Production Planning for Management Consultants & Business Analysts

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Production Planning for
Management
Consultants
A practical guide on how to do analyses during
consulting projects

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Consulting firms are hired very often to help with Supply Chain and Production. During such
projects you will quite often have to look at the production planning and optimize it.

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Optimizing production planning will require a good understanding of what drives
production capacity as well as how production impact the costs in the whole supply chain

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In this presentation, I will show you how to perform fast and efficiently
different types of analyses related to production planning.

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Target Group What you will learn What you will get
 Management Consultants &
Business Analysts
 Analysts working in PE, VC funds
 People responsible for production
planning or sales forecasting in
corporations
 Production and Supply Chain
Managers
 What the production capacity
depends on
 How to find optimal production
batches for different products in
Excel that will minimize costs in
the whole supply chain
 How to plan production capacity
for the future
 Ready made analyses in Excel
 List of Recommended readings
(articles, books)

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This presentation will help you perform fast and
efficiently production planning analyses on the
level of top management consultants

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How the presentation is
organized

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In business you have to make a lot of important decisions
In this presentation, I will show you how to perform fast and
efficiently different types of analyses related to production planning

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Essential Methods for
Production Planning
KPIs and goals for
Production Planning
What Production Planning
does in practices
Kanban & Continuous Flow
Advanced Methods for
Production Planning
Capacity management

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What you will see in this presentation is a part of my online course where you
can find case studies showing analyses along with detailed calculations in Excel
Production Planning for Management
Consultants & Analysts
$190
$19
Click here to check my course

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What Production Planning does in
practice

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What Production Planning does
in practice – Introduction

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Production Planning Department does a lot of things. In this section we will
briefly show you what kind of issues they have to deal with in their daily work.

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In this section we will talk about the following things
What Production Planning
has to decide on?
Optimal sequence of
production batches
Definition of terms used in
Production Planning
What the production
capacity depends on?
Theory of constraints and
bottlenecks
Links to Sales Forecasting &
Purchasing Planning
Where Production Planning
can be in the organization?
Production Planning role in
different production
systems

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Definition of terms used in
Production Planning

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In production planning, we use many terms, which are not always clear.
In this part, we will discuss briefly the most commonly used terms.

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Let’s go through some definitions we will use in production planning
Production schedule Production batch
SKU
Bottleneck
Optimal production batch Capacity estimation

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Let’s start from SKU
 Stands for Stock Keeping Units
 SKUs represent different product characteristics, like: producer,
brand, size, color
 The main role of SKU’s is to help you keep track of your inventory
 SKU is defined as a code used by online or offline stores to identify
a specific product
SKU =

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So if you have big bottle of milk and small bottle they will be 2 different
SKUs although inside of the bottle you have the same product

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Let’s have a look at the definition of production schedule
 It is a timetable of production activities for products, assembles and
manufactured goods in given period of time
 Can be created in different planning stages: production planning,
material planning and manufacturing planning
 In short it shows you what will be produce on what machine or a
production line
Production schedule =

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Let’s have a look at an example of production schedule in a factory
producing juice
Production schedule Duration of the task
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Production Line 1
Production Line 2
Production Line 3
Production Line 4
Production Line 5
Apple juice
Blueberry juice
Orange juice

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Let’s have a look at the definition of production batch
 It’s a group of similar products that are produced together
 Each batch goes through one stage of the production process
before moving onto the next stage
 Bigger production batches tend to have lower cost of production
 On the other hand bigger production batches mean also much more
inventory
Production batch =

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Demand is usually scattered in time. By grouping it and producing it as one
batch we save money
Day 1 Day 2 Day 3 Day 4 Day 5
Demand Production batch

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Obviously I can have different batches. Only 1 of them will be optimal
Batch 1 Batch 2
Batch 3

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Let’s have a look at the definition of optimal production batch
Optimal production
batch
=
 A production batch that helps you achieve usually minimal costs or
maximal total gross margin
 In other words it’s the Optimal quantity of units that can be
produced at a minimal cost or maximal gross margin
 In the costs we include production costs as well as warehousing
costs, cost of frozen capital

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Let’s have a look at the definition of bottleneck
 It is a part of the process that limits the output of the whole system
 Bottleneck blocks the system and does not allow it to do more
 We can distinguish two types of bottleneck: short-term bottlenecks
and long-term bottlenecks
 Short-term bottleneck is caused by temporary problem
 Long-term bottleneck is caused by repetitive problems and have
impact on the whole production process
Bottleneck =

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Bottlenecks are dangerous as their hurt the efficiency of the whole system

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Example 1
7 5 7
Example 2
5 10 20
Example 3
5 5 3
x Stage capacity
What is the throughput of every system and where is the bottleneck?

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Let’s have a look at the definition of capacity estimation
 Allows you to define maximum production output with the
available resources in a given period of time
 The capacity can be estimated over days, weeks or months
 You have to know the capacity of specific machine, factory to be
able to plan properly the production
Capacity estimation =

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Let’s have a look at some example of capacity estimation in a cinema
Number of screens x
Number of seats
per 1 screen
=
Daily cinema
capacity
Number of movies
per day per screen
x
5 x 400
=
12 000 6
x
5 x 400
=
6 000 3
x
5 x 500
=
15 000 6
x
10 x 500
=
30 000 6
x

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What Production Planning has
to decide on

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Production planning defines several things. In this lecture, I will show
you what Production Planning Department has to decide on.

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Production planning is responsible for defining some of the following
things
Define sequence of
production
Capacity estimation
(by groups)
Optimal production
batches
Identity bottlenecks
Production schedule

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Let’s see how it works in practice
Sales demand / Sales
forecast
Production planning
Inventory level
Production capacity
Availability of people
Availability of machines
Availability of resources
Production schedule
Sequence of production
Production orders with
deadlines

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Optimal sequence of
production batches

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Next thing that quite of production planning does is to put batches in
some order. Let’s imagine we will produce 3 types of juice
Apple juice Blueberry juice Orange juice

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Let’s look how we can order those bottles
Option 1
Option 2
Option 3
Option 4
Option 5
Option 6
Apple juice Blueberry juice Orange juice
Apple juice Orange juice Blueberry juice
Orange juice Apple juice Blueberry juice
Orange juice Blueberry juice Apple juice
Blueberry juice Orange juice Apple juice
Blueberry juice Apple juice Orange juice

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To select the optimal option you have to take into consideration the
following parameters
Loss of juice in liters
Set-up time
Price of juice
Shifts
Lack of inventory of some
type of juice
Speed of the machine for
different types of juice

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Bottlenecks & Capacity –
Introduction

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We have discussed shortly main terms that are useful in production
planning
SKU
Bottleneck
Capacity estimation
Smart batching

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In the next few lectures, I will concentrate on bottlenecks and capacity
estimation
SKU
Bottleneck
Optimal Sequence of
production batches

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Bottlenecks are dangerous as their hurt the efficiency of the whole system

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Example 1
7 5 7
Example 2
5 10 20
Example 3
5 5 3
x Stage capacity
What is the throughput of every system and where is the bottleneck?

