F
ROM TECH
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SUPPLIER TO
S
MART
F
ACTORY
S
OLUTION
P
ROVIDER
A hands-on approach for suppliers of production technology to
become a valuable partner for OEM’s with Smart Factory ambitions
F
ROM TECH
-
SUPPLIER TO
S
MART
F
ACTORY
S
OLUTION
P
ROVIDER
A hands-on approach for suppliers of production technology to
become a valuable partner for OEM’s with Smart Factory ambitions
Author:
Name: Jelle Jansma
Student number: S2730731 Address: Nieuwstraat 124 Postal and city: 9724KS Groningen
Phone: +31 6 15617461
E-mail: jellej@hotmail.com
University:
Name: Rijksuniversiteit Groningen / University of Groningen Faculty: Economics and business
Master: Master of Business Administration Program: Small Business and Entrepreneurship
Supervisors:
1st supervisor: Dr. Frits Wijbenga
2nd supervisor: Dr. Evelien Croonen
Thesis:
Submission date: 24-3-2017
Word count: 20311
Abstract
Een voorwoord uit het hart
Een voorwoord is meestal niet langer dan een halve A4. Voor mij is dit niet afdoende, daar ik de dank heb uit te spreken aan een groot aantal personen, die de afsluiting van mijn studententijd en
daarmee de definitieve entree in het burgerbestaan tot een succes hebben gemaakt. Voor u ligt het papiergeworden afstudeeronderzoek van een masterstudent die met groot enthousiasme terugkijkt op de medewerkingen aan zijn onderzoek. Het onderzoek heeft plaats gevonden in een zeer complex terrein, dat ik nog niet half had kunnen ontginnen zonder de medewerking van een groot aantal enthousiaste specialisten die mij een geweldige ondersteuning hebben verleend. Gezien de rol die de personen in kwestie hebben gespeeld, zou dit artikel voor mij als incompleet voelen als ik hen niet zou bedanken door ze in dit voorwoord de eer te bezorgen die hen ten dele valt.
Beginnend bij de man die veel tijd en energie in mij en mijn onderzoek gestoken heeft: Hans Praat. Hans heeft mij in zijn hoedanigheid als bedrijfsbegeleider overspoelt met al zijn kennis en mij deel laten nemen aan andere projecten binnen het RoSF-consortium, waarmee hij mij helemaal thuis heeft gemaakt in zowel het smart factory concept als binnen het RoSF-consortium. Hans heeft mij vanaf het begin al zijn vertrouwen geschonken en heeft mij bovendien goede diensten bewezen door dit vertrouwen ook uit te dragen naar alle andere consortiumleden die ik later in mijn onderzoek zou gaan betrekken. Hierdoor begon ik iedere kennismaking met een 1-0 voorstand. Daarnaast heeft Hans altijd voor mij klaar gestaan: Als ik iets wou weten, als ik zijn contacten nodig had, als ik een paar uren uit zijn drukbezette agenda claimde, Hans was er. Bedankt Hans, ik heb de periode als uiterst plezierig ervaren en ik hoop dat we onze samenwerking in de toekomst voort kunnen zetten! Een speciale bijdrage was er ook van Wilbert van den Eijnde, Anno Cazemier en Jan Post, die mij met hun positie binnen het RoSF-consortium van veel nuttige informatie hebben kunnen voorzien. Wilbert wist alles van onze technologie leveranciers, Anno is specialist van de Nederlandse maakindustrie en Jan Post kon ik alles vragen over het smart factory concept. Ook deze heren hebben altijd voor mij klaar gestaan en hebben mij, allen vanuit hun eigen expertise, ontzettend interessante perspectieven en bruikbare adviezen kunnen geven. Bedankt heren, door jullie bijdragen ben ik in staat geweest het onderzoek de praktische inslag te geven die we voor ogen hadden!
Deze alinea heb ik te wijden aan onze technologie leveranciers, voor wie ik dit onderzoek feitelijk heb geschreven. Ik heb ze volledige anonimiteit beloofd, wat zou betekenen dat ik ze hier niet bij name zou kunnen noemen. Ik zou ze daarmee wel te kort doen: Het enthousiasme waarmee ik bij jullie ontvangen ben, de heerlijke lunches, de vele uren die sommigen van jullie in mijn onderzoek
gestoken hebben, de openheid waarmee jullie de gesprekken in zijn gestapt, de enorme hoeveelheid informatie die jullie mij gegeven hebben, het is te veel om jullie onbenoemd te laten. Daarom schend ik mijn eigen regels door jullie toch te noemen, zij het bij voornaam: Kjeld, Joost, Jan L., Cor,
Theodoor, Gerrit Jan, Gert-Jan, Bas, Theodoor, Harry, Marco, Arthur, Erwin, Jan B., Menno en Marko. Heren, mijn eerzame dank!
Het is jammer dat uitgerekend de hekkensluiter één van de belangrijkste personen in mijn onderzoek geweest is. Frits: Zo is het allemaal niet bedoeld, het leek mij gewoon logisch om eerst de
opdrachtgevers te bedanken. Frits Wijbenga heeft opgetreden als supervisor vanaf de
er bovendien bij aanvullen dat ik onze besprekingen altijd als zeer plezierig ervaren heb. Frits: Zoals je merkt ben ik voornemens je voortaan te tutoyeren en bij je voornaam te noemen. Het lijkt mij leuk om in de toekomst het één en ander voor elkaar te kunnen blijven betekenen, zoals we reeds
meermaals besproken hebben.
Dat gezegd hebbende, wens ik de lezer veel leesplezier!
Jelle Jansma
IMPORTANT REMARK BEFORE READING!
We guaranteed complete anonymity to the RoSF-partners, which is why we replaced all their brand names with placeholders.
