Your Supply Chain Is Probably Lying to You

The most dangerous KPI isn't the one that's wrong. It's the one that's technically correct but tells you the wrong story.

We all love KPIs.

On-time delivery: 96%.
Forecast accuracy: 91%.
Inventory: down 12%.
Manufacturing efficiency: 94%.

Sounds like we're doing a pretty good job, right?

Well… maybe.

One of the things I've learned working with supply chains is that a KPI can be perfectly correct and still give you the wrong answer.

Imagine a company with a 95% On-Time Delivery rate.

At first glance, that's great.

But then you ask a few more questions:

- On time according to the original customer request date, or the date we changed it to?

- How many orders were delivered on time because the customer accepted a later date?

- How many required an expensive expedited shipment to make the deadline?

- And how many orders were already late before someone realized there was a problem?

Suddenly, that 95% doesn't look quite as impressive.

The same happens with lead time.

Suppose we reduce our average end-to-end lead time from 30 days to 25.

Success!

But what if the average improved because a few large orders moved much faster, while smaller orders became significantly more unpredictable?

The average tells us we improved.

The customer experience might tell us something completely different.

And that's the problem with averages and isolated KPIs.

They measure what happened. They don't necessarily explain why it happened.

This is why I think the next step in supply chain performance management isn't simply adding more KPIs.

It's connecting the KPIs and understanding the story behind them.

Instead of asking “What's our lead time?” we should ask “Where is the time actually being spent?”, is it manufacturing? Transportation? Waiting for an approval? Quality release? Planning? Customs? Or simply waiting for someone to make a decision?

And instead of asking “Are we on time?” maybe we should ask “What did it take to be on time?”

Because there's a huge difference between delivering on time through a stable, predictable process and delivering on time after three escalations, an emergency production slot and an expensive air shipment.

Same KPI. Completely different supply chain.

That's why I believe good supply chain analytics should work a bit like detective work.

The KPI tells you where to look, the process tells you what happened and root-cause analysis tells you what needs to change.

So the next time your dashboard is full of green numbers, don't just celebrate.

Ask a few uncomfortable questions, what is this KPI really telling us? What is it hiding? and perhaps most importantly: Are we measuring performance… or just measuring the symptoms?

Because sometimes the biggest supply chain problem isn't a bad KPI, it's a KPI that makes us believe everything is fine.

Master Production Scheduling (MPS): The Heart of Effective Manufacturing Planning

Master Production Schedule (MPS) is often described as the bridge between business strategy and operational execution. While sales forecasts, customer demand, and strategic objectives define what an organization wants to achieve, the MPS translates those goals into a realistic and actionable production plan.

Simply put, the Master Production Schedule specifies what products will be produced, in what quantities, and when. It serves as the primary driver for Material Requirements Planning (MRP) and provides visibility across manufacturing, procurement, inventory management, and customer service functions.

A well-developed MPS helps organizations balance demand and supply while optimizing resources. It enables companies to:

• Meet customer delivery commitments with greater confidence
• Improve inventory management and reduce excess stock
• Enhance production stability and resource utilization
• Identify capacity constraints before they impact operations
• Support cross-functional alignment between sales, operations, and manufacturing

Without a disciplined MPS process, organizations often experience frequent schedule changes, inventory imbalances, expedited orders, and declining customer satisfaction.

Key Inputs to the MPS:

• Demand forecasts
• Customer orders
• Inventory status
• Production capacity
• Business policies and strategic objectives

The scheduler must continuously evaluate these inputs to ensure that production plans remain feasible and responsive to changing market conditions.

An effective Master Production Schedule is:

- Realistic – aligned with available capacity and resources.

- Stable – minimizes unnecessary schedule changes that disrupt operations.

- Responsive – adapts to legitimate shifts in customer demand.

- Visible – provides a clear roadmap for manufacturing, procurement, and distribution teams.

Organizations often use time fences and schedule freezing policies to balance stability with flexibility, protecting near-term production commitments while allowing adjustments further into the planning horizon.

In today's environment of volatile demand, supply chain disruptions, and increasing customer expectations, the Master Production Schedule is more than a planning tool, it is a strategic capability. 

