White Paper

Demand Driven Material Requirements Planning (DDMRP)

&
Simio Adaptive Process Digital Twins

2023
DDMRP & Simio Adaptive Process Digital Twins

The Demand Driven Institute is the global authority on Demand Driven methodology, education, training,
certification, and compliance. With affiliates, compliant software alliances and instructors throughout the
world we are changing the way businesses plan, operate, think, and evolve. Every business has a choice
in the volatile, uncertain, complex and ambiguous (VUCA) world; continue to operate with rules, metrics
and tools developed more than fifty years ago or make a break from convention, recognize the complex
supply chains we live in and make a fundamental change in the way it does business. Learn more about
our mission at www.demanddriveninstitute.com.

A pioneer in simulation and trusted by thousands of users over the past 15 years, Simio is committed to
continuous innovation and delivering solutions designed to meet the challenging requirements of complex
supply chains and manufacturing environments. Simio’s state-of-the-art simulation-based digital twin
technology is architected to use a single detailed constraint model of your process to provide
comprehensive support for DDMRP, detailed scheduling, and process optimization. One of the key
advantages of a digital twin implementation powered by Simio is the tight integration with other enterprise
systems such as ERPs, MESs, planning applications, IoT devices, and structured data sources
(databases, data warehouses & data lakes). This enables the Simio digital twin model to be generated
and driven through current (near real-time) data – making it extremely accurate and effortless to maintain
when changes occur in your environment. To learn more about Simio’s digital twin for supply chain
planning, manufacturing scheduling, and process optimization, visit us at www.simio.com.

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Table of Contents

1. INTRODUCTION ................................................................................................................... 4
2. WHAT IS DEMAND DRIVEN MATERIAL REQUIREMENTS PLANNING (DDMRP)? ....................... 7

2.1 Component 1: Strategic Decoupling Points ............................................................. 9

2.1.1 Decoupling Implications for Planning Systems...................................................... 10

2.1.2 Placing Decoupling Points ..................................................................................... 12

2.2 Component 2: Buffer Profiles and Levels .............................................................. 14

2.3 Component 3: Dynamic Buffer Adjustments.......................................................... 16

2.3.1 Recalculated Adjustments ..................................................................................... 16

2.3.2 Planned Adjustments............................................................................................. 17

2.4 Component 4: DDMRP Planning ........................................................................... 18

2.5 Component 5: DDMRP Execution ......................................................................... 20
2.6 Component 6: DDMRP Model Adaptation ............................................................. 21

2.6.1 Tactical Review ..................................................................................................... 22

2.6.2 Tactical Projection ................................................................................................. 22

2.7 Summary ............................................................................................................... 23

3. SIMIO FOR THE DESIGN AND IMPLEMENTATION OF DDMRP ................................................ 24

3.1 DDMRP Functionality in Simio .............................................................................. 24

3.2 DDMRP Simulation Workflow ................................................................................ 26
3.3 Typical Results from DDMRP Digital Twin Models................................................ 31

3.3.1 Buffer Zones .......................................................................................................... 31

3.3.2 Resource Utilization............................................................................................... 34
3.3.3 Warehouse Capacity and Inventory Cost .............................................................. 35
3.3.4 Performance Metrics ............................................................................................. 37

4. CONCLUSION .................................................................................................................... 38

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1. Introduction
Just a few years ago it may have seemed unbelievable, but with our rapidly changing world the
unbelievable has become reality – we are now in a state of VUCA. VUCA stands for volatile,
uncertain, complex, and ambiguous. Most people will probably agree that VUCA is not
desirable, and it is also not something many companies had planned for – especially related to
the design and operation of their supply chains. Companies that rely on complex supply chains
to control the flow of products from manufacturing through distribution to customers are
witnessing VUCA wreak havoc on the operational efficiency and effectiveness of their supply
chains. VUCA is a concept highlighted by Carol Ptak and Chad Smith in their book “Adaptive
Sales and Operations Planning” where they described the world we live in today as follows:

Volatile: We see that both the frequency and the magnitude of disruptions to supply chains are
increasing. Recent examples include the COVID-19 pandemic, war in Ukraine, severe weather
phenomena, etc.

Uncertain: It is becoming increasingly difficult to predict what will happen. The more detailed
and remote in time our predictions and plans are, the more we can expect variances to be
experienced. This is true despite massive amounts of investments and advancements in
forecasting technology.

Complex: Supply chains are not and never have been linear systems. Most supply chains have
more relationships, interdependencies, and nodes than ever before in history, leading to more
complications. The net effect of these complications is that delays in supply chain flow
accumulate and propagate while gains do not. There is now more than ever a growing
disconnect between customer expectations and the reality of fulfilling those expectations reliably
and consistently.

Ambiguous: This is distinct and different from trying to predict what will happen. In this context,
ambiguous is referring to the fact that it is often very difficult to tell what is happening at any
point in time. Organizations are literally drowning in oceans of data but at the same time are
starved for truly relevant information. Worse yet, what organizational personnel may believe is
relevant information often leads them to make decisions and take actions that harm long-term
sustainability and resilience in the VUCA world.

