Payaro supply chain ddmrp neoma jun 2020

Supply Chain & DDMRP
Andrea Payaro, Ph.D.

Andrea Payaro.
Logistician of the year 2019
• Degree in Management Information System
• PhD in Business Management
• Post Doc in Business Management
• Management consultant - ELA Certification
• CEO P&P Consulting & Service
• Lecturer at Italian Trade Agency – Ministry of Foreign
Affairs
• Lecturer at International Business Schools
• Vice President of SCM-Academy – Turin
• Scientific Committee Member International Congress of
Contemporary Marketing Issues (ICCMI)
• More than 15 International Scientific Presentations and 8
International Scientific Papers
Payaro Andrea - ISC Academy - June 2020

Agenda
• From logistics to supply chain
• Main trends in the supply chains
• The 3C model
• Forrester (bullwhip) effect
• Vertical and horizontal integration
• Push Vs Pull
• Models of integration to improve forecast
• Bill of Material
• Material Requirement Planning
• The execution of MRP
• Decoupling point
• Demand Driven MRP
• Strategic inventory positioning
• Strategic buffers
• DDMRP planning and execution
• Metrics and analytics

From logistics to supply chain
• The terms logistics and supply chain management are sometimes used
interchangeably. Some say there is no difference between the two terms, that
supply chain management is the “new” logistics.
• Supply chain management is an overarching concept that links together multiple
processes to achieve competitive advantage, while logistics refers to the
movement, storage, and flow of goods, services and information within the
overall supply chain.

Logistics issues

SC Trends
• Global sourcing and demand
• Shorter product life cycles
• Shorter customer tolerance times
• More product complexity and/or customization
• Pressure for leaner inventories
• Inaccurate forecasts
• More product variety
• Long lead time parts/components

Global sourcing and demand
Tanned
leather
Carf
Seats
production
Customer
Car Manufacturer

Global sourcing and demand

Shorter product life cycles
Volkswagen Golf – analysis of generations
The product lifecycle is that period of time covered by
the introduction or acquisition, use and eventual
disposal of a product (Day 1981).
The evolution of customer requirements for vehicle
Day, G. (1981). The product lifecycle: Analysis and applications issues. Journal of Marketing. Autumn 1981 Vol.45 No.4 p. 60-67.
Volpato G. & Stocchetti A. (2008). Managing product life-cycle in the auto industry: evaluating carmakers effectiveness. MPRA, June
2008. 1-15. Retrieved from: http://mpra.ub.uni-muenchen.de/29381/ ,

Shorter customer tolerance times
Source: 2019, https://www.hotjar.com/customer-experience/trends-and-stats/

More product complexity - customization
Source: Sabadka D., Molnár V., Fedorko G., (2019), Shortening of Life Cycle and Complexity Impact on the Automotive Industry, TEM Journal. Volume
8, Issue 4, Pages 1295-1301

Product variety
Source: Aichner, T. and Coletti, P. - Aichner, T. and Coletti, P. (2013) Customers' online shopping preferences in mass customization. Journal of Direct, Data
and Digital Marketing Practice 15(1), 20-35.

Long lead time parts/components
• The priority ranking of the ‘Service’ dimension is third in the ranking. Service
quality of suppliers is important to evaluate during the production process by the
manufacturing department. ‘Accuracy of product and quantity delivered’ turned
out to be the most important.
Source: Keith R., (2013), A Biological Paradigm for Supply Chain Management, Industry Week

Decreasing accuracy forecasting
• Benefits of Accurate Volume Forecasts
• Companies that are best at demand forecasting average;
• – 15% less inventory
• – 17% higher On Time In Full delivery performance
• – 1/10 the stockouts of their peers
• + 5% increase in forecast accuracy increases delivery performance to customer
request date by 2%
• + 3% increase in forecast accuracy increases profit margin by 2%
Source: AMR Research, 2008

Key elements of supply chain
• Supply chains can be defined as long, complex and interwoven sequences of
order-connected firms (see Kotzab & Otto, 2004).
• The management of such chains is concerned with the identification of an
optimal strategy for the complete chain which induces an integration of business
processes amongst a number of companies (Ryu et al., 2013).
• This integration is based on different levels of interaction of the involved firms
ranging from
• harmonizing or synchronising activities (= coordination)
• to working together as equal partners (= cooperation)
• and even acting as one single entity (= collaboration).
Source: Kotzab, H., & Otto, A. (2004). General process‐oriented management principles to manage supply chains: theoretical identification and discussion.
Business Process Management Journal, 10(3), 336-349
Ryu, K., Moon, I., Oh, S., & Jung, M. (2013). A fractal echelon approach for inventory management in supply chain networks. International Journal of
Production Economics, 143(2), 316-326.

CCC model
Source: Son, B.-G. (2004). Managing a successful supply chain partnership (PhD thesis). City University London, London

Bullwhip effect
• The bullwhip effect is a major problem in today’s supply chains
• Lee et al.(1997) define it as the observed propensity for material orders to be
more variable than demand signals and for this variability to increase the further
upstream a company is in a supply chain. Main causes:
• No visibility of end demand
• Multiple forecasting
• Long lead time
• High order cost
• Price fluctuation
• Ignorance of supply chain conditions

Counter measures
• Access sell through or POS data
• Lead time reduction
• Computer orders
• Everyday low pricing. It refers to a pricing strategy in which a seller offers its
customers consistently low prices on every product, without running sales or
price promotions.
• Shared capacity and supply information - Integration

Integration benefits
• The availability of shorter lead time, access to POS data and supply information is
possible when the chain is integrated.
• Integration can be vertical or horizontal

Vertical integration example
• One example of vertical integration is the fast fashion
• Fast fashion is an approach to the design, creation, and marketing of clothing
fashions that emphasizes making fashion trends quickly and cheaply available to
consumers.
• Brand name organizations, such as Zara and H&M, are implementing a vertical
integration (VI) supply chain model. Their hope is to ‘enable faster decision-
making’, while also improving the overall customer experience.
• This model also benefits companies in the following ways:
• Control of value chain
• Reduction of distribution costs
• Increases ‘access to more production inputs, distribution resources and process and retail
channels’
• Alignment of pace with changing fashion trends

Horizontal integration example
• Badge engineering, sometimes called rebadging, is the practice of applying a
different badge or trademark (brand, logo or manufacturer's
name/make/marque) to an existing product (e.g., an automobile) and
subsequently marketing the variant as a distinct product
• Due to the high cost of designing and engineering a new model or establishing a
brand (which may take many years to gain acceptance), economies of scale make
it less expensive to rebadge a product once or multiple times than to create
different models.

Push/Pull View of Supply Chains
Procurement,
Manufacturing and
Replenishment cycles
Customer Order
Cycle
Customer
Order Arrives
PUSH PROCESSES PULL PROCESSES

Push Vs Pull
PUSH PULL
Forecast Demand
Production planning Production planning
Material requirement planning Material requirement planning
Manufacturing processes Manufacturing processes
Storage Storage
Demand Delivery
A pull production system is one that explicitly limits the amount of work in process that can
be in the system. [...] a push production system is one that has no explicit limit on the
amount of work in process that can be in the system. (Hopp and Spearman, 2004)
Hopp W., Spearman M.L., (2004), To Pull or Not to Pull: What Is the Question?, Manufacturing & Service Operations Management 6(2):133-148

Push vs. Pull
 Push:
 Maximizing capacity utilization
 Using forecast / approximation
 Top-down scheduling
 Large batches
 High WIP / inventory level
 Poor communication
 Pull:
 Producing only what is needed
 Following the real need / precision
 Upstream pulls from downstream
 Small batches
 Optimized stock level (shop-stock)
 Visual communication (signals)
• Make all we can.
• Inventory is helpful!
• Make only what’ needed
and when needed.
• Inventory is costly!