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The are 4 rules that you should follow when it comes to bottlenecks
 Identify what is the bottleneck
 Increase its throughput by lowering the time needed for everything that goes
through the bottleneck
 Add new resources to bottleneck
 Adjust everything to the bottleneck – so it works at the same pace

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Imagine that you are working in a company working in a content marketing
Research topics for a
post
Write a post Create illustration
Edit and modify
post, add illustration
and schedule
20 5 7 10
# of post that can
be done in a week
by 1 person
 Speed up the writing process (faster typing,
better tools, shortcuts for the most popular
words)
xx
20 8 7 10
10 9 10 10
 Make the researcher do also par time
writing and making illustration
 We have almost doubled the production of post with the same resources
 The same principles would apply if all the activities were done only by you – in this case it would mean that you
should not do too much research and illustration but rather improve the typing speed and match the number of
illustration and researches to your capacity in writing
 If you have no impact on the process (you are one of the guys above just doing his own part and your boss does not
want to listen to you then simply do less – identify the bottleneck and adjust your speed to his speed.
 On the other hand if you are the bottleneck then speed up because the whole team depends on you.

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Check the video on YouTube for more details
Click here to go to the video

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Content marketing agency –
Introduction

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Your friend Ivan works in a content marketing agency and wants to
improve the work of his team. Help him using the bottleneck framework

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A few things about Ivan team
He manages 4 people
Every person specializes only in
1 area
You measure your success in
number of posts produced

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Below you can see the production capacity for each and every stage.
1 person works on 1 stage only
post
Edit and modify
and schedule
20 7 10
# of post that can
be done in a week
by 1 person
xx
5

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Content marketing agency –
Solution

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Your friend Ivan works in a content marketing agency and wants to
improve the work of his team. Help him using the bottleneck framework

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Just as a reminder a few information about the content marketing
agency
He manages 4 people
Every person specializes only in
1 area
You measure your success in
number of posts produced

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Let’s see how we could increase production of posts
post
Edit and modify
and schedule
20 5 7 10
# of post that can be done in
a week by 1 person
 Speed up the writing process (faster typing,
better tools, shortcuts for the most popular
words)
xx
20 8 7 10
10 9 10 10
 Make the researcher do also par time
writing and making illustration
 We have almost doubled the production of post with the same resources
 The same principles would apply if all the activities were done only by you – in this case it would mean that you
should not do too much research and illustration but rather improve the typing speed and match the number of
illustration and researches to your capacity in writing
 If you have no impact on the process (you are one of the guys above just doing his own part and your boss does not
want to listen to you then simply do less – identify the bottleneck and adjust your speed to his speed.
 On the other hand if you are the bottleneck then speed up because the whole team depends on you.

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Production Capacity Improvement –
Case Introduction

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Now imagine that you were asked to increase the production capacity of a factory.
Try also to calculate the financial impact of the proposed scenarios.

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A few things about the firm
The product has to go through 3
stages
1 ton of product generates USD 30 of
margin after variable costs
You want to see to what level you can
increase production
Try to also estimate the impact on the
annual EBITDA of proposed changes

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Before we look at specific scenarios, we have to discuss 2 issues
What the production
capacity depends on?
How to calculate
monthly production
capacity

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What the production capacity
depends on?

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First let’s see what the production depends on assuming there is just 1
production stage
# of hours when
we were producing
=
Total number of
units produced
Units produced per
1 hour of work
x
Available time in
hours
x
% of Time used for
production
=
Total number of
units produced
Units produced per
1 hour of work
x
# of hours when
we were producing
Available time in
hours
x
% of Time used for
production
=

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Let’s calculate a simple example
Available time in
hours
x
% of Time used for
production
=
Total number of
units produced
Units produced per
1 hour of work
x
24 x 50%
=
Total number of
units produced
10
x = 120

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Now let’s imagine that we increase the % of Time used for production to 70%
Available time in
hours
x
% of Time used for
production
=
Total number of
units produced
Units produced per
1 hour of work
x
24 x 50%
=
Total number of
units produced
10
x = 120
24 x 70%
=
Total number of
units produced
10
x = 168

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The same results can be achieved by increasing units producer per 1 hour of
work
Available time in
hours
x
% of Time used for
production
=
Total number of
units produced
Units produced per
1 hour of work
x
24 x 50%
=
Total number of
units produced
10
x = 120
24 x 70%
=
Total number of
units produced
10
x = 168
24 x 50%
=
Total number of
units produced
14
x = 168

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Finally let’s see what other names we can use for this drivers of capacity
Available time in
hours
x
% of Time used for
production
=
Total number of
units produced
Units produced per
1 hour of work
x
Available time in
hours
x
Overall Equipment
Efficiency (OEE)
=
Total number of
units produced
Throughput per 1
hour
x

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How to calculate the capacity for
the whole month

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We said that the production depends on 3 drivers
Available time in
hours
x
% of Time used for
production
=
Total number of
units produced
Units produced per
1 hour of work
x
Available time in
hours
x
Overall Equipment
Efficiency (OEE)
=
Total number of
units produced
Throughput per 1
hour
x

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Now let’s see how it looks like for the whole month
Available time in hours
During the month x
Overall Equipment
Efficiency (OEE)
=
Total number of units
produced during the
month
Throughput per 1 hour
x
Available time in hours
During the month =
# of days during the
month when we work x
# of working hours per
day of work
produced during the
month
=
day of work x
Overall Equipment
Efficiency (OEE)
x

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Let’s have a look at a simple example
800 = 20 x 8 x 50% 10
x
produced during the
month
=
day of work x
Overall Equipment
Efficiency (OEE)
x

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You can increase production by changing different drivers
800 = 20 x 8 x 50% 10
x
produced during the
month
=
day of work x
Overall Equipment
Efficiency (OEE)
x
1 200 = 30 x 8 x 50% 10
x
1 200 = 20 x 12 x 50% 10
x
1 200 = 20 x 8 x 75% 10
x
1 200 = 20 x 8 x 50% 15
x
4 050 = 30 x 12 x 75% 15
x

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Production Capacity
Improvement – Scenarios

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Just as a reminder a few things about the firm
The product has to go through 3
stages
1 ton of product generates USD 30 of
margin after variable costs
You want to see to what level you can
increase production
Try to also estimate the impact on the
annual EBITDA of proposed changes

78
In previous lectures we said that the monthly production depends on 4
drivers
produced during the
month
=
day of work x
Overall Equipment
Efficiency (OEE)
x

