In today’s volatile and uncertain market, companies can no longer afford to operate without a structured, data-driven approach to forecasting demand. Demand planning is more than just predicting sales—it’s about building an integrated, agile business that can respond to customer needs while managing resources efficiently. Despite its importance, many organizations still rely on outdated tools, such as spreadsheets, which can lead to bias and siloed decision-making, ultimately compromising their forecast accuracy.

The potential improvements in predictive analytics and the integrated demand planning process can significantly streamline decision-making processes, create new insights, and save several business functions a huge amount of time and money.

Understand that a business will most likely invest in a new process to solve pain points, drive quantified savings, or deliver other clearly defined improvements. To successfully build a business case, you need to both help the organization understand the need and see the benefits.

Why Focus on Demand Planning?

Most companies that decide to invest or improve their process are primarily driven by one or more of the following:

• Obvious forecast accuracy challenges
• A highly variable process that requires a dedicated process to support it
• Detail-level forecasts are needed to support a more efficient manufacturing or distribution system
• Downstream inventory problems that are clearly driven by unseen variability
• An attempt to drive more cooperation between Sales and Operations through a consensus-based planning.

At its core, demand planning acts as the foundation for synchronized operations. It allows marketing, sales, supply chain, finance, and production to operate from a common set of assumptions. Without an accurate demand plan, supply planning becomes reactive; finance struggles with forecasting revenue, and customer service deteriorates due to stockouts or excess inventory.

Consumer behaviors have become increasingly unpredictable. Economic shifts, global disruptions, and rapid product cycles mean that relying solely on historical sales is no longer sufficient. Demand planning introduces a proactive lens that incorporates both internal drivers (such as promotions and price changes) and external signals (including market trends and customer insights) to create adaptive forecasts.

Inaccurate demand forecasts result in costly outcomes, including expedited shipping, excess working capital, lost sales, and markdowns. Improved demand planning helps reduce forecast error, allowing for better inventory placement, production planning, and supplier coordination. Even a 5- to 10-percent improvement in forecast accuracy can have a significant bottom-line impact.

Potential Improvements in Demand Planning

Organizations that invest in improving demand planning benefit from:

• Reduced Inventory Costs – Through better alignment of supply and demand.
• Improved Service Levels – By placing the right product in the right place at the right time.
• Higher Forecast Accuracy – Leading to more reliable plans across finance and supply.
• Faster Decision-Making – Enabled by real-time data and scenario analysis.
• Greater Agility – Ability to adjust to shifts in demand or supply quickly.

A mountain of research today shows that a mature demand planning process helps in improving forecast accuracy and delivers a high ROI. Improved forecast accuracy, when combined with software that translates the forecast into actionable insights, will decrease inventory and operating costs, increase service and sales, enhance cash flow and gross margin return on inventory investment (GMROI), and boost pre-tax profitability. The forecasting error, no matter how small, has a significant impact on the bottom line. In our experience, a 15 percent improvement in forecast accuracy will deliver a pre-tax improvement of 3 percent or higher.

In a previous IBF study of 15 U.S. companies, we found that even a one percentage point improvement in under-forecasting at a $1 billion company results in a savings of as much as $1.52 million, and for the same amount of improvement in over-forecasting, $1.28 million.[i]

The reduction in downstream finished goods inventory resulting from a well-established process and forecast accuracy improvements provides a one-time saving, as well as recurring savings arising from reduced carrying costs. There are significant benefits in a make-to-stock or distribution company. The downstream inventory reduction could range from 10 percent to 20 percent, as forecasting inaccuracies typically account for around 75 percent of the required safety stock.

Building and Investing in Demand Planning

• Build an Unbiased, Unconstrained, Consensus-Based Forecast: Organizations often confuse the demand plan with the sales target. Sales may overestimate to push for stretch goals, while operations may buffer to protect service. Demand planning needs to separate judgment from aspiration. Instituting a formal demand consensus process ensures that all voices are heard, while forecasts remain grounded in data and are evaluated against actual performance.
• Upgrade from Static Spreadsheets to Dynamic Models: Many companies still use Excel as their primary planning tool. While familiar, spreadsheets lack scalability, version control, and real-time integration. Upgrading to a dedicated demand planning system (or enhancing existing tools with forecasting models) introduces automation, improves collaboration, and enables real-time adjustments. It also supports more advanced techniques such as decomposition models or AI-based forecasts.
• Understand and Match Models to Patterns: Not all items follow the same demand pattern. Some are seasonal, some have trends, and others are highly volatile. Applying a one-size-fits-all model can lead to overfitting or underperformance. Instead, classify SKUs by their demand characteristics and apply the appropriate model, whether that’s exponential smoothing, moving average, or more complex causal models.
• Focus on Data Quality and Forecastability: Forecasting is only as good as the data behind it. Cleanse data for outliers, missing periods, and promotions. Measure forecastability using the Coefficient of Variation (CV) or Demand Intermittency. The demand planner becomes the integrator, ensuring that inputs from various departments are translated into a structured forecast. Establish accountability through KPIs such as bias, MAPE, and forecast value added (FVA).
• Invest in training and upskilling through IBF: Empower your teams with proven forecasting and planning knowledge by leveraging IBF’s certifications, workshops, and learning resources, building internal capability that drives consistent, confident decision-making.

Many companies are leaving money on the table with lost sales or poor service levels. An integrated demand planning process can result in increased revenue of 0.5 percent to 3 percent, along with improved inventory availability and demand shaping capabilities. Total annual direct material purchases, along with logistics-related expenses arising from demand variability and lost opportunities, can see direct improvements of 3 percent to 5 percent. We can also benefit from a 20 percent reduction in airfreight costs. Figure Y illustrates the anticipated benefits from a 15 percent improvement in forecast accuracy (these averages are based on individual results, which can vary depending on other variables and may be higher or lower for specific organizations).

Fig. Y | Graphic showing typical benefits from a 15 percent improvement in forecast accuracy

It is essential to understand these average savings amounts and determine what savings you believe you can achieve with a mature predictive analytics and demand planning process. Sometimes you need to know what finance and executive leadership anticipate in terms of benefits; you need to be on the same page in terms of expectations. It is here that the Institute of Business Forecasting Advisory Services (IBF.org) can shed some light on what is realistic based on past implementations.