 Production Approximation
 Anticipated Usage's
 Large Lots & High Inventories
 Waste is everywhere
 Management by Firefighting
 Poor Communication
 Make all we can just in case.  Make what’s needed and
when we need it.
 Production Precision
 Actual Consumption
 Small Lots & Low Inventories
 Waste is reduced
 Management by Sight
 Improved Communication
PARTS PARTS
PARTS
PARTS
PARTS
PARTS PARTS
PARTS PARTS
PARTS PARTS
PARTS PARTS

Push - Pull synergy
Yang, B., Burns, N.D., Backhouse, C.J. ,(2004), Postponement: a review and an integrated framework. Int.J.Oper.Prod.Manag.24(5), pp. 468–487.
P
U
R
E
P
U
S
H
P
U
R
E
P
U
L
L

Decoupling Point
Naylor, J.B., Naim, M.M. and Berry, D. (1999), “Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain”,
International Journal of Production Economics, Vol. 62, pp. 107-18.Payaro Andrea - ISC Academy - June 2020

Decoupling point
• The decoupling point (DP) is defined as: the point in the product axis to which the
customer’s order penetrates. It is where order driven and forecast driven
activities meet. As a rule, the decoupling point coincides with an important stock
point – in control terms a main stock point – from which the customer has to be
supplied (Mason- Jones and Towill, 1999, p. 16).
• In postponement final customization is delayed until customer orders are
received. The application of postponement in business dates back to 1920 which
was used by companies such as Benetton and Hewlett Packard (Boone et al.,
2007).
Boone, C.A., Craighead, C.W. and Hanna, J.B. (2007), “Postponement: an evolving supply chain concept”,
International Journal of Physical Distribution & Logistics Management, Vol. 37 No. 8, pp. 594-611.
Mason-Jones, R. and Towill, D.R. (1999), “Using information decoupling point to improve supply chain
performance”, The International Journal of Logistics Management, Vol. 10 No. 2, pp. 13-26.

Where order driven and forecast driven activities meet
• Forecast driven activities
• MRP schedules and tracks every production or purchasing order (Orlicky, 1975). It
works with the assumption that every order is potentially unique. It does
scheduling using a set of five key inputs:
Purchasing data
BOM
Lead times
Inventory data
Master
production schedule
Material requirement
planning programs
(computer and software)
WHEN
WHAT
HOW MANY
Orlicky, J., 1975. Material Requirements Planning. McGraw Hill, New York.

MRP
• MRP is a dependent demand technique that uses
• Bill-of-Material (BOM)
• On-hand inventory data
• Expected receipts (outstanding purchase orders)
• Master Production Schedule (MPS)
• Lead Time information
• to determine material requirements.
• Dependent demand:
• Demand for materials which are derived from the
build-plan of finished goods. Example: Wagon
handle, body & wheels
• Independent demand:
• Demand for the finished goods we sell to
customers. Example: Wagon Model#12
Independent demand: Red Wagon Model #12
Dependent demand:
The parts needed to make the wagon
• Handle – 1
• Body – 1
• Wheels – 4 Payaro Andrea - ISC Academy - June 2020

Planning process

Planning process 1
Change
production
plan?Master production
schedule
Management
Return on
investment
Capital
Engineering
Design
completion
Aggregate
production
plan
Procurement
Supplier
performance
Human resources
Manpower
planning
Production
Capacity
Inventory
Marketing
Customer
demand
Finance
Cash flow

Planning process 2
Is capacity
plan being
met?
Is execution
meeting the
plan?
Change
master
production
schedule?
Change
capacity?
Change
requirements?
No
Execute
material plans
Execute capacity
plans
Yes
Realistic?
Capacity
requirements plan
Material
requirements plan
Master production
schedule

Aggregate Production and MPS (Master Production
Schedule)
• The aggregate plan provides the general range of operation, the
master scheduler must specify exactly what is to be produced
• The master schedule deals with end items and is a major input
to the MRP process
• MPS tells MRP what to schedule, how many, and when they are
needed
• It is time-phased requirement system
• Usually end items and special order components

MPS
• Master production schedule (MPS): the time-phased plan specifying how many
and when the firm plans to build each end item

Time Fences
• Purpose:
• To maintain reasonably controlled flow through the production system.
• What they are:
• Periods of time within which the customer can make changes to the order
• Types
• Frozen: anything from no changes to only minor changes
• Moderately firm: allow changes so long as parts are available
• Flexible: allow almost any variations

Inventory Records File
• The inventory records file provides an accounting of how much inventory is
already on hand or on order, and thus should be subtracted from the material
requirements.
• Each inventory item carried as a separate file
• Status according to “time buckets” for all items
• On-hand quantities
• Scheduled receipt of order
• Lead times for all orders
• Lot size requirements
• Pegging
• Identify each parent item that created demand

BOM
• A bill of materials (BOM) is a list of all components and subassemblies needed to
make a final product or finished good. The list contains the description, the
quantity, and unit of measure.
• The end- result of a production BOM can be one of the following:
• Subassembly (used in another item)
• Finished good (an item ready for sale)

BOM Example
B(2) Std. 12” Speaker kit C(3)
Std. 12” Speaker kit w/
amp-booster1
E(2)E(2) F(2)
Packing box and
installation kit of wire,
bolts, and screws
Std. 12” Speaker
booster assembly
2
D(2)
12” Speaker
D(2)
12” Speaker
G(1)
Amp-booster
3
Product structure for “Awesome” (A)
A
Level
0

• Order: 50 pieces of A
B(2) Std. 12” Speaker kit C(3)
Std. 12” Speaker kit w/
amp-booster
E(2)E(2) F(2)
Packing box and
installation kit of wire,
bolts, and screws
Std. 12” Speaker
booster assembly
Part B: 2 x number of As = (2)(50) = 100
Part C: 3 x number of As = (3)(50) = 150
Part D: 2 x number of Bs
+ 2 x number of Fs = (2)(100) + (2)(300) = 800
Part E: 2 x number of Bs
+ 2 x number of Cs = (2)(100) + (2)(150) = 500
Part F: 2 x number of Cs = (2)(150) = 300
Part G: 1 x number of Fs = (1)(300) = 300

• Gross Requirements
• Requirements (demand) as taken from the MPS
• Scheduled receipts
• When new or outstanding orders are expected in
• Projected available balance (On-Hand)
• Available physical inventory
• Net requirements
• Requirements (demand) after available inventories and expected scheduled receipts are
consumed
• Planned order release
• When to place orders so they come in when needed

Orders
• Lot-For-Lot (LFL)
• Sets planned orders to exactly match the net requirements
• Produces exactly what is needed each week with none carried over into future periods
• Minimal Order Quantity (MOQ)
• A minimum order quantity (MOQ) is the lowest set amount of stock that a supplier is willing
to sell. If you can't purchase the MOQ of a specific product, then the supplier won't sell it to
you
• Economic Order Quantity (EOQ)

Economic Order Quantity
• Economic order quantity (EOQ) is the order quantity of inventory that minimizes
the total cost of inventory management. It is a measurement used in the field of
Operations, Logistics, and Supply Management. By using this model, the
companies can minimize the costs associated with the ordering and inventory
holding.

Economic Order Quantity weakness
• Without following assumptions, the EOQ model cannot work to its optimal potential.
a) The cost of the ordering remains constant.
b) The demand rate for the year is known and evenly spread throughout the year.
c) The lead time is not fluctuating (lead time is the latency time it takes a process to
initiate and complete).
d) No cash or settlement discounts are available, and the purchase price is constant for
every item.
e) There is no delay in the replenishment of the stock, and the order is delivered in the
quantity that was demanded, i.e. in the whole batch.
f) That replenishment is made instantaneously, i.e. the whole batch is delivered at once.

MRP Example
Lot
Size
Lead
Time
On
Hand
Safety
Stock
Order
quant
ity
Paren
t
Quant
ity
Low-
Level
Code
Item
Identi
ficatio
n
WEEK 1 2 3 4 5 6 7 8
LFL 1 10 - 1 0 A Gross Requirements 50
Scheduled Receipts
Projected On Hand 10 10 10 10 10 10 10 10
Net Requirements 40
Planned Order Receipts 40
Planned Order Releases 40
LFL 2 15 A 2 1 B Gross Requirements 80
Scheduled Receipts
Projected On Hand 15 15 15 15 15 15 15
Net Requirements 65
LFL 1 20 A 3 1 C Gross Requirements 120
Scheduled Receipts
Projected On Hand 20 20 20 20 20 20 20
Net Requirements 100
Planned Order Releases 100Payaro Andrea - ISC Academy - June 2020