79
Let’s instead of hours and throughput per day use hours and throughput
per shift
produced during the
month
=
day of work x
Overall Equipment
Efficiency (OEE)
x
produced during the
month
=
month when we work x # of shifts per day x
Overall Equipment
Efficiency (OEE)
Throughput per 1 shift
x

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In the case study we have 3 stages not one. So first we have to calculate
the potential capacity per stage
Total # of units going
through Stage 1 =
month when Stage 1
works
x
# of shifts per day for
Stage 1 x OEE for Stage 1
for stage 1
x
through Stage 2 =
month when Stage 2
works
x
for Stage 2
x
through Stage 3 =
month when Stage 1
works
x
for Stage 3
x
Total # of units
Produced =
Minimal # of units
going through every
stage

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In previous lectures we said that the monthly production depends on
number of drivers
through Stage 1 = 30 x 2 x 60% 12
x
x
x
Total # of units
Produced =
Minimal # of units
going through every
stage

82
In previous lectures we said that the monthly production depends on
number of drivers
through Stage 1 =
through Stage 2 =
through Stage 3 =
Total # of units
Produced = 432
432
720
540

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In the 1st scenario assumes that you will increase OEE for stage 1
The bottleneck is the stage 1 of
production
We want to increase the production
by moving OEE
Assume that you increase OEE from
60% to 70%
The change will require capex of USD
20 K

84
In the 2nd scenario assumes that you will increase the throughput for
stage 1
production
We want to increase the throughput
per shift
Assume that you increase throughput
from 10 to 16
The change will require capex of USD
30 K

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In the 3rd scenario assumes that you will increase the number of shifts
for stage 1
production
We want to increase the number of
shifts when we work
Assume that you increase the number
of shifts in stage 1
The change will require additional
Opex of USD 20 K

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In the 4th scenario assumes that you will combine all improvements
production
Combine all improvements from
scenarios 1-3
Remove the bottleneck in stage 3 –
increase throughput
The change will require additional
Opex of USD 20 K & Capex of 80 K

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One of the biggest problem for efficiency is the so called Parkinson’s
Law – Work expand so as to fill the time available for its completion

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People when asked to evaluate the time certain things will take build in
buffers
A B C
A + B + C
A + B + C
A B C Central buffer
Declared time
Buffer time
Real execution

90

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Links to Sales Forecasting &
Purchasing Planning

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Production Planning impacts a lot of areas. Let’s see how
it is linked to Sales Forecasting and Purchasing Planning.

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Production planning is linked with other planning activities
Sales forecasting Production planning Purchasing planning

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The way they influence each other will depend on the product and the
lead times for delivering necessary raw materials
Sales forecasting Production planning Purchasing planning
Sales forecasting Purchasing planning Production planning
Option 1 – short lead time or big inventory
Option 2 – long lead time

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Production Planning role in
different production systems

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The role of production planning will depend greatly on the
production system used. Let’s discuss this briefly.

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Bear in mind that production planning will have different role in
different production system
 You produce only if you have an order from the customer for specific good and quantity
 You don’t produce anything in advance
Make to order
 You produce ahead of time without a customer order
 You are trigged by changes in stock
 There is no direct link to the behavior of customers
Make to stock
 This is a mix of make to stock and make to order
 You produce components, semi products ahead of time (make to stock)
 The assembling of the finished product requires an order from customer (make to order)
Make to assemble
 You design product with the customer first
 Based on the design you start the production process
Make to engineer

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Let’s have a look at some examples
 Computers – Dell in B2B channel
 Carmakers – BMW
 Luxury products and accessories – Louis Vuitton
 Food – sushi
Make to order
 FMCG – L'Oréal,
 Fashion – H&M, GAP
 Food – mass production
 Computers – Dell, Asus in B2C channel
Make to stock
 Food – Subway
 Carmakers – spare parts, cheaper brands like Volvo, GM brands
 Chairs
 Fashion – Zara
Make to assemble
 Custom-made furniture manufacturers
Make to engineer

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Let’s see how the role of the production planning will differ
Make to order
 Aggregate orders from customers
 Check availability
 Create production order
 Define sequence of production
What production planning does
Make to stock
 Define optimal production batches, lead time and trigger mechanisms (on the basis of the inventory level)
 Define sequence of production
 Create production order for components
Make to assemble
 Define optimal production batches, lead time and trigger mechanisms (on the basis of the inventory level)
for components
 Define sequence of production of components
 Create production order for components
 Aggregate orders from customers (for finished products)
 Define sequence of assembling finished products
 Create production order for assembling

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Where can Production Planning
be located in the organization?

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In many cases, Production Planning is a part of the Production Department
CEO
Production Supply chain Sales Controlling / Finance
Production planning

102
In many cases, Production Planning is a part of the Supply Chain
Department
CEO
Production planning

103
It may also be a part of the Sales Department.
CEO
Production planning

104
Some firms prefer to place the Production Planning under the Controlling
Department
CEO
Production planning

105
Production planning can be also a separate department
CEO
Production Supply chain Sales Controlling Production planning

106
For more details and content check my online course where you can find case
studies showing analyses along with detailed calculations in Excel
$190
$19

107
Essential Methods for Production
Planning

108
Essential Methods for Production
Planning – Introduction

109
Let’s now move to production planning methods. In this section I will
show you the essential methods for finding optimal production batches.

110
Calculating optimal
production batches – case
study in juice production
2 stage production – case
study in cosmetics
Simple framework for
calculating the optimal
production batches
Scenario Analysis applied
to optimal production
batches – bakery case study
Scenario Analysis

112
On a typical production line there are many products produced. One
instance of producing the same product is called production batch.

113
8 000 2 000 3 000 1 000 2 000 2 000
Production batch is measured in quantity units and can be of different
size

114
TPA 8s
TPA 8a
TPA 21
TPA 8b
845 hl
701 hl
618 hl
860 hl
1 732 hl 1 375 hl
1 300 hl 1 300 hl
Average production
batch
Optimal production
batch
Quite often when you compare optimal with actual production batches
you see that a lot of money could be saved

115
In most cases, for every product, you should set minimal and safety stock
level and calculate the optimal batch. Below how the stock will change
S - Safety stock
level
Q – Optimal
production batch
LT – Lead time for
production cycle
Maximum stock
level
Stocks of
one SKU
Time

116
 Stocks level used to find moment, when work order should be send to Production
Department and production should be started
Let’s have a look at the definition of main terms used when looking for an
optimal batch
Safety stock level
Maximum stock
level
Optimal
production batch
Lead time for
production cycle
 Stock level achieved when Safety stock level is increased by optimal production batch = S
+ Q
 Batch size optimized to minimize costs of production and warehousing costs
 Time needed for goods production, quality control check up and transport to the
warehouse or distribution centers
Definition