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Now more than ever, business is a bewildering and distracting combination of products,
services, materials, technologies, machines, and people skills. A successful supply chain
requires the orchestration, coordination, and synchronization of each of these elements
operating independently and cohesively together. Choosing the right strategy to achieve
success is always complicated, VUCA makes it even harder. So how do you identify the right
strategy to use in the design of your complex supply chain? We believe the answer to that is
built on the concept of flow. We define the three elements of flow to be:

• The flow of materials and/or services from suppliers, then through one or multiple
manufacturing plants, and then through delivery channels to customers
• The flow of information to all parties about what is planned and required, what is
happening, what has happened, and what should happen next
• The flow of cash back from the market to and through the suppliers

Very simply put, a business process must take materials, convert or assemble them into
different things or offerings, then get those new things or offerings to a point where others are
willing to pay for them. The faster the business can make, move, and deliver its products and
offerings, the better the performance of the organization tends to be. This incredibly simple
concept is best described in what is known as Plossl’s law:

“All benefits will be directly related to the speed of flow of information and materials”

To this end Ptak and Smith, the founders of the Demand Driven Institute, developed an
innovative methodology called Demand Driven Material Requirements Planning (DDMRP).
DDMRP is designed to manage the flow of material and information more effectively in a visual
and practical way to support this new world of larger product variety, smaller orders with shorter
lead times, and rapidly changing demand topped by constantly varying supply chain conditions.

DDMRP is a powerful methodology for managing flow, but the key to an effective
implementation starts with detailed insight into how your supply chain operates. Before investing
time, money, and energy into implementing this transformative supply chain methodology, there
are two topics to consider:

• Connections. Does your organization understand the connections between departments,

functions, resources, and people both at the local and global levels of the company?

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• Variability. Does your organization understand the different forms of variability that affect
the flow through the enterprise?

Having people in the organization who understand these key topics is very important to the
success of a DDMRP implementation – equally so is the technology involved in the
implementation. Leveraging the right technology can have a significant impact on the time and
effort involved in implementing DDMRP. The right technology will also have a significant impact
on the long-term success of using DDMRP.

Adaptive Process Digital Twins are a widely used technology in manufacturing, warehousing,
and supply chain applications. As the name implies, Adaptive Process Digital Twins mirror real-
life complex business operations and processes with powerful built-in functionality to manage
orchestration, coordination, and synchronization with a very high degree of accuracy and
precision. This makes Adaptative Process Digital Twin technology ideally suited for DDMRP.

Simio is a pioneer in the development of discrete event simulation. Discrete event simulation is
a versatile technology with a broad range of applications. Some examples include:

• Simulation and analysis

• Process design and optimization
• Planning and scheduling
• Shopfloor orchestration
• Design to operate process management
• Adaptive Process Digital Twins

At Simio, we have leveraged our discrete event simulation technology to create an agile
platform for developing Adaptive Process Digital Twins that can be used to facilitate
comprehensive digital transformation and process re-engineering initiatives. A Simio Adaptive
Process Digital Twin is a data generated and driven, object oriented, 3D simulation model that
accurately replicates the physical behavior of complex processes when run on Simio’s discrete
event simulation platform. Some key capabilities associated with a Simio Adaptive Process
Digital Twin allow an organization to:

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• Capture the current process, including all physical constraints, business rules, and
detailed decision logic into a 3D simulation-based digital twin to serve as a process and
operational knowledge base and reference model.
• Create a process performance benchmark to understand current performance and
accurately predict future performance of the factory/supply chain in addition to validating
any changes such as automation, new equipment, and replenishment policies such as
DDMRP.
• Create feasible plans and schedules for shop floors, warehouses, factories & supply
chains that support autonomous execution and fully configurable time ranges.
• Leverage the adaptive, data generated and driven process digital twin to maintain a
“current status” reference model for determining future performance of the factory and
supply chain for transformation projects and investment decisions.
• Establish a predictive role to provide relevant and timely information about expected
future behavior and results, or in a prescriptive role to provide detailed task lists and
material requirements for execution.

Recognizing that an Adaptive Process Digital Twin is an ideal technology to take the time-tested
DDMRP methodology to the next level, Simio and the Demand Driven Institute began a
collaboration that has led to groundbreaking advancements in managing material flow.

This white paper will distill down the vast quantity of DDMRP content available online, in
textbooks, and through training courses into a succinct foundation of knowledge. We will then
build on that knowledge to explain how bringing DDMRP together with a Simio Adaptive
Process Digital Twin has resulted in a new and exciting development in the supply chain
industry – and an ideal solution for the design, test, optimization, and execution of a Demand
Driven Material Requirements Planning process.

2. What is Demand Driven Material Requirements Planning (DDMRP)?

In 2011, an evolved form of material requirements planning (MRP) called demand driven
material requirements planning (DDMRP) was introduced to the world in the book Orlicky’s
Material Requirements Planning 3E (Ptak and Smith, McGraw Hill). DDMRP is a formal multi-
echelon planning and execution method to protect and promote the flow of relevant information
and materials in volatile, complex, uncertain, and ambiguous (VUCA) supply chain
environments.

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The evolution of MRP rules and tools was born out of necessity. It is important to note that
conventional MRP rules were codified by 1965. However, as changes in the world and supply
chains occurred, MRP rules and their basic assumptions did not. Every conventional MRP
schedule output consists of the following assumptions:

• Demand is known and will not change

• There is enough time to accomplish all required activities
• All components will be available at the planned time (no variability in execution)

Over time these assumptions have become increasingly less valid as large amounts of supply,
demand, and internal variability have proliferated in the evolving supply chain environment –
some imposed and some self-imposed. The pace of change continues to accelerate and over
the last few years we have witnessed world events impacting MRP on a massive scale.
Examples of these include expanding cumulative lead times, shrinking customer tolerance
times, expanding product portfolios, shortening lifecycles, rising forecast errors, ballooning
inventories, increasing frequency of expedites, and constantly changing shortage reports that
have become a standard part of everyday life. Companies have attempted to expand their use
of spreadsheet-based tools and analyses in an effort to keep up with all this change, but that
path is error prone, labor intensive and highly inconsistent.