MRP Example
Lot
Size
Lead
Time
On
Hand
Safety
Stock
Order
quant
ity
Paren
t
Quant
ity
Low-
Level
Code
Item
Identi
ficatio
n
WEEK 1 2 3 4 5 6 7 8
LFL 2 10 B,C 2+2 2 E Gross Requirements 130 200
Scheduled Receipts
Projected On Hand 10 10 10 10 10
Net Requirements
Planned Order Receipts 120 200
Planned Order Releases 120 200
LFL 3 5 C 2 2 F Gross Requirements 200
Scheduled Receipts
Projected On Hand 5 5 5 5 5 5
MOQ 1 10 50 B 2 3 D Gross Requirements 390 130
Scheduled Receipts
Projected On Hand 10 10 10 0 0
Net Requirements 380 130
Planned Order Receipts 400 150
Planned Order Releases 400 150
MOQ 2 0 100 F 1 3 G Gross Requirements 195
Scheduled Receipts
Projected On Hand 0 0 0
Planned Order Releases 200Payaro Andrea - ISC Academy - June 2020

Lead time
• We need 7 weeks to
sattisfy the order
| | | | | | | |
1 2 3 4 5 6 7 8
Time in weeks
F
2 weeks
3 weeks
1 week
A
2 weeks
1 week
D
E
2 weeks
D
G
1 week
1 week
2 weeks to
produce
B
C
E
Start production of D
Must have D and E
completed here so
production can begin
on B
Figure 14.4

Conventional Inventory Management Effects
We know there are two universal
points with regard to inventory.
Between these points there is
an optimal range to maintain.
Too MuchToo Little
A B
0
Warning Optimal Range Warning

Conventional Inventory Management Effects
Most companies exhibit a “bi-modal
distribution” – most of the inventory
is either too low or too high
90% of companies report this issue!
Too MuchToo Little
#ofpartsorSKU
0
With every MRP run an oscillation
effect often occurs in which
inventory quickly moves from one
distribution to the other.
A B

The New Normal and Inventory Implications
Supply chains have elongated
and fragmented while customer
tolerance times have dropped
dramatically.
This disparity means holding
stock at some strategic point is a
must to keep and/or grow sales.
Also, there are more products
with shorter life spans to
manage - many use common
components and resources.
This means managing stock
positions effectively is a must for
effective capital and resource
management.
This also means that planning
horizons are more remote from
actual demand realization
(longer range forecast).
This also means that detailed
item level forecasting is much
more difficult.
The three rules of forecasts:
1. They start out wrong
2. The longer the range, the
more wrong they are
3. The more detailed, the
more wrong they are

New normal
Supply Chain Characteristics 1965 Today
Supply Chain Complexity Low High
Product Life Cycles Long Short
Customer Tolerance Times Long Short
Product Complexity Low High
Product Customization Low High
Product Variety Low High
Long Lead Time Parts Few Many
Forecast Accuracy High Low
Pressure for Leaner Inventories Low High
Transactional Friction (total direct and
indirect costs associated with the
execution of a financial transaction)
High Low
Conventional planning rules have not
appreciably changed since the 1960s.
MRP still plans today the way it did 50
years ago!
Today’s supply chains look VERY different
from 1960’s supply chains when
conventional planning rules were
formulated but…

Demand Driven Supply Chain
• Demand-Driven Supply Chain systems or Demand-Driven Supply Networks
(DDSN) reflect a close enough efficient pattern for building modern business
cultures for SCs that use the method of DDSN in response to customized demand
orders (Bramham and MacCarthy, 2004 ).
• Appropriate pieces of information run upstream from customer’s points to
supplier’s points (pull systems) and accurate capacity of materials run
downstream from suppliers’ points to customers’ points
• The decoupling point (DP) is defined as: the point in the product axis to which
the customer’s order penetrates. It is where order driven and forecast driven
activities meet. As a rule, the decoupling point coincides with an important stock
point – in control terms a main stock point – from which the customer has to be
supplied (Mason- Jones and Towill, 1999, p. 16).
Bramham J, MacCarthy B. The demand driven chain. Manufacturing Engineer 2004;

Decoupling point
FORECAST - PUSH DEMAND - PULL
SUPPLIER
CUSTOMER
where order driven
and forecast driven
activities meet.

Demand Driven MRP
A method to model, plan and manage supply chains to protect and
promote the flow of relevant information and materials. DDMRP uses
strategic decoupling points to drive supply order generation and
management throughout a supply chain.
Position, Protect and Pull
Material
Requirements
Planning
(MRP)
Distribution
Requirements
Planning
(DRP)
Lean
Theory of
Constraints
Six Sigma Innovation

The five components of DDMRP
Strategic Decoupling
Buffer Profiles and
Levels
Demand Driven
Planning
Position Protect
1 2 3
Pull
4 5
Dynamic
Adjustments
Visible and
Collaborative
Execution

Position – Strategic Decoupling
This stops the transfer and
amplification of variabilityin
BOTH directions where it
matters most.
End Item
AssemblerFoundry Component
Sub-
Assembler
Strategically places decoupling points of inventory
within the product structure and supply chain.
Demand Signal Distortion
Supply Continuity Variability
Planning horizons shorten AND
lead times compress.

Decoupling Placement Criteria
Decoupling Point Placement Considerations
Customer Tolerance Time The time the typical customer is willing to wait before seeking an alternative source.
Market Potential Lead Time
This lead time will allow an increase of price or the capture of additional business
either through existing or new customer channels.
Sales Order Visibility Horizon
The time frame in which we typically become aware of sales orders or actual
dependent demand.
External Variability
Demand Variability: The potential for swings and spikes in demand that could
overwhelm resources (capacity, stock, cash, etc.).
Supply Variability: The potential for and severity of disruptions in sources of supply
and/or specific suppliers.
Inventory Leverage and Flexibility
The places in the integrated bill of material (BOM) structure (matrix bill of material) or
the distribution network that enables a company with the most available options as
well as the best lead time compression to meet the business needs.
Critical Operation Protection
These types of operations include areas that have limited capacity or where quality
can be compromised by disruptions or where variability tends to be accumulated
and/or amplified.

Position – MRP versus DDMRP
MRP (Everything Coupled) DDMRP (Strategically Decoupled)
MRP was never designed to decouple! It
makes everything dependent forcing longer
planning horizons and variability accumulation.
Critical Difference:

Buffer profiles
• The placed stocks are defined as replenished strategic
and dynamic buffers.
• The second step aims at sizing the proper level of the
buffers with the goal of absorbing shocks and
compressing the lead time.
1. Green: the heart of the order generation aspect of
the buffer determining the frequency of order
generation and the minimum size of each other.
2. Yellow: the heart of the demand coverage in the
buffer.
3. Red: the safety embedded in the buffer position.

Buffer sizing
• The sizing of the buffer must be made as follows:
• The dimension of the green zone is defined by the maximum among
• Minimum Order Quantity (MOQ)
• 𝐷𝑒𝑠𝑖𝑟𝑒𝑑 𝑂𝑟𝑑𝑒𝑟 𝐶𝑦𝑐𝑙𝑒 (𝐷𝑂𝐶)∗𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑎𝑖𝑙𝑦 𝑈𝑠𝑎𝑔𝑒 (𝐴𝐷𝑈)
• 𝐴𝐷𝑈∗𝐷𝐿𝑇 ∗ 𝐿𝑒𝑎𝑑 𝑇𝑖𝑚𝑒 𝐹𝑎𝑐𝑡𝑜𝑟 (𝐿𝑇𝐹)
• • For the yellow zone, the dimension is given by 𝐴𝐷𝑈∗𝐷𝐿𝑇
• • The Red Zone dimension is given by the sum of
• Red Zone Base: 𝐿𝑒𝑎𝑑 𝑇𝑖𝑚𝑒 𝐹𝑎𝑐𝑡𝑜𝑟 (𝐿𝑇𝐹)∗𝐴𝐷𝑈∗𝐷𝐿𝑇
• Red Zone Safety: 𝑅𝑒𝑑 𝑍𝑜𝑛𝑒 𝐵𝑎𝑠𝑒∗𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐹𝑎𝑐𝑡𝑜𝑟 (𝑉𝐹)

Buffer sizing
• This is the normal work area. There are no necessary
actions on these parts
• The protection coverage of our flow and the heart of our
system playing the role of damper. When coins are in this
area, they must be rescheduled.
• It’s the security stock. If any parts are in this area, they
must be renewed urgently

Lead time & Average Demand
• The Reorder Point (ROP) is the level of inventory which triggers an action to replenish that particular inventory stock.
• It is normally calculated as the forecast usage during the replenishment lead time plus safety stock.
• Reorder point is a technique to determine when to order; it does not address how much to order when an order is
made.