Demand planning is not just a supply chain function; it’s a strategic business process that empowers smarter, faster decisions. In an environment where disruption is the norm and expectations are high, companies that implement disciplined, data-driven demand planning will not only survive, they will lead.

The path forward is clear: Separate judgment from strategy, invest in tools and talent, and build a collaborative process that evolves with your business.

In a world of uncertainty, demand planning offers clarity. It’s not just about predicting the future, it’s about preparing for it. Companies that invest in robust, unbiased, and collaborative demand planning are the ones that outperform, outmaneuver, and outlast their competition.

But you don’t have to do it alone.

The Institute of Business Forecasting (IBF) has been the trusted authority in forecasting, demand planning, and Sales and Operations Planning (S&OP) for over four decades. Whether you’re just starting your planning journey or looking to refine and elevate your process, IBF offers the training, certification, tools, and global community to help you succeed.

Join IBF and take the next step:

• Get certified with globally recognized credentials
• Attend industry-leading conferences and events
• Access exclusive research, case studies, and best practices
• Learn from and connect with top planning professionals around the world

[i] Chaman L. Jain (2018). The Impact of People and Processes on Forecast Error in S&OP. IBF research report #18. August 31, 2018

In today’s volatile and uncertain market, companies can no longer afford to operate without a structured, data-driven approach to forecasting demand. Demand planning is more than just predicting sales—it’s about building an integrated, agile business that can respond to customer needs while managing resources efficiently. Despite its importance, many organizations still rely on outdated tools like spreadsheets or allow bias and siloed decision-making to corrupt their forecast accuracy.

Employing predictive analytics and integrated demand planning can significantly streamline decision-making processes, create new insights, and save several business functions a lot of time and money.

This article explains why businesses need to leverage demand planning to improve their operations and explains the quantifiable value of doing it so that it can be sold within an organization.

Why Focus on Demand Planning?

Most companies that decide to invest in demand planning or improve their process are primarily driven by one or more of the following:

• Forecast accuracy challenges
• A highly variable process that needs improvement
• Need for a more efficient manufacturing or distribution system
• Downstream inventory problems driven by unseen variability
• Desire to improve cooperation between sales and operations

At its core, demand planning synchronizes operations. It allows marketing, sales, supply chain, finance, and production to operate from a common set of assumptions. Without an accurate demand plan, supply planning becomes reactive, finance struggles with forecasting revenue, and customer service deteriorates from stockouts or excess inventory.

Consumer behaviors have become increasingly unpredictable. Economic shifts, global disruptions, and rapid product cycles mean relying on historical sales alone is no longer sufficient. Demand planning introduces a proactive lens incorporating internal drivers (promotions, price changes) and external signals (market trends, customer insights) to create adaptive forecasts.

Inaccurate demand forecasts translate to costly outcomes: expedited shipping, excess working capital, lost sales, and markdowns. Improved demand planning helps reduce forecast error, allowing for better inventory placement, production planning, and supplier coordination. Even a five to ten percent improvement in forecast accuracy can have a significant bottom-line impact.

Potential Improvements Resulting From Demand Planning

Organizations that invest in improving demand planning benefit from:

• Reduced inventory costs through better alignment of supply and demand.
• Improved service levels by placing the right product in the right place at the right time.
• Higher forecast accuracy can lead to more reliable plans across finance and supply.
• Faster decision-making is enabled by real-time data and scenario analysis.
• Greater agility because of the ability to quickly adjust to shifts in demand or supply.

A mountain of research today shows that a mature demand planning process helps improve forecast accuracy and deliver a high return on investment (ROI). Improved forecast accuracy, when combined with software that translates the forecast into meaningful actions, will decrease inventory and operating costs, increase service and sales, improve cash flow and gross margin return on inventory investment (GMROI), and increase pre-tax profitability. The forecasting error, no matter how small it is, significantly affects the bottom line. In our experience, a 15 percent forecast accuracy improvement will deliver a 3 percent or higher pre-tax improvement.

In a previous IBF study of 15 U.S. companies, we found that even a one-percentage-point improvement in under-forecasting at a $1 billion company delivers a savings of as much as $1.52 million, and for the same amount of improvement in over-forecasting, $1.28 million.[i]

The reduction in downstream finished goods inventory resulting from a well-established process and forecast accuracy improvements provides a one-time saving, as well as recurring savings arising from reduced carrying costs. There are great benefits in a make-to-stock or distribution company, the downstream inventory reduction could range from 10 to 20 percent since forecasting inaccuracies typically drive around 75 percent of the required safety stock.

Building and Investing in Demand Planning

Here are some best practices when it comes to demand planning.

• Build an unbiased, unconstrained, consensus-based forecast. Organizations often confuse the demand plan with the sales target. Sales may overestimate to push for stretch goals, while operations may buffer to protect service. Demand planning needs to separate judgment from aspiration. Instituting a formal demand consensus process ensures all voices are heard, but forecasts remain grounded in data and evaluated against actual performance.
• Upgrade from static spreadsheets to dynamic models. Many companies still use Excel as their primary planning tool. While familiar, spreadsheets lack scalability, version control, and real-time integration. Upgrading to a dedicated demand planning system (or enhancing existing tools with forecasting models) introduces automation, improves collaboration, and enables real-time adjustments. It also supports more advanced techniques such as decomposition models or AI-based forecasts.
• Understand and match models to patterns. Not all items follow the same demand pattern. Some are seasonal, some have trends, and others are highly volatile. Applying a one-size-fits-all model can lead to overfitting or underperformance. Instead, classify SKUs by their demand characteristics and apply the appropriate model, whether that’s exponential smoothing, moving average, or more complex causal models.
• Focus on data quality and forecastability. Forecasting is only as good as the data behind it. Cleanse data for outliers, missing periods, and promotions. Measure forecastability using the Coefficient of Variation (CV) or Demand Intermittency. The demand planner becomes the integrator, ensuring inputs from various departments are translated into a structured forecast. Establish accountability through KPIs like bias, MAPE, and forecast value add (FVA).
• Invest in training and improving skills with IBF. Leverage IBF’s certifications, workshops, and learning resources to empower your teams with proven forecasting and planning knowledge, building internal capability that drives consistent, confident decision-making.