Inhoud
Een voorwoord uit het hart ... 3
Inhoud ... 5
1. Introduction ... 1
1.1. Background ... 1
1.1.1. Smart Industries ... 1
1.1.2. Region of Smart Factories ... 2
1.2. Research Problem... 4
1.2.1. Market uncertainty ... 5
1.2.2. Need for eco-system building... 5
1.2.3. Roadmapping framework ... 5
1.3. Research Questions ... 6
1.3.1. Sub questions ... 7
1.4. Used theories ... 7
1.5. Conceptual model and conclusion ... 7
1.6. Contributions to the literature ... 8
1.6.1. Commercialization of smart solutions ... 8
1.6.2. Combining effectuation with causation for designing a partnership ... 9
1.6.3. Roadmapping for partnerships ... 9
2. Literature review ... 11
2.1. General ... 11
2.2. Strategy forming ... 13
2.2.1. Strategize to the high market uncertainty ... 14
2.2.2. Strategize to ecosystem building ... 14
2.3. Personalized roadmap planning tools ... 16
3. Methodology ... 18
3.1. Part 1: Designing market and ecosystem strategy ... 19
3.2. Part 2: Designing roadmapping-tool ... 21
4. Part 1: Commercial and technical ecosystem strategy ... 23
4.1. Technological infrastructure ecosystem ... 23
4.1.1. Interfaces ... 24
4.1.2. Connecting to existing infrastructure ... 24
4.2.1. Technological aspects ... 27
4.2.2. Organizational aspects ... 27
4.2.3. Steering committee: ... 28
4.3. Institutionalizing phase: Period where causation takes over... 28
4.3.1. Target applications ... 28
4.3.2. Organization: Formal entity... 29
5. Part 2: Roadmapping toolbox ... 36
5.1. Design choices ... 36
5.2. Final architecture ... 41
6. Discussion & conclusion ... 42
6.1. Limitations ... 43
6.2. Academic reflections ... 43
6.3. Contributions to the literature ... 44
6.4. Suggestions for future research ... 45
Appendix 1: References ... 46
Appendix 2: Interview guide ... 47
Step 1: From theories to conceptual variables ... 47
Step 2: From conceptual variables to investigative questions ... 48
Step 3: Additional information ... 48
Step 4: Definite interview guide ... 49
Appendix 3: Processing interview results ... 51
Market-related problem analysis ... 51
Ambitions ... 51
Stumbling stones ... 52
Empirical perspectives on solutions: ... 55
Ecosystem-related interview results ... 56
Problem analysis: Ambitions ... 56
Problem analysis: Stumbling stones ... 58
Empirical perspectives on solutions: ... 61
Appendix 4: Interview results with specialist about technological infrastructure of ecosystem ... 63
Summary ... 63
Drawing for work ecosystem development ... 64
Drawing for executing client projects -> Connecting to non-ecosystem infrastructure ... 65
Answering to the 13 concerns of the interviewees... 68
Answering to 11 Effective Practices for Platform Leadership (Gawer & Cusumano, 2014) ... 69
Answering to 13 success factors (Sarja, 2015) ... 71
Appendix 6: Set of practices for roadmapping ... 74
Implementation phase ... 74
1. Introduction
We start with an introduction into the research. This chapter leads you through the background of the research and works up via the problem statements to the approach of the research.
1.1. Background
This research is conducted on behalf of and in close cooperation with Region of Smart Factories, a consortium in Northern Netherlands for developing the smart factories market in the Northern three provinces of the Netherlands.
1.1.1. Smart Industries
It is globally recognized as the next “big thing” in the manufacturing sector: smart industry, or industry 4.0, with the “4.0” referring to what is called the “fourth industrial revolution”. Smart Industries as a concept cannot be described as a complete new revolutionary technology. Rather is it a very advantageous confluence of a bundling of relatively new technologies that reach a technical maturity at this moment and are becoming available to the masses. On top of that, these new technologies are more and more easily interconnect to each other, due to developments like big data, Internet of Things and highly intelligent software. The possibilities that these developments bring for manufacturing seem to be endless, many of them would have been regarded as science-fiction ten years ago: additive manufacturing is going to enable highly customized one-piece mass-production, smart software is going to ensure first-time right product development and machine automation, build-in sensor technology in complicated end-products is going to send the products’ behaviours back to manufacturers for use in new product developments and new business models become possible as complex machines get the ability to become globally connected. Key features of smart industries can be found in:
Nullifying defects by highly-automated self-learning machines.
Reducing product development cycles from months to days by software enhanced highly detailed visualization, error detection and big data of as well product as production.
Reducing production time from days to hours with super-efficient processes by technologies such as robotized logistics and providing employees with augmented reality to steer them. Highly customized mass production by smart software drivers for additive machines like
3D-printers and CNC-machinery, making it so efficient that production costs are independent of batch-size.
Products that are going to communicate its behaviours and maintenance-needs by IoT to operators and/or management for reducing maintenance costs and downtime and improvements on new products.
A long list, which is not even exhaustive by far. Smart Industries seems to finally bring the solution for becoming completely LEAN, however the philosophy seems a little different: LEAN tries to find solutions for problems, smart industry tries to find what problems can all be solved by the great opportunities that new technologies bring.
the coming 10 years at annually €1,2bln up to €3,7bln. (Smart Industry, 2016). One thing is clear: we are at the beginning of a new era in manufacturing.
1.1.2. Region of Smart Factories
The just named Smart Industry institute initiated 29 field labs in The Netherlands, aimed at (regionally) development of Smart Industry ecosystems. The research is conducted for the by far largest (in terms of budget) of these 29: The Region of Smart Factories, which covers the northern three Dutch provinces and contains 40 partners, existing amongst others of OEM’s, tech-suppliers and universities. RoSF focuses on the manufacturing-specific areas of smart industries; smart factories. Within the smart factories concept, the RoSF covers the following fields:
1. Smart engineering
Smart engineering covers manufacturers’ primary process from customer request to work preparation. Smart engineering offers solutions for both new product development or product (mass)customization. Solutions can for example be found in automation of engineering processes, digital testing of product designs and smart ways to cooperate in engineering processes.
2. Smart manufacturing
Smart manufacturing covers the production processes and offers tools for self-learning and self-steering machinery, enabling them to produce super-efficient, defect-free and highly customized.
3. Smart products
Smart products can be anything and everything, ranging from sailing boat’s sails to
horseshoes. What make them smart is that they are fitted with all kind of sensors that are in one way or another able to deliver valuable information; sensors on sails are combined with actual weather conditions and water currents telling captains how to sail and smart
horseshoes indicate autonomously when they need to be replaced or when a horse’ walking behaviour deviates from its usual behaviour, indicating injuries. The RoSF does however focus on a secondary usage: collect these data and use it for processes related to for instance new product development or maintenance.
RoSF has its activities spread over 3 action lines. In the first action line, smart factory competences are developed in ten cluster projects, which are divided in three core themes:
1. Zero defect manufacturing
Developing 100% predictable and manipulable production processes, not by “pouring them in concrete” but by implementing intelligence, making them self-learning and self-steering. Result: Radical increase in productivity and efficiency.
1.1. Self-learning production processes for shavers.
Processes are real-time measured and analysed, after which decision-rules are generated automatically. Benefits can be found in higher efficiency, lower error margins and above all: significant lower commissioning times for new products
1.2. Wireless headsets
This project shows the real value of smart factories; nowadays this producers’ headsets are manually assembled in Malaysia but since the technology of these headsets becomes too miniaturized for human hands and the smart factory technology increasingly
advances, the producer invests in an automated assembly line for production in the Netherlands
The product is a miniature, wearable, diesel-powered generator for military. As it needs to comply with the high military requirements, even the smallest deviations lead to rejection. In this project, production quality of all separate parts are getting in-line and real-time monitored instead of off-line and afterwards.