ROP : Constant demand and lead time
• For basic EOQ model with const. demand and const. lead time to receive an order
is equal to the amount demanded during lead time.
Reorder Point (R) = Normal consumption during lead-time
R = d x LT
Where d = demand rate per period(units per day or week)
LT = lead time in days or weeks
6
4

ROP : Variable demand and const. lead time Expression
• R = dLT + z d 𝑳𝑻
where d = average daily demand
LT = lead time
d = standard deviation of daily demand
Z = number of standard deviations corresponding to the service level
probability
z d 𝐿𝑇 = safety stock
The term d 𝐿𝑇 in this formula for the reorder point is the square root of the sum of
the daily variances during lead time:
Variance = (daily variance) x (number of days of lead time)
= d2 𝐿𝑇
Standard deviation = d2 𝐿𝑇
= d 𝐿𝑇
From table for
service level
0.9000, z = +1.28

Z-table for service level

Lead time factor and Variability factor
Lead Time
Lead Time
Factor
Variability
Variability
factor
Raw Material
Lead Time
Long
0,25 High 0,75
Lead Time
Average
0,4 Average 0,5
Lead Time
Short
0,7 Low 0,25
Lead Time
Lead Time
Factor
Variability
Variability
factor
Sub Set made
Lead Time
Long
0,25 High 0,75
Lead Time
Average
0,4 Average 0,5
Lead Time
Short
0,7 Low 0,2

Lead time factor and Variability factor
Lead Time
Lead Time
Factor
Variability
Variability
factor
Purchased parts
Lead Time
Long
0,3 High 0,75
Lead Time
Average
0,5 Average 0,5
Lead Time
Short
0,7 Low 0,25

Example: Lead time factor and Variability factor
• Buffer profile:
• First letter represent Make Items (Parent – Intermediate - Purchased)
• Second letter represent Lead time factor (Long – Medium – Short)
• Third letter is the variability factor (High – Medium – Low)
Lead Time
Factor
Variability
Category
Variability
Factor
Parent Make Items (M)
Long Lead Time 9+ Days .25 High .75
Medium Lead Time 3-8 Days .40 Medium .50
Short Lead Time 1-2 Days .70 Low .25
Intermediate Make Items (I)
Short Lead Time 1-2 Days .70 Low .20
Purchased Items (P) C .
MMM:
PARENT
MEDIUM LEAD TIME FACTOR : 0,4
MEDIUM VARIABILITY FACTOR: 0,5

Buffer sizing example
ADU Average Daily Usage pcs 10
MOQ Minimal Order Quantitypcs 50
DOC Desired Order Cycle days 7
DLT Decoupled Lead Time days 12
Buffer Profile MML
Item type M
LTF Lead Time Factor 0,5
VF Variability Factor 0,33
GREEN
Minimal Order Quantitypcs 50
DLT * ADU * LTF pcs 40
DOC * ADU pcs 70
GREEN QUANTITY pcs 70
YELLOW DLT * ADU pcs 120
RED
Red Base pcs 60
Red Safety pcs 20
RED QUANTITY pcs 80

Buffer adjustment
• Recalculated adjustments are automiated adjustments to buffer levels based on
changes to individual part attributes or buffer profile adjustments.
• There are three critical factors for all buffered parts that directly impact the
buffer equations:
1. ADU,
2. lead time, and
3. Minimum Order Quantity.
• ADU and lead time tend to have the most dramatic impact because they are
involved in all three zone determinations.
• The minimum order quantity is only involved in green zone determination.

Buffer adjustment (ADU)
As an example of each input change, next figure shows the buffer inputs over a
time frame. Every input is static except ADU.
Date Red _Yellow _Green ADU Red Base Red Safety DLT LTF VF
1 Jan 70 100 50 10 50 20 10 0,5 0,4
15 Jan 105 150 75 15 75 30 10 0,5 0,4
1 Feb 161 230 115 23 115 46 10 0,5 0,4
15 Feb 266 380 190 38 190 76 10 0,5 0,4
1 Mar 315 450 225 45 225 90 10 0,5 0,4
15 Mar 364 520 260 52 260 104 10 0,5 0,4
1 Apr 385 550 275 55 275 110 10 0,5 0,4
15 Apr 406 580 290 58 290 116 10 0,5 0,4
1 May 378 540 270 54 270 108 10 0,5 0,4
15 May 392 560 280 56 280 112 10 0,5 0,4
1 Jun 406 580 290 58 290 116 10 0,5 0,4
15 Jun 371 530 265 53 265 106 10 0,5 0,4

Example buffer adjustment
Buffer adjustment over six months (ADU change)

Buffer adjustment (ADU, Lead Time)
Date Red _Yellow _Green ADU Red Base Red Safety DLT LFT VF
1 Jan 70 100 50 10 50 20 10 0,5 0,4
15 Jan 105 150 75 15 75 30 10 0,5 0,4
1 Feb 161 230 115 23 115 46 10 0,5 0,4
15 Feb 266 380 190 38 190 76 10 0,5 0,4
1 Mar 315 450 225 45 225 90 10 0,5 0,4
15 Mar 254,8 260 182 52 260 104 5 0,7 0,4
1 Apr 269,5 275 192,5 55 275 110 5 0,7 0,4
15 Apr 284,2 290 203 58 290 116 5 0,7 0,4
1 May 264,6 270 189 54 270 108 5 0,7 0,4
15 May 274,4 280 196 56 280 112 5 0,7 0,4
1 Jun 284,2 290 203 58 290 116 5 0,7 0,4
15 Jun 259,7 265 185,5 53 265 106 5 0,7 0,4

Buffer adjustment (ADU, Lead Time)

Buffer adjustment (ADU, Lead Time, MOQ)
Date Red _Yellow _Green ADU Red Base Red Safety DLT LFT VF MOQ
1 Jan 70 100 50 10 50 20 10 0,5 0,4 0
15 Jan 105 150 75 15 75 30 10 0,5 0,4 0
1 Feb 161 230 115 23 115 46 10 0,5 0,4 0
15 Feb 266 380 190 38 190 76 10 0,5 0,4 0
1 Mar 315 450 225 45 225 90 10 0,5 0,4 0
15 Mar 254,8 260 182 52 260 104 5 0,7 0,4 0
1 Apr 269,5 275 192,5 55 275 110 5 0,7 0,4 0
15 Apr 284,2 290 400 58 290 116 5 0,7 0,4 400
1 May 264,6 270 400 54 270 108 5 0,7 0,4 400
15 May 274,4 280 400 56 280 112 5 0,7 0,4 400
1 Jun 284,2 290 400 58 290 116 5 0,7 0,4 400
15 Jun 259,7 265 400 53 265 106 5 0,7 0,4 400

Buffer adjustment (ADU, Lead Time, MOQ)

Planned adjustment
• Buffers also can be manipulated through planned adjustments.
• Planned adjustments are based on certain strategie, historical, and business
intelligence factors.
• These planned adjustments are manipulatìons of the buffer equation that affect
inventory positions by raising or lowering buffer and their corresponding zones at
certain points in time.
• The demand adjustment factor (DAF) is a manipulation of the ADU input within a
specific time period. This manipulation occurs by adjusting the ADU to a
historically proven or planned position based on an approved business case or as
a reaction to rapid changes in demand within short periods of time.

Product introduction
• When introducing a new product that will be strategically buffered, a company
has to establish a buffer position. True demand adjustment factors can be used
with regard to a new-product launch.

Product introduction
Date Red _Yellow _Green
Projected
ADU
DAF
Adjusted
ADU
Red Base
Red
Safety
DLT LFT VF
1 Jan 0 0 0 50 0 0 50 20 10 0,5 0,4
15 Jan 35 50 25 50 0,1 5 75 30 10 0,5 0,4
1 Feb 70 100 50 50 0,2 10 115 46 10 0,5 0,4
15 Feb 105 150 75 50 0,3 15 190 76 10 0,5 0,4
1 Mar 140 200 100 50 0,4 20 225 90 10 0,5 0,4
15 Mar 175 250 125 50 0,5 25 260 104 10 0,5 0,4
1 Apr 210 300 150 50 0,6 30 275 110 10 0,5 0,4
15 Apr 245 350 175 50 0,7 35 290 116 10 0,5 0,4
1 May 280 400 200 50 0,8 40 270 108 10 0,5 0,4
15 May 315 450 225 50 0,9 45 280 112 10 0,5 0,4
1 Jun 350 500 250 50 1 50 290 116 10 0,5 0,4
15 Jun 350 500 250 50 1 50 265 106 10 0,5 0,4

Product deletion
• The demand adjustment factor in bringing a buffer down in anticipation of the
product no longer being offered to the market..