DDMRP was developed through two decades of research and application across a wide array of
industrial segments to address the evolving supply chain landscape and the challenges of using
conventional MRP in that landscape. DDMRP combines relevant aspects of MRP and
distribution requirements planning (DRP) with the pull and visibility emphasis found in Lean and
the Theory of Constraints, along with the variability reduction emphasis of Six Sigma. These
elements are successfully blended through key points of innovation.

For experienced planning practitioners, DDMRP is not about forgetting or abandoning what they
know. Instead, DDMRP is about building on top of that foundation through the incorporation of
known and accepted principles to meet the circumstances of modern supply chains. DDMRP
has three basic assumptions inherent in its configuration and operation:

• Outside of explicit sales orders, demand is generally not known and is subject to change
• The gap between cumulative lead times and customer tolerance times forces stock to be
held at key points to compress response times

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• There will be variability in execution

DDMRP is a proven approach to producing significant gains across a wide array of industries.
Table 1 shows the summary of research conducted by Camelot Management Consultants
related to the benefits of DDMRP for three key performance indicators: service level increases,
lead time reductions and inventory reductions across four major industrial verticals.

Table 1: DDMRP improvement across various industries

There are six components involved with DDMRP. A brief description of each will be provided in
this section. For a more complete description of the DDMRP methodology including links to
textbooks, videos, and case studies readers should visit:
https://www.demanddriveninstitute.com/ddmrp

2.1 Component 1: Strategic Decoupling Points

First and foremost, DDMRP recognizes that the extensive sources of variability experienced in
supply chains cannot be eliminated. Instead, DDMRP seeks to mitigate the effect of the
transference, amplification, and accumulation of variability in both up and downstream directions
on the most important objective of a supply chain – the maintenance and protection of flow of
product to consumers. The benefits of this maintenance and protection of flow are numerous.
When supply chains flow well, service levels are bolstered, inventories are minimized, costly
expedites and overtime are minimized, and cash follows the rate of product to the market. All of
this means that the maintenance and protection of flow directly connects to better return on
asset performance.

To foster this flow, DDMRP places decoupling points at strategic locations of the product
structure or supply network. A decoupling point is like a planning and execution firewall or

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bulkhead, isolating the events on one side of the point from the other. It is a fundamental break
from the conventional MRP calculations which, by design, tightly couple all dependencies.

2.1.1 Decoupling Implications for Planning Systems

The use of decoupling has no impact on the three required inputs for MRP. A product structure
and its attributes are still required. Inventory policies and status are still required. And finally,
there must also be a source of demand. With decoupling in place, however, the way these
inputs are considered and processed will be altered.

Netting in a Decoupled Environment

With decoupling employed, a critical change to the basic netting logic must occur. Decoupling
points should never be netted to zero. Netting a decoupling point to zero means that it ceases to
be a decoupling point. Thus, a level of decoupling point inventory must be maintained to
guarantee a decoupling point’s effectiveness. This means that an effective process or method to
set and adjust this level is required as well as amended netting logic to determine requirements
against the position. This will be discussed in more detail later in this section.

Lead Time Compression

Decoupling point buffer placement has huge implications for planning lead times. By decoupling
supplying lead times from that certain point in a supply chain, lead times are instantly
compressed to the customer. This lead time compression has immediate service and inventory
implications. Market opportunities can be exploited while working capital required in the higher-
level inventory positions in the product structure (end item or closer to complete) can be
minimized.

Shorter Independent Planning Horizons

Decoupling point placement creates a series of shorter independent or decoupled planning
horizons. The length of these planning horizons is determined by the longest time sequence
between the decoupling points. This span of time is called decoupled lead time, which will be
discussed in more detail in the next section.

More Accurate Demand Signals

With the compression of the planning horizon comes an increase in the accuracy of the demand
signal that can be used to drive supply order generation. When high-level decoupling points are
placed inside customer tolerance time or the order visibility horizon, it will allow for the system to

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exclusively use actual demand, the most accurate and relevant form of demand available, for
planning. This means that there are no planned orders derived from forecast and no
nervousness associated with their reconciliation in the close-in horizon.

Decoupled Lead Time

Decoupling point placement has significant implications for the lead times recognized by the
planning system for the purposes of calculating planned order receipts and releases as well as
the necessary decoupling point protection level. The use of decoupling points in a planning
system led to a key innovation, a new form of lead time called decoupled lead time.
Decoupled lead time (DLT) is the longest unprotected sequence in a bill of material or
distribution network. DLT is essentially a qualified cumulative lead time for a decoupled item.
This sequence is determined solely by decoupling point placement. It is the time between either
two decoupling points, the last decoupling point and a customer or the first decoupling point and
a supplier. Decoupled lead times play a key role in the following:

• Compressing response times to market required ranges

• Determining realistic due dates when needed
• Setting decoupling point buffer levels properly, which will be explained later in this
section
• Finding high value inventory leverage points for decoupling point placement

Figure 1 shows a conceptual summary of the previous impacts of decoupling points to a supply
chain.

Figure 1: The impact of decoupling points

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The “Decoupled Explosion”

In DDMRP, blending the use of decoupling points while still managing and accounting for
dependencies is accomplished by using a decoupled explosion. A decoupled explosion is the
cessation of a dependent requirements explosion at any decoupling point. The term itself seems
to be an oxymoron since it literally means independent dependence. Yet that is exactly what is
occurring with a decoupled explosion. Figure 2 illustrates the nature of the use of decoupled
explosion in a DDMRP environment. The decoupling points are represented by the large “X”s in
the graphic.