2. First-time right product & process development
In this core theme, products and processes are designed First Time Right, meaning that products and (production)processes are designed so well beforehand that no errors arise during production. This is amongst others achieved by replacing time consuming empirical engineering methods by model-based engineering. Results can be found in shorter lead times, lower development costs and nullifying errors.
2.1. Simulation shaving heads
While a shaving head is only a very small part of a shaver, the production process is highly complex, the success of the process is determined by hundreds of parameters. Simulating the process in a digitized model allows to set all parameters right for the actual production process, taking variables like material composition and thickness into account.
2.2. Ship design
A frontrunner in the area of CAD/CAM software, is after a world premiere of developing a new interface between CAD/CAM and PDM software. A major step for First Time Right development of ships.
2.3. 3D-sheetwork
In this pilot, a tool is created that automatically translates clients’ product data into production data, for a producer of formed steel sheet work with complex geometries. Benefits can be found in eliminating the work preparation process and eliminating human errors.
3. Customization
Development of effective solutions for tailor-made production, with possibilities of one-piece production having the same cost prices as mass production, making OEM’s better equipped for future market developments
3.1. Lenses
A producer of contact lenses teams with another producer of intra-ocular lenses, to develop a unique 3D vision-system for inline 100% quality control of their fully tailored lenses. This technology allows for serial production of 100% tailored lenses.
3.2. Smart Sailing
Development of customized sails is costly and time-consuming, while testing still happens in practice (empirical). A ship engineering firm therefore starts developing numerical models for predicting sail-characteristics. To make this possible, a sensor system is developed that measures sail-characteristics.
3.3. Heath-tech devices
The Internet of Things opens the door for customer-specific products and services. A sensor-system that allows for high customization is getting developed for one of the partners’ personalized consumer care products and services.
3.4. Personalized vision
In the second action line, competences are secured and knowledge is shared; a Centre of Expertise is created which involves sharing and involvement of universities and the Northern-Netherlands’ region is getting branded as the region of smart factories. This research will be part of the third action line: Business development for tech-suppliers. This action line is meant for commercialization of the above named smart factory competences that have been developed in the first action line. Within this action line, a total of 12 technology suppliers participate, which are all front-runners in smart solutions in a certain field:
1. Partner A: An all-round machine engineer and constructor, already one of the Dutch leaders in smart manufacturing solutions.
2. Partner B: Complete engineering software for shipbuilders, currently putting a lot of effort in adding smart engineering tools to it.
3. Partner C: Offers a modular platform for smart automations in engineering processes: Tell it the boundary conditions of the desired product and the tool engineers it automatically. 4. Partner D: Smart collaboration software for Engineer-to-Order projects, reducing horizontal
as well as vertical integration to a simplicity.
5. Partner E: An all-round machine engineer and constructor, already offering multiple smart manufacturing solutions
6. Partner F: Collecting and analysing data from manufacturing processes and presents it in dashboard and reports, aimed at smart manufacturing with predictive maintenance, efficiency and effectivity, operator support and track-and-trace
7. Partner G: In-line collecting and analysing data from manufacturing processes, spotting trends and translate it to adaptions in machine drivers for preventing deviations in the process.
8. Partner H: All-round industrial automation, currently specializing in complex vision systems, robotics and experimenting with new human interfaces like the HoloLens
9. Partner I: Front-runner in industrial automation using PC-controls. The modular tool enables not only smart automation for much lower investments than traditional controls but also making smart connections to other tools and integrating complex algorithms.
10. Partner J: Machine engineer and constructor, specializing towards mechatronics. Develops towards machinery for reshoring of high-tech (consumer) electronics.
11. Partner K: Front-runner on IoT sensor products, particularly suitable for embedding into Smart Products.
12. Partner L: Offers an algorithm that autonomously spots correlations from big data, no matter where the data comes from (machined, internet, etc.). The algorithm can be used for all kinds of objectives in all kinds of sectors, apart from industrial environments also for (social) media, health sector, etc. For smart factories, this algorithm could serve for generating knowledge rules, used for creating Smart Engineering models.
Note: For the sake of readability we will from now on use the term “partners” for the technology suppliers in the consortium. Strictly speaking, this is not an accurate term, as there are also many non-tech-suppliers within the consortium but as we concentrate strictly on this group of partners, we found this a suitable approach.
1.2. Research Problem
conducted yet on strategies for entering this market. The two major problems for entering will be covered:
1.2.1. Market uncertainty
A main challenge that the partners experience is the smart factory concept being highly tech-push, while clients for a great part are used to market-pull. This results firstly in that partners’ solutions are well beyond the OEM’s demand and secondly in that many OEM’s show fear for revolutionary solutions that still have to prove itself.
1.2.2. Need for eco-system building
Because technology becomes more and more complex and needs to communicate to each other across borders, all indicators are that partnering with other tech-suppliers comes to great importance. Most tech-suppliers are however used to operate on their own. It even seems that partnering is not enough, as many researches point to the importance of creating standardized platforms suppliers can attach their solutions to. For instance, McKinsey, one of the largest consultancy firms that put a lot of effort in Smart Industries, included this in her top 5 pragmatic recommendations for capturing value in Smart Industries (M. Breunig, 2016)
Note that, while both problems clearly have different topics and different approaches, the solutions on both problems need to complement each other. The RoSF is searching for one strategy to solve all problems, which is why we aim at a strategy that finds the synergies in solving both problems. What is also worth mentioning is that one may miss interaction with (potential) clients in the research. This has been two sided a choice; first, the time is not ripe for a market analysis. It’s the classic hurdle going along with technology push marketing: the client’s demand is above all latent. One of the biggest problems that partners are experiencing is that the vast majority of the market is unfamiliar with the smart factory concept and/or the concept is misunderstood. This research is one step ahead of doing market analyses. This research is about positioning the partners in such a way that they become valuable providers of smart factory solutions that catch the potential clients’ attention (technology push) and eventually gains momentum in the market (demand pull). Of course this cannot be done without basic knowledge on client’s demand, and this is where the second rationale comes in: The partners are generally already several years engaged in selling their tools, most often before the term “smart factory” or “Smart Industry” existed. Therefore, we reason that there must be enough knowledge for gathering the basic understanding of clients’ demand that we need.
Additionally, it can be mentioned that we do not focus on finding short-term demand. Among the consortium, twelve potential clients, which have in common that they are all known for their role as front-runners in applying advanced technologies, already showed their interest in making their factory smart. This makes customer demand not a short-term problem. The long-term problem is how to get the clients on board that have no particular focus on advanced technologies yet. Aim of the research:
Research on strategy forming alone is however not completely answering to the quest of the RoSF, as they are searching for a hands-on approach that can be used for business development of the
framework contains a systematic approach that partners can use to construct their own tailored roadmap: In this roadmap, partners can position their current situation and their desired situation in the smart factory market. Subsequently, partners can define their personal stumbling stones
between their current and desired situation, pick the practices that address these stumbling stones and map these practices out in their roadmap. This roadmap can then serve as a planning tool, to be used to guide their business towards a successful position in the smart factory market.
