Production planning & control (PPC)

PRODUCTION PLANNING &
CONTROL
Simple Sustainable Solutions 1
Production Planning & Control
Trainers:
Hakeem–Ur–Rehman
&
Sajid Mahmood
Simple Sustainable Solutions

Outline:
 DAY–1:
o What is Production Planning & Control (PPC)?
o Forecasting Methods & Its Applications
o Understanding Data Patterns & It’s Methods
 DAY–2:
o Aggregate Production Planning (APP)
o Inventory Management
 DAY–3:
o Master Production Scheduling (MPS)
o Material Requirement Planning (MRP)
Hakeem-Ur-Rehman & Sajid Mahmood

What is Production Planning & Control (PPC)?
Simple Sustainable Solutions Hakeem-Ur-Rehman & Sajid Mahmood 3
 The highest efficiency in production is obtained by
manufacturing the required quantity of the product, of the
required quality, at the required time, with the best and
cheapest method.
 PPC regulates and controls “how”, “where”, and “When” work is to be
done.
PRODUCTION
SYSTEM
 Four Factors involve:
o Quantity
o Quality
o Time
o Price
Production Planning
& Control (PPC)
BRAIN

Why Forecasting?
The Effect of Inaccurate Forecasting on the Supply Chain

Forecasting:
“A prediction of future events used
for planning purpose”
Principles of Forecasting:
1.Forecasts Are Almost Always
Wrong (But They Are Still Useful)
2.Forecasts are more accurate for
shorter than longer time horizons
3.Forecasts are more accurate for
groups or families of items rather
than for individual items.
Objective: “Better future Forecast
by Minimizing the error (Actual
Vs Forecasted Demand)”

Types of Forecasting Methods:
FORECASTING METHODS
Little or no quantitative
data available
 Quantitative historical data available
 Evidence of a relationship between the variable of
interest and some other variable(s)
Quantitative Techniques
Qualitative Techniques
o Market surveys
o Delphi method
o etc.
Time Series Models: “Future
is a function of time”
o Moving Average
o Weighted Moving Average
o Exponential Smoothing
o Linear Regression
o etc.
Causal Models: “Future is a
function of ‘any other factors’
other than time ”
o Linear Regression
o Multiple Regression

Forecasting Methods – Demand Patterns Over Time:
Any time series (i.e. data over time) is composed of the following:
DATA = Level + Trend + Seasonality + Cycles + Random Variation
DATA = PATTERN + Random Variation
Time
Quantity
(a) Level or Horizontal Pattern: Data
follow a horizontal pattern around the mean
Time
Quantity
(b) Trend Pattern: Data are progressively
increasing (shown) or decreasing
(c) Seasonal Pattern: Data exhibit a regularly
repeating pattern
Time (Quarters)
Quantity
Time (Quarters)
Quantity
(d) Cycle: Data increase or decrease over time
(Data patterns created by economic fluctuations)

Time–Series Forecasting Methods: Level or Horizontal Pattern
Averaging Techniques: work best when a
series tends to vary about an average (i.e.
smooth variations around mean)
 They can handle step changes or gradual
changes in the level of a series Techniques:
o Simple Moving average
o Weighted moving average
o Exponential smoothingTime
Quantity
(a) Level or Horizontal Pattern: Data follow
a horizontal pattern around the mean
Case Study–1:
(Open Excel
Sheet for the case
study data)
Case Study-1.xls
Week Demand Week Demand Week Demand Week Demand
1 415 14 365 27 351 40 282
2 236 15 471 28 388 41 399
3 348 16 402 29 336 42 309
4 272 17 429 30 414 43 435
5 280 18 376 31 346 44 299
6 395 19 363 32 252 45 522
7 438 20 513 33 256 46 376
8 431 21 197 34 378 47 483
9 446 22 438 35 391 48 416
10 354 23 557 36 217 49 245
11 529 24 625 37 427 50 393
12 241 25 266 38 293 51 482
13 262 26 551 39 288 52 484
Week Demand Week Demand Week Demand Week Demand
1 415 14 365 27 351 40 282
2 236 15 471 28 388 41 399
3 348 16 402 29 336 42 309
4 272 17 429 30 414 43 435
5 280 18 376 31 346 44 299
6 395 19 363 32 252 45 522
7 438 20 513 33 256 46 376
8 431 21 197 34 378 47 483
9 446 22 438 35 391 48 416
10 354 23 557 36 217 49 245
11 529 24 625 37 427 50 393
12 241 25 266 38 293 51 482
13 262 26 551 39 288 52 484

