common for companies to establish KPIs as quantitative measurements of performance—to assess how well the organization is meeting its yearly objectives.

Even so, it is important to note that KPIs are not only limited to quantitative applications, as they can also be used to communicate good stories. Diligently analyzing and interpreting these indicators, far beyond comparing them to a target, enables the discovery of what they are truly communicating. By learning how to interpret KPIs, past events can be better understood and used as valuable evidence to predict trends, propose impactful business actions, and effectively communicate them across the organization.

Additionally, integrating and effectively managing KPI indicators through financial statements results, provides organizations greater visibility and improved decision-making processes that support their financial health, and enhance their ability to sustain long-term growth and profitability. For example, integrating Sales and Operations Planning (S&OP) KPI metrics, such as forecast accuracy, inventory turns, customer service level, supply chain costs, and working capital ratios, among others, to the Income Statement and Balance Sheet, allows businesses to achieve greater operational efficiency and financial results, which by effect, lead to sustained competitiveness and increased market value.

LINKING KPIS TO THE INCOME STATEMENT

The income statement is an essential financial statement that provides insights into a company’s economic position, profitability, and efficiency in generating revenues and managing expenses.

Income statement figures can reflect actions taken by demand and supply planning to make a positive impact on revenue, generate cost savings across various areas of the business, increase profitability, and optimize costs—such as selling and marketing costs.

Linking S&OP KPIs to the income statement, facilitates a direct understanding of how operational performance impacts financial results, and establishes clear correlations between them, thus leading corporations to strengthen their ability to make informed decisions that drive profitability and sustainable development.

From a revenue perspective, accurate forecasting ensures that the right products are available to meet customer demand, hence preventing lost sales opportunities. Higher customer service levels also drive repeated business, further boosting revenue. As such, improvements in forecast accuracy and customer service levels contribute to positively impacting the top line.

From the Cost of Goods Sold (COGS) viewpoint, effective inventory management reduces carrying costs associated with excess inventory, and minimizes the risk of obsolescence—resulting in lower COGS. Moreover, effective supply chain management facilitates negotiating better prices with suppliers, reducing procurement costs, and optimizing resource utilization. Overall, these cost-saving strategies directly impact the bottom line of the income statement by reducing operating expenses and consequently improving profitability.

The combined effect of higher revenue and reduced costs leads to improved gross margins. Gross margin improvement is a key indicator of the company’s operational efficiency and profitability, benefiting the income statement by impacting the company’s bottom line, while enhancing profitability and shareholder value.

LINKAGE TO THE BALANCE SHEET

The balance sheet is another essential financial statement used by organizations to provide a clear picture of the company’s financial position at a specific point in time. It presents the company’s assets, liabilities, and shareholders’ equity. While balance sheet items are not typically considered KPIs themselves, certain financial ratios and metrics derived from the balance sheet can serve as metrics to assess the company’s financial health and performance.

A typical indicator measured on the balance sheet is the efficiency of working capital management. Efficient planning techniques directly impact working capital management by optimizing the weight between current assets and liabilities. For example, improving forecast accuracy and inventory management reduces the need for excess working capital tied up in inventory. This by result liberates cash that can be used for other operational needs or investments, and at the same time, improves liquidity and financial stability, ensuring that the organization can meet its short-term obligations and/or invest in other significant growth opportunities. Another efficient planning technique is tighter accounts receivable management resulting from improved customer service levels that by effect can reduce the Days Sales Outstanding (DSO), further improving working capital efficiency.

Other S&OP KPIs such as inventory turns and inventory levels can directly influence the balance sheet as well. Higher inventory turnover ratios imply efficient inventory management practices, that lead to lower inventory levels, reduced excess inventory, and minimized carrying costs. Further, optimizing inventory levels reduces the risk of inventory write-downs and obsolescence, which can impact the company’s financial health and asset valuation. These reductions also free up cash for additional investment, and could even lead to debt reduction. Improved financial performance resulting from effective planning practices can positively impact debt management, enhancing profitability and liquidity for better debt management, reducing interest expenses and financial risk.

In summary, a strong balance sheet with healthy ratios, such as higher profitability and better liquidity ratios resulting from improved working capital management, can enhance the company’s creditworthiness, access to capital, and reduce its reliance on debt financing.

REPORTING KPI RESULTS TO EXECUTIVE LEVEL TEAM

Executives are mostly interested in the company’s financial health and sustaining its long-term growth and profitability. As such, KPIs play a crucial role in driving financial performance and aligning operational activities and efforts with strategic goals and objectives, ensuring that everyone is working towards the same outcomes.
The linking and analysis of KPIs to the company’s financial results is not fully successful until information is effectively reported to the Executive Level Team (ELT), as actions and important decisions need to be made accordingly. Reporting KPIs to Executives in a frequent manner can serve as the basis for productive discussions and collaboration among Executives, as this encourages dialogue around strategic planning, performance trends, challenges and opportunities, and enables informed decision-making.

When communicating and reporting KPIs to the ELT, it is of great importance to determine clearly what each KPI measures and why it is important for the business. It is also crucial to consider setting clear communication and presentation goals where relevant and meaningful information is presented to ensure that Executives have a comprehensive understanding of the organization’s performance and strategic direction.