117
Optimal production batch =
2 * Expected demand * Cost of readjusting production line
Cost of frozen capital
 Expected demand – average weekly sales in pieces
 Cost of readjusting production line – Costs of employees working during starting up production cycle, costs of media used and
waste of juice
 Cost of frozen capital – unit costs of raw materials used for production of goods
Total costs of production =
Cost of readjusting
production line +
Expected
demand
Optimal
batch
Optimal
batch
1
2
Cost of frozen
capital
* *
*
Fixed costs Variable costs
Minimizing
Below are the most typical used formula for optimal production batch
(EOQ – Economic Order Quantity)

118
Planning for 2-stage production

119
Bundle 1 Bundle 2 Bundle 3 Bundle 4 Bundle 5
Sometimes forecasting gets a bit complicated if you have to look at
bundles

120
Forecast bundles
Translate it into
products
Calculate optimal
batches
Prepare assemble
plan
Prepare production
plan
Produce &
assemble
For bundles you have to translate them into products and account for
optimal production batches

121
Scenario Analysis –
Introduction

122
Future is pretty difficult to figure out. You don’t know what will happen.
In those cases it is a good idea to consider a few different scenarios

123
Future is pretty difficult to figure out. You don’t know what will happen.
In this cases it is a good idea to consider a few different scenarios

124
Imagine that you are ice cream producer and you have to decide how much ice-
cream to produce for the next day without knowing what will be the weather.
Therefore, you have to consider different scenarios
Scenario 1 Scenario 2 Scenario 3
100 70 30

125
The scenario analysis consists of 5 steps
Define the thing
(goal function) you
want to analyze
Define which drivers
are the least certain
Define the scenarios
Define your
behavior / policy
Check the goal
function for every
policy
 You should be
analyzing the things
that are threatened
by different
scenarios and are
important for your
business
 It can be profit,
NPV from new
investment,
inventory you
should have etc.
 It is good to define
3-5 different
scenarios
 In every scenario
the main drivers
will have different
value
 You should assign
certain probability
to every scenario
 Scenarios do not
depend on you but
your behavior does.
 You can define a
policy / behavior
that helps you in a
specific situation
 Concentrate on
drivers that have
big impact and big
volatility
 The aim of this step
is to pick the right
policy, given the
scenarios and their
policy
 The best policy is
the one that gives
you highest
benefits (highest
goal function)

126
In the next lectures I will show you how to create and use scenario
analysis in practice using an example from airplane industry
Which price formula is the best
for my profits

127
Which price formula is the best for
my profits – Introduction

128
Now we will try to see which price formula is better for aircraft
maintenance service company
2 sites – in Poland and Croatia
Consider 4 different formulas
Consider 3 different scenarios

129
Materials
Scenario 1
 $ 30 K
Number of
manhours needed
 3 000 man-hours
Probability of the
scenario
 30%
Scenario 2
 $ 20 K
 3 400 man-hours
 25%
Scenario 3
 $ 15 K
 3 800 man-hours
 45%

130
Materials
Times & Materials
 Cost of Materials
increased by 15%
markup
Labor
 $ 50 per 1 man-hour
 We look at the real
man-hours needed
Fixed Fee
 $ 25 K
 $ 140 K
Mixed Option 1
 $ 25 K
 Fixed: $ 140 K
 On top of that 15% of
the labor cost
calculated using Times
& Materials formula
Mixed Option 2
 $ 25 K
 Fixed: $ 140 K
 On top of that for all
man-hours above 2
800 we use the Time
& Materials formula
but using the price of
$ 90 per 1 man-hour

131
Which price formula is the best for
my profits – Solution

132
Just as a reminder we were trying to decide which pricing formula is the
best for the MRO organization
2 sites – in Poland and Croatia
Consider 4 different formulas
Consider 3 different scenarios

133
It seems that the Mixed Option 2 price formula is the best solution
Gross Margin
In thousands of USD
90
58
84
117
Times & Materials Fixed Fee Mixed Option 1 Mixed Option 2

134
Production batch estimation for
a bakery – Case Introduction

135
Let’s imagine that a local bakery would like you to estimate the optimal
production batch for muffins. You will use for that scenario analysis.

136
A few information about the firm
30 locations in Poland
They sell coffee, own made cakes,
sandwiches and quiches
They would like to optimize the muffin
production process

137
The muffin production process consist of 3 main stages
Purchase of
materials
Preparation of
dough
Forming Baking Sales
 Prepare the
workplace
 Gather and add
needed ingredients
 Mix the ingredients
 Divide the dough
into two smaller
parts
 Store the part of
the dough for next
process
 Fill in the cake
molds
 Prepare the oven
 Transport the
muffins to bakery
oven
 Remove the muffins
from the bakery
oven after they
have been baked
and prepare them
for transport to
stores
 Muffin sales in own
stores
 Purchase needed
ingredients

138
Production process looks as follow
Stage 1 - Preparation of dough Stage 2 - Forming
Stage 3 - Baking

139
Production batch estimation for a
bakery – Solution

140
Just as a reminder you work for a local bakery that wants you to estimate the
optimal production batch for muffins. You used for that scenario analysis.

141
5 locations in Poland
They sell coffee, own made cakes,
sandwiches and quiches
They would like to optimize the muffin
production process

142
Options for Dough
Options for Baking and Forming
Option A - preparation of
250 pieces in 1 batch
Option B - preparation of
Option C - preparation of
Option D - preparation of
1200 pieces in 1 batch In Days of Sales
Option 1 - preparation of
120 pieces in 1 batch 35 892 34 230 33 111 32 205 0,4
200 pieces in 1 batch 31 546 29 884 28 766 27 860 0,6
400 pieces in 1 batch 28 288 26 626 25 507 24 602 1,2
800 pieces in 1 batch 26 658 24 996 23 877 22 972 2,4
In Days of Sales 0,8 1,2 2,4 3,6
Let’s have a look at the change in monthly cost of producing muffins
Currently
After
changes
Monthly cost of muffin production – cost of labor and warehousing
In EUR

143
$190
$19

144
KPIs and goals for Production
Planning

145
KPIs and goals for Production

146
In production planning, you have to decide how to organize production so that it is
optimal not only from the point of view of production but the whole supply chain

147
In this section, you will learn 3 things
Goals of production
planning
Production Planning value
drivers
Typical problems in
Production Planning

148
Goals of Production Planning

149
The primary goals of Production Planning is to efficiently produce, with
low inventory and providing at the same time on time delivery
Efficiency
of production
High Customer
Service
Low
inventory
 WIP
 Finished Goods
 Raw Materials
 High utilization
of machines
 Smooth production
 Low costs
 On time delivery
 Delivery according to
the order

150
Production Planning –
Value Drivers

151
Several drivers related to production planning have impacts on value
generation
Production planning
Allocation of products to
machines/ production routes
Machine utilization
Batch order
Batch size
Driver Impact on
 Machine efficiency, throughput,
waste level
 Delivery time, throughput, costs
 WIP level, lead time, efficiency of
machines, waste level
 Waste level, efficiency of
machines (set up time), lead time
Variability
 Delivery time, throughput, waste
level