Apart from the goal of business development for the partners, two additional wishes arise from the RoSF:
• Appointing the right partners to the right projects
Within the RoSF project, a system with the name Smart Factory Assessment (hereafter: SF-Assessment) is being developed. This assessment is going to be used for analysing
manufacturers on their smartness on multiple topics. When gaps in the manufacturers’ smartness are defined, the RoSF wants a systematic approach to appoint the partners that are best suited for providing a solution. The roadmaps should provide all the information that is necessary to position the partners within the organization, or even better, find a close connection to the SF-Assessment.
• Creating a wide solution range
The roadmapping approach forms a great opportunity for creating distinctiveness between partners within the RoSF-consortium. The partners are completely free to choose their own paths but partners may feel the desire to distinct themselves from comparable consortium partners. The benefits for the individual partners would be that they do not have to compete against partners in their own consortium and for the consortium that they are able to market a broad solution range. When roadmaps are shared, partners have a very specific overview of their partners’ activities. Secondly, the smart factories concept contains technological solutions on numerous different fields. We already know that, with the solutions that the current partners of the RoSF-consortium provide, not all fields are covered. The roadmaps would account for a great overview in not only the fields that already are covered but also the fields that partners admire to cover in the (near) future. The RoSF-consortium has the ambition to increase the number of partners in the future. Using the roadmaps, the consortium can define new candidates based on fields that are not covered yet by the current partners or where current partners have no ambitions in.
1.3. Research Questions
We define the following research question:
“How can a roadmap framework be developed that helps partners evolve into valuable smart factory solution providers?”
For further research, we make use of the following definitions: Partners
“Partners” is how we name the consortium partners of the RoSF that are suppliers of production technology, as named in the paragraph Region of Smart Factories. Valuable smart factory solution providers
Smart factories ask for new solutions and new market approaches. Technology suppliers become valuable smart factory solution providers when they possess these.
Roadmap framework
1.3.1. Sub questions
The sub questions are based on the two major challenges named in the Research Problem
1. How can a plan be designed that addresses the high market uncertainty and the need for ecosystem building?
First we are going to explore the possibilities that answer to the high market uncertainty and need for ecosystem building. This is based on as well literature as interview results, in which partners showed their ambitions, stumbling stones and perceptions on solutions. In the end, we make a design that answers to partners’ concerns on market uncertainty and the
perceived need for ecosystem building.
2. How can the plan be captured in a personalized roadmapping framework?
This sub question is about how the plan from the previous sub question can be given a hands-on approach: The plan is translated into a roadmapping framework
1.4. Used theories
Theories that will be used as foundations are:
Effectuation-theory (Carter & Jones-Evans, 2012)
13 success factors for technology-push marketing (Sarja, 2015) Minimum Viable Product (Ries, 2009)
“Effective Practices for Platform Leadership” (Gawer & Cusumano, 2014) Roadmapping (Phaal, Farrukh, & Probert, 2004)
1.5. Conceptual model and conclusion
Figure 1: Conceptual model
In Block 1, we are triggered by how partners can become valuable smart factory solution providers
(see 1.3. Research Questions). In the search for where a valuable position in the smart factory market
1: Becoming valuable smart factory solution providers
3: Problem analyses: - Partners’ ambitions - Partners’ stumbling stones
Solution design: - Literature perspectives - Partners’ perspectives 4: Tailored strategy roadmaps for technology suppliers 2: Literature review
- Where to find value in the Smart Factory market? - How can the partners
address the areas. From this literature background, we start with researching the problems as perceived by the partners, after which we formulate a solution design from as well literature as perceived perspectives from partners (Block 3). Finally, we reach the end-goal, in which we process the solution design in a roadmapping strategy (Block 4).
1.6. Contributions to the literature
This research gives three main contributions to the academic literature. At first, there is the result of this particular research, as this domain has never been researched before. Secondly, there are two additions to existing theories. These are explained below.
1.6.1. Commercialization of smart solutions
When searching for “smart factories” or closely related keywords like “Smart Industries” or “Industry 4.0”, it is clear that this item is “hot” in the academic world: there is a lot of literature and what is very notable is that all these articles are very recent, the large stream started somewhere in 2014 and increased exponentially, with most articles published in 2016. What is also notable, is how the subjects of the great majority of literature indicate the immaturity of smart factories to the market. Many articles are aimed at defining smart factories, predicting where smart factories will lead in the future or are technical oriented.
When adding the keyword “supplier”, “commercialization”, “marketing” or “ecosystem” to the just mentioned keywords, search results are getting close to nullified in the academic databases. The subject of turning smart factory solutions into commercialization, clearly is a step further in de development of smart factories. Articles that are touching this subject often describe the subject very briefly or only for very specific products or services.
The commercialization of smart factory solutions does however deserve a specific place in academic literature, as it differentiates on two very elementary characteristics from marketing as we know it. At first, individual technologies do not stand alone but have to be seen as complementary to a bigger whole. This phenomenon exists in two different manners. The one manner is more classical; a solution requires multiple suppliers. One could think of a mechatronics supplier which cooperates with a sensor supplier and an industrial automation supplier to optimize a machine. The second manner is typically for smart factory solution providers, which cannot just suffice with supplying its products but have to think about what the value of the product could be, not only for now but also in the future. A big-data supplier for instance does not (only) work for creating statistics that help to solve a specific problem but tries to create a platform that is able to collect and present as much data as possible, in the hope that its analytics lead to valuable possibilities or insights that did not exist yet. This second manner already indicates the second elementary characteristic of smart factory solutions: smart factories (actually, the smart industries concept as a whole) are strongly driven by technology push instead of market pull, while other concepts that have proven its popularity to manufacturing industries in the recent past, like LEAN and Six Sigma, have been largely market-pull. Next to these challenges, partners also have to deal with high technological uncertainty. Considering this, it makes a lot of sense to develop a roadmap that guides partners through the maze.
While this research focuses solely on suppliers of smart factory solutions, it is likely that the results in the future will be useful for all kinds of (technology related) initiatives in the era that internet of things, big data and artificial intelligence seems to seriously emerge. The technology can be adapted for all kinds of applications in all kinds of environments, only the implementation differs. For
Smart health
It could be useful for a supplier of smart hospital solutions to link its platform to a smart home care solutions platform, so that a just treated elderly patient can be observed at home after release from the hospital. For instance: A smart solution for hospitals detected a complication to lungs, that have to heal. The patient therefore needs to be watched for certain coughs and, right now, consequently needs to stay in the hospital for multiple nights. But when smart home care solutions watch for the coughs, the patient could go home, where the patient may even be monitored more effective as the patient is monitored 24/7. Smart cities
A supplier of vision systems may monitor traffic on the roads. It may be beneficial to partner with a supplier of traffic lights, so that traffic lights adapt to the actual traffic situation and optimize traffic flows.