When presenting KPIs, it is best to present them in a visually concise, focused, appealing, and easy-to-understand format via charts, graphs, and dashboards to illustrate trends, comparisons, and key insights. Including contextual information and analysis alongside may allow Executives to better interpret the data accurately. When deemed necessary, identify key findings and insights derived from the KPIs and highlight actionable steps or strategic decisions that need to be taken based on the results. Monitoring progress against KPIs over time is fundamental to track changes in performance, evaluate the effectiveness of strategies implemented, and determine the need to adjust KPIs or initiatives as necessary.

CONCLUSION

In conclusion, in today’s dynamic marketplace, it is vital for any organization in search of fostering a holistic approach to performance management, to be able to translate operational efficiencies into financial successes. As such, aligning a company’s operational KPIs with its financial statements can be of great help in support of this goal. The benefits of linking operational KPIs to the income statement and the balance sheet can be substantial, as they can drive organizations to better understand the financial implications of their performance metrics and make better-informed decisions to drive revenue growth, improve profitability, and achieve strategic objectives.

The importance of demand forecasting is clear. Robust forecasting improves critical KPIs like customer service levels, inventory turns, and cash. 

However, demand planning is only as effective as the data informing it. Demand forecasters may find the results less trustworthy and reliable if the content fed into a forecasting system has errors or duplicate records. Creating and adhering to a thorough information collection and processing strategy prevents such outcomes.

Decide Which Data to Use

The first step is determining which data the company will use for its demand planning. The information collected in a point-of-sale system could be valuable for highlighting sales patterns, such as which times of the year specific products are most popular and what other things people typically buy at the same time. A practical approach for companies with numerous retail outlets is to gather data showing which stores have the most robust or slowest sales.

“Inventory tools give a broader picture by showing how stock levels change over time”

Alternatively, inventory tools give a broader picture by showing how stock levels change over time. Seeing that historical context can help decision-makers determine how long upswings and downturns might last, whether these events previously occurred and what caused them.

A related question is whether those working on data quality within the organization know the location of the information identified as worth using. Many companies still maintain rigid silos that create challenges for collecting and using information across departments or teams.

Establish a Data Quality Baseline

What’s the current state of the company’s data for demand planning? A data quality baseline answers that question. People must start by identifying the critical data elements (CDE). These collectively represent the information that will shape leaders’ future decisions.

Examples of CDEs in demand planning include:

• Supplier and customer names
• Order quantities and dates
• Restock frequency
• Merchandise prices
• Average order fulfillment timelines
• Dates associated with short-term promotions
• Most and least-popular product names and descriptions
• Inventory management system reports
• Distributor names and locations

The next step is to establish data quality indicators with input from those who understand and value the importance of demand forecasting in modern businesses. We should typically measure the following:

• Timeliness
• Uniqueness
• Accuracy
• Consistency
• Completeness
• Validity

They often rely on specialized tools to show data quality gaps and begin developing improvement plans. However, it’s also important to discuss challenges experienced by the people who collect and use it daily in their roles. They’ll likely have valuable input for changes that might have been overlooked.

Understand Data Governance Needs

Data governance encompasses keeping information usable, secure and available while retaining its high quality. Maintaining it is a team effort of ongoing collaboration to create and uphold standards. People on the data governance team will also help establish organizational norms by training employees to handle them and reduce the chance of errors.

Data governance policies will differ in an organization depending on the type of information used for demand planning. Anything containing payment details or personal info must be treated with more care.

“Many companies use third-party service providers to meet their data-handling needs”

Many companies use third-party service providers to meet some of their data-handling needs. In such cases, data governance plans must include steps to take so those outside businesses don’t compromise quality.

Documentation is also a major part of data governance. Keeping an ongoing record of the data source, location and associated security protections helps organizations use the information and address oversights.

It’s becoming more common for companies to collect data with Internet of Things (IoT) sensors. This gives a more detailed view of what’s happening with the information. Although confirming data sources can initially be time-intensive, the increased analysis opportunities are worthwhile. Estimates indicate the IoT sensor market will experience 24.9% growth in 2027, suggesting decision-makers are interested in using them.

Create and Maintain Data Preparation and Use Processes

Those overseeing data quality and usage within the organization must develop a preparation process everyone can use before feeding the information into platforms for further analysis.

For example, people must check the data for anything that could skew the results. Under- or overestimating demand can add to the organization’s costs, and mistakes often cause these outcomes. Thorough preparation requires looking for duplicate records, misspelled product or customer names, and any information in the wrong format. All those things could result in miscalculations or data not being included in an analysis.

The resultant process must be well-documented and easy for others to follow. Those qualities will be instrumental in getting usable, consistent results within the organization.

Next, people must make a framework for how people within the organization can and should use the data for demand planning. Which tools will they use? Must leaders invest in automated solutions or other products to support the process? Which employees will be directly involved in collecting or using the information? Getting feedback from those parties before and after making the data usage framework should optimize outcomes.

Teach the Importance of Demand Forecasting to Employees

Once the responsible parties design the processes for preparing and using data, they must communicate and teach it to all others handling the information. When all relevant employees understand the importance of demand forecasting, they’ll play important roles in upholding the requirements.

Allow plenty of time for people to get used to new tools or processes. Encourage them to give feedback about everything new and provide insights about further improvements.

Some organizations still use spreadsheets to track activities across the global supply chain. Lasting change will take longer to enact in such companies, and people may feel overwhelmed initially. However, most can adapt to new processes if their managers are patient.

“Employees who understand how to maintain high data quality will feel empowered”

Discuss how seriously the organization takes the importance of demand forecasting and explain why. Employees who understand how to maintain high data quality will feel more motivated and empowered.

