Download - CSI approach to your Production Management
CSI approach to your Production Management
Director Andrius Gudaitis 2013.05.171
Nova days key success factor in production and business environment is a correct Information
Flow Management.
Almost all companies work with constantly grown SKU’s and raw materials numbers, that leads to working with smaller and smaller batches, shorter and shorter lead times and higher and higher quality standards which brings high complexity in effective production flow management.
To manage and visualize Information Flow became necessary condition for effective production, business management or any kind of improvements – LEAN, TOC, Six Sigma or TLS.
2
Meaning of Proginta’s service is to arrange company Information Flow in such way that Company Management or Lean, TOC, Six Sigma consultants could easily recognize where to put their attention to increase efficiency of
production flow and reliability of client service.
3
Proginta inc.
— Proginta inc. combine different competences, software development and ERP deployment with deep international consultant expertise based on supply chain and production management using latest TOC and LEAN tools and methodology.
— Proginta inc. provide Information Flow management services in Ukraine and Europe from 2008. Services included classic consulting, IT solutions, daily work with data flow and Reports. The goal of our service is to help a client to improve their financial result.
4
We would like to repeat
Proginta inc. strive to present analytical information in a way that it is easily comprehended even without consultant’s help.
Thus, company management gets a clear understanding where to focus their efforts for a much greater improvement in current
results.
5
Methods we have competencies in:
6
Proginta are focusing on three main flows or processes
Information flow
Money flow
Products and services flow
7
Proginta offer a long term service of:
—Systemic analysis of the entire company
or
—Localized analysis of client’s choice according to Lean and/or TOC as a pilot project.
8
Systemic analysis
9
Quantum cryptography vs. Business
10
Production efficiency depends on the efficiency of every link in production chain: supply, production departments and logistics.
Problems that disrupt production plan are generally known and taken care off as they arise.
This “fire-fighting” method does not provide systemic improvement, because it fights symptoms and not the cause of the problems.
11
Systemic analysis must specify production disturbance causes and their ratings.
This allows to determine causes that affect production plan the most weekly and monthly.
3/5/2
013
3/6/2
013
3/7/2
013
3/8/2
013
3/9/2
013
3/10/2
013
3/11/2
013
3/12/2
01305
101520253035
Defective RMOrder sequence changesIssues with working toolsQuality defectsEquipment failureRaw materials/components supply
Raw materials/components supply
26%
Equipment failure12%
Quality defects16%
Issues with working tools21%
Order sequence changes
7%
Defective RM19%
12
Production process
Ord
er d
ispa
tch
Mat
eria
l arr
ival
Mat
eria
l ord
erin
g
Mat
eria
l iss
uing
Ele
men
t tra
nsfe
r to
dep
artm
ent X
Ele
men
t tra
nsfe
r to
dep
artm
ent Y
Pro
duct
arr
ival
to w
areh
ouse
13
Systemic analysis measures:
— Reliability of material supply;— Planned production starts;— Timely transfers between departments;— Timely production task completions;— Timely order dispatches;— Etc.
14
Production process
Ord
er d
ispa
tch
Mat
eria
l arr
ival
Mat
eria
l ord
erin
g
Mat
eria
l iss
uing
Ele
men
t tra
nsfe
r to
dep
artm
ent X
Ele
men
t tra
nsfe
r to
dep
artm
ent Y
Pro
duct
arr
ival
to w
areh
ouseMeasures in selected points:
— DDP%,— OS,— TVD.
15
Rating’s purpose:
DDP (Due Date Performance) % indicates reliability of a link.
If a link does not have buffer or excess production capacity, then the following link receives a lag.
Common causes:— Untimely material supply;— No material supply;— Previous link changed production tasks;— Incorrectly planned production scope;— Etc.
16
Rating’s purpose:
OS – Over Stock
Common causes:— Overprotection;— Production oriented efficiency;— Preceding departments starts tasks too early;— Incorrectly planned queue in preceding departments;— Large production batches policy;— Etc.
17
Rating’s purpose:
TVD (throughput value days)
This rating evaluates financial lag of delays.
It allows to analysis financial impact of delays and to allocate TVD points to the source of delay.
18
Required data:
DDP%
Initial data – detailed production plan:— Production start time, end time and planned
amount;— Equipment preparation time;— Single element processing cycle;— Equipment standby time.
Operations data:— Reasons for changing production plan;— Amount produced between X and Y hours;— Accumulated lag.
19
Data required for planning:
— Number of resources in a department;
— Resources’ work schedule;
— Shifts’ work schedule.
20
Data required for planning:
— Material supply schedule
Evaluation of material supply reliability (ordered vs. arrived)
Material arrival terms (lead time, on request)
Procurement plan forming
21
,etc.
Your ERP system
Data (or VSM) required for planning:
— Production route.
— System that plans according to production routes.
22
Data entry forms - Production routing (operations sequence and resources) description
If current systems cannot store required data…
23
Data entry forms – Bills of material and Product assembly tree
If current systems cannot store required data…
24
Data entry forms – Job sequence management with raw control
If current systems cannot store required data…
25
Data entry forms – Job execution management with barcode or custom instruments
If current systems cannot store required data…
26
Data entry forms – Job sequence management per resource usage
If current systems cannot store required data…
27
Acquiring actual dataSupply Production Warehouse
28
Acquiring actual data
Procurement data:
1) Ordered materials: item number, quantity, planned delivery time;2) Materials’ total received quantity;3) Factors that cause delivery lag;4) Reasons for changing delivery time before delivery term.
First two acquired from business management system, others – from analysis.
Supply Production Warehouse
29
Acquiring actual data
Production data:
1) Does the task have all required materials?2) Which tasks executed according to plan?3) What are the reasons for changing production plan?4) What have caused production lag?5) How tasks are queued?6) What production facilities are required?