Smart farming
It may for instance be useful for a supplier of field fertilization technology to cooperate with a supplier of satellite technologies. With photo analyses, the satellite technology supplier can monitor the fields and point what parts are growing how well. The supplier of field fertilization could use this information to distribute the fertilization according to crop growth.
While we make a division to sectors here, this division may even disappear in the future. Another total imaginary example is how a boundary between smart farming and smart manufacturing may be bridged in the future: A supplier of milking machines may measure fat percentage of milk. This data may be shared with a dairy plant, that uses it to transport milk according to fat percentage in
separate compartments of the milk truck and once processed in the dairy plant uses the milk that fits best with the product of process (light fat percentage for light-products, high percentages for cheese, etc.).
1.6.2. Combining effectuation with causation for designing a partnership
Where causation is about predicting the future and planning towards it, effectuation is about making the future without a clear goal. The effectuation logic is developed from venture building. In venture building, effectuation and causation is often used simultaneously to each other, opting for one of both depending on which is most efficacious. We however are going to show how to make a plan in which we do not constantly opt for causation or effectuation, but for causation and effectuation: The design of a causal plan using effectual principles. It will show that this is a very effective approach for ecosystem building in a partnership.
Further on, we are going to show how the effectuation theorem can be specified towards
commercial and technical ecosystem building, which is no literature on yet. We show how failures of past developments in partnerships that the RoSF partners took stake in are related to causation and find an effectual alternative that the partners find trust in.
1.6.3. Roadmapping for partnerships
participate in the ecosystem. The roadmap architecture is getting customized with partnership-specific axes and must provide insight in what they can mean for the ecosystem:
1. Partners’ specializations for now are defined and serve for finding the right partners for the right projects (effectuation)
2. Partners’ goals for the future are defined and serve for business development efforts from the partnership (causation)
2. Literature review
This chapter needs to be read as follows; in 2.1 General, we engage in the literature about
effectuation. These effectuation theories form the fundaments of the research in the methodology section and the results section. All other literature that is used during the research, is built upon the literature about effectuation that we show in 2.1. In 2.2, we engage into the literature that we specifically make use of for designing our strategy that address the first sub question. In 2.3 we present the literature that we use for roadmapping, related to the second sub question.
2.1. General
In general, it is clear what the smart factory concept is in its infancy for the time being and what the future will bring is highly unpredictable. A theory that addresses this problem is developed by Saras Sarasvathy (Carter & Jones-Evans, 2012).
“Are entrepreneurs born or made?” is a question that is often raised. To answer this question, researches have tried to isolate personality traits and characteristics of (successful) entrepreneurs over several decades. It turned out that there was a lot of contradicting evidence in personality traits, it appeared that people with completely different personality traits were able to start and
successfully run different kinds of companies (Carter & Jones-Evans, 2012).
Consequently, researches began turning their attention to what entrepreneurs actually do, rather than focusing exclusively on who they are. Sarasvathy found how experienced and successful entrepreneurs make use of another distinctive style and logic of decision-making than novice, unexperienced entrepreneurs, that is called “Effectual logic”, or “Effectuation” for short. This logic is perceived as particularly useful in start-up phases of new ventures (Carter & Jones-Evans, 2012). The linkage to our research is that Sarasvathy found that in general, expert entrepreneurs do not believe in causation, or trying to predict the future, like most standard scholars aim at (Carter & Jones-Evans, 2012). Effectuation is about controlling an unpredictable future, which is what we need to do here as we find the future highly unpredictable. The interesting question here is: How do you control a future you cannot predict? It turned out that expert entrepreneurs have figured out techniques for co-creating the future, as the following five principles illustrate (Perry, Chandler, & Markova, 2012), (Carter & Jones-Evans, 2012)..
1. The bird-in-hand principle
Causation: Beginning with a given goal
Effectuation: Beginning with a set of given mean
Instead of beginning with some vision of a great opportunity, in effectuation entrepreneurs start with their actual means and turn them into a new format. A good example given by Carter & Jones-Evans (2012) is Sears: a great American department store chain, founded by a train conductor that incidentally bought a batch of watches very cheaply and sold those watches to passengers on his route, with high profits. By starting with available means, entrepreneurs are freed from the necessity of coming up with a brilliant idea as well as the need to find funding. You can start right away.
2. The affordable loss principle
Causation: Focusing on expected returns Effectuation: Focusing on what you could lose
flows and calculate their net present value. Effectual entrepreneurs do not spend much time trying to do that. Instead they think through the downside potential and ask themselves whether or not they can live with that and if they would want to do the venture even in their investments may be lost. In other words: Expert entrepreneurs invest only what they can afford to lose.
3. The crazy quilt principle
Causation: Emphasizing competitive analysis
Effectuation: Emphasizing strategic alliances and pre-commitment
Where classic scholars focus on who could be your enemies: Who are your competitors, what differentiates you from them, how will they react at you, et cetera. Expert
entrepreneurs find themselves focusing on who could be your friends, instead. First of all: They shy away from notion of the market, which has a known set of competitors. Expert entrepreneurs begin to imagine ways to make market (any market) by allowing a variety of stakeholders to self-select into the venture-building process. Depending on what partners come aboard and what they commit to the venture, the growing network of partnerships determines which markets they end up in or end up creating. An example given by Carter & Jones-Evans (2012) is the success of CNN: CNN was only a run-down television station called WJRJ back in 1969. The station was bought by a billboard exploiter, that used the 15% that billboards lay blank for advertisement for his newly acquired television station and also used his advertisement resources to drum up ads for his new channel. Due to this bond, CNN became the great broadcaster that we know today.
4. The lemonade principle
Causation: Exploiting pre-existing knowledge Effectuation: Leveraging environmental contingencies
“When life gives you lemons, make lemonade”. This is an American saying that indicates how bad surprises can be turned into good opportunities. Classic scholars preach to carefully select clear goals, make detailed plans, use proven techniques to achieve those goals and then seek to avoid surprises which might upset those plans. Effectuation is about starting with flexible goals and focus on doing the doable, making contingencies become resources to be leveraged rather than distractions to be avoided. This may seem a little vague but on the flipside: contingencies may always occur, also when there are well thought out plans in place.
5. The pilot in the plane principle
Causation: The future is an external factor that needs to be dealt with Effectuation: The future is in the making, take control
The essence of effectuation. Effectuators see humans not as small cogs in the large machine but rather as prima movers capable of co-creating their future. Effectuators or not utopians seeking to improve the world, rather they focus on issues that they can control.