Leaders should also be open to hearing about any problems, concerns or challenges that arise as employees work to keep data quality high within the organization. People are more likely to be honest about the highs and lows of this transition if they know managers will hear and respect them.

Treat Data Quality Standards as Works in Progress

High data quality allows leaders to make effective and confident demand planning decisions, no matter what a company sells or how many customers it serves. However, even those who will never act on what the information says are instrumental in gathering and preparing it.

Although these steps will assist company representatives in creating data quality processes, people must periodically revisit the current procedures and assess whether they’re still working as intended. It’s not a sign of total failure if they aren’t. However, it’s a strong indicator it’s time to get to the bottom of what’s going wrong and work to improve the shortcomings.

Data quality standards may also change as a company grows, begins offering new products or must follow updated regulatory requirements. People who understand this and know data quality is never a static measure will collectively help their organizations reach new demand planning goals.

To read more of Emily’s work across business, science and technology, head over to her online magazine, Revolutionized.

To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.

As the saying goes, what gets measured gets improved. The same is the case with supply chain management. End to end supply chain operations related to planning, sourcing, production and logistics have specific metrics that help us improve processes and overall performance.

For every metric, there is a target number or goal that needs to be agreed upon. Then, each of those metrics is linked with a strategic objective or a key value driver (KVD), and is further supported by planned initiatives that help in achieving the target or goal.

Demand Planning Metrics

Demand planning metrics relate primarily to forecast error metrics. They include:

• Mean Absolute Percentage Error [MAPE]
• Weighted Absolute Percentage Error [WAPE]
• Mean Squared Error [MSE]
• Forecast Bias, and
• Forecast Value Added [FVA]

Of these, Forecast Bias and FVA are critical and should be tracked over the planning time horizon. Bias indicates the direction of the forecast errors and helps us understand whether we are under-forecasting or over-forecasting.

FVA is a measure of the value added during the forecasting cycle. It is measured by comparing forecast accuracy before and after each activity in the demand forecasting and planning process to determine if that activity improved the accuracy of the demand forecast. Tracking FVA during the forecasting cycle enables us to find the right mix of quantitative and qualitative approaches that result in fewer errors.

Supply Planning Metrics

Supply planning metrics are related to sourcing and production. They include:

• Order Confirmation to Delivery Lead Time
• OTIF % [On Time in Full]
• Material Availability for Monthly Production Schedule
• % Rejections and % Returns
• Spend Analysis [By Product Mix and Supplier]
• Direct and Indirect Category Analysis and
• Supplier Performance Index

These metrics allow us to understand supplier performance and to perform spend analysis.

• Production/Manufacturing metrics include:
• Daily schedule adherence
• Production loss
• SKUs with excess production
• SKUs with shortage in production
• Line Utilization %,
• OEE% (Overall Equipment Effectiveness)

It is essential to segregate the metrics into daily, weekly, monthly and quarterly buckets. However, the most crucial performance indicator is OEE % as it gives a snapshot of equipment productivity, product quality, utilization and speed.

S&OP Metrics

During S&OP meetings, the appropriate metrics are reviewed and discussed with the respective functional teams during each phase or step of the S&OP cycle.

Logistics & Distribution Metrics

This section covers metrics related to warehousing, transportation and distribution operations.

Warehousing metrics

•  Dock to Stock Cycle Time
• Inventory Accuracy %
• Operating Costs [Budget vs Actual]
• Detention Costs
• MHE Utilization %
•  Process Productivity Metrics
• Order-Delivery Cycle Time and
• Environment-Health-Safety Metrics

The metrics that are critical and need extra attention are the ones related to inventory management and customer order management. Accurate inventory management within the warehouse is critical to ensure correct order picking, packing, staging and loading operations. End to end order management from order receipt to fulfilment to proof of delivery are central to warehouse operations.

Transportation metrics

• Truck Placement Reliability
• Loading Time
• Unloading Time
• Transportation Lead Time
• Operating Costs
• Vehicle Capacity Utilization %

In the case of transportation, vehicle arrivals at the appointed times are critical for timely onward operations. Monitoring transportation lead times are also essential, especially for last mile deliveries.

Distribution metrics

• Despatch Schedule Adherence
• OTIF [On Time-In Full] %
• Customer Order Confirmation to Delivery Lead Time
• Product Returns %
• Customer Satisfaction Score

As an extension, customer satisfaction and product returns metrics need to be measured and monitored. Most 3PL and 4PL service providers invest in processes and systems related to customer issues. Order fill rates and customer satisfaction metrics need to be focused upon and prioritized.

Other Relevant Metrics

The advent of e-commerce and quick commerce has resulted in ever-increasing reverse logistics and material flows. Therefore, metrics related to reverse flows are as important as forward flows. Also, macro-economic factors, geopolitics, climate events, trade flows, fluctuating commodity prices, etc. have necessitated the measurement and monitoring of resiliency metrics.

Reverse logistics metrics

• Logistics Costs and Product Returns as a % of Sales

The challenge here is to minimize or reduce product returns and therefore reverse logistics costs. Just like the forward supply chain, designing the reverse supply chain is essential in ensuring that returned products can be inspected, segregated and reused, repurposed or refurbished depending on the type of product

Resiliency metrics

• Time to Survive (TTS)
• Time to Recover (TTR)
• Financial Impact (Sales and Profitability)

Resiliency metrics became extremely important for supply chains following the COVID-19 pandemic. Companies soon realized how every supply node in the chain could affect downstream activities due to material shortages and supply delays.