Product deletion
Projected
ADU
Demand
Adjustme
nt Factor
Adjusted
ADU
Red Base
Red
Safety
DLT LFT VF
1 Jan 350 500 250 50 1 50 50 20 10 0,5 0,4
15 Jan 315 450 225 50 0,9 45 75 30 10 0,5 0,4
1 Feb 280 400 200 50 0,8 40 115 46 10 0,5 0,4
15 Feb 245 350 175 50 0,7 35 190 76 10 0,5 0,4
1 Mar 210 300 150 50 0,6 30 225 90 10 0,5 0,4
15 Mar 175 250 125 50 0,5 25 260 104 10 0,5 0,4
1 Apr 140 200 100 50 0,4 20 275 110 10 0,5 0,4
15 Apr 105 150 75 50 0,3 15 290 116 10 0,5 0,4
1 May 70 100 50 50 0,2 10 270 108 10 0,5 0,4
15 May 35 50 25 50 0,1 5 280 112 10 0,5 0,4
1 Jun 0 0 0 50 0 0 290 116 10 0,5 0,4
15 Jun 0 0 0 50 0 0 265 106 10 0,5 0,4

Seasonality
• Another application of demand adjustment factors occurs with regard to
seasonality
• Tackling seasonality will involve both ramp-up and ramp-down adjustments.
• But several interactive considerations should be taken into account when
considering when and to what extent to apply demand adjustment factors to
compensate for seasonality of strategically buffered items:
1. Severity of the seasonality (length and significance). The first consideration is the known
length and severity of the seasonal swing. The higher the change and the shorter the
window, the more severe the seasonality.
2. Length of the ADU calculation period. The length of the ADU calculation period must be
known and considered in relation to the severity of the season. The longer the ADU
calculation period and the more severe the season, the higher the likelihood that the ADU
will be dramatically understated during the initial seasonal period.

Seasonality
3. Past, forward, or blended ADU. In many cases, a forward-looking ADU negates the need
for a planned adjustment factor, as it anticipates the seasonal demand change. A blended
ADU approach may be reactive enough depending on the severity of the season and the
length of the calculation period. In most cases a past-looking ADU will leave the buffers
vulnerable to significant seasonality, and demand adjustment factors will be needed to
compensate.
4. Lead times of critical components. Long lead time items (including parent items with long
lead time components) become a factor in determining when demand adjustment factors
should be applied. Employing a demand adjustment factor too late will mean that despite
the buffers being properly sized

Seasonality
0
10
20
30
40
50
60
70
80
90
100
0
500
1000
1500
2000
2500
1 Jan 15
Jan
1 Feb 15
Feb
1
Mar
15
Mar
1 Apr 15
Apr
1
May
15
May
1 Jun 15
Jun
01
Jul
15
Jul
1
Ago
15
Ago
1
Sept
15
Sept
1 Oct 15
Oct
1
Nov
15
Nov
1
Dec
15
Dec
Red Yellow Green Adjusted ADU

Seasonality
Projected
ADU
DAF
Adjusted
ADU
Red Base
Red
Safety
DLT LFT VF
1 Jan 0 0 0 0 0 0 50 20 10 0,5 0,4
15 Jan 35 50 25 50 0,1 5 75 30 10 0,5 0,4
1 Feb 70 100 50 50 0,2 10 115 46 10 0,5 0,4
15 Feb 105 150 75 50 0,3 15 190 76 10 0,5 0,4
1 Mar 140 200 100 50 0,4 20 225 90 10 0,5 0,4
15 Mar 175 250 125 50 0,5 25 260 104 10 0,5 0,4
1 Apr 210 300 150 50 0,6 30 275 110 10 0,5 0,4
15 Apr 245 350 175 50 0,7 35 290 116 10 0,5 0,4
1 May 280 400 200 50 0,8 40 270 108 10 0,5 0,4
15 May 315 450 225 50 0,9 45 280 112 10 0,5 0,4
1 Jun 350 500 250 50 1 50 290 116 10 0,5 0,4
15 Jun 350 500 250 50 1 50 265 106 10 0,5 0,4
01 Jul 350 500 250 50 1 50 50 20 10 0,5 0,4
15 Jul 315 450 225 50 0,9 45 75 30 10 0,5 0,4
1 Ago 280 400 200 50 0,8 40 115 46 10 0,5 0,4
15 Ago 245 350 175 50 0,7 35 190 76 10 0,5 0,4
1 Sept 210 300 150 50 0,6 30 225 90 10 0,5 0,4
15 Sept 175 250 125 50 0,5 25 260 104 10 0,5 0,4
1 Oct 140 200 100 50 0,4 20 275 110 10 0,5 0,4
15 Oct 105 150 75 50 0,3 15 290 116 10 0,5 0,4
1 Nov 70 100 50 50 0,2 10 270 108 10 0,5 0,4
15 Nov 35 50 25 50 0,1 5 280 112 10 0,5 0,4
1 Dec 0 0 0 0 0 0 290 116 10 0,5 0,4
15 Dec 0 0 0 0 0 0 265 106 10 0,5 0,4

Min Max Buffer
• Parts given the min-max designation should be non-strategic, readily available,
and/or extremely stable parts
• The green zone of a min-max buffer is calculated in the exact manner as
replenishment buffers: there are three potential determinants and the largest
number is recommended
• The red zone must be a minimum quantity of at least ADU x lead time in order to
provide the minimum amount of average protection required. Safety, however,
can be added through the use of the variability factor.
Source: Ptak & Smith; DDMRP, PWC Industrial Press – 3° Version (pp. 125)

Min Max Buffer example
PART NUMBER EX-GREEN RED
MOQ Minimal Order Quantity pcs 25
DOC Desired Order Cycle days
Buffer Profile MML
Item type M
Minimal Order Quantity pcs 25
DOC * ADU pcs 0
Red Base (ADU * DLT) pcs 120
Red Safety (ADU * DLT*VF) pcs 24
RED QUANTITY pcs 144
GREEN
RED
Source: Ptak &
Smith; DDMRP, PWC
Industrial Press – 3°
Version (pp. 125)

Planning: Net flow equation
• Unique to DDMRP – used to create supply order recommendations
• On‐hand + On‐order – Qualified Sales Order Demands = Net flow position
On‐Hand
On‐Order
Today
Sales order demand
Day 1 Day 2 Day 3
Inventory
physically in
stock
The quantity of stock
that has been ordered
but not received
Sales Orders due today, in
the past and qualified future
spikes.

Qualifying order spikes
• Order Spike Horizon = Length of time in the future in which spikes are considered
• Order Spike Threshold = Quantity in daily buckets which would qualify for available stock equation
inclusion
• Total Qualified Demand = The sum of sales order past due, sales order due today and qualified spikes.
9
TotalQuantityOrdered
1 2 3 4 5 6 7 8

Part/SKU Info
35 units
70 units
52 units
Replenishment Lead Time = 7 days
Lead time = 7 days (Medium – 50%)
ADU = 10
Medium Variability (50%)
Order Spike Horizon = 7 days
Order Spike Threshold = 26 (50% Red)
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
Order Spike Threshold (50% of red)
Order Spike
Horizon
▼
Supply Orders
-4
Sales Order Demand
-3 -2 -1 1 2 3 4 5 6 7
As time progresses Sales Orders and Supply Orders advance toward the buffer

Order Spike
Horizon
▼
Order Spike Threshold
35
Sales Order Demand Supply Orders
On-Hand
Open Supply
Sales Order Demand
65
72
10
37
-7 -6 -5 -4 -3 -2 -1
Net Flow Position: 127 Today’s Order Recommendation: NONE
▪
▪
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 1
On-Hand
Available Stock
10 18 17
6 5
9 10
1 2 3 4 5 6 7
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

