smart manufacturing -...
TRANSCRIPT
How Manufacturing is Becoming the Center of the Enterprise
lnsresearch.com
CONNECT:
SMART MANUFACTURING
SMART MANUFACTURINGHow Manufacturing is Becoming the Center of the Enterprise
lnsresearch.com
TABLE OF CONTENTS
SECTION 1: The State of Manufacturing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
SECTION 2: Smart Manufacturing in Tomorrow's World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
SECTION 3: Smart Manufacturing and the Extended Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
SECTION 4: Make the Move to Smart Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
SECTION 5: Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
SECTION 1
The State of Manufacturing Systems
BIG DATA
IIoT
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Manufacturing executives have been bombarded by new terms and
acronyms over the last few years; we are in the fourth Industrial Rev-
olution, Smart Manufacturing and Industry 4.0, not to mention Inter-
net of Things (IoT), Industrial Internet of Things (IIoT) and a plethora
of three and four letter acronyms. The feeling of needing to do some-
thing, but not knowing what can lead to paralysis by confusion.
Some manufacturing companies are taking a lead in these new
technologies since they are already well automated, partially in-
tegrated, and work in a mode of continuous improvement across
the enterprise. However, there are a vast number of manufacturers
whose journey is less mature or has yet to start. In this eBook, we
shall help those less mature to gain confidence and begin the jour-
ney to a digitized world. We examine what Smart Manufacturing
means and why companies should start the journey now, and offer
help to get started.
Introduction
INDUSTRY 4.0
SMART MANUFACTURING
IoT
APM
INTERNET OF THINGS
ASSET PERFORMANCE MANAGEMENT
SMART CONNECTED SUPPLY CHAIN
INDUSTRIAL INTERNET OF THINGS
MES
DIGITAL TRANSFORMATION
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The data presented in this eBook represents over 300 survey re-
sponses from mid-2015 to mid-2016. LNS Research employs a social
research model where our online format English language surveys
are open to the general public. Companies participate in LNS Re-
search surveys to gain access to the LNS Research library, meaning
survey participants are research consumers as well. An LNS Research
analyst follows up with each respondent by email and phone and the
analyst reviews each response for accuracy.
The industry demographics of the survey largely match the broad-
er demographics of the industrial landscape, with discrete being the
largest segment, followed by process and batch industries. Our re-
search also has a broad split across industries and company sizes.
Research Demographics
COLOR BY INDUSTRYCOLOR BY HQ LOCATION
Process Manufacturing
Discrete Manufacturing
Batch Manufacturing
North America
Europe
Asia/Pacific
Rest of World
2016 Metrics That Matter SurveyINDUSTRY
2016 Metrics That Matter SurveyREVENUE
2016 Metrics That Matter SurveyGEOGRAPHY
COLOR BY COMPANY REVENUE
Small: Less than $250 Million
Medium: $250 Million - $1 Billion
Large: More than $1 Billion
45% 49%41%
10%28%
15%
12%
37%
48%
15%
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To prepare for success today, it is helpful to consider the previous in-
dustrial revolutions and the impact they have had. The First (real) In-
dustrial Revolution embodied three revolutionary changes: machine
manufacturing, steam power and the move to city living for people
who had previously been agriculturalists. An important enabler was
the quality of agricultural stability at the time: people had enough
food without subsistence living, freeing up some to move to the cit-
ies and work in industry.
During the Second Industrial Revolution, the production line and
mass manufacturing drastically reduced the cost of consumer and
industrial products.
The Third Industrial Revolution was barely a revolution as elec-
tronics and control systems gradually penetrated manufacturing, al-
lowing increased flexibility and much more sophisticated products
at even lower cost. Although many would argue that this was on a
wholly smaller scale than the other three, it is included as everyone
now calls our current revolution the Fourth Industrial Revolution, In-
dustry 4.0 or Smart Manufacturing.
The Industrial Revolution is Here to Stay
From Industry 1.0 to Industry 4.0
1800 1900 2000 Today
FIRSTIndustrial Revolution
Through the introduction of mechanical production facilities with the help of water and steam power
SECONDIndustrial Revolution
Through the introduction of a division of labor and mass production with the help of electrical energy
THIRDIndustrial Revolution
Through the use of electronic and IT systems that further automate production
FOURTHIndustrial Revolution
Through the use of cyber-physical systems
First mechanical loom, 1784 First assembly line, Cincinnati slaughter houses, 1870
First programmable logic controller (PLC), Modicon 084, 1969
DEGREE OF COMPLEXITY
© DFKI, 2011
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The Fourth Industrial Revolution is happening around us right now.