For firms that had a global network of suppliers, the ‘Time to Recover’ took several weeks and months. Moreover, supply related constraints had an adverse impact on demand that eventually affected sales and profitability numbers. Firms have started to invest in tools and systems that enable ‘what-if’ scenario analysis – what the financial impact might be in case a supply node is unable to perform as planned.

Linking Operational & Financial Metrics

The various operational metrics and performance indicators listed above have a direct or indirect impact on business performance – more specifically, financial performance. Therefore, it is essential to establish a link between operational metrics and financial metrics.

The key financial metrics to focus on include:

• Cash to Cash Cycle Gross and Net Working Capital
• Inventory Turnover Ratio
• Gross Margin and Net Margin %
• Return on Assets (ROA)
• Return on Capital Employed (ROCE)

Cash to Cash Cycle, Gross and Net Working Capital and Inventory Turnover Ratio are the three metrics that should be on the radar of the supply chain management team. All the demand, supply and distribution plans and their execution have a direct bearing on the cash conversion cycle and working capital levels. ROA and ROCE are impacted by supply chain operating costs. Any reduction or saving in procurement costs, inventory handling costs, production and logistics costs shall have a positive impact on ROA and ROCE. The operational metrics can be dovetailed into a relevant business/financial scorecard every month, quarter, and financial year.

People, Processes & Systems

Finally, for any performance management philosophy to work, there is a need to put in place the right team with suitable skills and understanding of the supply chain domain in addition to devising and deploying the appropriate processes and procedures for data management (data recording, reporting, monitoring and analysis) and selecting the right tools, software and systems for reporting, analysis and visual management.

Visual management tools such as dashboards, functional control towers and related systems could go a long way in monitoring key operational metrics on a real time basis to enable course correction and development of corrective and preventive action plans going forward.

To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.

There are many factors that a demand forecaster must consider when developing a demand forecast. One of these factors is the pricing power of the company and of the brand. Pricing power will affect demand volume when prices change. It is important that the demand forecaster is familiar with pricing actions taken by the company and anticipate their impact on demand.

Price is an important element of the value perception of the customer/consumer. From a finance perspective, pricing power is also an important consideration for company investors where greater pricing power is typically rewarded with a higher company valuation and/or stock price.

What is Pricing Power?

If a company does not have much pricing power, an increase in their prices reduces demand for their products. A company that has substantial pricing power is often one that provides a rare or unique product with few rivals or substitutes in the market.

Scarcity of resources can also give a company high pricing power. If the resources for a product cannot be easily obtained, the price of those resources will increase because there is insufficient supply to meet demand, which pushes up the price of the final product for customers/consumers.

In these cases of pricing power, if the company raises its prices, the increase may not affect demand much – if at all – because there are no alternative products on the market that consumers can choose instead. So, when forecasting demand, the demand forecaster needs to consider the degree of pricing power the company has, as well as the degree of pricing power of the company’s competitors. A company’s pricing power is linked to price elasticity of demand for its products, a metric which can aid the demand forecaster in understanding how price changes will impact demand.

Price Elasticity

Price elasticity is a measure of the responsiveness of the quantity demanded of a product or service to a change in its selling price. It is calculated as the percentage change in demand volume divided by the percentage change in price. Price elasticity of demand can be classified into three categories: elastic, inelastic, and unitary. If the price elasticity of demand is greater than 1, the product is considered elastic, meaning that a small change in price leads to a large change in quantity demanded. If the price elasticity of demand is less than 1, the product is considered inelastic, meaning that a change in price has less or little effect on the quantity demanded (a strong brand will usually exhibit this characteristic.) If the price elasticity of demand is 1, it is unitary.

Apple iPhone: An Example of Price Inelasticity

An example of inelastic demand is the Apple iPhone. When the iPhone was initially introduced by Apple, the company had strong pricing power because it was essentially the only company offering a smartphone and associated apps. At the time, iPhones were expensive, and there were no rival devices. Even as the first competitor smartphones emerged, the iPhone still occupied the high end of the market in terms of pricing and expected quality.

As the rest of the industry began to catch up in service, quality, and app availability, Apple’s pricing power diminished. Apple began to offer new models of iPhones including cheaper models for budget-minded consumers. Even at that, Apple still reflects very favorable pricing power which contributes to its relatively high value as a high multiple of earnings in the stock market.

Cross Elasticity of Demand

Another metric of value to the demand forecaster is the cross elasticity of demand which measures the responsiveness in the quantity demanded of one product when the price for another product changes. Also called cross-price elasticity of demand, this measurement is calculated by taking the percentage change in demand volume of one product and dividing it by the percentage change in the price of the other product. (Companies often use cross elasticity of demand to determine and set prices of their products and services.)

Products with Perfect Substitutes: The demand forecaster can use the cross elasticity of demand to make comparisons of products that are considered perfect substitutes for one another or those that are complementary to one another. For substitute products, cross elasticity of demand remains positive, which means prices increase when demand for one product rises. Demand for complementary products drops when the price rises for another. This is called negative cross elasticity of demand. (Unrelated products do not affect one another.)

Products with no Substitutes: Products with no substitutes have the ability to be sold at higher prices because there is no cross-elasticity of demand to consider. Incremental price changes for products with substitutes can be analyzed to determine the appropriate level of demand desired and the associated price of the product.

Help Marketing & Sales in Their Pricing Decisions

The metrics above that relate to pricing power can also be used by the Financial Planning & Analysis (FP&A) function within the company to evaluate the financial effects (positive and negative) of pricing strategies and actions. Cooperation between demand forecasting and FP&A is important in aiding Marketing and Sales in their product pricing decisions. Pricing and pricing strategy is an important consideration for profitability and cashflow.