Figure 2: Illustration of decouple explosion

When a supply order is generated at a higher level, decoupling stops the explosion of the bill of
material at decoupling points placed at lower levels. The explosion can be stopped without risk
because that decoupling point is carefully maintained with decoupling inventory. The explosion
then restarts only when the decoupled position (through an independent calculation) determines
that it needs a resupply. This independent calculation is called the “net flow equation” which will
be covered in more detail later in this section.

2.1.2 Placing Decoupling Points

But the immediate question arises, where to decouple? Decoupling everywhere is an extreme
position; one that would create a floodplain of inventory investment across an environment.
Thus, the answer can only be to decouple in places that have the most value in protecting and
promoting flow. There are six factors considered in combination to determine decoupling point
placement.

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Customer Tolerance Time (CTT)

This is the time the typical customer is willing to wait before seeking an alternative source.
Customer tolerance time can also be referred to as demand lead time. Determining this lead
time often takes the active involvement of sales and customer service. What do customers
expect? What lead times do competitors offer? How good are the competitors at hitting those
lead times?

Market Potential Lead Time

This lead time will allow an increase in price or the capture of additional business through either
existing or new customer channels. Determining this lead time takes the active involvement of
sales and customer service. Be aware that there could be different stratifications of market
potential lead time. Properly segmenting the market will maximize the possible revenue
potential for the company and provide excellent revenue growth control.

Order Visibility Horizon

This is the timeframe in which a company or position typically becomes aware of sales orders or
actual dependent demand. In retail the order visibility horizon is essentially zero, however, in
most manufacturing scenarios, there are sales orders conveyed in advance of expecting receipt
of the item. Often the sales order visibility either matches or exceeds customer tolerance time.
The farther out the visibility to sales orders, the better the capability of the environment to see
potential spikes and derive relevant demand signal information.

Imposed Variability
There are two forms of variability to be considered on potential decoupling point positions:

Demand Variability
This refers to the potential for swings and spikes in demand that could overwhelm the
decoupled position. This variability can be calculated by a variety of equations or determined
heuristically by experienced planning personnel. If data required for mathematical calculations
does not exist, companies can also use the following criteria to group items into categories:

• High-demand variability – Products and parts that are subject to frequent spikes
• Medium-demand variability – Products and parts that are subject to occasional spikes
• Low-demand variability – Products and parts that have little to no spike activity, demand
is stable

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These groupings can help us to understand where to place decoupling points. In addition to
decoupling point determination, these groupings will also be used in the next component of
DDMRP – determining the level of protective inventory employed.

Supply Variability
This is the potential for and severity of disruptions in sources of supply or specific suppliers.
This can also be referred to as supply continuity variability. It can be calculated by examining
the variance of promise/schedules dates versus actual receipt dates. If the data required for
mathematical calculations does not exist, the following heuristics can be used to group items
into categories:

• High supply variability – Frequent supply disruptions

• Medium supply variability – Occasional supply disruptions
• Low supply variability – Reliable supply

As with demand variability, the groupings will help us to understand where to place decoupling
points but will also be used in determining the level of protection.

Inventory Leverage and Flexibility

There are places in the matrix bill of material or the distribution network that provide a company
with the most available options as well as the best lead time compression to meet the business
needs. Within manufacturing, these places are typically represented by key purchased
materials, subassemblies, and intermediate components. This becomes more critical in
environments with BOMs that are deeper and more complex (broader) and have more shared
components and materials.

Critical Operation Protection

The longer and more complex the routing structure and dependent chain of events (including
interplant transfers), the more important it can be to protect identified key areas of capacity.
These types of operations include areas where there is limited capacity, where quality can be
compromised by disruptions, or where variability tends to be accumulated or amplified.

2.2 Component 2: Buffer Profiles and Levels

For the decoupling points to maintain their decoupling integrity, there must be a level of
protection that absorbs demand and supply variability at the same time. This protection is
manifested by a concept called decoupling inventory. In DDMRP, decoupling point inventory is

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referred to as “stock buffers”. Decoupling point buffers are amounts of inventory that will provide
reliable availability for stock consumption while at the same time allow for the aggregation of
demand orders, creating a more stable and efficient supply signal to suppliers of that buffered
position.

Figure 3: Each buffer zone purpose and equation

Decoupling point buffers are sized through a pragmatic, proven, and transparent process. They
incorporate a three-color zonal schema (Green, Yellow, Red). Each zone serves a specific
purpose in the way it protects and helps to manage the decoupling point. Figure 3 shows both
the purpose of each buffer zone and its respective equation.

The zonal values and total buffer value are determined through a combination of two elements.
First, part positions chosen for decoupling are sorted or grouped by like attributes called buffer
profiles. These attributes are:

• The type of part (manufactured, intermediate, purchased, or distributed)

• Lead time category (long, medium, short)
• Variability category (high, medium, low)

Next, the individual part traits are combined with these attributes to create unique zonal and
cumulative buffer values. These individual part attributes are:

• Average daily usage (ADU) – The ADU can be determined through a variety of ways
including historical usage, forecasted usage or some blend of both over a defined
horizon
• The item’s decoupled lead time (DLT)
• Minimum order quantities or cycles if applicable (MOQ or MOC)

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Figure 4 shows the output of a typical DDMRP buffer sizing equation combining part and buffer
profile attributes.