Effectual entrepreneurs are good in synthesizing their visions through the process, rather than setting out with a clear vision that they hold on to at any cost. This does however not mean that effectuation is a complete replacement for causal logic. Effectuation is better when “inventing the wheel” where causation is best when the concept is already known, as is illustrated in Figure 2
Effectuation versus causation. In an additional article, Wiltbank & Sarasvathy (2010) declare that
effectuation and causation are usually used simultaneously, depending on “which is more efficacious
under what circumstances”. This is beneficial, as we assume that we will need some planning to at
planning however, we can make use of effectual logic. Wiltbank & Sarasvathy (2010) also state that effectuation is not restricted to the domain of entrepreneurship. While a partnership may certainly have entrepreneurial elements in it, we are going to use the effectual principles for building a partnership.
Log
ic
Effectual
Causal
Time and experience
Start-up firm Novice
entrepreneur Large firm
Expert entrepreneur
Figure 2 Effectuation versus causation
While there is not much written about the use of effectuation for great development in partnerships, we did find an article that gives us a solid trust in opting for the use of effectual principles in joint ecosystem building. Solesvik en Gulbrandsen (2013) did a case study of late-stage open-innovation projects in high-tech environment (R&D on hybrid propulsion for ships). They found that effectuation is a suitable approach for open-innovation development under our circumstances (note however that we are not only developing an ecosystem but also commercialize it). They found that where
innovations are related to sensitivity of information outflows, participant prefer to deal with known partners that they trust over new partners. The RoSF-consortium is at the moment of writing just a year old and it is too early to state that the partners all have close bonds to each other, however, there have already been multiple cooperations between partners and they already are familiar to each other, so we take that to our advantage. According to Solesvik en Gulbrandsen (2013) this firms retain certain benefits like limited secrecy and first-mover advantages, even while working in open-innovation mode. Solesvik en Gulbrandsen (2013) also found how in a partnership where partner selection is based on current trustful relationships but is only small in the number of partners, the partnership still resulted in inventions that were possibly new to a global market.
2.2. Strategy forming
2.2.1. Strategize to the high market uncertainty
The highly tech-push driven smart factory concept would imply that partners’ products would be significant ahead of most customers’ demand.
A recent literature study defined 13 success factors for technology-push marketing (Sarja, 2015), which we can use to specify for the RoSF partners (see Table 1 Success factors for technology-push
marketing).
Table 1 Success factors for technology-push marketing
An approach we will use for getting a deeper understanding of where technology matches demand is the bottom-up approach we found in Ries’ Minimum Viable Product (MVP) theorem (Ries, 2009). We do use the theorem in a slightly different manner however. Ries developed its theorem for LEAN-manufacturing which is mainly demand-driven. In LEAN, the MVP theory is used for developing a product that has just enough features for customers to be willing to pay for it, after which the actual MVP can be produced, sold and further developed. As in our environment the solutions are already ought to be developed (hence: tech-push), we use the MVP theory to define the part of the product range that is readiest to commercialize.
An interesting additional research we can use for defining the current customers’ demand is done by McKinsey, focusing on practical approaches for tech-suppliers to capture value in the smart factory market. (M. Breunig, 2016).
2.2.2. Strategize to ecosystem building
Ecosystem building for smart devices is not new, multiple parties have for instance engaged in ecosystem building for home automation. Where those approaches often suffice by just having a technical platform, our case differentiates significantly from this approaches. A partnership in smart factory solutions need more than only a technical platform that interlinks each other’s solutions, for two reasons:
1. To continue on the just mentioned comparison to home automation: In smart factory solutions, the partners’ solutions are not just about turning off your hallway lighting or controlling your vacuum cleaner. Our partners’ solutions are about engineering anything from a bicycle to a rocket, about making the most complex machinery self-learning, about collecting data of millions of Smart Products, and so on: technologies are highly complex. Connecting highly complex systems to each other automatically means high development costs. This makes it important to have a long-term relationship with partners; if the
developments are not being yielded over a longer length of time, the development costs may never be recovered.
Market related success factors Product related success factors 1. Market-pull methods used
2. Focus on customer needs 3. Market development 4. Alternative study
5. Adoption time and technophobia
6. TP for difficult adopted 7. Life cycle
8. Fill an unrecognized need 9. Technological advantages
Management related success factors Organization related success factors 10. Management support
11. Degree of funding
2. Where home automation can make use of commodities that you could just buy straight from the internet, have delivered one day later and installed within seconds using a Plug & Play protocol from your smartphone, implementing smart factory solutions demand quite some more care. Each installation needs to be done in a multiple-partner project taking at least days but, more plausible, weeks or months and each project asks for its own technology and own approach. Also here, the need is stressed for having partners within the partnership that can successfully cooperate with each other.
This is why we have searched for an approach that looks beyond the technology. We found an article by Gawer & Cusumano (2014), who did a case-study in 2014 in which they observed ecosystems, or “industry platforms” as they name it themselves, which they defined as follows:
What may be noticed is that this definition defines a platform as one that is developed by one or more firms, on which other firms can build complementary products. This applies to our case in the sense that we want to develop the platform in collaboration with the actual partners of the RoSF. From their observations, Gawer & Cusumano (2014) defined the practices that successful platform leaders engage in. They present them as “Effective Practices for Platform Leadership” (see Table 2
Effective Practices for Platform Leadership). This set of practices not only looks at the technical
aspects but also at business-specific practices, especially in terms of business development for participants and also in terms of marketing.
We will use Gawer & Cusumano’s (2014) set of practices as a causational framework for designing the ecosystem.
[…] We have defined external or industry platforms […] as products, services or technologies developed by one or more firms, and which serve as foundations upon which a larger number of firms can build further complementary innovations, in the form of specific products, related services or component technologies. […]
Table 2 Effective Practices for Platform Leadership
2.3. Personalized roadmap planning tools
Technology roadmapping originates from the field of business. Motorola has been widely recognized to be the founder of roadmapping (Willyard & McClees, 1987). Since then, technology roadmapping has widely helped companies to interline strategy with long-range planning on fields like markets, products, technology, and much more. (Phaal, Farrukh, & Probert, 2004). Companies have used roadmaps to plot their business goals on (usually visual) roadmap architectures and plan their developments in line with them, in order to reach them. The principle has gotten its great academic attention in the early 2000’s, when it was picked up by Robert Phaal. In the first years, Phaal
researched the subject to give us a deeper understanding of what roadmapping is and how
companies apply the technique. In 2004, Phaal, Farruk & Probert (2004) released an article in which they presented a toolbox for roadmapping, for all kinds of different purposes: Product planning, Service planning, Strategic planning, etcetera.