It extends the digital impact of the third revolution and merges it
with the physical and natural worlds. As the fourth revolution takes
hold, it will impact everything that we do; manufacturers and their
extended supply chains will change forever as the virtual and real
world come together to deliver Smart Manufacturing. Change and
disruption happen today at an exponentially increasing pace, and the
pool of technologies and information available to manufacturers is
almost unimaginable – yet we have just begun. Much of the changes
are due to digital technologies applied in amazing new ways – 3D
printing could revolutionize factories and supply chains, new materi-
als will change countless products, and data will change the way we
perceive the world.
Looking at the world today, highly developed countries and re-
gions move rapidly towards the fourth Industrial Revolution. De-
veloping countries may take a different path. There are still many
countries the majority of whose population survive by subsistence
farming with very little mechanization. They will experience a differ-
ent Industrial Revolution as they skip the first three waves and cata-
pult straight to a digital world.
The Industrial Revolution is Here to Stay (Cont.)
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What is Smart Manufacturing?
Smart Manufacturing is a term used to define the complete digital
manufacturing universe including:
• Theextendedsupplychain,
• Newproductintroduction–fromideatodesign,
virtualworld,3D
• Thedigitized(orsmart)factory
• Newmanufacturingtechnologiessuchasadditive
manufacturing(3DPrinting)
• Standardsofcommunicationanddatatoallow
interoperability
Data – lots and lots of data – is what all these elements have in
common, and potentially, Big Data. Manufacturers must find solu-
tions to help them derive value from the data, analyze it and use it in
business processes and by people.
What Is Smart Manufacturing?
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LNS surveys include questions about current and planned deploy-
ment of manufacturing-centric software solutions. The list of choices
is broad but not overwhelming. The research reveals key unmistak-
able facts about Smart Manufacturing applications:
• ThereisstillplentyoflifeleftinManufacturingOperations
Management(MOM)
• Standaloneapplicationsaredroppingawayrapidly
• Analyticsistheonlyfieldgrowing
Adoption of Manufacturing Software
Actual and Planned Software Implementation
Data historian
MOM / MES suite
Visualization / HMI software
Production execution software
Statistical process control (SPC)
Manufacturing process management / workflow
Recipe management software
Plant scheduling software
Plant quality management software
Plant and process simulation software
Advanced process control (APC)
Plant analytics
Operations intelligence / Manufacturing intelligence
Mobile applications for visualization and/or control
Predictive modeling
Process analytic technology (PAT)
0% 5% 10% 15% 20% 25%
Plan it
Have it
8%
16%
4%
5%
5%
5%
3%
3%
3%
2%
4%
4%4%
9%
1%
1%
19%
17%
13%
12%
11%
10%
10%
10%
10%
7%
7%
6%
6%
6%
6%
23%
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The results depict an industry at the cusp of change, and it demands
more investigation. LNS Research daily interaction with vendors and
users of manufacturing software reveals different approaches in the
software market.
The thirty-year history of MOM and its predecessors have wit-
nessed a diverse collection of vendors with one thing in common:
they sell to manufacturers. Beyond that, there is a tremendous array of
offerings. Large ERP vendors, huge industrial engineering companies
who sell control systems, start-up software companies, and compa-
nies that sell product design software all compete for a wide variety of
MOM opportunities. Many vendors cover more than one flavor, like a
business software vendor that has an IIoT platform offering.
As we move into Smart Manufacturing, MOM software will only
be part of the overall solution, and so the potential sources of soft-
ware will inevitably expand. When making software choices, choose
vendors with primary Smart Manufacturing strategies that align
closely with your business. For example, it would be unusual for a
company to choose an ERP vendor’s MOM solution if it doesn’t use
that vendor’s ERP solution unless those systems are distinct and can
stand alone. Later stages in your Smart Manufacturing journey might
well have an impact on a company’s choice of MOM provider.
Adoption of Manufacturing Software (Cont.)
Smart Manufacturing Drivers
Control systems
Business software
Product development
Pure MES
IIoT, analytics
PRIMARY BUSINESS INTEGRATION CONNECTIVITY DATA MANAGEMENTMULTI-SITE ANALYTICS
SECTION 2
Smart Manufacturing in Tomorrow’s World
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Smart Manufacturing will reach most businesses in phases, and the
first step is the move to an integrated manufacturing environment.