Companies that establish a disciplined MPS process gain better operational control, improved service levels, and stronger financial performance.

Successful production planning is not about creating the perfect forecast; it is about developing a realistic plan that aligns demand, supply, and organizational objectives. The Master Production Schedule remains one of the most powerful tools available to achieve that balance.

 

Master Planning of Resources: The Backbone of Supply Chain Excellence

In modern supply chain management, successful planning depends on aligning business strategy with operational execution. 

The MPR framework helps organizations balance demand, capacity, inventory, and resources while improving customer service and operational efficiency. Each stage builds upon the previous one, creating an integrated planning process that supports better decision-making across the entire supply chain.

1. Business Plan

The process begins with the Business Plan, which defines the organization’s strategic direction. This high-level plan establishes financial objectives, growth targets, market positioning, product strategies, and overall business priorities.

The Business Plan typically covers a long-term horizon of one to five years and serves as the foundation for all operational planning activities. It provides guidance on revenue expectations, investment decisions, expansion opportunities, and resource allocation.

2. Sales & Operations Planning (S&OP)

Sales & Operations Planning translates strategic business objectives into an achievable operational plan. At this stage, cross-functional teams from sales, operations, finance, procurement, and supply chain collaborate to balance market demand with supply capabilities.

S&OP creates alignment between customer expectations and operational capacity. Organizations review forecasts, inventory levels, production constraints, and financial targets to develop a consensus plan that supports both profitability and customer service.

3. Demand Management

Demand Management focuses on understanding, forecasting, and managing customer demand. This step combines forecasting techniques, market intelligence, customer orders, promotional plans, and historical sales data to generate accurate demand projections.

Effective demand management reduces uncertainty and improves responsiveness across the supply chain. Companies that maintain accurate demand visibility are better positioned to optimize inventory, improve service levels, and minimize operational disruptions.

4. Master Production Scheduling (MPS)

The Master Production Schedule converts demand plans into a detailed production timetable. The MPS determines what products will be produced, in what quantities, and when production will occur.

This stage acts as the critical link between customer demand and manufacturing execution. A well-structured MPS ensures production stability while maintaining flexibility to respond to changing customer requirements.

5. Rough-Cut Capacity Planning (RCCP)

Once the Master Production Schedule is developed, Rough-Cut Capacity Planning evaluates whether sufficient capacity exists to support the production plan. RCCP focuses on critical resources such as labor, machinery, production lines, and key work centers.

The objective is to identify potential bottlenecks before detailed planning begins. If capacity constraints are detected, planners can adjust schedules, increase resources, or revise production priorities.

6. Material Requirements Planning (MRP)

Material Requirements Planning calculates the materials, components, and raw materials needed to support the production schedule. MRP systems analyze bills of materials, inventory balances, lead times, and planned production orders to determine procurement and manufacturing requirements.

MRP plays a central role in ensuring materials are available when needed while minimizing excess inventory and carrying costs. It also improves supplier coordination and purchasing efficiency.

7. Capacity Requirements Planning (CRP)

Capacity Requirements Planning expands on RCCP by performing a more detailed analysis of production capacity at the operational level. CRP evaluates workload requirements for specific work centers, machines, and labor resources.

This step helps organizations validate whether production schedules are realistic and achievable. Detailed capacity analysis supports improved scheduling accuracy, resource utilization, and operational efficiency.

8. Production Activity Control (PAC)

Production Activity Control represents the execution phase of planning hierarchy. PAC manages the release, scheduling, monitoring, and control of production orders on the shop floor.

At this stage, organizations track actual production performance against planned schedules, manage work-in-process inventory, resolve operational issues, and ensure timely order completion. Effective PAC improves production visibility, reduces delays, and supports continuous operational improvement.

Balancing OEE with Other Supply Chain KPIs: Navigating the Tradeoffs

In the world of operations, few metrics get as much attention as Overall Equipment Effectiveness (OEE). OEE measures how effectively a manufacturing asset is utilized by combining three factors: availability (uptime), performance (speed vs. ideal cycle time), and quality (good units produced vs. total units).