In this toolbox, we found a roadmap architecture that answers to what we are looking for: The strategy roadmap architecture allows for plotting strategy and zooms in at underlying aggregation levels mapping strategy. This framework allows to position the “Current” and the “Vision” state on multiple layers of a company (see Figure 3 Strategy roadmap by Phaal et al.). Subsequently, strategic options for bridging the gaps between current and vision are explored. These strategic options are positioned in the middle section of the architecture. This middle section now forms the roadmap. Phaal et al. (2004) use the layers Market, Business, Products, Technology, Skills & Organization in their elaboration. We will however aim at characteristics that are specifically tailored for
tech-Develop a vision of how a product, technology or service could become an essential part of a larger business ecosystem
Build the right technical architecture and ‘connectors’
1. Identify or design an element with platform potential (that is, performing an essential function, and easy for others to connect to). 2. Identify third-party firms that could become
complementors to your platform (think broadly, possibly in different markets and for different uses)
3. Adopt a modular technical architecture, and in particular add connectors or interfaces so that other companies can build on the platform
4. Share the intellectual property of these connectors to reduce complementors’ costs to connect to the platform. This should incentivize and facilitate complementary innovation.
Build a coalition around the platform: Share the vision and rally complementors into co-creating a vibrant ecosystem together
Evolve the platform while maintaining a central position and improving the ecosystem’s vibrancy
5. Articulate a set of mutually enhancing business models for different actors in the ecosystem
6. Evangelize the merits and potentialities of the technical architecture
7. Share risks with complementors 8. Work (and keep working) on firm’s
legitimacy within the ecosystem. Gradually build up one’s reputation as a neutral industry broker
9. Work to develop a collective identity for ecosystem members
10. Keep innovating on the core, ensuring that it continues to provide an essential (and difficult to replace) function to the overall system, making it worthwhile for others to keep connecting to your platform
suppliers that enter the smart factory solutions market. The strategy roadmap architecture allows for lots of customization, which Phaal & Muller (2009) elaborated on in another article that discusses in further details how to approach this subjects.
The example of the architecture above contains 6 layers, starting at the highest aggregation level, downwards zooming further into layers on what is needed to achieve business goals. In this example: For your strategy, what kind of market do you need, then what kind of business do you need, then what kind of product you need, etcetera. These layers can be changed according to your needs, and even be completed with sub-layers: For instance, on the market layer, when you serve 2 markets. Also, there are 3 columns in this example. The current column is for plotting the current situation, the vision column is for the desired situation. Migration paths are defined that bridge the gaps between both situations, these migration paths form the strategic planning and are placed in the middle. The article also advises to divide the middle section into multiple timeframes.
The roadmapping process is the process in which a company plots its own roadmap on the
architecture. Typically, According to Phaal & Muller (2009), there is no standard process. Each firm needs its own approach. This depends on many factors, which we will quote:
As in the case of the RoSF it is needed to engage in this process for about 10 partners which are all different in nature (size, solutions, people, culture, etc.), this process cannot be made generic for our purposes. Therefore, we limit ourselves to providing a roadmap architecture in this research and leave the design of processes to further investigation.
Current Migration paths Vision Market Business Product Technology Skills Organization
Figure 3 Strategy roadmap by Phaal et al.
[…] The process that is most suitable depends on many factors, including the level of available resources (people, time, budget), nature of the issue being addressed (purpose and scope), available information (market and technology), other processes and management methods that are relevant (strategy, budgeting, new product development, project management and market research). Strategic planning usually involves balancing an external view of the firm (market and business environment) with an internal view (tangible and intangible assets). […], combining these external and internal perspectives (opportunities, threats, strengths and weaknesses) enables a set of product-technology options to be identified and evaluated. […]
Phaal & Muller (2009)
3. Methodology
While this research aims at solving the problems of the RoSF-consortium, there is a broader purpose. The outcome needs to be generalized in that way that not only RoSF partners benefit from it but also other parties, even outside of the smart factory domain (see: 1.6.1. Commercialization of smart
solutions). Also, we want to make additions to the literature on effectuation and roadmapping.
Simplified: We use an actual business problem for generating generalized academic knowledge. An approach that has proven its use for these kinds of researches is developed by Van Aken, Berends, & Van der Bij (2012) (see Figure 4 Reflective redesign process - Van Aken et al. (2012)). To ensure that the results of this research not only become a good contribution to the academic literature but also a valuable hands-on approach for the partners, we must make sure that their interests are really met. Therefore, this research will be conducted following the Engaged Scholarship framework (Van de Ven, 2007). This framework proposes a large role for stakeholders in order get a thorough research that solves actual problems. During all stages of the research, partners will be approached for:
1. Obtaining their perspectives and advice in conducting the research 2. Co-produce knowledge
3. Obtain their involvement in designing the market strategy and ecosystem strategy
Figure 4 Reflective redesign process - Van Aken et al. (2012) (the loop was added by ourselves)
Chapter 1 Introduction already discussed subjects around Phases 1 and 2. In this chapter, we will discuss how we are going to address Phases 3 and 4. Phase 5 will follow from the results of Phase 3 and 4.
The research approach can be divided in two separate parts, which have a sequential follow-up. These parts are aligned with the two sub-questions:
Phase 1
•Business phenomenon (type of business problems) •Solutions not adequately adressed in academic literature
Phase 2
•Selection of company's business problems with on or more problem owners and a linkage to business performance (within the type of business problems)
Phase 3
•Analyses and diagnosis
•Data collection and data analyses •Academic literature
Phase 4
•Solution •Design
•If possible: (pilot) implementation •Evaluation
Phase 5
•Academic reflection
Figure 5 Research parts
With the information we have after finishing the first part, we can start with the second part. These parts are explained below:
3.1. Part 1: Designing market and ecosystem strategy
Part 1 is divided in 3 sections, which refer to phase 3 and 4 of Van Aken et al. (2012) Below is a visual model of the research lay-out, where after the model is explained.
Figure 6 Research lay-out Part 1
Although we clearly base Phase 4 on Phase 3B which on it turns follows on Phase 3A, this sequence is not necessarily maintained in terms of processing. As the double arrowed bar in Figure 6 Research
lay-out Part 1Figure 6 Research lay- and the loop on Phases 3 and 4 in Figure 4 Reflective redesign process - Van Aken et al. (2012) already indicate, as the research proceeds there will be looped
between these phases continuously.
Block 3A.1: Literature research
In this block we collect relevant literature. This literature is presented in paragraph 2.1 General, about effectuation and in 2.2 Strategy forming about strategizing
Block 3A.2: Interviewing partners
Block 3A.2 is about interviewing partners. We opt to interview all the 12 technology supplying partners of the consortium. This may seem a high number for academic research but there is a clear explanation: At first, all partners are different in size, solutions offered, management style, maturity of their products, etcetera. We do not want to overlook perspectives that do count for the one but not for the other partner. Secondly: This research does not only serve an academic interest but also the interest of the RoSF, that want to proceed their activities based on this research. Therefore, it is in their interest to know exactly how each individual partner is “in the game”. And lastly, we want to prevent that some partners do not feel heart in this research.