152
Typical problems in Production
Planning

153
There are a few typical problem usually occur in production planning
Potential Problem
 Production planning process not linked with
sales planning process
Analysis needed
 Production planning does not take into account
machine park abilities, production plan does not
optimize OEE, i.e.:
 Short production batches
 Frequent setups / losses of raw materials
 Products not ascribed to machines on which
their production is optimal
 Low stability of production plans
 Real production differs from production plans
 Short planning horizon
Production
planning vs
sales planning
process
 Analysis of production planning process (process mapping)
 Number and scale of plan corrections a month / week
Production
planning vs
production
 Analysis of the logic behind planning model used (production for
warehouses vs. production as a realization of orders)
 Analysis of production planning efficiency (impact on OEE):
 Analysis of planned downtime (frequency and length of setups)
 Analysis of production batches length in comparison with optimal
batches length and scale of orders / sale in regarded period
 Analysis of the level of shortages on different production lines / for
different length of production batches
 Production planning horizon vs. „lead time” and stock management
model
 Real production vs. production plan

154
Advanced Methods for Production
Planning

155
Advanced Methods for Production

156
Now let’s have a look at more advanced methods of finding optimal
production batches. They can be very useful in more complicated case studies.

157
In this section, we will talk about the following things
Simulation method applied
in sales – case study
in logistics – case study
What is a simulation
method?
in optimal production –
case study

158
Simulation analysis –
Introduction

159
Future is pretty difficult to figure out. You can use scenario analysis or
you check ALL the potential options and see which is optimal

160
Imagine for a second that you have a small bakery trying to decide what is the optimal
number of cakes that you should bake. You want to use simulations to find out

161
For the producer of cakes that at the same time can bake from 1 to 10 cakes
using the simulation to find optimal production batch would entail calculating
the costs for all options

162
There are plenty of things you can do thanks to simulations
Find optimal solutions
Carry out sensitivity analysis
Plan & Forecast
Test the boundaries of the
system
Find weak spots
…..

163
In the next lectures I will show you how to use simulation in practice. I
will be talking about 2 examples
What will be the effect of the
price increase
Simulation of the whole
Logistics System

164
What will be the effect of the price
increase – Introduction

165
The impact of the price change on your profit will depend on a few
factors
How big the increase is
What your competition does?
How aware of prices are the
customers?
Price sensitivity
The role of the product you
are increasing the price
Components of the average
basket

166
Imagine that you want to estimate the price change impact for a small
chain of local coffee shops
20 location in Poland
Sell coffee, cakes, sandwiches and
quiches
3 different motives for going there

167
What will be the effect of the
price increase – Solution

168
Imagine that you want to estimate the price change impact for a small
chain of local coffee shops
20 location in Poland
Sell coffee, cakes, sandwiches and
quiches
3 different motives for going
there

169
If we look just at coffee gross margin, we should increase the price of coffee by
9%. If we look at the total gross margin, 4% price increase makes more sense
0
2 000
4 000
6 000
8 000
10 000
0% 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%
0
5 000
10 000
15 000
20 000
0% 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%
Gross Margin – Only for Coffee vs price increase
In thousands of USD
Gross Margin – Coffee and Cakes vs price increase
In thousands of USD

170
Juice Producer – Simulation of the
whole Logistics System – Introduction

171
Let’s imagine that you have to optimize the distribution
system of a juice producer located in Serbia.

172
Let’s imagine that you have to optimize the distribution system of a
juice producer located in Serbia
Introduction:
 You were employed by a producer of local brand of Cola to carry out an operational audit.
 The producer located in Serbia where it has a significant market share. Currently, apart from warehouse next to the production facility
in Subotica, it has two additional distribution centers in Novi Sad and Belgrade.
 Currently, the goods are collected from the distribution centers by shops
Tasks:
 Calculate the cost of the current system assuming that you have warehouses in all locations
 Assume that you use 24-tonnes trucks for transportation

173
Let’s imagine that you have to optimize the distribution system of a
juice producer located in Serbia
Subotica
Novi Sad
Belgrade
Niš
Kragujevac
Prijepolje

174
whole Logistics System – Tips

175
Simulations are pretty difficult, therefore, we will try to provide you
certain tips that will help you understand what we do in Excel.

176
Future is pretty difficult to figure out. You can use scenario analysis
or you check ALL the potential options and see which is optimal.

177
Imagine for a second that you have a small bakery trying to decide what is the optimal
number of cakes that you should bake. You want to use simulations to find out.

178
For the producer of cakes that at the same time can bake from 1 to 10 cakes
using the simulation to find optimal production batch would entail calculating
the costs for all options

179
There are plenty of things you can do thanks to simulations
Find optimal solutions
Carry out sensitivity analysis
Plan & Forecast
Test the boundaries of the system
Find weak spots

180
In our cases study, we will consider different options. To do the
simulation we should first estimate how many options there are.

181
Imagine that you are working for FMCG company that has 3 warehouses and is
wandering whether no to cut down number of warehouses. How many
different options you would have to examine?

182
In order to solve it thing about the supply chain system as if it was a system
consisting of 3 independent parts, 3 warehouses.
W1 W2 W3

183
Every warehouse can be a part of the system or can be excluded from it. In
other words, we have for every warehouse 2 options: closed or open
W1 W2 W3
Open
Closed

184
This means that to calculate number of all options we multiply the number of
options for every warehouse. Since we have 2 options per warehouse we get 8.
W1 W2 W3
2 2 2
x x = 8
Open
Closed

185
You may decided to exclude the option where there are no warehouses (all are
closed). In this case we would have 7 options we have to consider
W1 W2 W3
2 2 2
x x = 8
=
-1
7
Open
Closed

186
The last thing we want to do is to have automatic generator of
options. We know we have 8 different options we can consider.