LNS Research estimates that the uptake of MOM systems in factories
around the world is only about 20%. The other 80% should start the
move by considering the level of data collection and cross plant inte-
gration that will define a suitable starting point.
Simply collecting data and making it available to a wide audience
within a plant and across the business will lead to almost immediate
returns. If everyone from business leader to shop floor operator has
access to appropriate information at the relevant time, they will do
a better job. More productive people lead to better business deci-
sions and more profit. Once information becomes available, analysis,
feedback, and action deliver new value, and safer, cleaner factories.
This is only the first stage of Smart Manufacturing with no hype or
expectation of instant transformation.
Smart Manufacturing – Getting Connected
OF FACTORIES do not have MES systems80%
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LNS has created a Digital Transformation framework to help industri-
al companies conduct multiple transformation initiatives in an orga-
nized and structured environment with respect to people, process,
and the required technology. Many enterprises should start a digital
transformation journey with limited ambition and build the basis for
further Digital Transformation in the future.
Digital Transformation and Smart Manufacturing
CEO/COO
Business Leaders
CDO, IT/OT Leaders
Functional Managers, SMEs
Business, IT/OT PractitionersSOLUTION SELECTION
BUSINESS CASE DEVELOPMENT
OPERATIONAL ARCHITECTURE
OPERATIONAL EXCELLENCE
STRATEGIC OBJECTIVES
Eliminating Bias and Finding Long Term Partners
Evaluation
Team
Research
Pilot
RFPDISCOVERY
PLANNINGBUSINESS CASE
SELECTION
ProjectCharter
Defining Immediateand Long Term ROI
Managing IT-OT Convergence and Next-Gen IIoT Technology
Realigning People,Process, andTechnology
Reimagining BusinessProcess and Service Delivery
COSTS TOTAL YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5
HARDWARE
SOFTWARE LICENSING
THIRD PARTY SOFTWARE
APPLICATION SOFTWARE
DOCUMENTATION & TRAINING
MAINTENANCE
INSTALLATION
INTEGRATION
LEGACY DATA LOADING
PROJECT MANAGEMENT
SUPPORT
TOTAL:
CONNECTIVITY
SMART CONNECTED ENTERPRISE
APPLICATIONDEVELOPMENT
CLOUD
BIG DATA ANALYTICS
IoT Enabled Business SystemsL4
Smart Connected Operations - IIoT Enabled Production, Quality, Inventory, MaintenanceL3
L2 L1 L0
IIoT EnabledNext-Gen Systems
L5 IoT Enabled Governance and Planning Systems
Smart Connected Assets -
IIoT Enabled Sensors, Instrumentation, Controls, Assets, and Materials
APMEHS
ENERGY QUALITY OPERATIONS
People – Process – TechnologyOperational Excellence Platform
OPERATIONAL EXCELLENCE SUPPORT
Fall short on any pillar and your OpEx platform becomes tippy
Fall short on two or more pillars and yourOpEx platform becomes totally unstable
DIGITAL TRANSFORMATION FRAMEWORK
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Many manufacturing software vendors claim to offer
Big Data analytics but most still only provide old an-
swers to old questions, shown in the bottom left corner
of the matrix. Assess vendor capability by asking: “Can
its analytics system answer questions we didn’t know
to ask?” This may sound far-fetched in today’s Enter-
prise Manufacturing Intelligence (EMI) systems that are
usually the most powerful analytic tools in a modern
factory. However, some solutions can bring together
structured, time series and unstructured data, and lay
artificial intelligence (AI) based analytics on top – these
are the solutions answering unasked questions – to
drive real and unexpected value.
What is Big Data
BIG DATA
ANALYTICS ML ANALYTICS
DATA
A B C
NEW ANSWERS
to OLD QUESTIONS
OLD ANSWERS
to OLD QUESTIONS
NEW ANSWERS
to OLD QUESTIONS
NEW ANSWERS
to NEW QUESTIONS
BIG DATA ANALYTICS FRAMEWORK
DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE
What happened
What willhappen
What actionto take
Why it happened
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Across the world of manufacturing companies, there is a huge dis-
parity in levels of automation and integration. Digital Transforma-
tion demands answers to two questions:
• Whatisourstartingpoint?
• Whataretherightfirststepsforus?
Companies that have invested in integrated solutions with com-
plex networks and data sharing may not want to re-architect every-
thing to invest in new IIoT technologies and Smart Manufacturing
solutions. On the other hand, businesses with limited infrastruc-
ture and little or no higher level manufacturing systems may want
to take only cautious first steps toward Smart Manufacturing.