35
-3 -2 -1
37
-7 -6 -5 -4
Net flow position:
On-Hand
Open Supply
Sales Order Demand
55
72
48
79 Today’s Order Recommendation: 78
SPIKE
▪
▪
▪
▪
◄SPIKE!
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 2
On-Hand
Available Stock
18 17
6 5
9 10 30
2 3 4 5 6 7 8
37
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
-7 -6 -5 -4
Available Stock Today:
72
115
47
37
-3 -2 -1
140
▪
Today’s Order Recommendation: NONE
▪
▪
▪
78
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 3
On-Hand
Available Stock
SPIKE
17
6 5
9 10 30
5
3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
-7 -6 -5 -4
55
115
36
37
-3 -2 -1
134 Today’s Order Recommendation: NONE
78
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 4
On-Hand
Available Stock
SPIKE
6 5
9 10 30
5 6
4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
49
115
35
37
-7 -6 -5 -4 -3 -2 -1
Available Stock Today: 129 Today’s Order Recommendation: NONE
78
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 5
On-Hand
Available Stock
SPIKE
5
9 10 30
5 6
9
5 6 7 8 9 10 11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
-7 -6 -5 -4
44
115
39
37
-3 -2 -1
120 Today’s Order Recommendation: 37
78 Order Spike Thre dshol
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 6
On-Hand
Available Stock
SPIKE
9 10 30
5 6
9 10
6 7 8 9 10 11 12
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
72
115
40
78 Order SpikeT
37
-7 -6 -5 -4 -3 -2 -1
Available Stock Today: 147
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 7
On-Hand
Available Stock
SPIKE
hreshold
10 30
5 6
9 10 20
7 8 9 10 11 12 13
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
62
115
30
78 Order SpikeThreshold
37
-7 -6 -5 -4 -3 -2 -1
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 8
On-Hand
Available Stock
SPIKE
30
5 6
9 10 20
6
8 9 10 11 12 13 14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
32
115
5
78 Order Spike Threshold
37
-7 -6 -5 -4 -3 -2 -1
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 9
On-Hand
Available Stock
5 6
9 10 20
6
9 10 11 12 13 14 15
11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
-7 -6 -5 -4
105
37
6
-3 -2 -1
37
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 10
On-Hand
Available Stock
6
9 10 20
6
10 11 12 13 14 15 16
11 10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
-7 -6 -5 -4
99
37
9
-3 -2 -1
37
▪
▪
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 11
On-Hand
Available Stock
11 109 10 20
6
20
11 12 13 14 15 16 17
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
90
37
10
Today’s Order Recommendation: 40
37
-7 -6 -5 -4 -3 -2 -1
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 12
On-Hand
Available Stock
11 1010 20
6
20
12 13 14 15 16 17 18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
80
77
20
3740
-7 -6 -5 -4 -3 -2 -1
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 13
On-Hand
Available Stock
11 1020
6
20
13 14 15 16 17 18 19
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
97
40
6
40
-7 -6 -5 -4 -3 -2 -1
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 14
On-Hand
Available Stock
11 10
6
20
14 15 16 17 18 19 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
91
40
11
Today’s Order Recommendation: 36
40
-7 -6 -5 -4 -3 -2 -1
▪
▪▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 15
On-Hand
Available Stock
11 10 20
15 16 17 18 19 20 21
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position

On-Hand
Open Supply
Sales Order Demand
80
76
10
40
-7 -6 -5 -4 -3 -2 -1
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 16
On-Hand
Available Stock
10 20
16 17 18 19 20 21 22
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position
36
▪

On-Hand
Open Supply
Sales Order Demand
70
76
20
40
-7 -6 -5 -4 -3 -2 -1
▪▪▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪ ▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
Order Spike
Horizon
▼
TOG = 157 ►
TOY = 122 ►
TOR = 52 ►
DAY 17
On-Hand
Available Stock
20
17 18 19 20 21 22 23
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Net Flow Position
36
▪
▪
▪
▪
▪

DAY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
DEMAND 10 18 17 6 5 9 10 30 5 6 9 10 20 6 11 10 20
NET FLOW POS 127 79 140 134 129 120 147 147 142 136 127 117 137 131 120 146 126
ON-HAND 65 55 72 55 49 44 72 62 32 105 99 90 80 97 91 80 70
Average Daily Usage over 17 day
period = 11.8
Average Net Flow Position = 71.6

Cases distribution
Too MuchToo Little
A B
0

DAY TODAY
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
On
Hand
On
Order
Qualif
Dem
Net
Flow
Order
amount
65
1 0 37 0 0 0 35 0 65 10 18 17 6 5 9 10 30 65 72 40 97 60
2 60 0 37 0 0 0 35 55 18 17 6 5 9 10 30 5 55 132 48 139 0
3 0 60 0 37 0 0 0 72 17 6 5 9 10 30 5 6 72 97 47 122 35
4 35 0 60 0 37 0 0 55 6 5 9 10 30 5 6 9 55 132 36 151 0
5 0 35 0 60 0 37 0 49 5 9 10 30 5 6 9 10 49 132 35 146 0
6 0 0 35 0 60 0 37 44 9 10 30 5 6 9 10 20 44 132 39 137 0
7 0 0 0 35 0 60 0 72 10 30 5 6 9 10 20 6 72 95 40 127 0
8 0 0 0 0 35 0 60 62 30 5 6 9 10 20 6 11 62 95 30 127 0
9 0 0 0 0 0 35 0 92 5 36 9 10 20 6 11 10 92 35 41 86 71
10 71 0 0 0 0 0 35 87 36 9 10 20 6 11 10 18 87 106 36 157 0
11 0 71 0 0 0 0 0 86 9 10 20 6 11 10 18 12 86 71 9 148 0
12 0 0 71 0 0 0 0 77 10 20 6 11 10 18 12 8 77 71 10 138 0
13 0 0 0 71 0 0 0 67 20 6 11 10 18 12 8 6 67 71 20 118 39
14 39 0 0 0 71 0 0 47 6 11 10 18 12 8 6 5 47 110 6 151 0
15 0 39 0 0 0 71 0 41 11 10 18 12 8 6 5 8 41 110 11 140 0
16 0 0 39 0 0 0 71 30 10 18 12 8 6 5 8 2 30 110 10 130 0
17 0 0 0 39 0 0 0 91 18 12 8 6 5 8 2 12 91 39 18 112 45
18 45 0 0 0 39 0 0 73 12 8 6 5 8 2 12 20 73 84 12 145 0
19 0 45 0 0 0 39 0 61 8 6 5 8 2 12 20 9 61 84 8 137 0
20 0 0 45 0 0 0 39 53 6 5 8 2 12 20 9 8 53 84 6 131 0
21 0 0 0 45 0 0 0 86 5 8 2 12 20 9 8 5 86 45 5 126 0
CUSTOMER ORDERSSUPPLIER ORDER

Main formulas
• On Hand (t) = On Hand (t-1) + Supplier Order (t-1) – Customer Order (t-1)
• On Order = 𝑖=−6
−1
𝑆𝑢𝑝𝑝𝑙𝑖𝑒𝑟 𝑂𝑟𝑑𝑒𝑟 (𝑖)
• TOR (Top Of Red) = 𝐷𝐿𝑇 × 𝐴𝐷𝑈 × 𝐿𝑇𝐹 + 𝐷𝐿𝑇 × 𝐴𝐷𝑈 × 𝐿𝑇𝐹 × 𝑉𝐹
• OTS (Order Threshold Spike) = 0,5 TOR
• Qualified Spikes = 𝑖=2
8
𝑖𝑓(𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑂𝑟𝑑𝑒𝑟 𝑖 > 𝑂𝑇𝑆; 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑂𝑟𝑑𝑒𝑟 𝑖 ; 0)
• Qualified sales order demand (t): The sum of sales order (t+1) + Qualified Spikes
• Net Flow (t) = On Hand (t) + On Order (t) - Qualified sales order demand (t)
• Order Amount (t) = if (Net Flow (t) < TOY; TOG – Net Flow (t) ; 0)

Prioritized share supply order
• Discount optimization
• Suppliers frequently offer discounts for meeting certain conditions.
• These discounts often represent a significant economic advantage to the buying
entity if properly managed. Examples might include free freight or a percentage
discount for orders that meet a given threshold. This threshold might be that the
total dollar amount of the order must reach a specified minimum or that the total
order must fill a truck.