Evaluate how much data is in BMS (SAP, Oracle & etc) and how much to give to external system.
Supply Production Warehouse
30
Acquiring actual data
To collect information that is transmitted between work centers/departments required :
1) What was the plan?2) What was produced at a specified time and in
what quantities?3) What have caused lags?4) How much spoilage was produced? What was
it?5) What was material spoilage?
Is it possible to indicate in IT system, that department has excess resources or buffer?
Supply Production Warehouse
31
INSTRUMENTS:
+
Acquiring actual dataSupply Production Warehouse
32
Bottleneck operations data:
1) How production is queued?2) What is equipment's preparation time?3) What is the time for single element cycle?4) What causes lags?5) How much spoilage produced? What was it?6) What was material spoilage?7) Etc.
Data sources include automated data collection systems (Lean2S) and IP video cameras monitoring operations or employees’ behavior.
Acquiring actual data
Orders data:
1) What is order dispatch plan?2) How many orders dispatched according to plan?3) What have caused lags?4) Reasons for changing dispatch time before dispatch term.
First two acquired from business management system, others – from analysis.
Supply Production Warehouse
33
Our aim to identify what causes plan changes and lags, order completion lags, production starts too early or produce to much.
It enables to isolate recurring causes that adversely affect the results.
It also enables systematic application of Kaizen or POOGI improvement mechanism.
34
Automated data collection
35
Simple & Smartsolution
36
The importance of data transmission reliability
While researching production efficiency solutions we discovered that data reliability is very important:
Given example demonstrates data distortion. First chart depicts actual work cycle. Second charts depicts situation when there were no connection to database between minutes 5 and 10:— System did not record 7:00–9:00 downtime.— This distributed and hid the 5:00–6:00 spike.— Spike that occurred right before the downtime compensated the latter. This would not be visible in summarized data and would not draw attention to such event.
Summarizing distorts data.
People who make decisions divide into camps: those
who agree with the data and those who do not.
People who do not agree with the data do not participate in
decision realization.
1
2
37
1:002:00
3:004:00
5:006:00
7:008:00
9:0010:00
11:0012:00
13:0014:00
15:0016:00
17:0018:00
19:0020:00
0
10
20
30
40
50
60
pcs/min
1:002:00
3:004:00
5:006:00
7:008:00
9:0010:00
11:0012:00
13:0014:00
15:0016:00
17:0018:00
19:0020:00
0
10
20
30
40
50
60
pcs/min (from 05 to 10 min lost connection)
pcs/min lost connection
Protection against data loss
Considering possible data loss, we have implemented solutions that minimize the risks to an absolute minimum:
1) Data receiver preserves data of up to 10 hours of work while database is down.
2) Data receiver preserves collected data indefinitely while database is down.
3) Software informs personnel via e-mail or SMS when communication is down, allowing quick troubleshooting and data preservation.
38
Data integrity
Companies usually use various types of equipment from different vendors.
Consequences of processing data by different software:
1) Different software presents the results differently.
2) Users must learn what those results mean and what to look at.
3) There is no way to analyze equipment interdependences.
4) Employees who move to another department may have to learn new analytical tools that may considerably differ from the previous ones.
39
Flexibility of our equipmentBecause we are the developers, we are able to adapt our solution to a specific project.
Data collection unit is constructed using industrial controller (PLC) that enables to:— Read data from any type of sensor.— Adapt data collection algorithms to specific cases.— Control external equipment.— Accumulate data internally when communications are down.
Data transferred to PC using:— Direct connection via COM port.— Industrial grade wireless connection.
Data collection from external systems:— We implemented means to exchange data with external systems.— As the software developer, we are able to adapt data exchange between any hardware and software.— It is possible to export collected data to Your ERP system.
Duomenų mainai su išorinėm sistemom
40
Objectives
— Generating analytical reports. The purpose of the analytical reports is to identify negative factors using historical data.
— Identifying negative factors in real-time. The purpose of real-time analysis to inform personnel about identified negative factors ASAP.
41
Analytical reports
Measurements and Δ change:EA – Equipment AvailabilityEEP – Equipment Efficiency PerformanceEQP – Equipment Quality PerformanceOEE – Overall Equipment EfficiencyMachine operation and tuning
42
3/4/2
013
3/5/2
013
3/6/2
013
3/7/2
013
3/8/2
013
3/9/2
013
3/10/2
013
3/11/2
013
3/12/2
013
3/13/2
013
3/14/2
013
3/15/2
013
3/16/2
013
3/17/2
013
3/18/2
013
3/19/2
013
3/20/2
013
3/21/2
013
-60%-40%-20%
0%20%40%60%80%
100%120%
EA
EA ∆
Spoilage analysis
43
Visualizing equipment operation
44
Real-time analysis:
— Report workflow status every two hours.
— Notification about unplanned downtime that is not not resolved in predefined time.
— Notification about lag exceeding predefined time interval. (For example through text message)
45
Our proposal -
to start from an
analysis stage:
46
Analysis achievements:How and when does information flow now?
Identify what causes information lags.
Assess what current information we can use.
Identify current system’s resources that we can use for analytics.
Determine external IT tools required for systemic analysis.
Develop a project to fill information gaps in the current system.
Define responsibilities for entering information.
Develop a project scope and estimate budget for systemic analysis data collection and processing.
47
Andrius GudaitisPhone +370 37 30 08 12
Mobile +370 699 9 26 59
E-mail [email protected]
Website www.lean2s.eu
www.facebook.com/proginta
Darius RadkevičiusPartner
Mobile +370 698 4 10 27
E-mail [email protected]
48
Proginta was created to be your business personal CSI (Corporate System Investigator)!
49