Manufacturing Data Becomes the Enterprise Heartbeat
TRADITIONAL VALUE CHAIN TECHNOLOGY ARCHITECTURE
L5 Governance and Planning SystemsADOPTION: Moderate DECISIONS: Months/Years NETWORK: Enterprise
L4 Business SystemsADOPTION: Broad DECISIONS: Days/Weeks NETWORK: Enterprise
L2 Equipment and Process ControlADOPTION: Broad DECISIONS: Sub-Second NETWORK: Plant
L1 Sensors, Instrumentation, Data CollectionADOPTION: Broad DECISIONS: Sub-Second NETWORK: Plant
L0 Production Assets and Materials
L3 Manufacturing Operations ManagementADOPTION: Limited DECISIONS: Seconds/Minutes/Hours NETWORK: Enterprise/Plant
MODERATE INTEGRATIONCustom > Web Services
LIMITED INTEGRATIONCustom > Web Services
LIMITED INTEGRATIONProprietary > Open, IP-Based
BROAD INTEGRATIONProprietary > Open, IP-Based
BROAD INTEGRATIONProprietary > Open, IP-Based
Traditional Value Chain Technology Architecture
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One rapidly growing trend in 2016: manufacturers using data in an IIoT
environment to address very specific issues. Companies are starting
to question the use of data they already have but don’t use. In particu-
lar, those that have complex machines for the manufacturing process
often gather no information from the machine other than production
numbers. Operations engineers would highly value more information
about the performance of each machine and, indeed, the means to
improve that performance. Many company executives feel pressure to
not to be left behind as digitization accelerates.
Manufacturing Data Becomes the Enterprise Heartbeat (Cont.)
Do not understand or know about IoT
We understand and our customer demands are driving us
We are still investigating the impact
We understand/are aware and see value to our operators/customers or both
We understand but see no impact at this time
We understand and have already seen dramatic impact
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
2016
2015
19%
33%
18%
13%
8%
8%
44%
21%
16%
9%
6%
4%
The need for more data and information and the desire for prog-
ress are two factors that meet perfectly in early stage Smart Manu-
facturing transformation. A good early project will include:
• Collectinformation(perhapsincludingspecialIIoTdevices
fordataacquisition)fromthemachine(s),
• AnalyzeitusingIIoTBigData(orfairlybigdata)analyticap-
plications,
• Deriveactionstoimprovemachineperformance,and
• Maketheinformationavailabletomorepeopleinatimely
andrelevantmanner.
Please indicate how the IoT is impacting your business today
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Q1
DOCUMENTATION
EVALUATION
Q2 Q3 Q4
Map Processes & Sign Off
Compile RFP
Team Assembled
Vendor Demo 1 (All)
Vendor References
Integration Proofing Pilot
Negotiation& Contracts
Vendor Demos (shortlist)
Team Reviews
Issue RFP 4 week
Compile Scripts for Demos
RFP Deadline Decision
Selection+
1 Reserve
All this can be done using your choice of IIoT platform, or other
data gathering system, and analytic tools. Incremental growth from
this starting point will deliver ever more value.
There are almost unlimited places to start the Smart Manufac-
turing journey. Many industrial companies that are asset intensive
(e.g. refining, chemicals, energy) look to Asset Performance Man-
Manufacturing Data Becomes the Enterprise Heartbeat (Cont.)
agement (APM) as an excellent starting point for advanced analyt-
ics and other Smart Manufacturing related topics. The benefits are
clear and starting on a small scale is straightforward.
While Smart Connected Assets is certainly one option, LNS has
covered that extensively, and so this eBook examines two other
stating points –Supply Chain and MOM.
SECTION 3
Smart Manufacturing and the Extended Supply Chain
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THE SUPPLY CHAIN
CABLE
PROCESS
PEOPLE
TECHNOLOGY
FORECASTING
PLANNING
SCHEDULING
PRODUCTION PLANNING
PRODUCTION EXECUTION
DEMAND MANAGEMENT
VISIBILITY
CONNECTIVITY
DATA MANAGEMENT
ANALYTICS
CULTURE
EMPOWERMENT
TRAINING
The Smart Manufacturing journey begins at the machine level; the
opposite extreme is Smart Manufacturing across the supply chain.
Almost all manufacturing companies face a rapidly changing mar-
ket, in particular from more demanding customers and consumers.