Time–Series Forecasting Methods: Level or Horizontal Pattern…
0
200
400
600
800
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52
Demand
Time (Weeks)
Demand Pattern Over Time
Demand
Case Study–1: Neither seasonality
nor cyclical effects
can be observed
Is Seasonality or Cyclical effects Present?
Is Trend Present?
Coeff. Stand. Err t-Stat P-value Lower 95%Upper 95%
Intercept 369.27 27.79436 13.2857 5E-18 313.44 425.094
Weeks 0.3339 0.912641 0.36586 0.71601 -1.49919 2.166990.71601
Run Linear Regression to test 𝜷 𝟏 in the model 𝑦𝑡 = 𝛽0 + 𝛽1t + 𝜀𝑡
P–Value > 0.05; No Linear trend in
the data
Conclusion: A stationary model is
appropriate (i.e. Averaging techniques).

Time–Series Forecasting Methods: Level or Horizontal Pattern…
AVERAGING TECHNIQUES:
Simple Moving Average: Technique that averages a number
of the most recent actual values in generating a forecast.
1
tMA
n
t i
i
t
A
F
n


 

Weighted Moving Average: The most
recent values in a time series are given
more weight in computing a forecast.
1 ( 1) 1 1...t n t n n t n tF w A w A w A       
Simple Exponential Smoothing: The smoothed value Lt is the weighted average of
Ft+1 = 𝐿 𝑡 = 𝛼𝐴t + (1 − 𝛼)𝐹𝑡
o The current period’s actual value (with weight of α).
o The forecast value for the current period (with weight of 1–α).
The smoothed value Lt becomes the forecast for period t+1.
An initial “forecast” is needed to start the
process

Performance of Forecasting Methods: Error Performance Measures:
 Which one of these forecasting methods gives the “best” forecast?
𝑴𝒆𝒂𝒏 𝑨𝒃𝒔𝒐𝒍𝒖𝒕𝒆 𝑫𝒆𝒗𝒊𝒂𝒕𝒊𝒐𝒏 𝑴𝑨𝑫 =
𝐴 𝑡 − 𝐹𝑡
𝑛
𝑴𝒆𝒂𝒏 𝑺𝒒𝒖𝒂𝒓𝒆 𝑬𝒓𝒓𝒐𝒓 𝑴𝑺𝑬 =
(𝐴 𝑡 − 𝐹𝑡)2
𝑛
Smaller the Error Performance Measure (i.e MAD, MSE, MAPE) better the method.
𝑴𝒆𝒂𝒏 𝑨𝒃𝒔𝒐𝒍𝒖𝒕𝒆 𝑷𝒆𝒓𝒄𝒆𝒏𝒕 𝑬𝒓𝒓𝒐𝒓 𝑴𝑨𝑷𝑬 =
𝐴 𝑡 − 𝐹𝑡
𝐴 𝑡
𝑛
∗ 100
 MAPE > 30%  Forecast is more or less inaccurate
 MAPE < 30%  Forecast is reasonably good
 MAPE < 20%  Forecast is good
 MAPE < 10%  Forecast is Very good

Performance of Forecasting Methods: Selecting Model Parameters
Use the performance measures to select a good set of values for each model
parameter.
 For the Simple Moving Average:
o the number of periods (n).
 For the Weighted Moving Average:
o The number of periods (n),
o The weights (Wi).
 For the Simple Exponential Smoothing:
o The exponential smoothing factor (α).
Excel Solver can be used to determine the values of the model parameters.
Relationship between exponential smoothing and simple moving average:



2
k
 An exponential smoothing forecast “based on large number
of periods” should have a small “α”

Time–Series Forecasting Methods: Linear Trend Pattern
Time
Quantity
(b) Trend Pattern: Data are progressively
increasing (shown) or decreasing
Techniques for Trend:
 Linear Regression equation
 Holt’s Linear Exponential Smoothing
(Trend-Adjusted Exponential Smoothing)
 Non-linear trends
o S–Curve trend
o Exponential trend equation
o etc.
Case Study–2:
(Open Excel
Sheet for the
case study data)
Case Study-2.xls

