All the techniques used in the JMark Services Information Environment Advanced Analysis (IEAA) course apply to supply chain networks. I’ve selected three and added a new fourth type based on Project Socrates (see endnote xi). Our first step is to characterize the IE to understand the battlespace. We begin with baselines used in IEAA practical exercises.[ix]
The IE is ever-changing, so we need to identify its various states relevant to our problem set at any time. Three enduring questions about the international system can help us decide what to include in a baseline.[x] Who are the relevant actors? What do they want to do to each other? What can they do to each other? These questions are not all-inclusive. They serve as a starting point for advanced analysis (includes synthesis).
Baselines are informational, “normal” starting contexts filled with dynamic relationships among actors and environmental forces. Four types of baselines are deeply relevant to supply chain networks:
This baseline provides vital information on patterns and behaviors deriving from traditions, beliefs, attitudes, identities, and social norms. Culture tends to be described and interpreted as it is. Most people don’t explain culture as actionable causes because it presumably takes a long time to change, though the IE may be changing that for socially immersed populations. Consequently, “thick descriptions”[xi] are being replaced by a mishmash of historical and psychological factors characterized as mindsets, interpretations, personalities, perceptions, approaches, and worldviews. Adherents often claim their culture as unique or exceptional to find or reinforce their identities. Such peculiarities are critical to understanding behavior that otherwise may seem unfathomable or unexpected. Cultural differences affect supply chain labor and management issues in multiple ways–avoiding and accepting uncertainty, performing a job in collectivist and individualist ways, establishing trust, and implementing best practices.
This baseline is more tangible than culture. I’ll describe technical factors from Ervin Ackman’s perspective of creating advantage in three key attributes and related purposes.[xii] First, the structure of interconnected technologies provides insight into a state, society, or firm’s capability to compete, survive, and advance. Critical information includes the data, sensors, tools, equipment, material, skills, and knowledge to satisfy market wants and needs. A prime example today is the race to dominate the 5G technology that connects other technologies. Second, technology flow across individuals and groups reveals advances and gaps. Being ahead or behind affects performance and can change rapidly. A venue technology like 5G can shape advances in other technologies such as robotics and remotely controlled sensors and shooters. Third, the duration and timing of technology structure and flows characterize past and current conditions so we might anticipate future conditions. From differences in technology’s pace and rate change, actors develop advantageous upstream and downstream pathways. Again, 5G technology is a critical example, particularly if an authoritarian regime controls the global telecommunications network of devices.
Functional baselines reflect the IE’s diversity and potential for integrated control. Controlling AI technology relies on objective functions (tasks) that can vary, such as past performance versus the ability to learn. Here are four types of functional baselines subject to change. Attitudes and opinions among the public or select decision-makers function as inputs to a product or service’s demand, affecting supply. Economic performance such as revenue from energy sales, current account deficit or surplus, government debt, and income levels can influence sales and investment instantly. Government structure and informal power networks shape communication flows and incentives for suppliers across regions and locales. Finally, national and private media may be controlled or decentralized, serving as a censor or facilitator of marketing, shaping production and distribution options. The value that humans or a future artificial general intelligence place on a function is information that determines optimal solutions across different states and space.
Sources of Competitive Advantage. All the previous baselines harbor sources of competitive advantage. Cultural advantages such as collectivist or individualist tendencies are not objective facts; they are contextual subjects that vary according to conditions. So, superior awareness of cultural practices and beliefs is an “information intelligence” (see ICSL Paper #39) advantage over competitors about what and whom to trust. Technological advantages drive competitive supply chain networks and much more, whether domestically developed or acquired abroad. Theft of intellectual property rights can create a technological advantage if domestic innovation does not suffer. Functional advantages derive from a technology of some sort but not necessarily from a technological advantage. For instance, the strategic application of old technology can outperform “superior” technology ill-suited to the social context.
Developing baselines that characterize the competitive space is an artful science. All the baselines above impact a supply chain’s sources, manufacturing, and delivery. Baselines are critical reference points that need to be updated with other advanced techniques, starting with linkages.
Mapping the nodes and relationships in the IE can simplify the complexity of the information environment. At first, it may not look like simplification until we recognize the structure of the complexity. When it’s unclear which relationships matter to our problem, collecting more data and assigning meaning to convert it to information can generate just another complex morass. AI-assisted human-directed collection, filtering and sorting can help us understand structures and relationships.
Savant X Seeker is an example of a hyperdimensional relationship analysis platform that finds interconnections among keywords and concepts in a database. I provided the human direction, framing “information” as described in Paper #48: operating system information (input-change-output) in physical and psychological dimensions.
Figure 4 is the initial visual rendering of complex relationships (the node and linkage output) after providing keywords (the input highlighted on top). In this case, the problem was to characterize the breadth of Iranian influence strategy. Seeker’s latent semantic analysis program calculated 4-6 dimensions of insight (the change). I provided the database in the form of credible articles and reports. Each relationship in the figure pointed back to passages in the database.
I adjusted the scale and scope of the search using the Node Control, Settings, and Nodes features to the left of the picture. This search led me to discover regional and global connections that otherwise would have required more research time at best. I might not have discovered them at all. Seeker also has an “Insights” feature that I successively activated for specific terms in the visual depiction. This sequence enabled me to examine fourth, fifth, and sixth orders of relationships beyond the initial 3-D picture (3-D insights yield 4th-dimensional relationships; insights from those reveal 5th, etc.)
This type of analysis could include keywords for time-sensitive chains in any industry. A database, desired performance effects, and multiple risks would be appropriate inputs, ingested by Seeker for subsequent human-directed search.
In this Iran example, what initially appeared as an intractably complex sphere yielded insights into proxy warfare, political interference, cyberattacks, narrative warfare, and financial flows.[xiii] Mapping linkages is an initial characterization of the IE. We also want to know how the linkages change over time. To do that, we turn to the next analytical technique, pattern and trend analysis.
One way to capture the dynamic variety of relationships is to characterize patterns and trends as linear or curvilinear. I use these terms instead of “linear” and “non-linear” because they are more accurate. “Nonlinear” or “disproportionate” examples still use lines to describe relationships; they’re just curved lines as in an exponential increase or decrease.
A pattern is a consistent or repeating set of traits, acts, or any observable information. A linear pattern continues for a period in fixed ratios, perhaps changing to other fixed ratios. The pattern is usually represented in 2-D or 3-D.
A trend is a longer-term pattern, long enough to reasonably expect it to continue. We can calculate trends as derivatives of patterns. Just as the derivative of position is velocity (position over time) and the derivative of velocity is acceleration (velocity over time), the derivative of a pattern is a trend. A linear trend is a direction or vector—up or down, more or less, west or east, and so forth.
The US stock market is an excellent open-source example of linear patterns and trends.