The last decades since the third Industrial Revolution have seen an in-
exorable move towards low-cost manufacturing. This caused massive
moves to manufacturing in low-cost regions and a complete change in
the supply chain to support remote manufacturing and global deliv-
ery. This shift satisfied consumer demand for ever lower cost but often
at the cost of
fewer available jobs in local markets. It has also led to monolithic and
inflexible supply chains poorly suited to today’s consumer and cannot
handle major disruption from natural disasters or man-made chaos.
Today’s supply chain leaders break down the supply chain into
more nimble parts that help to overcome some of the global issues
they face. Just as LNS Research has prescribed modular MOM, we
recommend the same for the supply chain. However, this is not
enough. Consumer demands for choice, speed of delivery and low
cost create opportunities for companies to disrupt the entire supply
chain in some markets. Whether it be through local manufacturing,
additive manufacturing (3D printing), advanced robotics or design to
order, Smart Manufacturing in a Smart Connected
Supply Chain creates a vast opportunity to drive a
fundamentally different supply chain.
A Strong Case for Manufacturing Data to Enrich Supply Chain Performance
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To succeed in this new world manufacturers must sell what people
want, deliver in a convenient manner and at an acceptable price.
Only those that have a flexible supply chain, and use it constantly
to manage change will succeed. All the economic, social and busi-
ness arguments to drive more manufacturing to the US will not suc-
ceed unless US factories perform to the highest standards. Wheth-
er these plants deliver finished consumer goods or are part of a
complex B2B supply chain, flexible, efficient and very high-quali-
ty manufacturing are pre-requisites for performance in the Smart
Connected Supply Chain.
A Strong Case for Manufacturing Data to Enrich Supply Chain Performance (Cont.)
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The key to extend the value of Smart Manufacturing beyond the
factory is data. Manufacturing excellence means using more data in
better ways. That data can begin as early as discussions about ideas
for new products right through development and new product in-
troduction, to manufacturing and, ultimately, consumer use.
Today, we hardly scratch the surface to define and use data re-
lated to manufactured products except in the design office and
factory. The ability regularly to improve design and implement it
through software updates will become the norm for more than
smart phones and computers. We already see this in cars at a very
limited level, but the future will bring petabytes of data about cars
in use. The future will bring detailed performance data similar to
that found in today’s Formula 1 cars and endless updates related to
autonomous driving information and the environment. Companies
shouldn’t worry overmuch about handling so much data; under-
standing where the end game might be in your industry is useful
when considering pilot projects.
From Idea to Consumer – Information Redefines Relationships
PRODUCTDESIGN
PROCESSDESIGN
MANUFAC-TURING
SUPPLYCHAIN
SERVICE& USE
CLOUD
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When the topic of Big Data analytics comes up, it almost always in-
cludes questions about what exactly it is. A simple way to recognize
Big Data analytics is an answer to a question that didn’t even exist.
Similarly, when we look at what will change in a specific industry,
we must be able to imagine outcomes that almost nobody would
ask about today. Businesses must analyze the array of possibilities
to develop long-term strategic objectives and make shorter term
plans to invest in projects leading to these objectives.
From Idea to Consumer – Information Redefines Relationships (Cont.)
SECTION 4
Make the Move to Smart Manufacturing
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The journey must start at the top of any organization. The strategic
objectives must include Digital Transformation from the current state
to a more mature one where manufacturing delivers more value to
the enterprise. For many, initial objectives will be around improving
communication and use of data. The top of the list will also include
analytics that can feed back into manufacturing for improved per-
formance and quality. There are many other areas, especially Asset
Performance Management (APM) in asset-intensive industries. The
most important first step is to ensure top management is fully com-
mitted to the objectives and that Smart Factory projects can meet, at
least partially, those objectives in one or more plants.
There are many stages in the journey to Smart Manufacturing.
While the full-fledged IIoT approach, with integration across the en-
terprise and beyond, should be a long-term goal for many manufac-
turers, today’s goals will be simpler for most. Eighty percent of man-
ufacturers do not have an MES system in place. Companies should
address this, and other gaps before they even start down the road.
Building the business case is always the best way to secure execu-
tive buy-in for the improvement program. Some organizations try to
use pilot projects as an excuse for lack of committed financial bene-
fits. In the case of digital transformation, there is no option; companies
that fail to change will be overtaken by more agile, disruptive players.
Set the Scene – Strategic Objectives
COMMUNICATION
ANALYTICS
DATA
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With objectives in place, a path to real projects can begin. In this
eBook we focus on companies that have not yet taken many steps
towards a digitalized future and want to start a journey with data col-
lection, analysis, and feedback to improve performance.