It is important for demand forecasters to understand demand and price interactions and inter-relationships within the product portfolio of the company and with competitor products in the marketplace. Price elasticity analytics can aid in their assessing the effects of promotions, pricing actions of the company, and the pricing actions of competitors. This is important to the demand forecaster’s effort to develop more accurate forecasts for use by management in the supply chain and other management processes of the company.

To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.

It is a great time for business forecasting and planning and those who do it! We have technology and data analytics tools that could not have been imagined just 30 years ago. We have able to shift from manual, time-consuming data collection and analysis to enabling better business decisions quicker and easier, adding more value to our businesses.

It has been a time of great expansion of data, and of great advancement in forecasting tools to exploit it. We can better understand the purchasing behavior of customers and consumers. And we have better forecasting methods and models to generate demand forecast and revenue projections.

Demand Forecasters and Planners are now business partners in decision making across the entire enterprise – and that should be our goal.

Your Role Is Only Getting More Important

Operational and financial plans can are only as good as the underlying demand forecasts and plans. This places Demand Planners in uniquely important position, and one with great responsibility when it comes to the success of the company. Welcome to the 2020’s where Demand forecasting and demand planning are foundational. Welcome to a time of multi-dimensional business thinking. Having seen planning transition from a traditional approach of reverse engineering from the top line to the bottom-line performance, it’s like shifting from 2-dimensional chess to 3-dimensional chess.

Welcome to the 2020’s where Demand forecasting and demand planning are foundational

Shift Away From Short-Term Forecasting

What can we do to leverage the full range of capabilities available to us? Currently, a limiting factor for many is the continued use of spreadsheets. Spreadsheets are still widely used by Demand Planners in companies of all sizes. Spreadsheets require significant time to import data, maintain it, perform modeling, and export data for use in other software. Integrated software solutions within the company should be standard, freeing up time for Demand Planners to create insights of greater value to the company. Innumerable cost-benefit studies support this. Time is money.

It is important that time be dedicated to identifying and analyzing the drivers of demand

A Demand Planner’s time should shift from away purely performing short-term forecasting, for which Machine Learning (ML), and Artificial Intelligence (AI) can be readily deployed. If technology can assist in short term forecasting, what should we do with this extra time? It is important that time and effort be dedicated to identifying and analyzing the drivers of demand (and the forces that affecting these drivers), both now and in the future. This places the Demand Planner in a position to understand and anticipate bigger picture forces impacting the enterprise, and to inform and assist the strategic discussion and decision-making. That is enormously valuable.

Make Collaboration Your Superpower

Given all functions throughout the company plan and depend on demand forecasts and plans, development of relationships across functions is a major value-added. Become familiar with the terminology used by the functional areas participating in and using information from your demand forecasting and planning processes.

Help them to use the information you provide more effectively for their unique roles. Determine how you may be able to reshape information that you provide in a better format or segmentation structure. Ask them to share information with you from their functional area that may add to the effectiveness of the work in demand forecasting and planning, such as industry publications, research reports, market research, and other inside and outside information that they use.

Ask them about their views on events, situations, competitors, and other developments that impact their area of the business. Ask them how they might expect this to affect the company in the future. Ask them how it might affect their function in the future. Ask them to suggest conferences that they attend that they believe would add to the quality of the work that Demand Planners are doing. Ask and listen to inside and outside authoritative sources of information. Be genuinely interested in others and their functions.

Use Technology to Elevate Your Role to Business Partner

Implement technology solutions that enable better operational forecasts more quickly, while serving the company with insights and longer term forecasts that will drive strategic initiatives. Think of yourself as a trusted advisor for the company, aiming to deliver on enterprise goals and objectives and interested in its business success – and well qualified to guide the business in the right direction. This reframing of your role can enhance your value to the company and contribute to its future success and sustainability. Look constantly for ways to improve your functional area and the company as a whole.

Think of yourself as a trusted advisor for the company

As ML and AI develop in the coming years, they will likely be one of our best enablers and sources of rapid research and information for the work of demand forecasting and planning for future company success. The pace of change is rapid and unlikely to slow down in the future. Acquiring tools and applications that enable us to work more effectively and efficiently is essential. We should not fear technological developments but embrace them as they will be fundamental to our success, both as individuals and companies.

 There have been amazing changes for Demand Forecasters and Demand Planners in the past 25-30 years. And more change will come even faster in the next 5-10 years. It will be a great time and opportunity for business forecasting and business planning and for those who do it!

To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.

We all know that machine learning (ML) and AI gets the analytics and data science community excited. Every self-respecting forecasting department is developing ML algorithms to predict who will click, buy, lie, or die (to borrow the title of Eric Siegel’s seminal work on the subject). All analytics conferences and publications are filled with AI buzz words.

But when it comes to real-life implementation, the majority of demand forecasters are somewhat cautious about implementing machine learning. Why is that? Isn’t machine learning all about predicting, which is literally a Forecasters job? Let’s explore the opportunities and pitfalls of applying machine learning in forecasting.

Demand Forecasters & Data Scientists Define ‘Prediction’ Differently

There is a subtle difference in the way forecasting and ML define ‘prediction’. When Forecasters say ‘prediction’ we mean a prediction about the future. Traditional forecasting prediction methods include Time Series modelling, algebraic equations and qualitative judgement calls. As a result traditional forecasting is somewhat manual and time consuming, and may be swayed by human judgement. However, the outputs are easily interpreted and it is an agile process; the Forecaster knows where the numbers are coming from and may easily make corrections as needed. Further, traditional forecasting may be done with limited data.