Priority: discount (30 pallets)
Part# Priority
Priority %
NFP/TOG
NFP TOG Order
Qty/Pa
llet
Pallets
Req
Tot
Pallet
12 YELLOW 41% 700 1700 1000 100 10 10
10 YELLOW 57% 800 1400 600 150 4 14
11 YELLOW 59% 850 1450 600 150 4 18
6 YELLOW 63% 600 950 350 50 7 25
9 YELLOW 64% 800 1250 450 150 3 28
1 GREEN 58% 375 650 0 300 0 0
2 GREEN 69% 450 650 0 300 0 0
8 GREEN 85% 850 1000 0 300 0 0
3 GREEN 88% 750 850 0 300 0 0
4 GREEN 89% 800 900 0 250 0 0
5 GREEN 94% 850 900 0 150 0 0
7 GREEN 95% 950 1000 0 200 0 0

Part# Priority Priority % NFP TOG Order
Qty/Pal
let
Pallets
Req
Tot
Pallet
Pallet
+1
New NFP (Q.ty
Pallet +1 + NFP)
12 YELLOW 41% 700 1700 1000 100 10 10 11 1800
10 YELLOW 57% 800 1400 600 150 4 14 5 1550
11 YELLOW 59% 850 1450 600 150 4 18 5 1600
6 YELLOW 63% 600 950 350 50 7 25 8 1000
9 YELLOW 64% 800 1250 450 150 3 28 4 1400
1 GREEN 58% 375 650 0 300 0 0 1 675
2 GREEN 69% 450 650 0 300 0 0 1 750
8 GREEN 85% 850 1000 0 300 0 0 1 1150
3 GREEN 88% 750 850 0 300 0 0 1 1050
4 GREEN 89% 800 900 0 250 0 0 1 1050
5 GREEN 94% 850 900 0 150 0 0 1 1000
7 GREEN 95% 950 1000 0 200 0 0 1 1150

Qty/Pa
llet
Pallets
Req
Pallet
+1
New NFP
(Q.ty Pallet
+1 + NFP)
New NFP /
TOG
OTOG
12 YELLOW 41% 700 1700 1000 100 10 11 1800 105,9% 5,9%
10 YELLOW 57% 800 1400 600 150 4 5 1550 110,7% 10,7%
11 YELLOW 59% 850 1450 600 150 4 5 1600 110,3% 10,3%
6 YELLOW 63% 600 950 350 50 7 8 1000 105,3% 5,3%
9 YELLOW 64% 800 1250 450 150 3 4 1400 112,0% 12,0%
1 GREEN 58% 375 650 0 300 0 1 675 103,8% 3,8%
2 GREEN 69% 450 650 0 300 0 1 750 115,4% 15,4%
8 GREEN 85% 850 1000 0 300 0 1 1150 115,0% 15,0%
3 GREEN 88% 750 850 0 300 0 1 1050 123,5% 23,5%
4 GREEN 89% 800 900 0 250 0 1 1050 116,7% 16,7%
5 GREEN 94% 850 900 0 150 0 1 1000 111,1% 11,1%
7 GREEN 95% 950 1000 0 200 0 1 1150 115,0% 15,0%

Qty/Pa
llet
Pallets
Req
Pallet
+1
New NFP
(Q.ty Pallet
+1 + NFP)
New NFP /
TOG
OTOG
1 GREEN 58% 375 650 0 300 0 1 675 103,8% 3,8%
6 YELLOW 63% 600 950 350 50 7 8 1000 105,3% 5,3%
12 YELLOW 41% 700 1700 1000 100 10 11 1800 105,9% 5,9%
11 YELLOW 59% 850 1450 600 150 4 5 1600 110,3% 10,3%
10 YELLOW 57% 800 1400 600 150 4 5 1550 110,7% 10,7%
5 GREEN 94% 850 900 0 150 0 1 1000 111,1% 11,1%
9 YELLOW 64% 800 1250 450 150 3 4 1400 112,0% 12,0%
8 GREEN 85% 850 1000 0 300 0 1 1150 115,0% 15,0%
7 GREEN 95% 950 1000 0 200 0 1 1150 115,0% 15,0%
2 GREEN 69% 450 650 0 300 0 1 750 115,4% 15,4%
4 GREEN 89% 800 900 0 250 0 1 1050 116,7% 16,7%
3 GREEN 88% 750 850 0 300 0 1 1050 123,5% 23,5%

Minimize overstock
Order Spike
Horizon
▼
20
14 15 16 17 18 19 20
OVERSTOCK
Order Spike
Horizon
▼
20
14 15 16 17 18 19 20
OVERSTOCK
PRODUCT PART 6
PRODUCT PART 9
OTOG Calculateions permits to minimize overstock
NFP actual state
NFP future state
NFP future state
NFP actual state
This quantity
exceed the TOG
This quantity
exceed the TOG
This solution minimize
Overstock or the quantity
exceeds TOG

Spend threshold
• Free freight for order above 19.000$
• The planner will need to find the best combination of additional pallet orders to
meet the minimum spend threshold.
Part# NFP TOG
Priority
(NFP/TOG)
Order (TOG -
NFP)
Qty Pallet $ Pallet
Pallet Req
(Order / Qty
pallet)
$ Ordered NFP After
10 800 1400 57.1% 600 150 1000 4 $ 4.000,00 100.0%
12 700 1700 41.2% 1000 100 600 10 $ 6.000,00 100.0%
11 850 1450 58.6% 600 150 825 4 $ 3.300,00 100.0%
6 600 950 63.2% 350 50 450 7 $ 3.150,00 100.0%
9 800 1250 64.0% 450 150 525 3 $ 1.575,00 100.0%
$ 18.025,00

1. Calculate pallet request
2. Calculate cost per pallet ordered
3. Is the total cost less than 19000 $?
4. If YES, determine the OTOG.
5. Create the new list OTOG
1. Make the order of item in the OTOG list until
you reach 19000 $
Part# Priority NFP TOG Priority %
Qty/Palle
t
Order Pallets Req Pallet +1
New NFP
(Q.ty Pallet
+1 + NFP)
New NFP /
TOG
OTOG
$ per
Pallet
Cost per
pallets
ordered (Pal)
New cost per
order
1 GREEN 375 650 57,7% 300 0 0 1 675 103,8% 3,8% $ 400,00 $ - $ 400,00
6 YELLOW 600 950 63,2% 50 350 7 8 1000 105,3% 5,3% $ 450,00 $ 3.150,00 $ 3.150,00
12 YELLOW 700 1700 41,2% 100 1000 10 11 1800 105,9% 5,9% $ 600,00 $ 6.000,00 $ 6.600,00
11 YELLOW 850 1450 58,6% 150 600 4 5 1600 110,3% 10,3% $ 825,00 $ 3.300,00 $ 3.300,00
10 YELLOW 800 1400 57,1% 150 600 4 5 1550 110,7% 10,7% $1.000,00 $ 4.000,00 $ 4.000,00
5 GREEN 850 900 94,4% 150 0 0 1 1000 111,1% 11,1% $ 480,00 $ - $ -
9 YELLOW 800 1250 64,0% 150 450 3 4 1400 112,0% 12,0% $ 525,00 $ 1.575,00 $ 1.575,00
8 GREEN 850 1000 85,0% 300 0 0 1 1150 115,0% 15,0% $1.200,00 $ -
7 GREEN 950 1000 95,0% 200 0 0 1 1150 115,0% 15,0% $1.200,00 $ -
2 GREEN 450 650 69,2% 300 0 0 1 750 115,4% 15,4% $ 600,00 $ -
4 GREEN 800 900 88,9% 250 0 0 1 1050 116,7% 16,7% $ 825,00 $ -
3 GREEN 750 850 88,2% 300 0 0 1 1050 123,5% 23,5% $ 300,00 $ -
$ 18.025,00 $ 19.025,00

Main results
FG DC2FG DC1PackMfgMfgMfgRM
RM RM
RM
forecast
Suppl.
Suppl.
Suppl.
Suppl.
Cust.
Forecast error induced variability
FG DC1Pack.MfgMfgMfg
RM
sales orders
Suppl.
Suppl.
Suppl.
Suppl.
Cust.
RM
FG DC2
RM
RM
Demand variability
SFG
Planned but independent stock
buffers
FG DC2

Example: FPA FPA
201 203
301 302P
401P
303
402P 403P
1
2
4
30 30
12
5
4
10
PART L.T. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
401P 10
402P 30
403P 30
301 4
302P 12
303 4
201 5
203 2
FPA 1

Example: FPA FPA
201 203
301 302P
401P
303
402P 403P
1
2
4
30 30
12
5
4
10
PART L.T. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
401P 0
402P 0
403P 0
301 4
302P 0
303 4
201 5
203 2
FPA 1 Payaro Andrea - ISC Academy - June 2020

Example: FPA – 201 decoupled FPA
201 203
301 302P
401P
303
402P 403P
1
2
4
30 30
12
5
4
10
PART L.T. 1 2 3 4 5 6 7 8 9 10
401P 0
402P 0
403P 0
301 4
302P 0
303 4
201 0
203 2
FPA 1