Taking steps to practice demand planning optimally will increase the bottom-line benefits you gain from it.

Bottom Line Benefits for Practicing Demand Planning

Many companies leave money on the table with lost sales or poor service levels. An integrated demand planning process can translate to increased revenue of 0.5 percent to 3 percent with improved inventory availability or demand shaping capabilities. Total annual direct material purchase, along with logistics-related expenses arising from demand variability and lost opportunities, can see direct improvements of 3 to 5 percent. We can also benefit from a 20 percent reduction in airfreight costs. The figure below shows the anticipated benefits from a 15 percent improvement in forecast accuracy. (These are averages and individual results for organizations. They are dependent on many other variables and can be higher or lower.)

This illustrates the possible benefits from a 15 percent improvement in forecast accuracy

It is essential to understand that these are average savings amounts. It is up to you to determine what savings you believe you can drive with a mature predictive analytics and demand planning process. Sometimes you need to know what finance and executive leadership anticipate in terms of benefits; you need to be on the same page in terms of expectations. It is here that the Institute of Business Forecasting Advisory Services can shed some light on what is realistic based on past implementations.

Demand planning is not just a supply chain function; it’s a strategic business process that empowers smarter, faster decisions. In an environment where disruption is the norm and expectations are high, companies that implement disciplined, data-driven demand planning will not only survive but also lead.

The Benefits of Demand Planning: The Final Word

The path forward is clear: Separate judgment from strategy, invest in tools and talent, and build a collaborative process that evolves with your business.

In a world of uncertainty, demand planning offers clarity. It’s not just about predicting the future, it’s about preparing for it. Companies that invest in robust, unbiased, and collaborative demand planning are the ones that outperform, outmaneuver, and outlast their competition.

But you don’t have to do it alone.

The Institute of Business Forecasting (IBF) has been the trusted authority in forecasting, demand planning, and S&OP for over four decades. Whether you’re just starting your planning journey or looking to refine and elevate your process, IBF offers the training, certification, tools, and global community to help you succeed.

Join IBF and take the next step:

• Get certified with globally recognized credentials
• Attend industry-leading conferences and events
• Access exclusive research, case studies, and best practices
• Learn from and connect with top planning professionals around the world.

[i] Chaman L. Jain (2018). The Impact of People and Processes on Forecast Error in S&OP. IBF research report #18. August 31, 2018

The essence of Sales and Operations Planning (S&OP) is the active participation of different departments. Participants come from varied backgrounds, each with different daily objectives and challenges, and they need to reach consensus to advance the process. Achieving this alignment in an environment of trust while balancing risks and opportunities is the main challenge.

The Demand Review is a foundational step in this process It is where a picture of future demand is created by taking a statistical forecast and adjusting it according to information about clients and the market from Sales & Marketing. It is where consensus on future demand is achieved.

The building blocks of the Demand Review include a statistical forecast, input from Sales and Marketing, and technology that supports the planning process to track the different clients, SKUs, and each person who collaborates in each stage of the S&OP process. In addition, transparency is key whereby all participants have the same information regarding past performance and future expectations.

Pre-Demand Review

Before the Demand Review, we must build a statistical forecast. I highly recommend having a quick meeting at the beginning of each month to show the results of the main KPIs, similar to the daily meetings proposed in the agile methodology. This helps us to understand the main difficulties of the last month and encourages conversation between the Sales team and Demand Planners. It is important not to focus too closely on each SKU and to keep it high level. I have been in Demand Reviews that lasted more than one hour per category, causing managers to leave the meeting. At this stage, keep discussions concise and focused.

In these pre-meetings, we can gain information about products or clients. For example, we might have a deviation from the forecast because our competitors increased their prices, or a client decided to increase shelf space, or conversely, the client decided that the product will be sold in fewer stores.

At the same time, it’s important to look at our service levels for different clients. Variations in sales could be due to internal problems. I recommend discussing Fill Rate, Market Share, and Days on Hand (DOH) for each client/category. If our colleagues have this information, we can react faster to changes in sales. For example, if we see that a retailer has increased its DOH, it is highly possible that our sales for next month will be lower. Information from the past helps us identify new sales trends and focus on products or clients that could pose risks or opportunities for our projections.

The desired level of aggregation depends on the planning time horizon. We take a more granular view the closer the horizon, and a higher level view for longer horizons. I suggest breaking it down as shown in Figure 1.

Figure 1 | Aggregation by planning horizon

Inputs We Need Before The Demand Review

We need a few different types of data for our Demand Review, which we collect from different sources.

Statistical forecast: We forecast based on historical data, assuming what happened in the past could happen again. The unconstrained demand forecast is our foundation for further discussion. This forecasting step can be improved with technology. For example, with APO or IBP, it is possible to keep a history of sales affected by out-of-stock situations or promotions, leading to a cleaner history and allowing Demand Planners to create better forecasts.

Input from Sales: Collaboration from the salesperson for each client is crucial, as they know the client’s perception best and will execute the sales plan. Therefore, they must be committed to the plan, considering it is usually their goal for the next month, with correlated financial incentives. The unconstrained forecast can be adjusted accordingly.

Input from Marketing: This area must incorporate knowledge of expected future share of the category, promotions, launches, or any product changes that could mean replacing a current SKU. At this point, the effectiveness of the Demand Review increases, as all the previous hard work will help have a decision-making meeting with managers. The unconstrained forecast can be adjusted accordingly.

During the Demand Review Meeting

During the Demand Review, I recommend starting with a one-page overview focusing on each category, showing the projections in terms of volume, price, and margin. This should be high level; only go into detail for SKUs that show significant deviations versus the last three months or have notable characteristics for the next months. In this meeting, we expect the participation of the sales manager, business manager, finance manager, or revenue manager.

The S&OP leader should encourage consensus to obtain a single plan that must be followed and executed. However, the S&OP leader also needs to create tension among the areas by asking questions such as:

• Are we considering pending orders in this demand?
• With the current projection, what could be our market share? Is it similar to what we expect?
• If we create a scenario in which competitors increase or decrease their prices, what is the expected volume difference?
• Which SKU poses the biggest risk? (If we anticipate higher sales than planned, we can project a bigger proportion of the demand at the beginning of the month, adapting the production schedule accordingly and react faster to potential out-of-stock situations.)
• Are we considering discontinued SKUs according in our revenue forecasts?
• Are we considering cannibalization between SKUs?
• Are we comfortable with this plan? What is the gap between this plan and the budget?
• What do we need to do to achieve our strategy? (We can adjust projections for a specific client, considering the sales manager’s participation in the meeting.)