187
First we will put 1 if the warehouse is open and 0 if it is closed
= Open = 1
= Closed = 0

188
We have 3 potential locations for warehouses.
Subotica Novi Sad Beograd

189
The following sequence of 1 and 0 would mean that Subotica is opened,
Novi Sad is closed, and Beograd is open
1 0 1

190
If on the other hand we had the following sequence, Subotica and Novi Sad
would be opened and Beograd would be closed
1 1 0

191
As you may remember we have 8 different options for 3 warehouse. For 3
warehouse you could list them for 10 it would be very difficult
1 0 0
0 1 0
1 1 0
0 0 1
1 0 0
0 1 1
1 1 1
0 0 0

192
The number of options can be calculated using the following formula.
This means that for 10 warehouses there are 1 024 options.
𝟐# 𝒐𝒇 𝒘𝒂𝒓𝒆𝒉𝒐𝒖𝒔𝒆𝒔
=
# of options
𝟐𝟑
=
8
𝟐𝟓
=
32
𝟐𝟏𝟎
=
1 024
# of warehouses
3
5
10

193
To speed up the generation of options we will treat the sequence of 1 and 0
that corresponds to specific option as a binary number
1 1 0

194
Binary number can be converted into well known decimal number. You
have to multiply the 0 or 1 by the right power of 2 (depending on the
position)
1 0 0
𝟐𝟐
𝟐𝟏
𝟐𝟎
𝟒 𝟐 𝟏

195
Let’s convert 1 0 0 in binary system into decimal system. As you can see it is
4 in decimal system.
1 0 0
𝟒 𝟐 𝟏
X X X
= = =
4 0 0 = 4

196
So, every option can be converted in decimal system
1 0 0
0 1 0
1 1 0
0 0 1
1 0 0
0 1 1
1 1 1
0 0 0
4
2
6
1
5
3
7
0 or 8
=
=
=
=
=
=
=
=

197
In Excel we will do the opposite thing. We will convert decimal number into
binary number. Every binary number will be a different, unique option
Number in decimal
system from 1 to 8
Binary Number A unique option
4 1 0 0
Warehouse only in
Subotica
2 0 1 0
Warehouse only in Novi
sad
7 1 1 1
Warehouses in all 3
locations

198
We do that to make a scalable solution. For 3 warehouse we could easily list all
options manually . For 10 warehouse it would not be possible
𝟐# 𝒐𝒇 𝒘𝒂𝒓𝒆𝒉𝒐𝒖𝒔𝒆𝒔
=
# of options
𝟐𝟑
=
8
𝟐𝟓
=
32
𝟐𝟏𝟎
=
1 024
# of warehouses
3
5
10

199
Juice Producer Case Study –
Steering variables

200
Since we want to use a simulation, we will have to make our calculations
dynamic. For that we can use the so-called steering variables.

201
Steering variables act like a switch that can easily allow us to include or
exclude certain part of the model, depending on the situation.

202
Let’s see how we would calculate the costs of warehousing for the whole
system assuming that we have 3 warehouse.
Costs of Warehouse in
Subotica +
Novi Sad
=
Cost of warehousing
Beograd
+

203
Unfortunately for every option we would have to have a different formula
Warehouse only in
Subotica
Subotica +
Novi Sad
=
Cost of warehousing
Beograd
+
Warehouse in Novi Sad
& Beograd
Novi Sad
=
Cost of warehousing
Beograd
+
Warehouse only in
Beograd =
Cost of warehousing
Beograd
Warehouse in Subotica
& Novi Sad
Subotica +
Novi Sad
=
Cost of warehousing
Warehouse only in
Subotica
Subotica
=
Cost of warehousing

204
Not to have every time a different formula we will use 3 steering variables
Steering
Variable for
Subotica
Steering Variable
for Novi Sad
Steering Variable
for Beograd
= S =
 Will be equal to 1 if the warehouse in
Subotica is open
Subotica is closed down
= N =
 Will be equal to 1 if the warehouse in Novi
Sad is open
 Will be equal to 0 if the warehouse in Novi
Sad is closed down
= B =
Beograd is open
Beograd is closed down

205
Thanks to this we can create one general formula that automatically will
adjust to the situation
Subotica
+
Novi Sad
=
Cost of warehousing
Beograd
+
x S x N x B

206
whole Logistics System – Solution

207
At the end thanks to the simulation we get the optimal solution. It
seems that regional warehouse don’t make economical sense
1 611
1 480
1 838
2 431
2 788
2 657
3 015
1 254
Warehouse only in
Subotica
Warehouse only in
Novi Sad
Warehouse in
Subotica and Novi
Sad
Warehouse only in
Belgrade
Warehouse in
Subotica and
Belgrade
Warehouse in Novi
Sad and Belgrade
Warehouse in all
locations
No warehouses
Total cost of end products distribution system
In thousands of USD

208
Juice Producer Case Study –
Number of trips

209
As you have probably noticed we calculate the number of trips to specific
location in a simplified way. Let’s see what is the logic behind.

210
Every truck has 2 limitations when it comes how much cargo it can take
Truck’s limits
Weight limits Cargo size limits
 24 tons of cargo  24 pallets

211
The number of trips you have to do will be based on the bigger number
of trips
Truck’s limits
# of trips based on
the weight
# of trips based on
the size
# of trips = Maximal of
both

212
Let’s see how we calculate the number of trips based on 2 criteria
Cargo Transported in tons
# of trips based on the weight =
Maxima cargo you can put in
the truck in tons
Cargo Transported in pallets
# of trips based on the size =
Maximal # of pallets you can
put in the truck

213
In the case of juice the weight of 1 pallet is around 0.6 ton and that is
why the # of trips based on the size will be also the maximal one
Cargo Transported in
tons
# of trips based on the
weight =
Maxima cargo you can
put in the truck in tons
Cargo Transported in
pallets
# of trips based on the
size =
Maximal # of pallets you
can put in the truck
1 440
=
24
2 400
=
24
= 100
= 60

214
That’s why we actually only calculate the number of trips based on the
size (number of pallets)
Truck’s limits
# of trips based on
the size
# of trips = # of trips
based on the size

215
Cheese Production –
Case Introduction

216
Let’s imagine that you are working for a cheese producer. Your task is to
find the optimal production batch. Use for this the simulation method.

217
Imagine that you want to estimate the production batch for your
company
One of the biggest cheese producer in
Poland
Production of long-ripening cheese
Cooperation with over 1 000 local
suppliers

218
$190
$19

220
Kanban & Continuous Flow –
Introduction

221
The role of the production planning will be different if you have implemented
continuous flow with kanbans. We will discuss in this section what is continuous flow.

222
How to implement
continuous flow
Continuous flow in services
What is Kanban?

224
Since each person is not talking to each other you are creating a lot of work
in progress (WIP) that you have to throw away
Cut the bread
Cut cheese
Cut the meat
Assemble the
sandwich
20
15
10
6
10
X
Hourly Capacity in pieces
Inventory in pieces
14
9
4

225
By introducing Kanban you limit the work in progress / inventory
Cut the bread
Cut cheese
Cut the meat
Assemble the
sandwich
20
15
10
6
10
X
Inventory in pieces
Kanban

226
Below some examples of kanban

227

229
Consulting is a place where the work is very volatile – one day you work 15
hours and next day you have nothing to do. What you want to do is use the
time of low activity to somehow prepare yourself and absorb periods of high
activity
1 2 3 4 5 6 7 8 9 10 11 12

230
Therefore you create a shelf of tasks to be done once you are free. This to-dos
should be properly selected and structured and can have the form of a Kanban