Figure 4: Example of a buffer sizing equation

2.3 Component 3: Dynamic Buffer Adjustments

Since today’s supply chains are incredibly dynamic, the initially determined buffer and zone
levels must adjust and adapt to changing conditions. There are two basic forms of buffer
adjustments; those that are implemented because of what has happened and those that are
implemented based on what we think will or plan to make happen.

2.3.1 Recalculated Adjustments

A recalculated adjustment is typically an automated adjustment to a buffer’s zonal values.

These changes can be driven by an individual part’s attributes changing, a change in its profile
assignment or a change in the profile value that the part is assigned to. Figure 5 is an example
of a decoupled part’s buffer adjusting through time.

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Figure 5: A buffer’s recalculated adjustment through time

2.3.2 Planned Adjustments

Buffers can also be manipulated through planned adjustments. Planned adjustments are based
on certain strategic, historical, or business intelligence factors as identified in tactical planning.
These planned adjustments are manipulations to the buffer equation that affect target inventory
planning positions by raising or lowering buffer levels and their corresponding zones at certain
points in time. These manipulations tend to be confined to demand manipulations, zonal
manipulations or lead time manipulations. Figure 6 shows a planned adjustment shaping a
buffer’s capability to meet an impending demand surge.

Figure 6: Planned adjustment applied to a buffer

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2.4 Component 4: DDMRP Planning

In DDMRP, stock buffers use a unique equation called the net flow equation to drive
replenishment supply order release. The demand element of the net flow equation has no
forecasted orders; only qualified sales orders or higher-level dependent demand within the
immediate time horizon are considered.

The net flow equation is simple:

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 + 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 – 𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄𝑄 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 = 𝑁𝑁𝑁𝑁𝑁𝑁 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃

Figure 7 illustrates the three components of the net flow equation. Two components of the
equation will be familiar with most supply chain personnel. On-Hand is the quantity of stock
physically available. On-Order is the quantity of stock that has been ordered but not received.
This could be a single incoming order or several incoming orders. The on-order quantity is the
total quantity that has been ordered but not received, irrespective of timing.

Figure 7: DDMRP’s net flow equation

It is the demand component that gives the equation its uniqueness. Qualified Sales Order
Demand is the sum of sales orders past due, due today and qualified spikes. Order spikes are
qualified through the combined use of an order spike horizon in future daily buckets (typically
equal to one DLT in the future) with a defined order spike threshold (typically set at 50% of the
of the red zone value). In Figure 7 the cross-hatched area represents both the value of the
horizon and the threshold.

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Figure 7 shows qualified orders for the net flow equation as highlighted “Today” and in “Day 3”.
There is no past due amount represented in this figure. There are two sales orders due today
and three sales orders due Day 3. These Day 3 orders have combined to create a qualified
spike. These two days of qualified demand are added together to get the total amount of
qualified demand for today’s computation of the net flow equation.

Figure 8: The net flow equation

When the net flow equation yields a position below the green zone in the buffer, an order
recommendation is created in a quantity sufficient to restore the net flow position to the top of
the green zone. The net flow equation is performed every day for every decoupled item. Figure
8 shows how the net flow equation for the given example produces both a net flow position as
well as a recommended order quantity of 21 units.

A key aspect of using the net flow equation is the visibility that planning personnel gain to
relative requirements priority. This relative priority distinction is a crucial differentiator between
the conventional MRP planning alerts and action messages and the highly visible and focused
DDMRP approach. Under the DDMRP approach, planners and buyers can quickly judge the
relative priority without massive amounts of additional analysis and data queries. This is
accomplished using a concept called planning priority. Planning priority incorporates two
aspects: color and a percentage value. The color is established by identifying the zone color of
the current net flow position. The percentage is computed by dividing the net flow position by
the top of green value of the buffer. Figure 9 shows an example of a DDMRP planning screen
sorted by planning priority value.

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Figure 9: Sort by planning priority

2.5 Component 5: DDMRP Execution

DDMRP carefully separates the process of planning buffer resupply (as defined by the net flow
equation) and the process of monitoring the status of a buffer’s current integrity as defined by its
on-hand amount. In conventional MRP, priority is always tied to due date – due dates that are
subject to constant date and quantity shifting. DDMRP all but disregards due dates for buffer
resupply in favor of a prioritization mechanism called on-hand priority.

Like planning priority, the on-hand priority is both a color and a percentage value. It is calculated
by dividing the current on-hand level of the buffer by the value of the red zone. This produces a
percentage of safety remaining at any point in time. At 100% or above the color is green. 99%-
50% produces a yellow color. Below 50% produces a red color. This general color and discrete
percentage reference allows personnel to focus expedite efforts and scarce resource activity on
the most critical items as defined by the integrity of the strategic decoupling points. Figure 10
shows an example of an on-hand prioritization display.

Figure 10: On-Hand status example

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There are additional DDMRP execution alerts but space limitations in this section prevent the
ability to explain those alerts in more detail. These alerts include:

• Projected on-hand status – projects the on-hand status in the future based on the timing
of open supply and known or average demand
• Material synchronization alerts – alerts personnel to potential misalignments to supply
and known future demand allocations
• Lead time alerts – a proactive alert designed to ensure the timely supply of strategic long
lead time components to critical scheduled receipts

2.6 Component 6: DDMRP Model Adaptation

The final component of maintaining an effective DDMRP system is the process of adapting the
DDMRP model through time. This adaptation cycle is driven both by historical performance as
well as anticipated events or future trends. The process of this tactical adaptation is referred to
as Demand Driven Sales and Operations Planning (DDS&OP).