One of the outputs of this meeting is a realistic yet challenging demand plan, with alerts for the next stage of the process. This will facilitate the next step in the S&OP cycle, the Supply Review. While uncertainty about the future always exists, we must take action with the best information available and prepare to react to new opportunities or risks. This is why it is essential to discuss the questions above.

Overcoming Bias & Achieving Alignment

I have been in Demand Reviews with biased behavior. We cannot forget that the demand agreed on at the end of the process will be part of the sales goal. Therefore, sales teams may try not to overcommit, thinking that selling more than planned is a good problem to have. At the same time, I have seen Marketing overpromise sales for new launches to ensure sufficient inventory, even at the risk of expiration. To promote cohesion, ensure that all participants share the same overarching goal (profitability for the business). Occasionally, let everyone adopt the CEO perspective. This approach helps identify what is best for the company and reduces siloed thinking.

Conclusion

In conclusion, the Demand Review is a critical step in the S&OP process and its success depend on the quality of the data and information used in preparation. This preparation allows for a decision-making meeting instead of an informational one. The S&OP leader must align the team through commitment, transparency, and trust, creating positive tension that addresses potential risks and opportunities.

This article first appeared in the fall 2024 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. 

]]> https://demand-planning.com/2023/11/20/the-supply-chain-planning-kpis-you-need-to-know/ Mon, 20 Nov 2023 22:14:34 +0000 https://demand-planning.com/?p=10208

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.

Q. What is the best method to forecast in the toy industry ? Or any industry where 70% + of the portfolio is renewed every year across thousand of SKUs ? Aggregation is understood, problems starts when entering SKU territory .

A. There is no special model for the toy industry. There are three types of models: (1) Time Series, (2) Cause-and effect, and (3) Judgmental models. They apply to all industries. We use the model that best captures the data pattern. SKUs are always most difficult to forecast. Usually, we first make a forecast of the category, and then allocate the share of each SKU using the ratio of each SKU to the total based on the rolling average of the last 12 months or so. That is, what the ratio of SKU 1 is of the total category, what the ratio of SKU 2 is of the total category, and so on. By using these ratios, we can make a forecast of each SKU.

I hope this helps.

Happy forecasting!

Dr. Chaman L. Jain

Editor-in-Chief,

Journal of Business Forecasting

Inventory – can’t live with with it, can’t live without it. Let’s talk about how to balance the competing trade-offs of high service levels, the need to control costs, and freeing up cash – and how to make managing your inventory a whole lot easier.

We want to have sufficient inventory on hand to service customers and maximize sales, especially in a make-to-stock environment. Inventory, is of course, necessary. And there are plenty of reasons to hold lots of it.

Why Inventory is Good

By committing to higher levels of inventory we can optimize batch sizing to lower production costs and cost per item. By ordering more stock/materials we can optimize transportation costs. We can get price breaks by ordering higher volumes on a monthly basis vs lower volumes on a weekly basis.

Having inventory on hand limits fines for late delivery in the case of retailers like Walmart and Amazon. And a high level of inventory limits costs for expediting delivery for stock we didn’t have readily available. Having inventory ahead of a sale is cheaper than having to source it last minute.

The flipside is the cost of tying up cash in stock that isn’t selling. Right now we have a perfect storm of rising inflation where inventory/materials are more expensive to source, debt is more expensive to service, and sales are going down. In such an environment your CFO will be on your back to reduce inventory.

Why Inventory is Bad

Beyond the hard cost of dollars being tied up in assets sitting in storage, there is an opportunity cost associated with tying up capital in inventory. With that extra cash your company could shore up the balance sheet, service debt or deploy it for new initiatives. Storage is also a cost, not just in terms of the space but in terms of people and equipment required for warehousing.

Inventory also comes with damage and pilferage. What’s more, companies with short lifecycles face obsolescence, never being able to shift stock for certain items which have to be disposed of (another cost). Insurance is yet another cost, the premium being paid on the total assets your company holds.

So there are advantages to holding inventory and disadvantages to holding inventory. Finding a balance between the two that is right for your company is the holy grail of planning.

Lean Into Your Company’s Priorities

If you’re thinking I want on-time, in-full to be 99%, I want to have 24 turns a year, I want to have less than one week’s worth of inventory in stock, and I want to maximize my margin – wonderful, everybody wants that! One of those objectives, one is going to win, and it is up to you to decide which is most important. What are your company’s objectives? In the Cost-Service-Cash triangle, your company will naturally lean into one dimension more than the others, and it’s up to you make the trade-offs that support enterprise strategy.

Prioritize customer service, and your costs will increase and cash will be tied up. Prioritize cash, and you’ll have to accept that customer service will suffer and costs will increase. Prioritize lower costs, and service will decrease and cash gets tied up. Which dimension you need to prioritize most will inform your safety stock levels.

Stop Self-Inflicted Uncertainty Now!

There are certain things companies do that unintentionally introduce demand uncertainty, making it more difficult to know the required safety stocks. There are certain supply planning actions we can take to make inventory management more effective.

Beware Demand Shaping: IBF research reveals that a 1% reduction in uncertainty equals a 6% reduction in my safety stock. Dynamic pricing and promotions shift demand, causing uncertainty that has makes inventory management more complicated. While promotions may be necessary, there are consequences to adding in that demand variability.

Reduce your lead times: It’s not just about demand forecasting; proper supply management pays dividends when it comes to reducing the cost of holding inventory. On average, every 1% reduction in lead time results in a 0.95% percent reduction of safety stock.

More SKUs equal more inventory: I can’t believe that some people don’t understand that logic. More SKUs serving the same demand adds uncertainty without increasing the top line. A 10% reduction in SKUs represents a  5% reduction in safety stocks.

Lower your service levels for a given customer: Some customers won’t necessarily need the service level you’re providing, meaning that you can afford to carry less inventory. What does customer X really need, and what can you reasonably get away with?