The market for data gathering systems is wide and ranges from data
historian and MOM systems to full-fledge IIoT platforms. For many
starting out, the tools provided by an appropriate MOM system will
provide data gathering tools and also the ability to extend connec-
tivity, communication, and timely information sharing and analysis.
Although the technical fit of a solution is vital, it is imperative that
decision makers consider how the solution will solve process issues
and benefit the people who will use it. Quite often manufacturing
plants that are not highly automated and have little technology to
support the manufacturing process have the most valuable people.
This is where to find the most valuable tribal knowledge about com-
plex processes. People as keepers of knowledge isn’t a sustainable
strategy, particularly with a rapidly aging workforce. Now is a perfect
time to help these valued people to move their knowledge into sys-
tems and processes that will outlive them.
Key Manufacturing Systems: for Today and Beyond
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Fit is the most important consideration when looking at different types
of MOM suppliers. Some technical considerations should also come
into play before you create a shortlist and dive deep into the details.
Any new solutions the company introduces to drive towards a
digitized manufacturing environment must live and grow alongside
existing systems or replace them. The Digital Transformation frame-
work by LNS Research defines the steps and provides tools for great
detail at each step of the transformation. Each business should de-
cide to follow the framework in part or whole, and to what degree
to avoid unnecessary constraints. However, every company must
define the goals for operational excellence and build an operation-
al architecture that includes existing and new solutions to achieve
long-term goals.
The Right Solutions for Unique Strategic Objectives
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With a defined operational architecture it is possible to build a busi-
ness case and move forward to solution selection. Since the initial
solution will likely be a MOM system (or some functions that make
up a MOM system), key considerations must include:
• Howmuchexperiencedoesthevendorhaveinyour
verticalmarket?
• Howwelldoesthefunctionalityaligntoyourmarketnow
andforthefuture?
• Doesthebusinessrequireascalablesolutionor
willitalwaysbesmallandcontained?
• Isthesolutionscalabletothesizeofyourbusiness,
anddoesthevendorhaveprovenexperiencewith
asimilarscope?
• Canthesystemintegratewithexistingsystemsor
willtherebecustomprogramming?
With a short list in hand, the process of func-
tional and financial comparison, choice of part-
ners and detailed functional definitions can start.
Every project will be different but one rule is al-
ways in place: involve all the right people.
The success of any MOM project, indeed any technology proj-
ect, is hugely dependent on the people who are (and who are not)
involved. Experience shows that projects with a well-defined orga-
nization, include people from executives to shop floor workers, and
are flexible to meet changing needs, succeed. It’s a simple concept,
but hard to do. If the project team is not thoughtful, no amount of
advice, time or money will help.
The Right Solutions for Unique Strategic Objectives (Cont.)
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For those just starting out on the Digital Transformation journey, re-
main cognizant that Smart Manufacturing is a huge topic – one that
many try to tackle with enterprise-wide IoT mega-projects. Most
companies will not succeed with that approach. Focused and steady
improvement plans with clear objectives will lead to a set of projects
that deliver defined results along a defined timeline.
With the example of a MOM solution, the first results will be data
collecting and organizing. Analytics could then follow to allow the
manufacturer to learn more about processes. The journey continues
with feedback about the process to drive value and quality. Each step
will have clear goals and measurable results. It’s a long journey that
will benefit from measuring long term benefits instead of grasping for
short-term ROI.
Manufacturers who want to take first steps in data gathering and
manufacturing execution should start with:
1. STRATEGIC OBJECTIVES: Drive from top-down, everyone should
understand the objectives.
2. PILOT: Choose a small project that reflects objectives. Define
clear success and failure criteria. Failure is fine but you must be
able to learn from it.
3. TECHNOLOGY ENABLERS: Select a platform, and one or more
partners with which to work. Platform and analytics partners can
be different if compatible.
4. EXECUTION: Implement project, measure and do another. After
that re-visit objectives and move into a more architectural approach
as rollout looms.
Analysts and other thought leaders in the manufacturing sys-
tems space love to discuss the latest ideas of IIoT, Big Data Analyt-
ics and everything in the cloud. These transformative technologies
will come, but the vast majority of companies need to take a more
pragmatic approach to digital transformation. Get connected and
define strategic objectives – the way forward needs to be clear, es-
pecially when it will be steady rather than rushed. Smart companies
are more important than Smart Manufacturing.
Recommended Actions
Presented by:Author:
© 2016 LNS Research.
lnsresearch.com
Connect:
AndrewHughes, Principal Analyst