Time–Series Forecasting Methods: Linear Trend Pattern…
TECHNIQUES FOR LINEAR TREND:
Linear Regression: Construct the regression equation
based on the historical data available.
 The independent variable is “time”.
 The dependent variable is the “time-series value”.
𝑦𝑡 = 𝛽0 + 𝛽1 𝑡 + 𝜀 𝑡
Holt’s Linear Exponential Smoothing: The trend adjusted forecast consists of two
components.
 Smoothed factor (i.e. Level)
 Trend factor
+1Ft t tL T 
Adjust the Level Lt , and
the Trend Tt in each period
Level:
Trend:
Initial Values:

Time–Series Forecasting Methods: Seasonality Pattern
Seasonality: “Seasonality is expressed in terms of
the amount that actual values deviate from the
average value of a series.”
(c) Seasonal Pattern: Data exhibit a regularly repeating pattern
Time (Quarters)
Quantity
o Multiplicative: Seasonality is expressed
as a percentage of the average (or trend)
amount which is then used to multiply the
value of a series in order to incorporate
seasonality.
𝒚 𝒕 = (𝑻 𝒕)(𝑺 𝒕)(𝑪 𝒕)(𝜺 𝒕)
“Many time series exhibit seasonal and cyclical
variation along with trend”
 MODELS OF SEASONALITY
o Additive: Quantity (i.e. added or subtracted)
from the time-series average in order to
incorporate seasonality
𝒚 𝒕 = 𝑻 𝒕 + 𝑺 𝒕 + 𝑪 𝒕 + 𝜺 𝒕
THE CLASSICAL DECOMPOSITION TECHNIQUE: To develop an additive or multiplicative model;
The time series is first decomposed to its components (trend, seasonality, cyclical variation).

Time–Series Forecasting Methods: Seasonality Pattern…
Case Study–3:
(Open Excel Sheet for the case study data) – Case Study-3.xls
The graph exhibits long term trend
The graph exhibits seasonality pattern

Day–1: Review
 Why Forecasting?
 What is Forecasting?
 Time Series Data Patterns
 Forecasting Methods
o Smoothing Techniques (i.e. Averaging Techniques)
o Linear Trend Techniques
o Decomposition Technique
 Forecasting Methods – Performance Measures
 Forecasting Methods – Selection of Model Parameters
o Using Excel Solver

Outline:
 DAY–1:
 DAY–2:
 DAY–3:

Overview of Production / Operations Planning Activities:

What is Aggregate Operations Planning?
Suppose the figure to the right represents
forecast demand in units.
Now suppose this lower figure represents
the aggregate capacity of the company to
meet demand.
What we need to do is to balance out the
demand and the capacity supply?
 Balancing Aggregate Demand and Aggregate Production Capacity – to specify
the optimal combination of:
o production rate; workforce level; inventory on hand …
A poor aggregate plan can result in lost sales, lost profits, excess inventory, or
excess capacity

Aggregate Operations Planning: Understanding Process
Aggregate
Planning

Aggregate Operations Planning: Inputs, Outputs & Goal
 Resources
o Workforce
 Policy statements
o Subcontracting
o Overtime
o Inventory levels
o Back orders
 Demand forecast
 Costs
o Inventory carrying
o Back orders
o Hiring/firing
o Overtime
o Inventory changes
o subcontracting
Aggregate
Planning
 Total cost of a plan
 Production/operation plan
o Projected levels of:
• Inventory
• Output
• Employment
• Subcontracting
• Backordering
 Goal: Specify the optimal combination (tradeoff) of “Production rate, workforce level,
overtime production, subcontracting (i.e. outsourced capacity), inventory / backlog” to
maximizes the firm’s profit over the planning horizon
 Product group ((or) family) or broad category (aggregation)
 Intermediate-range planning period: 6-18 months

Aggregate Operations Planning: Procedure (Process Steps)
1. Develop the aggregate sales forecast and aggregate planning value.
2. Translate the sales forecast into resource requirements.
3. Generate alternative production plans.
Aggregate Planning value: The term aggregate refers to a line of products (i.e.
Product family), not just one individual product. An aggregate unit is some kind of an
“average” unit.
 Example: A firm produces 6 different models of washing machines
Model
Number
Working Hours
Required
Total
Sales (%)
A5532 4.2 32%
K4242 4.9 21%
L9898 5.1 17%
L3800 5.2 14%
M2624 5.4 10%
M3880 5.8 6%
Aggregate Planning value = (0.32)(4.2) +
(0.21)(4.9) + (0.17)(5.1) + (0.14)(5.2) +
(0.10)(5.4) + (0.06)(5.8)
= 4.856 hours per aggregate unit
The manager decides to define an aggregate
unit of production as a machine requiring
some weighted total working hours where the
weights are taken from the total sales (%).