Improve your supply chain planning at IBF’s Supply Chain Planning Boot Camp in Nashville, TN, from August 9-11, 2023. Learn best practices across demand management, supply planning, S&OP, distribution planning, inventory models, and more. Register your place.

The following is the second in a two-part series covering the art and science of  demand planning. Read the first part on the art of planning here.

Why do we Need to Forecast?

Companies need to understand what the future will bring to make the right decisions: What is my optimal footprint? What is the right price based on future expected volume? How much capacity is needed, and which inventory investments will yield the best returns? Using a statistical forecast provides a good starting point to answer all these questions.

However, not everyone has the background to understand the math behind the process, the underlying assumptions used in the forecast, or the best way to integrate this information into demand planning decision making. This is why it is important to simplify the statistical forecasting principles and assumptions at a high level so everyone actively participate in the discussion and help build the demand plan.

The Science Behind Statistical Forecasting

“The only way to predict the future is to understand the present.” – Isaac Asimov

The first question that needs to be answered is: What are you trying to predict? Forecasting order entry or sales will provide different outcomes and maybe both are needed, but for different reasons. My experience is that to drive the supply chain process you are better off using order entry as an input, since sales includes variability related to supply availability. But if there is a conscious thought process behind the decision of what is being used, it should not be a problem. Let’s review a quick example to clarify this situation.

A few years ago, during a demand discussion among colleagues, we were seeing a high level of volatility in a certain SKU. The Demand Planner was not able to explain it well as the item had steady sales in the past. Then the Supply Planning Manager stepped in and mentioned that we had a recent shortage in this product and the peak in sales should be due to a big lot coming in and being shipped out in the same month.

These circumstances lead us to review the process and the data we were using, as we were trying to determine what we could sell if there was not any supply constraint. The best set of data to do this was orders entered with the requested date from the customer. Once we used these figures, the pattern was much smoother as order entry had the information of what the customer needed, and the sales information had the information of when we were able to provide it. These subtle differences in the input of the process provide a very different output.

Now we can move to discuss the process. The intention of this section is to understand from a ten thousand foot view the components of the models and the high-level assumptions behind them. There will be complex calculations involved and we will not get into the details behind the math. After all, the experts can handle this very easily, but it is the understanding of these concepts in general that helps us to drive constructive discussion and decision-making.

There are a lot of different methods that try to predict the future including a simple moving average, exponential smoothing, econometric models, linear programming methods or machine learning algorithms. We will focus on time series – a series of sequential data points ordered by time. The two main methods we will review are exponential smoothing and linear regression because they are the easiest models to understand and the most widely used.

Exponential Smoothing

The first technique is called exponential smoothing. This method uses historical information to decide how much weight to give more recent history, trend or seasonality. These are the three main factors that you want to spend some time reviewing as they will give you some insight into the current demand pattern. The math will tell you how important each of these is, but it is up to people that understand the market to explain why.

This type of modeling is very helpful as it is data driven. I remember a meeting where a person on the marketing team mentioned that a certain product family was seasonal and after reviewing the data, it turned out that this was not statistically true. After digging into the details, it turned out that some products in that category had a strong seasonality component, but at the aggregate level you could not distinguish that pattern.

Another way to predict the future is to tag demand to macroeconomic variables and see if there is some correlation between them. A good indicator used in the plumbing industry is housing starts since it is a leading indicator of economic activity that tells you how many more homes are being built. There are a lot of different economic indicators out there and probably one of them is a good fit for your industry. Finding this correlation would be very useful since there are public forecasts available that can be used to predict the demand of your products.

Underlying Assumptions & Caveats

“History never repeats itself, but it does often rhyme” – Mark Twain.

What happens in an environment where there is volatility, uncertainty and complexity? Well, some time is spent discussing the data but this is usually not enough to understand all the caveats and assumptions.

One of the most important principles in forecasting is the underlying assumption that history will repeat itself and that past information can provide a good representation of what will happen next. The math tries to find certain patterns hiding in the data, like determining if the recent past is more useful or if there is a trend or seasonality involved. Early on in my career, when forecasting a high-volume item, we got a very high forecast for the near future, which did not made sense. It turned out that we had a big promotion in the recent past that was skewing the numbers. Once we took this out, the forecast corrected itself. The lesson we learned was that it is very important to scrub history to find outliers in the data.

Another important assumption is that external factors in the environment will remain constant, allowing the forecast to be developed under similar circumstances every time. Variations in the industry outlook, regulatory changes or economic growth fluctuations provide a challenge to the models described above. An alternate way to incorporate this information into the demand plan is required in these cases. A recent example of this is that after years of economic growth, the economy is stagnating or even shrinking. While this is known and discussed in the news and at the watercooler, there is always a lag from when this starts to happen and when the forecasting model picks it up.

Finally, a few important factors to consider in the assumptions are the horizon that you are using to plan and the level of aggregation. Think about the weather for a moment – usually the forecast for tomorrow is very accurate but looking at any day next month is not worth it. The same is true for any type of industry forecast; the further your look out, the less accurate it becomes.

It is a similar situation for the level of aggregation. It might be easy to predict how much you will sell in dollars for next month but it is harder to determine how many dollars per SKU or per customer you will sell next month. This is important to understand, because I have faced situations where the business asks how much we will sell in five years at SKU level by customer. It does not make sense to do a detailed analysis in this situation.  It is better to provide a directional number that is easy to explain and that supports the business to make the right decisions.

Measuring The Output Of The Process

Some of the most popular phrases in demand planning are “If I could predict the future, I would go to Vegas” or “The forecast is always wrong” and, my favorite one, “My crystal ball is broken”.  It is a fact that no forecast will be exact, but you can get within a decent range and with a good level of confidence. This is why it is good practice to measure the accuracy of your statistical forecast to understand the reasons behind your top misses.

From my perspective, it is important to understand how good the forecast accuracy is in terms of mix and volume. A good way of measuring mix is through MAPE (mean absolute percentage error), which basically tells you by how much you missed the forecast – regardless if you missed up or down – compared to what your actuals were. The advantage of this metric is that it is easy to understand (since it is a percentage) and it does not net out negative and positive values as it uses an absolute error.

The way to measure volume is through understanding if there is a bias at the aggregated level of your forecast. For example, when aggregating all your forecasts at the total dollar level by month, if you find out that the actual sales number has been below the forecast for some time, you might want to understand the reasons behind this. Ideally you want to oscillate between being a little above and then the following period a little below over the time horizon.