The goal of the interviews for this research is two sided: At first we want to define problems, by asking partners for their ambitions and stumbling stones. Secondly, we also see the interviews as a chance to gain some empirical perspectives in the solution design. (see 1.5. Conceptual model). Although we want some concrete answers to certain questions, we foremost want to have as much information as possible in order not to miss anything, as this research is highly explorative. Therefore we opt for a semi-constructed interview with solely open questions that serve as a starting point for a probing-based session, aiming towards a fully-fledged conversation Interview topics are based on the literature as stated in Chapter 2 Literature review. The processing from literature background to interview guide can be found in Appendix 2: Interview guide.
Following the Engaged Scholarship framework, the interviews will form an interplay between an insider and an outsider perspective. This will be done from an insider perspective in the first place: the partners of our case-study will be interviewed about the stumbling stones they are facing in reaching their ambitions around serving the smart factory market. Interviewees will also be asked for their perspectives on solutions. Second, an outsider perspective will be taken. Using this method, partners are used as a source while they are also given the opportunity to suggest adjustments to the research. This can be on all kinds of topics: One could for instance imagine how partners could propose to make adaptions to the ecosystem design or suggest to investigate a certain domain that the researcher did not think of.
see Appendix 2: Interview guide for the interview guide and its emergence.
Block 3A.3: Interviewing specialists
This one is optional; on topics where we find the knowledge to be insufficient, we may make usage of one or more specialists that could help on certain topics where knowledge is insufficient.
Phase 3B: Processing
This phase is about processing the information that we collected in phase 3A into a set of problem statements and a set of solution analyses.
Block 3B.1: Problem statements
Out of the interviews of Block 3A.2 and complemented with the literature of Block 3A.1, we
articulate a set of problem statements: an interplay of what the partners’ ambitions are and what the stumbling stones are for reaching them. This set of statements is going to be addressed in the
Block 3B.2: Solution analyses
In this block, we process the data from Phase 3A into solutions for addressing the problem statements that are formed in Block 3B.2. The elaboration on the solutions can be found in the strategy designs, in chapter 4 Part 1: Commercial and technical ecosystem strategy
Phase 4: Designing
Finally, in Block 4.1, we problem statements are combined with the solution analyses into one design for marketing and ecosystem strategy. Ideally, the design of strategy need to meet all the problem statements as stated from Block 3B.1. Therefore, we will continuously use them as a check on the designs: Problem statements that are not met yet, will be further analysed until the point where as much problem statements as possible are met. The partners have a very important role in the elaborations, as the progress will regularly be presented to them. In these sessions, partners will be asked for input and validation and again given the opportunity for suggesting adjustments to the designs.
This block is also where marketing and ecosystem building come together; the strategy design is about the synergies between both.
3.2. Part 2: Designing roadmapping-tool
After Part 1 is finished, the strategy needs to be merged into a roadmapping toolbox. For explaining this process, we use a same kind of lay-out as for Part 1.
Figure 7 Research lay-out Part 2 Phase 3A: Data collection
In this phase, we collect our data which form the basis for the processing of Phase 3B
Block 3A.1: Literature research
The literature we use for Part 2 is stated in Paragraph 2.1 General (effectuation) and in 2.3
Personalized roadmap planning tools about roadmapping.
Block 3A.2: Marketing & ecosystem strategy
As the design of marketing & ecosystem strategy needs to be moulded in the roadmap toolbox, we use this output of Part 1 as an input for Part 2.
Phase 3B: Processing
Here, the data is processed. We develop as well the roadmap architecture (the framework) as the practices (the content of the framework).
Block 3B.1: Roadmap architecture
We use a top-down as well as a bottom-up approach here. We start with the top-down approach, which we base on the literature. Once this is completed, we continue with the bottom-up approach, where we make eventual adaptions to the architecture in order to make sure that the practices that we develop in Block 3B.2 fit within it.
Block 3A.2: Practices
The practices are a derivative of the marketing & ecosystem strategy. This strategy extract into practices, where each practice forms a building block. When combining the practices in the roadmap, it will lead to (a part of) the strategy proposed in Part 1.
Phase 4: Designing
4. Part 1: Commercial and technical ecosystem strategy
During the interviews it proved that almost all RoSF-partners expressed great troubles around approaching the market and also the concept of ecosystem building could count on lots of interest
(see Appendix 3: Processing interview results). In order to allow the reader to know in what context
this strategy is designed, we start off with a brief summary of the interview results.
About approaching the market, the troubles can be summarized as follows: It proved true that partners faced many problems with the high tech-push character of the smart factory concept. They almost all expressed how their solutions are beyond clients’ imagination, how their solutions solve problems that clients did not know they had and how unfamiliar clients are with the smart factory concept. As a result, partners find difficulties in finding solid ground in the market.
For ecosystem building, the rationale is as follows. Smart factories are all about connectivity. Smart factory solutions need to communicate with each other, also to solutions of other tech-suppliers, and this need will only become larger over time. It is no easy job to make durable and high quality
connections to other suppliers’ solutions. Each individual project works with different solutions from different tech-suppliers, which continuously creates the need to “reinvent the wheel” in order to make a new connection to another solution. That makes it very precious and risky to implement solutions. The strategy we propose is the creation of a cooperation among multiple, fixed and likeminded partners: A commercial and technical ecosystem that partners can ultimately “plug & play” connect their solutions to. The partners’ answer to the question if they were enthusiastic about such an ecosystem-approach, can be generalized as: “Absolutely! But…”. It is clear: The benefits of an ecosystem are well recognized among most partners but there many concerns too.
The design results from the partners’ concerns, as expressed in the interviews, and are combined with literature. The final design we present here finds the synergies between market approach and ecosystem building. Ultimately, this leads to a strategy that guides the partners into a valuable position in the smart factory market, with which it answers to the first sub-question.
This strategy is presented in the following sub-paragraphs. As we first need to understand the basic idea, we start off with the technological infrastructure of the proposed ecosystem. Hereafter we dive into the organizational aspects, which we divided into two phases: The first phase reflects the start-up phase of the strategy, which is characterized by the high degree of effectuation with some slight causational elements. The aim of the first phase, which will take a year, is gaining experience in operationalization and constructing a proof of concept. In the first phase, we lay the fundaments for the second phase which aims at operationalization and is characterized by causation, with still some space left for effectual ingredients.
Important: In the next paragraph we present the strategy’s design. We do not refer to the used literature or interview results, as the great amount of literature that we use would cause problems with readability. Instead we are going to reveal how this design reflects to both the used literature as the interview results in a checklist-styled overview, which can be found in Appendix 5: Reflections of
design choices on literature and interviews.