A key differentiation in DDMRP and the DDS&OP process is that there is no master production
schedule (MPS) in use. This is a huge departure from convention, one that many operational
personnel find refreshing. Why is there no MPS in DDMRP? The conventional use of an MPS
was a response to the problematic conditions described in the beginning of this section,
particularly the widening gap between cumulative lead times and customer tolerance times. As
mentioned previously, the use of decoupling points compresses planning horizons to a point
where more stable and actual demand can be used to drive supply order generation. This opens
the door to planning and managing model capability instead of attempting to plan order and
resource activities over an extended future period.

DDS&OP’s purpose is to plan and manage future capability as defined by the DDMRP model.
That model is defined by decoupling point placement, buffer profile definitions, individual part
traits and any buffer adjustments. This collective assembly of model determinants is referred to
as the master settings. DDS&OP refines these master settings by reviewing past DDMRP
performance (called tactical review) and projecting future performance based on what is
expected to happen (tactical projection).

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2.6.1 Tactical Review

The first element of DDS&OP looks at the past performance of the DDMRP model regarding its
reliability, stability, and velocity; the three primary operational metric objectives of DDMRP.
Reviewing the past performance of these objectives identifies areas or processes in the
DDMRP model that jeopardize performance, cause additional spend or present improvement
opportunities for model refinement.

Key past reports include the supply order integrity report and buffer run charts. The supply order
integrity report shows the timeliness and accuracy of supply order generation against the
performed daily net flow equation. Buffer run charts show the ability of a buffer to maintain its
on-hand performance against a targeted range over time. Figure 11 provides an example of
both types of tactical review reporting.

Figure 11: Examples of order integrity report and buffer run chart

2.6.2 Tactical Projection

DDS&OP is not just about reviewing the past performance of the DDMRP model. The DDS&OP
process also projects model performance given different scenarios within the tactical horizon
(typically one cumulative lead time into the future). To develop these projections, the DDS&OP
team must have an awareness of current and potential problems regarding demand, capacity,
supply disruptions, quality/yield problems and anomalous sales activity. Examples might
include:

• Significant seasonality
• Promotions or an expected surge in demand
• A planned shutdown at the plant
• A known supply disruption

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• The cutover to a new manufacturing process

• The impending opening of a new distribution facility
• The introduction of a new product

Figure 12 shows an example of a buffer projection with an expected large upsurge in demand
being countered by a planned adjustment to the buffer.

Figure 12: Buffer projection example

2.7 Summary
DDMRP can best be summarized as “Position, Protect, Pull and Adapt”. The migration to
DDMRP is not a wholesale abandonment of conventional planning rules. Instead, it employs
innovations that recognize changing supply chain circumstances and takes advantage of
advancements in certain aspects of technology to bring manufacturing and distribution assets in
closer alignment with actual demand.

Figure 13: DDMRP Basic Methodology

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Figure 13 displays all the components of DDMRP and their collective relationships. It is
important to note the feedback loop from component 6 (Adapt) back to components 1, 2 and 3.
This depicts what is referred to as the tactical adaptive loop in the Demand Driven methodology.

3. Simio for the Design and Implementation of DDMRP

Simio has created an agile platform for developing Adaptive Process Digital Twins that can be
deployed offline and/or online for both predictive and prescriptive applications to facilitate
comprehensive digital transformation and process re-engineering initiatives. A Simio Adaptive
Process Digital Twin is a data generated and driven, object oriented, 3D simulation model that
accurately replicates the physical behavior of complex processes when run on Simio’s discrete
event simulation platform. The Simio Adaptive Process Digital Twin captures the current
process in detail including all physical constraints, business rules, and detailed decision logic to
serve as a process and operational knowledge base and reference model that can be used to
evaluate all future changes and system performance. An Adaptive Process Digital Twin
powered by Simio’s discrete event simulation platform is an ideal solution for the design, test,
optimization, and execution of DDMRP as a replenishment methodology to manage material
flow that includes order generation for procurement, manufacturing orders, and stock transfers.

3.1 DDMRP Functionality in Simio

The Simio platform includes features and functions specifically developed to support the
accurate modeling of any DDMRP replenishment options as part of a factory or larger supply
chain. DDMRP specific functionality includes the DDMRP replenishment method as part of the
Simio inventory element, DDMRP specific calculators with associated data table
schema/templates for inputs and outputs, and DDMRP specific dashboards.

Features related to the creation, configuration, and running of a supply chain model using a
DDMRP project template are shown in Figure 14.

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Figure 14: Features for DDMRP Modeling

First, in the top left of the figure, the DDMRP project template includes a predefined set of
relational data tables for simulation input. Simio offers several different types of data connectors
to import data into these tables from external data sources such as databases, URLs, Excel
workbooks, and CSV files.

Second, in the top center of the figure, the DDMRP project template includes a predefined set of
customizable objects for supply chain modeling. This includes objects for modeling the physical
sites in the supply chain network such as retail, distribution, manufacturing, and supplier sites,
as well as objects for modeling transportation modes to deliver supply orders.

Finally, in the top right of the figure, the DDMRP project template includes tools for calculating
key inputs for DDMRP to size a model’s strategic inventory buffers and generate supply orders

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during simulation runs. These key inputs include average daily usage values, decoupled lead
times, buffer red, yellow, and green zone sizes, and qualified spike demand values for net flow
position calculations.

Features related to running a supply chain simulation are shown in the bottom half of Figure 14.
First, in the bottom left of the figure, a Demand-Driven MRP replenishment policy is used at
each strategic inventory buffer during a simulation run to determine when to generate a supply
order and the recommended order quantity.