Aggregate Operations Planning: Procedure (Process Steps)…
Example: ABC Electrical performs
three services: cable TV installations,
satellite TV installations, and digital
subscriber line (DSL) installations.
Estimated labor hours per installation:
0.4* 2 + 0.4 * 3 + 0.2 * 4 = 2.8 hours
Estimated supply costs per installation:
0.4 * $15 + 0.4 * $90 + 0.2 * $155 = $73
Estimated Resource Requirements at ABC’s Electrical
ABC expects (i.e. forecasted) total installations for the next three months to be 150,
175, and 200, respectively.

CASE STUDY: XYZ Cabinets Manufacturing Company
XYZ is a manufacturer of several different lines of kitchen and bathroom cabinets that
are sold through major home improvement retailers. XYZ’s marketing vice president
has come up with the following combined sales forecast for the next 12 months:
XYZ Cabinets Manufacturing
Company; Sales Forecast
Case Study: (Open Excel
Sheet for the case study
data) APP-Case Study.xls

Generate alternative production plans: Production Plan Strategies
 Pure Production Plan Strategies:
o Level production plan – in which production is held constant and inventory
is used to absorb the differences between production and the sales forecast.
o Chase production plan – in which production is changed in each time period
to match the sales forecast.

Aggregate Operations Planning: Alternative Production Plan
Level vs. Chase Production Plan
Advantages:
o Investment in inventory is low
o Labor utilization in high
Disadvantages:
o The cost of adjusting output rates
and/or workforce levels
Advantages:
o Stable output rates and workforce
Disadvantages:
o Greater inventory costs
o Increased overtime and idle time
o Resource utilizations vary over
time

Aggregate Operations Planning: Basic Formulas & Relationships
 Labour hrs. Req. per month = (Labour hr. per unit) * (Month’s forecasted
demand)
 # of workers Req. per month = (Labour hrs. Req. per month ) / (Working hrs. per
month per employee)
 # of workers in a period = (# of workers at the end of previous period) + (# of
workers at start of the period) – (# of laid off workers at start of the period)
 Regular Production per month = (Actual # of workers * Working hours per
month per employee)/(Labour hr. per unit)
 Inventory at the end of a period = (Inventory at end of the previous period) +
(Production in current period) – (Demand in current period)
 Cost for a period = Output Cost (Reg.+OT+Sub.) + Hire/Lay off cost + Inventory
cost + Back order cost

Aggregate Operations Planning: Alternative Production Plan…
Generate alternative production plans: Production Plan Strategies …
 Mixed Production Plan Strategy:
o Combination of Level Production and Chase Demand strategies
o Examples of management policies
• no more than x% of the workforce can be laid off in one quarter
• inventory levels cannot exceed x dollars
 Optimization Modeling to Aggregate Production Plan:
o Linear Programming Model – Using Excel Solver

Aggregate Operations Planning: Selecting the Production Plan…
LEVEL CHASE LP Model
Regular Prod. Cost $20,160,000.00 $19,712,000.00 $20,160,000.00
Overtime Prod. Cost 0 $474,260.00 0
Hiring/Layoff costs $16,250.00 $39,000.00 $26,625.00
Inventory Cost $114,800.00 $50,240.00 $20,400.00
Total Costs $20,291,050.00 $20,275,500.00 $20,207,025.00
Key Factors  Flat Production
level
Inventory level
grow high
 Minimal Inventory
 Significant
overtime
production
required in peak
months
 Optimum levels
but fractional
values
 Assumption
(Start & End
Inventory is ‘0’)
Summary of Alternative Production Plans for XYZ Cabinets Manufacturing
Company:

Inventory Management: What is Inventory?
 INVENTORY is the stock of any item or resource used in an organization. These
items or resources can include: raw materials, finished products, component parts,
supplies, and work-in-process
Independent Demand
A
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand
Independent demand is uncertain.
Dependent demand is certain.
 starting point of inventory management is customer
demand; Inventory exists to meet customer demand