In simple terms, it’s a snapshot of how close a machine or line is to running at its theoretical maximum potential. Improving OEE is often seen as a direct path to better productivity and lower costs, but like many metrics, focusing on it in isolation can create conflicts with other critical supply chain goals.

For instance, pushing OEE higher often means striving for longer production runs and fewer changeovers. That’s good for machine efficiency, but it can hurt inventory turns and customer responsiveness. A plant that maximizes OEE by producing large batches of a single SKU may end up tying up working capital in excess stock and reducing the ability to adapt to shifting demand. Similarly, prioritizing OEE can clash with on-time delivery if equipment schedules are optimized for efficiency rather than customer requirements.

Another tradeoff emerges with flexibility and innovation. To keep OEE high, operations teams may resist frequent product launches or engineering changes, both of which introduce downtime, lower yields, and slower cycle times. Yet in today’s market, agility and product variety often matter just as much as asset utilization.

So how do you balance these competing priorities? The key is to treat OEE not as an end in itself, but as one piece of a broader performance puzzle. A mature operations strategy aligns OEE with business objectives by:

• Defining the right horizon: Short-term dips in OEE may be acceptable if they support long-term goals like faster customer response or product diversification.
• Using tiered KPIs: Pair OEE with customer-facing measures such as fill rate, lead time, and service level, ensuring that equipment efficiency doesn’t come at the expense of market performance.
• Driving continuous improvement, not perfection: The pursuit of 100% OEE is unrealistic. Instead, focus on targeted improvements that also strengthen supply chain resilience.

In the end, OEE is a powerful tool for uncovering hidden losses and improving operations, but it should never overshadow the broader mission: delivering the right product, at the right time, at the right cost.

Balancing OEE with other KPIs ensures that efficiency gains translate into true supply chain value.

7 Characteristics of the best Demand Planners

Without a shadow of a doubt, this is one of the best articles I have read recently, and it encompasses all the key traits that make a great Demand Planner.

Difficult to pick up just a few key points, as all of them are too valuable to be left out, but here I go:

- The difference between success and failure may not be dependent on intellect or even analytical ability, but on leadership skills.

- They are not afraid of voicing their opinions and lead from the front, bringing solutions, not problems.

- They will work on being able to express ideas or information clearly and if they do this while seeking to understand the other person’s needs and concerns.

- They follow less their gut instinct and try to find a quantitative basis for an idea.

- They demonstrate an interest in personal learning and development, seek feedback from multiple sources about how to improve and develop.

-  They are being able to communicate risk and uncertainty.

- They are creative in their approaches and are not afraid to try something new. They are not locked in on the way we used to do things, they do not work in a silo, they collaborate with others and finally, they are very customer-centric, focusing on adding value both internally and externally.

- They have the ability to stand strong and be wrong with confidence.  They are not afraid to take chances and learn from their setbacks and failed attempts.

http://demand-planning.com/2019/02/26/7-characteristics-of-demand-planning-rock-stars/

Forecast bias

In this post we are going to touch on one of the most important KPIs that any demand planner should focus on: Forecast bias

Forecast bias is the general tendency for a forecast to be higher or lower than the actual value.

Forecast bias is distinct from forecast error in that a forecast can have any level of error and still be completely unbiased. For instance, even if a forecast is fifteen percent higher than the actual value half of the time and fifteen percent lower than the actual value the other half of the time it has no bias. If the forecast is on average fifteen percent higher than the actual value has both fifteen percent error and fifteen percent bias.

Bias can exist in statistical forecasting or in judgment methods. With statistical methods, the forecasting model must be adjusted or switched to a different model. For judgment methods however, bias can be conscious and driven by certain incentives provided to the forecaster or it can be unconscious.

How to simply calculate forecast bias at an aggregated level?

BIAS = Historical Forecast Units minus Actual Demand Units.

The impact of bias can mean that either an organization is holding too much inventory (over-forecast bias) or missing sales due to service issues (under-forecast bias).

Supply Chain fun Friday!

It's Friday! Some "Supply Chain fun"

Enjoy!