231
Below you have an example of defining of to-dos for the Kanban shelf
Product
development
Read articles
Read 5 articles
Read 5 articles
Read 5 articles
Read book
Read 50 pages
of 1 book
Read 50 pages
of 1 book
Read 50 pages
of 1 book
Product
proposal
Draft in pencil
Draft in PP
Fill in 5 slides
Fill in 5 slides

232
Tasks from the Product development exercise you put into the Kanban
Education Product development Sales

233
There are number of things that you can put on the shelf
Learning new tools
Learning new skills
Improving skills
Project preparation
Knowledge base preparation
Training preparation
Conduct training (esp. lesson
learnt)
Business development
Template preparation
Product Development

234
Continuous Flow –
Introduction

235
Ideally you would like to have a continuous flow of goods
 Each process “speaks” to each other and it is enough to
say to the last one what you want. The rest will follow
 Pull process not a push process
 We produce only what the customer needs and exactly
as much as he wants
 Hardly any inventory
 We use efficiently resources especially people

236
In order to implement it in real life we have to define some terms
Hourly capacity
 Number of semi-products / parts that can be
produced by a specific worker
Cycle Time (CT)
 Time in minutes needed to produce 1 semi
product /part by a specific worker
=
=
Hourly Capacity =
60
Cycle Time (CT)
Takt time
 Frequency with which the product is demanded
by the customer
=
Cycle Time (CT) ≈ Takt time

237
Continuous flow gives you a lot of advantages
Short cycle time
Less inventory
Higher quality
Fewer inefficiency
Better usage of people
Less space
Faster servicing of the customer
Lower need for
transportation
Lower costs

238
How not to make continuous flow
– sandwich factory

239
Imagine that you have a small factory producing sandwiches

240
You have 4 people. Each of them does the sandwich from beginning till the end
Cut the bread
Cut vegetables
Fry vegetables
Cut the cheese
Assemble the sandwich
Pack the sandwich
4
5
3
6
7
11
36
x
CT in
minutes

241
You have 4 people. Each of them does the sandwich from beginning till the end
Cut the bread
Cut vegetables
Fry vegetables
Cut the cheese
Pack the sandwich
4
5
3
6
7
11
36
36
x
CT in
minutes

242
If you divide the activities and give 1 activity per person you can lower the
waiting time of the customer
Cut the bread
Cut vegetables
Fry vegetables
Cut the cheese
Pack the sandwich
4
5
3
6
7
11
36
Cut the bread
Cut vegetables
Fry vegetables
Cut the cheese
Pack the sandwich
3
4
2
4
6
10
29
All operations done by 1 person Division of work and specialization
10
x
CT in
minutes

243
Yet since each person is not talking to each other you are creating a lot of work
in progress (WIP) that you have to throw away
Cut the bread
Cut vegetables Fry vegetables
Cut the cheese
Assemble the
sandwich
Pack the
sandwich
15
30
20
15 10
6
10
3
2
4
4 6
10
10
80
X
CT in minutes
Inventory in pieces
120
40
32
40

244
When we compare the 2 options we can see that there are some strong
advantages of the division of work yet is causing lot of waste
All operations done by 1 person Division of work and specialization
 4
# of people  6
 36 minutes
Total cycle time
needed to produce
the sandwich
 29 minutes
 We are not using the people – no
customer cannot do anything
Type of waste  We are wasting food that we have to
throw out at the end of the shift
 36 minutes
Time the customer
awaits for the
product
 10 minutes
 None; just raw materials
Inventory of Work
in Progress
 A lot . The biggest in vegetables – for 120
sandwiches

245
How to make continuous flow
– sandwich factory

246
If we want to limit the waste we will have to look at the cycle time of each
and every operation. As you can see this is due to the fact that some
process are much faster than the things that follow after them. You have to
get even cycles
3
2
4 4
6
10
Cutting Bread Cut Vegetables Cut Cheese Fry vegetables Assemble sandwiches Pack the sandwich
Takt time

247
The are number of ways in which you can try and get the even cycle
times
 Combine two operations
 Divide 1 operation into many
 Speed up the operation
 Put Kanban between the 2 process or FIFO lane and limit the time
of specific worker spend on the working station

248
We know that customers want to eat 6 sandwiches during the hour. It
means that we need cycle time of 10 for every process
10
6

249
Let’s see what we can do with our cycle times
3
2
4 4
6
10
Cutting Bread Cut Vegetables Cut Cheese Fry vegetables Assemble sandwiches Pack the sandwich
Takt time

250
We can combine some of the processes to get to the pace required by
the customer for every processes
7
6 6
10 10
Cutting Bread & Cut
Cheese
Cut Vegetables & Fry
vegetables
Assemble sandwiches Pack the sandwich Required by customer
demand

251
In this we lower down the inventory drastically and have fewer people
Cutting bread &
Cut Cheese
Cut & fry
vegetables
Assemble the
sandwich
Pack the
sandwich
10
8,6
10
6
10
7
6 6
10
10
0
X
CT in minutes
Inventory in pieces
11
21

252
Let’s see how the 3 options compare with each other
All operations done by
1 person
Division of work and
specialization
 4
# of people  6
 36 minutes
Total cycle time
needed to produce
the sandwich
 29 minutes
 We are not using the
people – no customer
cannot do anything
Type of waste  We are wasting food that we have to throw out at the end of the shift
 36 minutes
Time the customer
awaits for the
product
 6 minutes
Inventory of Work
in Progress
 A lot . The biggest in
vegetables – for 120
sandwiches
Continuous Flow CT
10; no limiting lanes or
Kanban
 4
 29 minutes
 6 minutes
 21 sandwiches are
thrown and 11 sets of
vegetables for
sandwiches

253
In this we lower down the inventory drastically and have fewer people
Cutting bread &
Cut Cheese
Cut & fry
vegetables
Assemble the
sandwich
Pack the
sandwich
10
8,6
10
6
10
7
6 6
10
10
0
X
CT in minutes
Inventory in pieces
11
21

254
If we put FIFO lanes and kanbans we can further improve
the customer experience and lower
Cutting bread &
Cut Cheese
Cut & fry
vegetables
Assemble the
sandwich
Pack the
sandwich
10
8,6
10
6
10
7
6 6
10
10
CT in minutes
Lane limiting the inventory
FIFO Lane
Max 1
FIFO
Lane
Max
2
FIFO Lane
Max 2
FIFO Lane
2
Kanban

255
Let’s see how the options compare with each other
All operations done by
1 person
Division of work and
specialization
 4
# of people  6
 36 minutes
Total cycle time
needed to produce
the sandwich
 29 minutes
 We are not using the
people – no customer
cannot do anything
Type of waste  We are wasting food that we have to throw out at the end of the shift
 36 minutes
Time the customer
awaits for the
product
 6 minutes
Inventory of Work
in Progress
 A lot . The biggest in
vegetables – for 120
sandwiches
Continuous Flow CT
10; no limiting lanes or
kanban
 4
 29 minutes
 6 minutes
 21 sandwiches are
thrown and 11 sets of
vegetables for
sandwiches
Continuous Flow CT
10; lanes and Kanban
 4
 29 minutes
 0 minutes
 2 packed sandwiches
 2 almost ready
sandwiches
 2 sets for sandwiches

256
Production Planning in Continuous
flow

257
Production Planning in
Continuous flow – Introduction

258
Production planning will look differently if you have implemented continuous flow and Kanban.
In this section, I will show you how to calculate optimal production batch and Kanban size.