Inventory Management: Inventory Costs
1.CARRYING COSTS: Cost of holding an item in inventory; Vary with the level
of inventory and the length of time an item is held
 Carrying costs include: Rent, Heating, cooling, lighting Security, Record
keeping
2.ORDERING COSTS: Cost of replenishing inventory; Expressed as a dollar
amount per order
 Vary with the number of orders made; As the order size increases, ordering
costs decrease and carrying costs increase
 Ordering costs include: Purchase orders, Transportation and shipping,
Receiving, Inspection, Handling and storage
3.SHORTAGE COSTS
 Temporary or permanent loss of sales when demand cannot be met because of
insufficient inventory
 Customer dissatisfaction and loss of goodwill
 For internal demand, shortage can cause work stoppage or create delays

Inventory Management: Definition & ABC Inventory Classification
 An INVENTORY MANAGEMENT SYSTEM is the set of policies and controls
that monitor levels of inventory and determines:
o what levels should be maintained,
o when stock should be replenished, and
o how large orders should be.
 THE ABC CLASSIFICATION SYSTEM: An inventory classification system in
which a small percentage of (A) items account for most of the inventory value.
o In ABC analysis each class of inventory requires different levels of inventory
control
o The higher the value of inventory, the tighter the control
 Class A
o 5 – 15 % of units
o 70 – 80 % of value
 Class B
o 30 % of units
o 15 % of value
 Class C
o 50 – 60 % of units
o 5 – 10 % of value

Inventory Management: ABC Inventory Classification…
 Example: The maintenance department for a small manufacturing firm has
responsibility for maintaining an inventory of spare parts for the machinery it
services. The department manager wants to classify the inventory parts according to
the ABC system to determine which stocks of parts should most closely me
monitored. The parts inventory, unit cost, and annual usage are as follows:

Inventory Management: Inventory Control Systems
 Continuous Inventory Systems (fixed-order-quantity)
o Constant amount ordered when inventory declines to predetermined level
referred to as reorder point
 Periodic Inventory System (fixed-time-period system)
o Order placed for variable amount after fixed passage of time
 ECONOMIC ORDER QUANTITY (EOQ) MODELS: To determine how
much to order in a continuous system the economic order quantity (EOQ) model is
used
o BASIC EOQ MODEL: EOQ model is to determine the optimal order quantity
that will minimize total inventory costs
• ASSUMPTIONS:
Demand is known with certainty and is constant over time
No shortages are allowed
Lead time for the receipt of orders is constant
Order quantity is received all at once

Inventory Management: EOQ Model …

Inventory Management: Safety Stock
 SAFETY OR BUFFER STOCK
o Inventory level might be depleted at a slower or faster rate during lead time
o Buffer added to on hand inventory during lead time
Reorder Point = (Average daily demand)*(Lead Time) + safety stock
 𝑅 = 𝑑𝐿 + 𝑧𝜎 𝑑 𝐿
 𝑅 = 𝑑𝐿 + 𝑧 𝐿𝜎 𝑑
2
+ 𝑑𝜎𝐿
2

Day–2: Review
 What is Aggregate Production Planning?
o Understanding the Process of APP
o Aggregate Production Planning – Procedure
o Aggregate Production Planning – Strategies
o Level Strategy
o Chase Strategy
o LP Model
 Inventory Management
o What is Inventory?
o Inventory Costs
o What is Inventory Management?
o ABC Classification
o Inventory Control Systems
o EOQ Model
o Safety Stock

Outline:
 DAY–1:
 DAY–2:
 DAY–3:

Resource Planning for Manufacturing:

Master Scheduling: Def. & Linking with APP
 Master Production Schedule (MPS): Time–phased plan
specifying how many and when the firm plans to build each
end item
 tracks production output and matches this output to actual
customer
Aggregate Plan
(Product Groups)
MPS
(Specific End Items)
Product–A
Product–B
Product–C
Breaking by Week & by Product
 In Reality
o Demand and Production numbers in the
master schedule are unlikely to match the
sales and operations plan exactly.
o Actual capacity requirements might not
match the planning values.
 safety stock,
 schedule overtime, or
 take other measures to make up
difference between the plan and
reality.