FPA before 201 decoupling
Buffer Profile MLM
Item type M
DOC * ADU pcs 750
Red Base pcs 1250
Red Safety pcs 625
GREEN
RED
FPA after 201 decoupling
Buffer Profile MML
Item type M
DOC * ADU pcs 750
Red Base pcs 700
Red Safety pcs 175
GREEN
RED

FPA comparison
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2
FPA before 201 decoupling FPA after 201 decoupling
FPA
RED YELLOW GREEN

201 comparison
Buffer worksheet (201)
Buffer Profile ILM
Item type I
Minimal Order Quantity pcs
DOC * ADU pcs 1950
Red Base pcs 3088
Red Safety pcs 1544
GREEN
RED
201 AFTER 401 DECOUPLING
Buffer Profile ILL
Item type I
Minimal Order Quantity pcs
DOC * ADU pcs 1950
Red Base pcs 1463
Red Safety pcs 293
GREEN
RED

201 comparison
0
5000
10000
15000
20000
25000
1 2
201 201
201
RED YELLOW GREEN

Relative priority
• This final DDMRP innovation will deal with the high degree of relevant visibility
and focus contained in DDMRP.
• This aspect is simply called relative priority, and it has important implications for
both planning and execution. The buffered positions of DDMRP provide a sense
of priority for the position itself as well as how that priority relates to other
buffered positions for both planning (supply order generation) and execution
(supply order management).
• In DDMRP, this is provided through a general reference of color combined with a
discrete reference of percentage. In this way planners can focus on protecting
strategic decoupling points in order to keep the flow of relevant materials
moving.

Relative priority
• This relative priority distinction is a crucial differentiator between the abundant
conventional MRP planning alerts and action messages highlighting even the
smallest imbalance and the highly visible and focused DDMRP approach.
• Conventional MRP is a binary system. The planner is either OK or not OK with
regard to each part. There is little sense of how parts compare to each other-the
planner needs to either act or not act.
• Under the DDMRP approach, planners and buyers can quickly judge the relative
prior¬ity without additional analysis and data queries.

ADU 10 TOG 157
GREEN 35 TOY 122
YELLOW 70 TOR 52
RED 52 OST 26
DAY TODAY
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
On
Hand
On
Order
65
1 0 37 0 0 0 35 0 65 10 18 17 6 5 9 10 30 65 72
2 60 0 37 0 0 0 35 55 18 17 6 5 9 10 30 5 55 132
3 0 60 0 37 0 0 0 72 17 6 5 9 10 30 5 6 72 97
4 35 0 60 0 37 0 0 55 6 5 9 10 30 5 6 9 55 132
5 0 35 0 60 0 37 0 49 5 9 10 30 5 6 9 10 49 132
6 0 0 35 0 60 0 37 44 9 10 30 5 6 9 10 20 44 132
7 0 0 0 35 0 60 0 72 10 30 5 6 9 10 20 6 72 95
8 0 0 0 0 35 0 60 62 30 5 6 9 10 20 6 11 62 95
9 0 0 0 0 0 35 0 92 5 36 9 10 20 6 11 10 92 35
10 71 0 0 0 0 0 35 87 36 9 10 20 6 11 10 18 87 106
11 0 71 0 0 0 0 0 86 9 10 20 6 11 10 18 12 86 71
12 0 0 71 0 0 0 0 77 10 20 6 11 10 18 12 8 77 71
13 0 0 0 71 0 0 0 67 20 6 11 10 18 12 8 6 67 71
14 39 0 0 0 71 0 0 47 6 11 10 18 12 8 6 5 47 110
15 0 39 0 0 0 71 0 41 11 10 18 12 8 6 5 8 41 110
16 0 0 39 0 0 0 71 30 10 18 12 8 6 5 8 2 30 110
17 0 0 0 39 0 0 0 91 18 12 8 6 5 8 2 12 91 39
18 45 0 0 0 39 0 0 73 12 8 6 5 8 2 12 20 73 84
19 0 45 0 0 0 39 0 61 8 6 5 8 2 12 20 9 61 84
20 0 0 45 0 0 0 39 53 6 5 8 2 12 20 9 8 53 84
21 0 0 0 45 0 0 0 86 5 8 2 12 20 9 8 5 86 45
CUSTOMER ORDERSSUPPLIER ORDER
PART#: EXAMPLE

Priority
TODAY 15/07/2020
PART ON HAND ON ORDER
QUALIFIED
DEMAND
TOR TOY TOG
LEAD
TIME
NET
FLOW
POS
ORDER
REC
REQ DATE
406P 401 506 263 750 2750 3250 20 644 2606 12/08/2020
403P 1412 981 412 1200 3600 4560 8 1981 2579 27/07/2020
401P 2652 6233 712 4063 10563 13813 10 8173 5640 29/07/2020
402P 601 753 112 540 1440 1800 9 1242 558 28/07/2020
405P 3400 4251 581 1756 7606 9556 9 7070 2486 28/07/2020
404P 1951 1560 291 1050 2550 3300 6 3220 0 -

4 methods of Demand Driven planning
• Supply Partner Collaboration - Collaborating with other parts of SCM
• Forecasting- consensus forecast – Balance historical sales to market input by
various parts of SCM.
• Information Technology – Streamlines operation
• Supply Chain Segmentation – Separating SCM into groups and work to full fill
their needs.

SMEs
• Small and medium-sized enterprises (SMEs) represent 99% of all
businesses in the EU. Staff headcount more than 50 and less than
250 employees, turnover between 10 and 50 m€ (EU Commission,
2003)
• Small and medium-sized firms contribute strongly to the
development of new ideas and technologies which drive the
growth of new industries (Perks & Bouncken, 2004).
• Lack of strategic alignment of information technology; lack of
awareness of potential benefits of supply chain technology and the
lack of motivation are the main barriers to supply chain information
integration among SMEs (Harland et al., 2007)
• The use of information technology has been found to improve
business competitiveness, with the internet providing the
opportunity for SMEs to compete on equal terms with Les.
Source: Payaro A., Papa A.R. 2019, Supply Chain Management implementations in Italian SMEs. IN THE PROCEEDINGS "4th
International Conference of Business and Management in Emerging Markets",OsloPayaro Andrea - ISC Academy - June 2020

SCM practices
• Many Supply Chain Management practices are developed in companies.
• Main practices are:
• Kanban
• Just In Time
• Milk Run
• Vendor Managed Inventory
• Consignment Stock
• Centralized Purchasing
• Co-opetition
• Blockchain

Suppliers candidate to integration
IMPORTANT SUPPLIER
GEOGRAFIC AREA
Source: Payaro. 2017, A. Lean Management, Esculapio ed.

Kanban
• The signaling device that indicates need for material manufacturing is often
referred to as a kanban, which is a Japanese word meaning “card” or “visible
marker.”
• Some companies, such as Toyota, utilize two different types of kanban; one that
authorizes the movement of material from one location to another, and one that
authorizes the production of more material. In such a two-card system , the type
of kanban that authorizes production of more material is often referred to as a P-
kanban or production kanban.

Just in Time
• Just in time (JIT) is an inventory management method whereby materials, goods,
and labor are scheduled to arrive or be replenished exactly when needed in the
production process.
Milk Run
• A Milk Run is a delivery method used to transport mixed loads from various
suppliers to one customer. Instead of each supplier sending a truck every week to
meet the needs of one customer, one truck (or vehicle) visits the suppliers to pick
up the loads for that customer.

Vendor Managed Inventory
• Vendor-managed inventory (VMI) is an inventory management practice in which
a supplier of goods, usually the manufacturer, is responsible for optimising the
inventory held by a distributor.
• In VMI the customer shares their inventory data with a vendor such that the
vendor is the decision-maker who determines the order size for both.
• The vendor is responsible for the customer's ordering cost, while the customer
has to pay for their own holding cost. This policy can prevent stocking undesired
inventories and hence can lead to an overall cost reduction.
Consignment stock
• Consignment inventory is the stock in hands of a customer, while the supplier
retains ownership until the products are sold.

Thanks for Your Attention

Payaro supply chain ddmrp neoma jun 2020

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Payaro supply chain ddmrp neoma jun 2020

Similar to Payaro supply chain ddmrp neoma jun 2020 (20)

More from Andrea Payaro

More from Andrea Payaro (20)

Recently uploaded

Recently uploaded (20)

Payaro supply chain ddmrp neoma jun 2020