- freely available
Appl. Syst. Innov. 2018, 1(1), 5; https://doi.org/10.3390/asi1010005
- RQ1: How can the status and state-of-the-art of development in the areas of production (CPS, additive manufacturing, Industry 4.0, etc.), transportation (PI, IoT) and artificial intelligence (AI) be described?
- RQ2: Applying the method of technology forecasting and evaluation, what looming innovations and prognoses can be derived from that?
- RQ3: What interactions and innovation potentials can be assumed bringing the three areas of production, transportation and artificial intelligence together?
2. State of the Art and Recent Advances
- In the first generation of cruise control applications, the system steadily maintained a constant, preset speed level. This was only steering the gas and propulsion system of the truck.
- Second, the system was able to follow a preceding vehicle with a present distance length with varying speed, therefore already combing the management of gas, automated gearshift and brake.
- Third is the competence of current dynamic cruise control systems to anticipate the route characteristics via GPS positioning in combination with map material. This allows the system e.g., to decelerate and upshift before downhill passages or accelerate and downshift before uphill road segments. This combines gas, brake, gearshift as well as GPS and navigation capabilities. The driver is furthermore only steering and supervising the comprehensive system.
2.3. Artificial Intelligence
- Cost savings: AI applications are expected to achieve cost savings, especially in the area of personnel costs as robotics and automated intralogistics and transportation applications are expected to provide faster and durable—and therefore finally cheaper—solutions in the logistics domain.
- Earnings increases: In many cases AI applications are expected also to increase revenue volumes—whether directly by increasing available product items at the point of sale (avoiding out of stock situations), or indirectly by allowing retailers to match customer preferences and expectations better and therefore increase customer satisfaction and re-buy rates. This is already partly obvious in the online shopping environment as well as with new automated customer support systems such as ‘Google Home/Assistant‘ or ‘Amazon Alexa/Echo’ [47,48].
- Increased speed: Obviously especially in logistics and transportation contexts, an area of great expectations is also the dimension of speed, especially by a more aligned cooperation of different actors within operations and transportation processes—e.g., between picking, production, packing and outbound transportation. This may significantly decrease lead times and time to market rates with the application of AI and automation.
- Increased flexibility: Finally, especially with speedy AI applications, most logistics and SCM managers also expect increasing flexibility for intralogistics as well as transportation setups; this is a very valuable asset in logistics environments and can be especially crucial in peak times. For example especially in delay and ‘troubleshooting’ situations very complex and fast decisions have to be taken e.g., for or against express transportation, re-shipments or others—in such situations, AI decision support could be crucial for maintaining customer satisfaction and competitiveness for logistics service providers as well as manufacturers and retailers.
“In 1963, Dennis Gabor, Nobel laureate for his invention of the holograph, said ‘The future cannot be predicted, but futures can be invented’ (Gabor, 1963). This statement has become a mantra in recent times, attributed to many who are simply rephrasing Gabor. Alas, the slogan, wonderful though it may sound, is false. The most successful inventions transform the world in ways that are impossible to foresee at the time of the invention. The statement should really be yet another of my laws: My law of prediction: ‘The future cannot be predicted, not even by trying to invent it. Although inventions can change the future, their long-term impact cannot be predicted.’ So, invent all you like, just don’t try to predict the impact several decades later.”
3. Technology Forecasting and Evaluation
3.1. Theory and Method Framework
- Delphi method (expert interviews);
- Analytical hierarchy process AHP (expert opinions/prioritization);
- Patent analysis;
- Bibliometric analysis;
- Relevance trees;
- Growth curve;
- Case studies;
- Scenario writing.
4. Discussion of Innovation Fields
4.1. Field A—‘Advanced CPS’
4.2. Field B—‘Advanced Transportation Planning’
4.3. Field C—‘Expert/AI CPS’
4.4. Field D—‘Expert/AI Transportation Systems’
4.5. Field E—‘Integrated SCM Sytems’
4.6. Field F—‘Global Meta-SCM Systems’
5. Case Analysis Autonomous Truck Driving and Maintenance
5.1. Theory Framework
5.2. Description Status Quo
5.3. Technology Forecasting
6. Conclusions and Outlook
Conflicts of Interest
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|Backgammon||1979: The backgammon program BKG by Hans Berliner defeats the world champion—the first computer program to defeat (in an exhibition match) a world champion in any game—though Berliner later attributes the win to luck with the dice rolls. |
1992: The backgammon program TD-Gammon by Gerry Tesauro reaches championship level ability, using temporal differences learning (a form of reinforcement learning) and repeated plays against itself to improve. In the years since, backgammon programs have far surpassed the best human players.
|Chess||1997: Deep Blue beats the world chess champion, Garry Kasparov. Kasparov claims to have seen glimpses of true intelligence and creativity in some of the computer’s moves. Since then, chess engines have continued to improve.|
|Scrabble||As of 2002, Scrabble-playing software surpasses the best human players.|
|Jeopardy!||2010: IBM’s Watson defeats the two all-time-greatest human Jeopardy! Champions, Ken Jennings and Brad Rutter. Jeopardy! Is a televised game show with trivia questions about history, literature, sports, geography, pop culture, science, and other topics. Questions are presented in the form of clues, and often involve wordplay.|
|Go||As of 2012, the Zen series of go-playing programs has reached rank 6 dan in fast games (the level of a very strong amateur player), using Monte Carlo tree search and machine learning techniques. Go-playing programs have been improving at a rate of about 1 dan/year in recent years. If this rate of improvement continues, they might beat the human world champion in about a decade. *|
* Addendum: AI application AlphaGo beat human world champion Lee Sedol in March 2016, [42,43].
Addendum II: The further improved AI application AlphaGo Zero was trained during 2017 without prior human knowledge, only with the game rules and by playing against itself—it is now unbeatable by humans and the strongest AI application regarding the game of Go .
|Poker||Addendum III: In 2017, also the first AI program was able to beat human players at head-up no-limit poker, in contrast to the above mentioned games of e.g., chess and go a non-perfect information setting [ 44,45,46]. Similarly to e.g., jeopardy! in this case it was expected to take much longer for AI instances to master such ‘human’ game settings including incomplete information and cheating.|
|Delphi method||Repeated implementation of expert interviews in a qualitative setting describing and evaluating the probability of situations in the future|
|Analytical hierarchy process||Pairwise comparisons of hierarchical decision criteria by experts; quantitative method requiring a predetermined hierarchy of criteria as well as group of experts applying the comparisons|
|Patent analysis||Tool for studying the information attached to patents as intellectual property, e.g., by using spreadsheet-based data analysis or software-based patent analysis tools|
|Bibliometric analysis||Statistical analysis of available (scientific) books and articles; citation analysis is based on constructing the citation graph, a network or graph representation of the citations between documents; many research fields use bibliometric methods to explore the impact of specific researchers, papers or ideas and thought schools|
|Relevance tree||Starting from an objective, the most appropriate path of the tree has to be identified by arranging the objectives, subobjectives and tasks in a hierarchical order; this should ensure that all possible ways of achieving the objectives are found; the relevance of individual tasks and subobjectives to the overall objective is evaluated|
|Growth curve||Application of a standard innovation and expansion model to nascent technologies e.g., by assuming specific growth rates or historical growth data|
|Extrapolation||Extension of existing (quantitative) data or (qualitative) knowledge based on historical events and data/knowledge|
|Case studies||Extended description of operational implementation based on an existing company or organizational setting|
|Scenario writing||Extended description of a comprehensive situation or application starting from a known point and technological status in time|
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