Second, the bottom center of the figure shows the ‘Digital Twin’ which refers to the collection of
detailed warehouse, factory, supplier, and delivery-related objects as well as sourcing policies
and any other decision logic necessary for simulating the order fulfillment process.

Finally, in the bottom right of the figure, potential output from a supply chain model is shown.
Scenario lookaheads provide valuable insight for decision-making, and the simulation results
include not only supply order forecasts and the expected schedules for production and delivery,
but also KPIs, target risk analysis, and other feedback measuring the potential impact of
variability in the system.

3.2 DDMRP Simulation Workflow

The DDMRP workflow can be summarized as 4 primary steps in the decision making and
execution process of the model. Figure 15 depicts the flow of information and material in the
supply chain through a circular arrow diagram. The process starts with supply order generation
using the Demand-Driven MRP process. Once the re-supply quantities have been determined
the Sourcing policies and options need to be evaluated to best fulfill the supply order. Based on
the type of supply order (procurement, manufacturing, or stock transfer), the Demand-Driven
Scheduling & Execution process is triggered to fulfill the orders. This is then followed by the
Delivery process to complete the fulfilment process. Each of the steps in Figure 15 are
described in more detail in this section.

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Figure 15: DDMRP Model Decision Workflow

Demand-Driven MRP (Supply Order Generation) Process

The first step of the DDMRP model decision workflow is to perform supply order generation
using the DDMRP replenishment methodology, which is shown in Figure 16. The inventory
position of each strategic inventory buffer in the model (calculated as quantity in stock plus
quantity on order minus quantity backordered) is monitored and updated during a simulation
run. The other key inputs for DDMRP are defined in the Buffer Zone Sizes and Qualified Spike
Demand tables that are calculated using Simio’s DDMRP calculators, which are shown in the
top left corner of the figure.

Continuous or periodic inventory reviews (e.g., daily reviews) are then performed using the
DDMRP replenishment policy (as described in Section 2).

At the time of each inventory review in the simulation, the DDMRP replenishment policy uses
the inventory’s net flow position (calculated as the inventory position minus the qualified spike
demand) and green zone to determine when to reorder and for how much. If the net flow
position is at or below the top of the yellow zone, then a reorder quantity is recommended to
return the net flow position to the top of the green zone.

As the Simio Adaptive Process Digital Twin simulation runs, it produces the recommended order
quantities for each SKU or SKU-Group that must be sourced and fulfilled to maintain proper flow
of material in the supply chain.

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Figure 16: Demand-Driven MRP (Supply Order Generation) Process

Sourcing Process
The recommended order quantity information then flows to the inventory’s sourcing policy which
determines whether the supply order is a manufacturing, purchase, or stock transfer order and
the site (or sites) to send that order to. Supplier-dependent order modifiers can be applied to
enforce minimum, maximum, or fixed order size requirements.

The focus of this step in the Simio simulation-based planning cycle is the sourcing decision. It is
important to emphasize a significant difference between the decision-making and information
flow shown in Figure 17 and a conventional master production schedule (MPS) approach. MPS
simplifies the planning problem by assigning work into time buckets at each factory, often based
on faulty assumptions about demand, lead times, and material and resource availability. Faulty
assumptions will produce unrealistic plans. During a supply chain simulation with Simio, instead
of static planning based on time-buckets, the sourcing decision for a supply order is made at the
time the order is generated by DDMRP. This creates the opportunity to apply both demand-
driven replenishment and dynamic sourcing strategies based on the current system state.

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DDMRP & Simio Adaptive Process Digital Twins

Accurate lead times are crucial to supply chain performance as they are key inputs for inventory
buffer sizing and other decision making such as sourcing. Rather than assuming static lead
times, the Simio platform can utilize the power of artificial intelligence (AI) to set lead times
dynamically to reflect current system state. One example of that would be to set manufacturing
lead times based on a factory’s current workload and product mix. Simio has native AI support
for neural network models created and trained directly within Simio as well as support for
bidirectional integration with other third-party AI applications.

Figure 17: Sourcing Process

Demand-Driven Scheduling and Execution Process

Once the sourcing decision has been made, a supply order is sent to the selected site model.
That site model is shown on the right side of Figure 18. This is the warehouse, factory, or
supplier part of the model that captures the detailed resource, business rules, and detail
decision logic, including any site-specific scheduling constraints as well as any other general
decision logic necessary to simulate the order fulfillment process.

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DDMRP & Simio Adaptive Process Digital Twins

This is a key part of the workflow to ensure the fulfilment plans and schedules are feasible
based on raw material supply, manufactured goods as well as any stock transfer orders from
stocking locations in the network. The schedules are not only resource and material feasible
within a time window (1 shift) but also feasible on the actual execution event timeline within
each shift.

Figure 18: Demand-Driven Scheduling and Execution Process

Delivery Process
When a supply order is ready to ship, a delivery model is then used to deliver the material to the
destination inventory site. This is shown in the bottom center of Figure 19. That delivery model
may simply be a delay time or a more complex model capturing details such as transportation
modes and capacity. The model may also include any shipment consolidation policies and
delivery routes. The level of detail to be included is important to ensure execution feasibility to
closely match planning to actual execution.

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DDMRP & Simio Adaptive Process Digital Twins

Figure 19: Delivery Process

3.3 Typical Results from DDMRP Digital Twin Models

As part of the DDMRP methodology implementation within the Simio platform, there are
customized results and dashboard views to measure performance and understand system
behavior. A selection of key results will be shown and discussed in more detail in the next
section. Simio focused on making the planning and execution of the material and information
flow efficient and understandable. Simio charts and tables employ the red, yellow, and green
DDMRP methodology paradigm to make results clear and easy to interpret by all stakeholders
for both decision making and execution management.