Machine learning or statistical model ‘prediction’ refers to predicting the past. This sounds a bit counterintuitive, but the idea is to compare the model ‘prediction’ with reality and measure the difference or error. These errors are used to finetune the model to predict the future. Consequently, model predictions are heavily driven by past performance and are almost impossible to finetune. Also, the interpretability of models is very limited. Another factor to consider is that by design ML requires a lot of data. On the upside, machine learning is quick and automated as well as objective, being free from human judgement.

Machine Learning was Built for the Digital World; Forecasters Work in the Real World

Machine learning and AI algorithms were created for a digital world with almost unlimited data on customer clicks, purchases and browsing data. As we know, these algorithms do an excellent job in luring us to make repeat purchases, buy complimentary items, and sign up for loyalty programs. The sunk cost of prediction error (lost sales) is relatively low. In addition, every error is an opportunity for the machine learning algorithm to improve itself.

The real world marketplace is quite different from the digital, marketplace, however. The data here might be limited to cash register sales, loyalty program data, or shipment data. The sunk cost of prediction error can be quite high as restaurants and retailers make procurements in bulk. Also, predictions cannot improve themselves as there is no automatic feedback loop. For these reasons, many brick-and-mortar retailers and their suppliers still rely on traditional forecasting methods. This does not mean that Machine Learning cannot offer opportunities in improving forecasting but there are a few considerations that need to be addressed before venturing into machine learning.

Machine Learning Requires Much More Data Than Time Series 

Any machine learning algorithm requires a lot of data. By a lot of data, I do not mean dates or variables. Machine learning models run on defined observation levels—this can be customer, store etc. You need at least a thousand of those (if not thousands) for machine learning to work. If the sample is limited to only 10 stores, it is probably better to refrain from machine learning and use Time Series techniques instead. Another factor to consider is the cost of maintaining the data. Is it readily available or does it need to be inputted manually? Does the data need to be engineered? Would that be a one-time effort or an ongoing process requiring human and computing resources? What would be the cost of storing data over the years?

Machine Learning is far Less Interpretable than Time Series 

By design machine learning is a black box. For example, predictions may be generated by a vote of thousands of decision trees. You can use colorful histograms to depict the weight of each factor in the model. These charts look very smart on presentation slides but are very far from intuitive. If the cost of a wrong prediction is millions of dollars, companies might be more comfortable with Time Series and arithmetic they can understand rather than a slick black box algorithm. This especially applies to new products with no sales data or limited test data.

There are a few workarounds for understanding machine learning. Playing with parameters might be a good indicator of the robustness of results. If one slight change to model inputs or specifications results in significant changes to predictions, this might be a red flag.

At the end of the day model trustworthiness may be proved by testing on new data. We don’t necessarily need to understand the ins and outs of algorithms if we are confident in the end result. Robustness of this argument may depend on your audience. Typically, analytics professionals are comfortable using machine learning predictions as long as they are tested. Supply chain leaders might be more cautious in making business decisions based on black box. A good sanity check is to run traditional forecasting methods in parallel to machine learning. If there is feasible difference between the results there might be either an issue with the model or an important consideration was left out when creating a traditional forecast.

The Cost Benefit of Machine Learning is not Always Clear

It goes without saying that when machine learning is set up right it is wonderfully efficient. All one needs to do is provide inputs and press the button. The ‘setting up right’ piece might be relatively straightforward or extremely difficult depending on prediction goal and data available. Repeat products with abundant history may be easily predicted using even out-of-the box ML packages such as SAS or Azure as long as the data is readily available. New product predictions may require intricate proxy algorithms to solve for limited data. This may require development of ML algorithms from scratch. In addition, there may also be a need to engineer data from different sources to feed the algorithm. This might require significant investment to either hire contractors, expand analytics team or put pressure on existing resources. Before ramping up a data science crew, companies would be well advised to consider how often the algorithm will be used, the efficiency gains, and the computing resources required for the project.

Impacts on Overall Business Planning 

Forecasting is the cornerstone of business planning. Any changes to the forecasting process may have an impact on other areas of the business such as Finance and Supply Chain. Typically, traditional forecasting methods rely on a top-down approach. A forecast is created in aggregate and then broken down by store/time period, etc. These breakdowns may be later used for financial targets or demand planning at store level. By design, ML Forecast utilizes a bottom-up approach. A prediction is created at store/time period level and later aggregated. When switching from traditional forecasting to ML, companies must ensure smooth transition at all stages of business planning. If not done right, this transition may result in discrepancies between the ML prediction vs the financial targets and supply plans.

To summarize, ML is a great instrument to streamline forecasting. As with any tool, it has its applications, benefits, cost, and risks. When utilizing ML for forecasting, companies should consider their data, business need, decision making culture, and planning workflow. A great place to start might be trying out ML on your data using online, off-the-shelf solutions such as Azure and SAS. Most of these solutions have step-by-step training videos that will help fit an ML algorithm to your data. Experimenting with these solutions may help decide whether ML is a good tool for your company’s forecasting, and whether an off-the-shelf solution is sufficient or there is a need for in-house development. Even if it turns out that for whatever reason ML is not a good fit for your company, there is no investment lost and some analytical knowledge will gained.