259
In a traditional push system, you send the production order to Stage 1. On top
of that you have to create plans, set priorities for other stages as well
Production planning Stage 1
Stage 2
Stage 3
Production Orders
& Main Plan
Plan
Plan

260
If you implement continuous flow and Kanban you send the plan to the last
stage that thanks to the pull system will relate it to other stages.
Production planning Stage 1
Stage 2
Stage 3
Kanban
Plan
Kanban Size
Kanban Size
Optimal
Production Batch
Optimal
Production Batch
Optimal
Production Batch

261
Kanban in Burger Chain
– Case Introduction

262
Let’s imagine that you have to define the
Kanban for French fries in a chain of burgers.

263
They have a chain of 200 restaurants
In the buffer there should be at least
French fries for 5 minutes
In the buffer there should be at most
French fries for 10 minutes
Find optimal size of the buffer /
Kanban for different situations

264
Kanban in Chair Production with 1
element – Case Introduction

265
Let’s imagine that you have to calculate the size of the
Kanban for legs in a furniture factory.

266
You have to produce 3 different
components for chairs
Before assembling there will be
Kanban for components
Estimate how much inventory we
need in the Kanban for legs
Estimate how many Kanban cards
there will be in circulation for legs

267
Let’s have a look how the whole production process looks like.
Produce set of legs
Produce the back
Produce the seat
Assemble the
chair

268
We want to put a supermarket with Kanban cards that will help us to keep the
assembling going and limit the inventory level at the same time
Produce set of legs
Produce the back
Produce the seat
Assemble the
chair
Kanban

269
We will concentrate now only on the production of legs
Produce set of legs
Produce the back
Produce the seat
Assemble the
chair
Kanban

270
Let’s have a look at the formula we will use to calculate how much
Inventory of prepared legs we need for assembling
Inventory of set of legs
required =
Time required to
create a production
batch of legs in hours
x
Average demand for
legs per 1 hour (from
assembling)
x
1 + Safety Margin in
percentage
required =
Lead time to create 1
production batch in
hours
x
Average demand for
assembling)
x
percentage

271
Obviously the inventory will change as the lead time, demand and
safety margin will change
required =
production batch in
hours
x
Average demand for
assembling)
x
percentage
25 set of legs = 2 x 10 x 125%
13 set of legs = 1 x 10 x 125%
50 set of legs = 2 x 20 x 125%
60 set of legs = 2 x 20 x 150%

272
The assembling is done in a different building so to inform the production of
legs we will use Kanban cards to send signals.
Produce set of legs
Assemble the
chair
Kanban
Kanban
card

273
1 Kanban card will mean that the production needs 10 new sets of legs.
1 Kanban Card =

274
Let’s see how we can calculate the number of Kanban cards that we
need to correspond to the required inventory
required =
production batch in
hours
x
Average demand for
assembling)
x
percentage
# of Kanban Cards =
Inventory of set of
legs required ÷
Number of set of legs
assigned to 1 Kanban
Card

275
The number of Kanban Cards will change with changes in inventory and
number of set of legs assigned to 1 card
# of Kanban Cards =
Inventory of set of
legs required ÷
Number of set of legs
Card
6 = 60 ÷ 10
5 = 50 ÷ 10
12 = 60 ÷ 5

276
Kanban in Chair Production with 2
elements – Case Introduction

277
Let’s continue our case study with the chair producer.
This time we will have 2 types of legs.

278
Now we produce 2 types of set of
legs: white and brown
Estimate the optimal production
batches
Estimate the inventory required
Estimate the number of Kanban cards
required

279
Now we will consider a situation where we have 2 types of legs – white and
brown
Produce set of legs
Assemble the
chair
Kanban
Kanban
Cards for white legs
Minimal production
batch
Kanban card for
brown legs

280
From the previous case study, we know how to calculate the inventory and
number of Kanban cards.
required =
production batch in
hours
x
The average demand
for legs per 1 hour
(from assembling)
x
percentage
# of Kanban Cards =
Inventory of set of
legs required ÷
Number of sets of legs
Card

281
Since we have now 2 types of components more tricky will be the lead
time
required =
production batch in
hours
x
The average demand
for legs per 1 hour
(from assembling)
x
percentage
# of Kanban Cards =
Inventory of set of
legs required ÷
Number of sets of legs
Card

282
Since we have now 2 types of components more tricky will be the lead
time
production batch in
hours
=
Full Process Time
needed to create 1
batch
Waiting time
+
Full Process Time
needed to create 1
batch
=
Pure production time
without setups to
create 1 batch
Change over / Set-up
time
+
Waiting time =
Waiting time to get
sufficient demand for
1 batch
Potential waiting time
to finish other
products
+
production batch in
hours
=
Pure production time
without setups to
create 1 batch
Change over / Set-up
time
+ +
Waiting time to get
sufficient demand for
1 batch
Potential waiting time
to finish other
products
+

283
How to calculate production batch
in continuous flow

284
In continuous flow we want to make the production batches as short as
possible. We calculate the minimal production batch using backward reasoning
Total Available time
Time needed to produce the goods without the set-up time
(changeover time)
Maximal time for set-ups
(changeover)

285
Once we have the total maximal time for set-ups I can calculate how many set-
ups I can do and the minimal size of production batch
Maximal time for
set-ups (changeover)
per 1 SKU
=
Maximal time for
for all SKUs
÷ # of products / SKUs
Maximal # of setups
per SKU =
Maximal time for
per 1 SKU
÷ Duration of 1 set-up
Minimal size of
production batch =
Total demand for
specific SKU ÷
Maximal # of setups
per SKU

286
Let’s have a look at an example
Total Available time = 480
Time needed to produce the goods without the set-up time
(changeover time) = 300
Maximal time for set-ups
(changeover) = 180

287
Let’s have a look at an example
Maximal time for
per 1 SKU
=
Maximal time for
for all SKUs
÷ # of products / SKUs
Maximal # of setups
per SKU =
Maximal time for
per 1 SKU
÷ Duration of 1 set-up
Minimal size of
production batch =
Total demand for
specific SKU ÷
Maximal # of setups
per SKU
90 = 180 ÷ 2
2 = 90 ÷ 45
200 = 400 ÷ 2

288
$190
$19

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