3.3.1 Buffer Zones

The red, yellow, and green buffer charts are probably the most used feature of Simio’s DDMRP
implementation as they provide the buffer status of all material at each decoupling point in
various formats. Following are three examples – buffer zones for planning, buffer status for
execution tracking, and buffer run charts for performance monitoring (both past and future).

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Figure 20 uses a static view of buffer zone and buffer status charts to explain the differences
between the two charts.

Figure 20: Buffer Planning and Execution Charts

Working within the Simio platform, these charts are created dynamically and with great accuracy
by replicating the behavior of the factory and/or supply chain using simulation. The Simio
Adaptive Process Digital Twin simulation will predict the future performance of inventory buffers
and create execution tasks at all levels of the process in a prescriptive manner (if required).
Figure 21 shows an example of a buffer zone chart for planning and Figure 22 shows the buffer
status chart for execution tracking.

The buffer run chart tracks the actual ‘on hand’ inventory against the target on hand and the
calculated optimal inventory range to protect and sustain flow with sufficient buffer based on the
average daily usage, MOQ, lead time, and variability factors of the process. Without a Simio
Adaptive Process Digital Twin, these charts are typically created looking at the actuals of the
past period. Using a Simio Adaptive Process Digital Twin simulation, it is possible to accurately
project forward in time to provide timely input about the expected behavior of the factory or
supply chain. An example buffer run chart is shown in Figure 23.

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Figure 21: Buffer Zone Chart for Planning

Figure 22: Buffer Status Chart for Execution Tracking

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Figure 10: Buffer Zone Chart for Planning

Figure 23: Buffer Run Chart for Performance Tracking and Prediction

3.3.2 Resource Utilization

For the system to be able to respond to new or changing demand, it is important to understand
the capacity constraints in the system. Unlike a cost-based system where nearly 100%
utilization is required to reduce unit cost, a flow-based system having available capacity allows
the system to respond to variability to maintain flow. As the utilization moves into the red zone,
the system’s ability to respond becomes more constrained. When utilization is in the green
zone, the system has sufficient capacity to respond to change. This ensures the right items are
produced or transferred to support actual demand, which leads to converting goods to cash in
the most efficient way while minimizing inventory. An example of a resource utilization chart is
shown in Figure 24.

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Figure 24: Resource Utilization Chart

3.3.3 Warehouse Capacity and Inventory Cost

In most supply chains warehouse capacity is at a premium and acquiring additional capacity is
very costly. By running a Simio Adaptive Process Digital Twin simulation, warehouse capacity
requirements can be predicted accurately over any range of time. This forward visibility will
provide the lead time needed to avert or address potentially costly problems and maintain
operations while minimizing infrastructure cost. The Simio Adaptive Process Digital Twin also
keeps track of all inventory and associated costs which can include detailed individual resource
costs as part of operations. This allows the business to make clear decisions based on both
warehouse capacity (Figure 25) and the associated cost of inventory (Figure 26).

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Figure 25: Warehouse Capacity Chart

Figure 26: Inventory Cost by Site

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3.3.4 Performance Metrics

Two of the key performance metrics for supply chain performance are the Inventory Target
Ratio (ITR) (Figure 27) as well as the Taguchi Capability Index (Cpm) (Figure 28).

The ITR is a method for calculating inventory values against the nominal point and the optimal
range as seen in the run charts. ITR is a quick way to judge conformance to the planned
inventory levels as per the operating model definitions.

Using the Taguchi Capability Index is an approach for evaluating the level of inventory
compliance against the total model for selected global considerations. This approach is a
function of the specification limits, the mean of the process, and a provided target.

Both these metrics are calculated during the Simio Adaptive Process Digital Twin simulation run
and provide valuable forward-looking predictive indicators of performance as shown below.

Figure 27: Inventory Target Ratio

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Figure 28: Taguchi Capability Index

4. Conclusion
Before companies invest significant amounts of money, time, and energy into new hardware
processes and systems, understanding the current supply chain behavior and key constraints
ensures that organizations can optimize their use of time and deployment of CAPEX. The Simio
Adaptive Process Digital Twin is a key success component for supply chain design and planning
going into the future. It provides both a system-wide aggregated view of the state of the system,
as well as a means of predicting forward in time (crystal ball approach) to see the expected
future state as illustrated in Figure 29 below. Currently companies mostly rely on analysis of
historical data and performance (rearview mirror approach) to help them decide what to adjust
and do differently to improve the process going forward. When attempting to implement a new
and innovative material management methodology such as DDMRP the ability to optimize the
master setting of the demand driven operating model (DDOM) before putting it into actual
operation is invaluable as it will prevent costly mistakes and avoid experimentation on the actual
factory/supply chain.

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Figure 29 – Backward and Forward-looking analysis

Simio differs from traditional simulation modeling tools in that it is designed from the ground up
to also execute as a live component of a factory or supply chain execution system. Simio
supports complex in-memory relational data, connections to real time data sources, complex
dynamic decision rules, detailed resource, material, and task logging, along with customizable
Gantts, reports, and use case specific dashboards for communicating results. This is essential
functionality for providing a connected Adaptive Process Digital Twin of the factory/supply chain.
Figure 30 depicts the relationship of Simio’s digital twin model to the ERP and MES/IoT, along
with the key capabilities that are enabled in the Adaptive Process Digital Twin.

Figure 30 – Simio Adaptive Process Digital Twin Relationships and Capabilities

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