This article first appeared in the summer 2023 issue of the Journal of Business Forecasting. To access the Journal, become an IBF member and get it delivered to your door every quarter, along with a host of memberships benefits including discounted conferences and training, exclusive workshops, and access to the entire IBF knowledge library. 

It is nearly impossible to avoid the current hype around ChatGPT.  ChatGPT is an artificial intelligence content creator that generates a variety of outputs by answering questions fed to it.  You would have to be living under the proverbial rock not to hear the news stories – ripe with examples of the ChatGPT application passing MBA tests, and Law School exams.  The hype piqued my curiosity, so I decided to spend a cold February weekend testing out the application.  

Not knowing exactly how to use it – I decided to just have some fun. I prompted the application: “Create a short biography of Willie Mays”. It created two paragraphs of clean content highlighting May’s greatness as a baseball player.  I repeated the question and added “ In the style of Hemingway.” The response astonished me in its clarity and precision and likeness to Hemingway’s style. I asked other silliness such as “Visualize entropy” and it described an organized and disorganized living room as a metaphor for high and low entropy.

Many, many years ago on a planet far, far away…I wrote a paper for my high school American History class. The paper was meant to emulate a well-researched college thesis, with a minimum of 50 pages, proper citations, a table of contents, and other related components. We were instructed to choose a topic that had either a historical or futuristic focus. Having just read Alvin Toffler’s best-selling “Future Shock,” I chose to write about the pace of technological change and its effects on society.

The End of the White Collar Class?

“Future Shock,” published in 1970, outlines the political, social, and technological impacts of rapid technological advancement. Toffler predicted the decline of traditional industries and the rise of knowledge-based careers leading to a constantly evolving job market where successful workers must be able to adapt and retrain quickly to maintain their employability. He foresaw the trend toward remote work, the gig economy, the Internet of Things, and even the planned obsolescence of products. Toffler’s insights, published more than 50 years ago, have proven to be largely accurate.

As I played more and more with ChatGPT, I could not help but wonder if this new tool and others like it would have a dramatic effect on the knowledge workers of today—the people foretold of in “Future Shock.” I tried to contextualize the impact of the technology. Would this automation be a help or a replacement for many of these workers? Could it help with some known labor shortages such as those in the supply chain? What would happen to the coders, content creators, illustrators, web designers, writers, and countless others engaged in careers that will be affected by this new tool?

This was the eureka connection to my high school thesis paper, I recalled considering the possibility that some forms of planned obsolescence might include people – expanding on the notion of technological unemployment first articulated by John Maynard Keynes. After a long career in supply chain and manufacturing, I clearly understand how the advance of breakthrough technology has shifted work. I have watched automated picking machines replace workers in warehouses, and robots replace legions of factory workers. And we are now on the cusp of automated driving vehicles that might replace truck drivers.

Through all of this “progress” I never once considered that white collar workers, the knowledge workers, would be impacted by advancing technology. I thought they were “safe”. I always assumed that the workers most likely to be “technologically unemployed” would be the folks working on a typical manufacturing production line, where a machine could be built or programmed to replace their physical labor.

After experimenting with my whimsical prompts, I gave ChatGPT a series of supply chain prompts such as : “Explain S&OP in simple terms.” Here again, I was amazed by the app’s near-perfect and grammatically accurate answer. ChatGPT was not a digital toy hardwired for fun and it is not just incremental improvement. It is a game changer. I was so enamored with this experience that I posted to LinkedIn my story of querying ChatGPT about S&OP. A former colleague, a senior marketing executive with a FinTech firm sent this reply to me:

“Saw your ChatGPT post. I’ve already started using it to write byline articles, but I would not say that publicly– I’d get scorched by copywriters, editors, etc. I’ve learned how to feed it to get decent fodder up front, and then I clean it up and add more nuanced info. It probably cuts article development time in half.”

My former colleague confirmed some of my concerns. ChatGPT is such game-changing technology that even in its embryonic form has already replaced human workers while improving outcomes. I don’t for a moment think the myriad of content creators or editors will lose their jobs immediately, but I can’t imagine many are happy with this new tool. They may eventually have to re-learn and re-tool to remain employable.

ChatGPT & Supply Chain Management

Channeling Toffler, I considered what this might mean in my own profession. What were the potential use cases within supply chain? Envisioning the possibilities of some natural language, quantitative ChatGPT “cousin” in the supply chain field, I can think of a hundred different ways I would leverage such a tool. As planners, we always search for that extra piece of data to help us perform our jobs more efficiently. Imagine someday using an app to inquire, “Where is the shipment of chemical X at the moment?” or “What is the forecast error for product Y?” or “Has product Z started being sold to Walmart?”

Imagine colleagues five years from now prompting their cell phones to “Generate a forecast for the new blue widget product line, using the red widget product line as an analog” or “Provide me with the economic and sustainability impacts of closing a warehouse in Memphis.” Then imagine a tool that could perform even more complex analyses: “What is the risk profile of supplier A?” or “What are the economic tradeoffs of a less than 100% fill level while serving Amazon?” When you consider all the data analyses that supply chain professionals perform on a daily basis, the opportunities for supply chain AI tools are limitless.

There is still much to learn as natural language artificial intelligence tools expand into many domains. To me, this is the real promise of Moore’s law – expansive computing power that provides digestible information at the speed of thought. The future is unknown (despite Toffler’s knack for prescience), but I suspect we will be talking about this breakthrough moment and its impacts for a long time.