Master Scheduling Process & Its Linkages:
MASTER
SCHEDULING
Beginning Inventory
Forecasted
Demand
Customer Order
(Booked Order)
Projected Inventory
Master Production
Schedule
INPUTS OUTPUTS
Master production schedule
linkages:

Developing Master Schedule Record:
 Master schedule records track several key pieces of information:
o Forecasted Demand (Estimated demand)  𝐹𝑡
o Booked orders (Confirmed demand)  𝑂𝐵𝑡
o Projected inventory levels  𝐸𝐼𝑡
o Production quantities (Master production schedule)  𝑀𝑃𝑆𝑡
• The amount of product that will be finished and available for sale at the beginning of
each week.
o Units still available to meet customer needs (Available To Promise)  𝐴𝑇𝑃𝑡
 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐄𝐧𝐝𝐢𝐧𝐠 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐸𝐼𝑡 = 𝐸𝐼(𝑡−1) + 𝑀𝑃𝑆𝑡 − 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 (𝐹𝑡, 𝑂𝐵𝑡)
 Available To Promise (ATP):
o ATP for first week of the Master Schedule Record:
• 𝐴𝑇𝑃𝑡 = 𝐸𝐼(𝑡−1) + 𝑀𝑃𝑆𝑡 − 𝑖=𝑡
𝑧−1
𝑂𝐵𝑖
o ATP for any subsequent week in which “MPS > 0”:
• 𝐴𝑇𝑃𝑡 = 𝑀𝑃𝑆𝑡 − 𝑖=𝑡
𝑧−1
𝑂𝐵𝑖
Case Study: (Open Excel
Sheet for the case study
data) MSR.xls

Material Requirement Planning (MRP):
 Materials requirements planning (MRP) Computerized inventory control and
production planning system
 how do firms actually organize things to turn materials into finished products?
Independent Demand
A
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand
Independent demand is uncertain.
Dependent demand is certain.
 “Once the independent demand is known,
the dependent demand can be determined”
 Dependent demand drives MRP
When to Use MRP?
Dependent and discrete items
Complex products
Job shop production
Assemble-to-order environments

Material Requirement Planning System: Def., Inputs & Outputs
 Based on a master production schedule, a material requirements planning
system:
o Creates schedules identifying the specific parts and materials required to
produce end items
o Determines exact unit numbers needed
o Determines the dates when orders for those materials should be released,
based on lead times
Material
Requirement
Planning (MRP)
Master Schedule
Bill of Materials
Inventory Records
Planned order releases
o Work Orders
o Purchase Orders
o Rescheduling notices
INPUTS OUTPUTS
 MRP process consists of four basic steps:
o Exploding the bill of material
o Netting out the inventory
o Lot sizing
o Time-phasing requirements

Material Requirement Planning System: Inputs …
 MRP Inputs:
1. Master Production Schedule
2. Bill of Material (BOM)
3. Inventory Record file
2. Bill of materials (BOM): A list of all of the items
needed to produce one unit of a product.
o Product structure tree: Visual representation of
BOM, where all components are listed by levels.
EXAMPLE # 1 (Product  Chair): BOM – Product Structure Tree
3. Inventory Record File: A database of information
on every item produced, ordered, or inventoried.
Gross requirements, Amount on hand, Lead times, &
more....
How many Legs we need
in order to produce 50
Chairs?

Material Requirement Planning System: Inputs…
EXAMPLE # 2 (Product  Clip Board): BOM – Product Structure Tree
Time-phased BOM: An assembly chart shows the lead time required to manufacture an item.
Assume Lead time for each item =1 week
How long it will take to assemble a
clipboard from scratch?
 Forward scheduling: start at today‘s date
and schedule forward to determine the
earliest date the job can be finished.
 Backward scheduling: start at the due date
and schedule backwards to determine when
to begin work.

Material Requirement Planning System: MRP Process & Matrix
 Exploding the bill of material
 Netting out the inventory
o Netting = (on-hand quantities + scheduled receipts) – Gross requirement
 Lot sizing: determining the quantities of items produced or purchased
 Time-phasing requirements
Lot Sizing in MRP Systems: MRP generates material orders; Order sizes / lots
can be chosen according to various objectives
o Lot-for-lot (L4L): Produce to cover next period
o EOQ: Apply the EOQ approximation for yearly demand
MRP
Matrix:

Material Requirement Planning System: MRP Process & Matrix…
EXAMPLE: School Mate Products

Following the same logic Gross
Requirements in Periods 4 and 5 develop
Net Requirements, Planned Order Receipts,
and Planned Order Releases

Following the same logic, the Lapdesk
MRP matrix is completed as shown

MRP & JIT / Lean Production:
How does
MRP work?
Units are PUSHED forward
according to the plan!
What about
JIT / Lean
Production?
Units are PULLED forward
only when needed!
Push / Pull
Decoupling
Point
Units are PUSHED forward to
a certain point.
Final configuration (PULL)
occurs only when the customer
demand occurs.

ANY QUESTION?
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Questions & Answers:

Production planning & control (PPC)

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