Discussing The Forecast During The Demand Planning Meeting

After understanding how a computer (or a Demand Planner) creates a forecast, it should not be a surprise why this needs to be reviewed in a group setting. Even if math is not your strength, a lot of value will come from having a thorough discussion of the historical data, the process used to come up with the numbers, the actual forecast, and metrics.

Below is a checklist of things to review. During this discussion, make sure that you balance time between the items that will move the needle, but covering in enough detail that allows you to understand the current situation.

• Start with the input. Are we using the right dataset and have we looked at history to review and scrub?
• How much importance do we give to more recent history versus the past? Is there a pattern in the data to be discerned, like trend or seasonality?
• Is there an economic indicator that we could peg to a group or family of SKUs that will help us determine what the future holds?
• Is there any external factor that could deter us from using our forecasting methods to determine the future values? Examples of this are changes in price (by us or the competition), changes in the economic or regulatory environment, or even new products that could cannibalize current demand.
• What are the forecast accuracy metrics telling us? Are we better than last month? What are the top offenders and why?

In conclusion, a good statistical forecast provides a good start for your demand planning process and a solid foundation. The data, assumptions and metrics need to be understood and discussed in depth. But keep in mind that this only the beginning of the process. There is an art component of the demand planning process that incorporates changes in the environment, market intelligence and in general a consensus between areas that should be aligned with the company strategy.

It is that time of year when leadership starts talking about strategy. The result will be very nice-looking slides that will be discussed in every town hall for the next month. When you are looking at the presentation, two questions will go through your mind.

First, how relevant are these initiatives to the company? You’ll have your own thoughts the direction the company wants to go in and wonder how serious the enterprise is about it. You’ve seen before how leaderships fails to follow through on their  own guidelines or change direction for some reason or another, making it hard for people on the ground to understand what the priorities are.

The second question that arises is: What’s my role in this in strategy? This is where you try to understand the impact it will have in your area and what will be required of you to support these strategic objectives Translating impactful PowerPoint slides to what we actually do day-to-day is easier said than done.

COVID showed business leaders the importance of Supply Chain Management so there is usually a section that links the overarching business plan to this area in a way that helps everyone involved in the process. As planning professionals, this is where we should focus our attention and seek to understand not what good enterprise strategy looks like but what good supply chain strategy looks like.

The Link Between Business Strategy & Supply Chain

“Plans are worthless, but planning is everything”, said Dwight D. Eisenhower. What we do as Demand Planners will invariably fail to reflect reality perfectly but are nevertheless valuable—indeed critical— to responding to demand and changing marketplace dynamics.

During the strategy ideation stage, the main question that the planning organization needs to answer is how to create value—value for the customer, employees, and even for our suppliers. If this is done successfully, it will set your company apart from the rest. I will not delve into the details of how this is done, rather I will provide an example of how to link this into the value chain.

Supply chain is a pilar that supports some of the core business objectives. How does your company’s strategy allow you to compete in it’s chosen market? And what supply chain model will support it most effectively? Supply chain can create value for our customers either through consistent and reliable delivery, truly short lead times, or great pricing. It is important that we choose the supply chain model that best aligns with the value creation strategy. Some examples of these are if we produce to a forecast, make to stock, only manufacturing when an order is received, or even only start designing the product once the order is confirmed. The following is a real-life example.

Real Life Example of a Planning Model

This happened during my first experience working in supply chain as a Master Scheduler. I worked for a factory that produced brass goods and had a wide assortment of products that kept growing over time. The main idea was to be able to manufacture these products in a reasonable amount of time and with an optimized amount of working capital. So, the model we used was Late Configuration. The company offered a wide variety of finished goods but with a lot of commonalities at the component and subassembly level.

The intent of the Late Configuration model was to wait and produce at the latest point of differentiation possible. To accomplish this, for example, you create buffers before a color change, or before you assemble the product and add a different option like another handle or trim. This allows you to absorb some of the demand variability of the finish goods in a subassembly that is common to several products, thus smoothing some of the variation by netting out puts and takes in the ordering pattern across different SKUs.

At the same time, it lets you reduce lead time as you are not starting productions from scratch and it has an inventory benefit as well, since the valuation of a semi-finished product is less than the finish goods and has a lower storage cost.

Finally, the supply signals were based on a pull system, using Kanban. This meant that if there was no demand, production would not be trigged and inventory would be kept at a component level. Components were acquired based on a forecast due to the long lead times, being sourced from Asia. This meant that if demand dropped after you filled the pipeline of semifinished products, all the excess inventory would be accumulated at the component level, which costs less and is cheaper to store. Obviously, the tradeoff is that you need to flex capacity. Adjusting staffing was the main way to change the output.

Real Strategy Vs Pie in the Sky

At the end, any strategy that you choose will be different depending on how you are creating value for the stakeholders in your business. But there is a sure way to identify a real strategy versus a wish list. Look at the tradeoffs. If you see a statement where the organization wants to provide an elevated level of service with little to no inventory, long lead times from suppliers and at a low cost, then this might be a clue. The classic tradeoff example that comes to mind when discussing this topic is about three attributes in a product or process. You can be fast, good, or cheap—but you can only pick two. This helps clarify supply chain decisions in a quite a straightforward way.

If you consider the Late Configuration example from the previous section, the model helped you reduce inventory, align production to demand, and have a reasonable lead time. But if demand changed a lot, you would have idle resources at the shop floor, creating additional costs or manufacturing variances to the financial plan. Another strategy for the company in question would be to produce all the finished goods assortment per the forecast. This could optimize manufacturing costs, reduce set ups, and slightly reduce lead time but will increase inventory and storage costs due to the complexity in product mix.

Going From Strategy to Execution 

“Culture eats strategy for breakfast”, said Peter Drucker. This highlights that while strategy is critical, it requires buy in and support from the whole organization to bring it to life. Since Management by Objectives was introduced in the 1950’s, the intention of closing the gap between what needs to be done and what is executed has been a very intensive journey. The combination of academic research and practical approaches has yielded a few frameworks that we can use. The main idea behind these concepts is that metrics drive behaviors and these in turn create a culture of execution in the company.