I recently watched a Ted Talk by Professor Tali Sharot, a specialist researcher in Experimental Psychology at University College London. She described how optimism bias is rooted in our brains to the point where it’s an evolutionary trait.

Human brains integrate positive evidence more efficiently and faithfully than we do negative evidence. Our tendency is to overestimate the likelihood of experiencing good events in our lives. In short, we are more optimistic than realistic, and we are oblivious to the fact. 80% of the population display an optimism bias to some degree, so it comes to no surprise that this translates into the business environment.

This got me thinking about optimism bias in the world of product forecasting – how we adapt our behaviour, especially regarding new or recently launched products where there is a significant judgemental contribution. New product launches typically show a forecast accuracy of 40%-55% (after the first year of launch) and forecasts are heavily reliant on the judgement of stakeholders.

The optimism bias challenge is so prevalent in the real world that the UK Government’s Treasury guidance now includes a comprehensive section on correcting for it. A real-life example is the cost of hosting the Olympic Games which, since 1976, is over forecast by an average of 200%. If future bidders wanted to safeguard against this bias, they should bear this in mind.

Tackling optimism bias is therefor crucial. Research done by Professor Sharot shows that being aware of the bias does not shatter the illusion. This is key to our understanding as having a bias KPI in forecasting is not enough for it to go away- though it is certainly a step in the right direction. It shows us the magnitude of our bias but measuring alone is not enough to eliminate the human and organisational behaviours that drive it.

To add to the above, let’s also consider that there are two types of optimism. One is closely related to belief in our success and essentially backing ourselves. Studies consistently show that this type of optimism is valuable as it leads to success in academia, sports, and politics. For a new product forecast, we are backing ourselves and our team’s ability to outpace the competition and all our assumptions being right as we are personally invested in its success. This type of optimism is in fact necessary for stakeholder buy-in and team motivation, even though the reality is that not all our efforts will bear 100% commensurate results; some might deliver higher results while others will go the opposite way.

The second and more alarming type is blind unrealistic optimism, i.e. overconfidence in our assumptions and our ability to deliver. Overconfidence is “the most significant of the cognitive biases” for new launches according to research by the Tuck School of Business at Dartmouth. It leads to the setting of unachievable goals which initially might look tempting but, once the hoped-for results do not transpire, can lead to a demotivated workforce underperforming. This is often manifest by overlooking the basic facts and fundamentals and not planning for “What if” alternative scenarios. This is where mature forecasting and S&OP processes can play a vital role as they define risks and opportunities, drive scenario planning, and encourage get stakeholders from Commercial, Marketing, Finance, and Supply Chain on the same page.

From my experience, here are some of the steps that I have seen work best when it comes to ensuring that a forecast reflects all the opportunities without being unrealistically optimistic.

1. Promote Diversity of Thought

The question here is, who has input into your forecasts? We should be wary if only one team is influencing the forecast as this could lead to homogenous personality types dominating the inputs who will inevitably have blind spots. It is common to see teams who are so invested in their new product that they discount some basic facts around its launch assumptions. The more diverse the teams who have a say in the process, the more likely we will have a robust set of assumptions which have been pressure tested. We must ask the question: Does our culture actively encourage individuals to speak up and have a say, especially if they have a different opinion?

2. Document the Assumptions That Underpin the Forecast

On occasions you may hear from a colleague who has been intimately involved with new products saying, “I feel this will perform better than the competition or better than we previously thought”. But as demand and S&OP managers, we need to understand the assumptions behind the “feel” factor and what is driving this. Feelings don’t have numbers, however assumptions driving price, competitive intelligence, and market share can be analysed and documented to be shared with stakeholders. This is where we need to keep investing in soft skill training for demand and S&OP managers.

3. Do Scenario Planning

We live in a world where our decisions are influenced by our environment and work culture. There might be pressure to hit growth P&L targets, internal politics, or an individual is overriding everyone else. In new products, it is important to acknowledge we will always have the “known knowns vs known unknowns” in our assumptions. Setting up a Base vs Ambitious case scenario opens this debate at the S&OP table in a constructive way and forces us to break down what we know and what we are less confident about. We can prepare and execute both plans and, importantly, know what indicators will show which trajectory we are on. Base vs Ambitious do not have to conflict with each other. Having this discussion early sets us up for decisions on safety stock, late-stage customisation, inventory order points, and risk of write off, among other parameters.

4. Peer Review your New Product Forecast

One of the most effective S&OP practices I saw practiced in my FMCG career was cross-checking of assumptions by an internal, non-aligned stakeholder. For example, in Pharma this could be the commercial lead of Antibiotics stress testing a Respiratory channel forecast. In retail, this could be somebody from Health and Beauty looking at Clothing channel forecast assumptions. If this is done in an open environment and constructively, it produces a robust discussion that ultimately ensures the strongest forecast possible. This works best where the leadership culture of the company is open to taking feedback from the broader organisation and doesn’t work in silos.

5. Learn From Historical Launches

What caused you to be off your forecast last time you launched? Which assumptions turned out to be incorrect? This might sound like a fundamental building block of the process, but it’s important to review your recently launched products on a 3 and 6 month rolling basis (shorter in FMCG) and have a learning feedback loop. Documenting this early on in a central repository is helpful. Often after-action reviews are in presentations that get lost over time as people move to new roles, thereby losing the institutional knowledge gained along with it.

Summary

To have success in our launches, we must believe we are equipped to execute every opportunity without becoming overconfident. A new product launch that has its forecast backed up by a robustly evaluated plan that considers all eventualities will have a significantly better chance of being a resounding win.