When the pandemic boosted demand from makers of electronic devices for those locked down at home, car firms found themselves low in the semiconductor pecking order: although they spent $40bn or so on chips in 2019, that accounted for only a tenth of global demand. Established car firms also long ago outsourced development of most technology, including electronic subunits, to big suppliers. These “tier 1” suppliers, such as Germany’s Bosch or Denso of Japan, buy circuit boards and microcontrollers to make components from suppliers in the next tier down, which in turn buy semiconductors from chipmakers. This has kept chip firms and car firms at arm’s length. Deloitte, a consultancy, talks of a “lack of visibility up and down the value chain”. Carmakers’ initial response has been to make more vehicles that require fewer chips, or to use the scarce resources to build their most profitable models. In the long run, the changing nature of the car will force them to think more creatively. Fully electric cars are packed with twice as many chips by value than fossil-fuelled ones, says , another consultancy. As Pedro Pacheco of Gartner, yet another firm of consultants, points out, software will become a significant source of profits as cars move from a disparate collection of chips to a centralised “brain” connected to the internet that can be updated remotely. In 2019 Tesla made an average of nearly $1,200 per vehicle from selling software updates, Mr Pacheco notes. To ensure that the hardware woes don’t stymie this ambition, carmakers may need to take another leaf from Tesla’s book. The Californian firm has been designing its own chips since 2016, which lets it launch new software-enabled features quickly. Volkswagen’s boss, Herbert Diess, has said that the German giant will develop its own chips and software for autonomous driving: “Software and hardware have to come out of one hand.” For now Volkswagen would like closer relations with chipmakers. So would its rivals. Few have the resources—or inclination—to design chips. Some will still need help developing their own software. Big suppliers, fearful of losing out as carmakers cosy up to chip firms, are being forced to up their game.
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But if there is an audit failure, the auditors will be loath to hand over their audit trail because it would expose what went wrong in the RPA. Robotic process automation (RPA) is a class of businesses processes that uses software, referred to as a “robot” (or colloquially as “bot”), that utilizes variants of artificial intelligence to perform tasks that are either repetitive or learnable by a software algorithm. Historically, RPA started as a form of “data scraping,” which obtained data from a fixed-format output (such as screens or paper printouts). Robotic software evolved to be both an enhanced graphic visualization software as well as an underlying data gathering technique (see Exhibit 1). With enhanced data gathering came software whose logic was primarily addressing repetitive activity such as tracing, matching, and vouching. Adding a layer of software-learned intelligence, exceptions, anomalies, and implicit-rule based analysis followed. (An example of applied matching can be found in J. Ciric, C. Seche, “Efficient canonical form for Boolean matching of complex functions in large libraries,” IEEE Xplore, June 2003.) Today, RPA has application in banking, mortgage application process, sales, generic big-data applications such as optical character recognition (OCR) and data extraction, and—increasingly—audits (Michael Cohen, Andrea Rozario, and Chanyuan Zhang, “Exploring the Use of Robotic Process Automation (RPA) in Substantive Audit Procedures,” The CPA Journal, July 2019). A 2017 PricewaterhouseCoopers article estimated that 45% of worldwide workforce task can be automated (https://pwc.to/3itWwnG), and in November 2019, Deloitte disclosed that it utilizes RPA extensively in its financial audit process (“Deloitte leverages RPA for audit,” Accounting Today, Nov. 27, 2019, https://bit.ly/3BnKWTD). The rapid adoption response from 2017 to 2019 indicates not only a near-perfect match between the technology and the need; it also indicates that the return-on-investment argument has been substantially demonstrated. The application of RPA to an audit is—like many smart software applications—a layered framework that begins with data gathering (a simple, repetitive task), and culminates with visualized deliverables (a changing, intelligent task): Data gathering underlies the RPA process. This is done by various techniques, such as OCR, extracting data from human-readable reports, importing data from data sources, or connecting to an existing database using open database connectivity (ODBC) methods. Although all these functions require human set-up and initial analysis, once the data gathering is set up, the learning of the robot software is generic, specific, and—most telling—it is repetitive. Rule-based activity is the matching, vouching, and tracing that can occur once data has been gathered. For example, once a set of invoices and delivery reports are obtained, either by OCR or some form of extraction from ledgers, rule-based software can create a matching set between the two, eliminating the need for a human to do so. The software can also be programmed to attempt to match multiple deliveries to a single invoice (or vice versa), by either obtaining details such as delivery dates from the underlying paper documents, or by assuming a certain date proximity. Of course, for entities that utilize extensible markup language (XML) or its business derivative format extensible business reporting language (XBRL), OCR is less relevant, and direct data extraction will result in fewer errors in the underlying process and the rule-based application that follows. Implicit rule analysis is more abstract and less specific than the preceding explicit rule-based phase (a.k.a. “implicit deep learning,” a discussion of the underlying science is outside the scope of this article).