So, the next logical step is to go from top-level guiding principles to long term objectives, zoom into what the annual operating plan will look like and, finally, link this to Key Performance Indicators (KPIs). This is a straightforward process, and there are several methodologies available, like the Hoshin Kanri matrix if you are a fan of the Toyota Production System, or a balanced score card if you prefer classical methods.

The important aspect is to understand which part of the high-level objectives your area will have a real impact on. Then the priority is to cascade the measurements that are important for the organization in general into specific metrics that your department will own and deliver. In my experience, this is a terrific opportunity to spend some time together with your team (offsite to avoid distractions) and talk about how the supply chain organization creates a positive impact in the company and how we can measure it. At the same time, you can combine this with some team building activities to create relationships conducive to the development of a high-performance team.

A widely used method to define and deploy objectives is SMART Goals (Specific, Measurable, Attainable, Relevant and Time-bound). A recent trend, which is now one of my favorites, is FAST Goals (Frequently-discussed, Ambitious, Specific and Transparent), created by Don Sull from MIT. The main components of the former are intensive communication and stretch targets; both are key factors in developing the necessary culture. I will go back to my own experience to explain how this works in practice.

Several years ago, I was hired for a turnaround role as a Supply Chain Manager for a manufacturing site. The challenge was to increase the service level. The metric we had in place was on-time in-full (OTIF). After getting my head round their process, two things became clear. First, the bottleneck was at the finished goods warehouse. Second, Production was focusing on their own efficiency metrics. From an operations perspective, we had to add an additional shift and create Standard Operating Procedures (SOPs) to remove the constraint; this was very straightforward. However, from a scheduling perspective, we had to implement a daily cadence to discuss production deviations from the plan and understand the root causes. At the same time, we published the metric all over the plant, including the cafeteria.

At first, it was hard to stomach lunch while looking at an extremely low fill rate (OTIF). However, it generated a lot of internal discussion and a noticeably clear sense of priority. This created a tense but positive environment that supported the daily scheduling meeting and finally the process enabled the team to change the priority to mix over volume and hitting committed dates versus reducing the amount of set ups at the plant. After a few weeks of this, the metric started taking off and since it was very visible all over the plant, it generated a positive feedback loop that helped gather engagement from everyone and changed a very defeatist environment into one where everyone wanted to participate and contribute.

This allowed us to move the meeting cadence from daily to weekly and it became part of the operational review and culture of the plant. From this example you can see the importance of frequent communication and how a prominent level of transparency around metrics helps link the strategy directly into the culture of the organization.

One last word of caution when deploying FAST Goals: It is extremely critical that when reviewing stretch targets that there is a range in place and that the incentive plans for leaders are set up in tiers, so you can recognize ‘good’ and really reward the achievement of ambitious goals.

The Framework in a Nutshell

• Identify the role that Supply Chain plays in the bigger picture and make sure that the model fits the strategy.
• Call out the tradeoffs in the supply chain strategy to clearly define priorities.
• Cascade the business strategy all the way down to metrics that will define what success looks like. This will generate visibility of the impact that supply chain has in the organization.
• Make sure that there is frequent discussion around the metrics and the current performance of the team. Recognize ‘good’ but really reward excellence.
• Make sure that these processes are incorporated into the culture of your team; this will enable the creation of a high-performance organization.

Next time you are sitting in the strategy town hall, make sure that you apply some of these ideas. This will change your perception of these meetings from pretty slides with all power and no point (pun intended) into meaningful ways to make a difference in your organization.

This article first appeared in the winter 2022 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. 

Some years ago I took up a new role as Director of Demand Planning at a global sporting goods company. I was charged with overhauling its planning function. This is challenging at the best of times, but this was complicated by the company’s unique growth-by-acquisition strategy. The following is a case study of the transformation project I led, covering the problems I inherited, the step-by-step improvements I implemented, and how it was designed to facilitate decisions that directly supported organizational priorities.

Company Background

This Indiana-based company imports and distributes multiple widely-recognized sporting goods and athletics brands globally. They do this through major retailers, specialty dealers, key online retailers, traditional department stores and eCommerce. The company operates primarily in North America with over fifty corporate accounts that includes companies like Walmart, and others. They also sell directly to Amazon and on Amazon marketplace. They launched their own internal website and fulfilment for direct-to-consumer sales last year and it already accounts for over 10% of their business.

Their business model was simple: grow through acquisition. During my time there, they owned forty-seven brands. While there was moderate organic growth within some of their brands, they relied on consolidation of the market to increase market share and top line growth. To do this, cash was King and the availability of capital was a key priority for the organization.

Their Existing Planning Process

Being their focus was on adding to their portfolio of brands, they had inherited a mishmash of various ERP systems and planning processes. For the most part their forecasting process was still somewhat manual, using traditional time-series methods like moving averages and seasonal random walk. They got some inputs from sales reps but their input was typically either about products that customers want in the current month or products that they thought were needed in inventory.

There were often competing objectives across inventory, purchasing, logistics, and manufacturing with attempts to get products at the lowest cost while constant pressures to reduce inventories. These problems were compounded with adding new brands and product mangers attempting to provide value to corporate accounts with unique offerings which added cost and caused SKU proliferation.

The Challenge: A Changing Marketplace

Over the past few years, they have seen a changing landscape in the way consumers are making purchases. This impacts how they needed to go to market. Direct-to-consumer was less than 10% just a few years ago. It now accounts for over 25% of their business. This includes all eCommerce business including Amazon, other retail websites, and the company’s own direct selling. It is estimated to grow by double digits over the next few years.

A major challenge they were facing is that their supply chain was designed around what retail stores were purchasing, i.e. the were planning for bulk orders with 2 week lead times. That is fine for retails order, but not for the increasing amount of direct-to-consumer orders that required single items to be delivered in 48 hours. This necessitated having inventory on hand instead of making to order, which required high quality forecasts.

To add to this challenge there is the issue of retail stores making up less of their total sales volume because now they are increasingly dealing directly with consumers. Given these shifts, their forecasts had gotten worse, as evidenced by a higher error percentage over the past couple of years.

Some challenges we faced were:

• Direct to-consumers expect a 36-hour delivery window. Prior retail customers traditionally allowed up to 2 weeks or more.
• Average lead-times to produce or source items has grown from 46 days on average to over 118 days as more products are now coming from China.
• Forecast accuracy at a weighted mean absolute percentage error (lag 1 WMAPE) with has gone from 68% to 85% due to SKU proliferation and complexity of new channels.
• Previous On Time and in Full (OTIF) was at 89%. It is now 77% due to the added volume of direct-to-consumers.
• Inventory turns have decreased from 4.2 to 3.7 as inventory rises due to SKU proliferation, longer lead-times, and poorer forecast quality.

The Solution: Integrated Planning

The company kicked-off a comprehensive digital transformation project whose goal was to standardize different planning processes to create competitive advantages, while Improving Total Cost and Enabling Inventory Optimization by integrating strategy, forecasts, planning, and perpetual inventory. Over an 18-month time horizon we would totally revamp the planning process, implement new platforms and technology across the entire organization, and introduce SKU rationalization, segmentation, and add predictive analytics—all of which was aligned to the organization’s growth-by-acquisition strategy.

Our initial focus was data and within first few months we went live with a new data warehouse and central data storage repository (DSR), and new business intelligence software (BI). These critical first steps helped the company find hidden issues in their data structure and in the information that was being used to make decisions. It provided visibility into data and was important for insuring they had the right data for planning and to create insights. It also allowed us to look at new attributes using web crawlers that extracted consumer information and other information about the new eCommerce channel that could be used in modeling.

Part of this new visibility included the development of new balanced scorecards and performance metrics to understand the trade-offs of decisions and how they impacted strategy.  We made the KPIs more relevant to what the business wanted to achieve: number of active items, minimum order quantities, and gross margin as return on investment (GMROI). Understanding more of the drivers and being able to see the interdependence of metrics, we could now decide at what cost we were willing to service our customers or not.

We knew the importance of cash to the business model and that the availability of capital was a key objective of the organization. To this point, we determined that it made strategic sense to not aim for higher levels of service at the cost of higher inventories or additional, specialized SKU’s. Further consideration was given regarding the tradeoff inherent with larger orders that have longer lead times, i.e. they save upfront costs but risk tying up cash in inventory if it is not sold quickly.

After the initial focus on data, visibility, and decision making, attention was given to people, process, and technology. By the end of the first full year of the project, we defined and created specialized roles and hired new planners and a data architect to augment the current team, and went live with an advanced planning system (APS). We used clustering methods to help segregate items and customers which allowed us not only to focus planning resources on the most important items, but to do SKU rationalization to eliminate poor performing items. We now had a planner focused on eCommerce and began forecasting weekly using a combination of traditional methods and new models such as decision trees using external data. One example of external data is social media comments about new products which we used to predict through sell through post-launch.

Results

The results came with much coordination, collaboration, challenges, and success. Due to these efforts, this company by the end of the second year saw a 10% improvement in fill rates, a 26% improvement in forecast accuracy, a 19% reduction in some supply chain costs, and an 11% reduction in excess inventory. Add to this real time visibility into data and new insights, they had a much better way to manage their business. Significantly, we saw a return on investment of the entire transformation project in less than 14 months. The company continues to be a leader in their industry and is taking full advantage of the changing consumer landscape.

This article originally appeared in the Winter 2022 issue of the Journal of Business Forecasting. To receive a print copy of the Journal every quarter, become an IBF member or subscribe. 

In the 1990’s when I was with Baxter Healthcare, we implemented a statistical forecasting solution for our European affiliates. In going through the user training I was intrigued by the functionality around aggregate level forecasting and the improved accuracy achieved.

The example they used was a company manufacturing bicycles. Rather than forecast the demand for different colors of a particular model the company would aggregate historical demand at the model level and then run their statistical modelling.

At this aggregate level the forecast accuracy was much better and downstream painting could be driven by Make to Order with much shorter lead times, a reorder point (ROP), or through a disaggregation technique for the higher level forecast.

At the time I was managing the European distribution of sterile surgical gloves and was excited about trying this approach. We had two SKU’s for each of the eight sizes for the five different types of surgical gloves, each with ten languages. We sourced these 80 SKU’s from our Malaysian manufacturing site into our European distribution center in Belgium and then shipped weekly to our twenty affiliates based on their actual inventories, forecast, and safety stock target.

I started by building a pyramid structure in our forecasting tool that allowed me to aggregate historical demand for these 80 SKU’s. I then began forecasting at this level each month and compared to the sum of the affiliate forecasts.

The results were astounding and I was able to demonstrate a greater than 20 point improvement in forecast accuracy using this method. I easily convinced my boss that we should use these forecasts for our manufacturing site in Malaysia.

I then calculated a ROP for each affiliate for each SKU based on historical demand variability and lead times and developed a dBase application to calculate weekly replenishment quantities based on actual inventory and the ROP. Getting commercial buy-in for this approach took more time but we did get agreement.

I also met monthly with our European product manager to ensure that any market intelligence was captured on top of the statistical model. This process worked so well that we were able to tell our affiliates that they no longer needed to spend time forecasting these products. We also well over achieved on our inventory targets.

A few years later I moved to our biotech division. I remember when my boss needed to provide a projection of QIV European sales for a blockbuster hemophilia product and asked me how much I thought we would sell.

I aggregated historical demand at the three dosage form levels, 250 AU (activity unit), 500 AU and 1000 AU lyophilized product in vials. I ran the statistical models and told him 90 million AU’s. Actual QIV sales came in close to 100 million IU’s and my forecast was much better than what he had received from Finance in the affiliates.

Since those days I have been with three different biopharmaceutical companies and have built a large network across the industry. It amazes me that not once have I seen this technique applied to improve forecast accuracy.

For many products the bulk unpackaged tablet, capsule, vial, syringe is the same across many markets and even globally. By aggregating demand at this level and then generating a forecast biopharmaceutical companies would be running their most constrained and expensive manufacturing operations with a much more accurate demand signal.

It goes without saying that this approach would have a profound impact on inventory levels. I am not suggesting that it be applied carte blanche but it should be strongly considered for any product from 3 – 5 years after launch through to late stage lifecycle.

With this approach one could use one of the strategies I mentioned above for downstream packaging and distribution. Make to Order would not work in this industry but reorder point is an option or using a technique to disaggregate the tablet/capsule/vial/syringe forecast down to the country level.