human can read) to become RPA workflow (RPA developer can read).
5 Create metrics to measure ROI in terms of calculated full‐time employee (FTE) labor hours.
6 Communicate results to management in global and regional offices.
7 Investigate and integrate RPA into each of the different business units.
8 Expand RPA locally by training more local RPA champions who can help to spread knowledge and increase the number of RPA processes.
9 Multiply the power of RPA by combining it with Excel macros.
10 Distribute messages and hold further training to create an RPA culture within offices.
11 Continue to invest in upskilling the team and get them certified.
Processes that Konica Minolta has built RPA bots for include updating shipping schedules and generating customer consumption reports. More examples can be found in the chart below that demonstrates how Konica Minolta deployed bots across various department functions (Figures 5.1 and 5.2).
In 2018, over 55 automatic bots were developed and deployed, with a similar number in the work pipeline. Konica Minolta could save around 19 000 hours in 2018 and expects to save 33 000 hours in the fiscal year 2019. The goal here was not to replace staff through RPA but to add more business value to their customers. The company's RPA journey gives invaluable insights to how automation can change a company's culture and the ways its employees work.
Though Konica Minolta is not a logistics company, this roadmap would look extremely similar if the implementation was happening in one. Identifying highly repetitive tasks, converting workflow to one that an RPA developer can read, forming a team to manage adoption and training, and creating metrics to quantify ROI would be standard steps in any company's RPA journey whether they provide logistics services or technology solutions. The positive results and hours saved that Konica Minolta have experienced since deploying their first bots therefore could be shared by logistics companies since many of the functions that the bots handle will be the same regardless of the industry that they serve. For example, Konica Minolta's use of bots for creating market data reports, generating quality assessment reports, and managing inventory could very easily be applied to a logistics provider as these functions will generally look quite similar across different organizations. In addition to this, Konica Minolta used RPA specifically to update their shipping schedules, a task that is done daily in many logistics companies. Later on in this section, we will look at an example of how DHL have seen equally impressive results to Konica Minolta's during their own RPA journey.
Figure 5.1 Deploying RPA in various operations throughout the value chain.
Source: Konica Minolta © (2020).
Figure 5.2 Internal RPA training course participants.
Source: Konica Minolta © (2020).
Use Cases for RPA in Logistics
Track and Trace
Logistics service providers are seeing customer expectations increasingly rise in areas where they simply do not have the margins to throw extra staff at the solution. E‐commerce and SaaS solutions are changing the visibility expectations of buyers. When you can price shop and buy a Boeing 747 online and track penny items turn by turn as they approach your house, the expectations in the business‐to‐business sector shift as well (Sheetz 2017). In international shipping, this has traditionally been a huge issue as carriers, forwarders, port authorities, rail operators, customs agencies, trucking companies, warehouses, and their customers often all have different levels of digital communication capabilities. In this scenario where a buyer of a container of goods in the United States wants to track his cargo from China to his warehouse door outside of Chicago, Illinois, they most often look to their freight forwarder as an aggregator of data and communication window. The freight forwarder typically does not directly own or control any of the carriers along the way, so they must find ways of grabbing event milestones to give updates to their customers. They have a few different options when it comes to retrieving such milestones. First, they could have direct integration with each of these carriers via EDI, although it requires a significant investment in mapping event fields from the carrier's system back to the forwarder's system. Second, they could have their staff locally go to each carrier website and retrieve the information as it is updated and key it back into their tracking database manually. Third, they could identify low‐cost labor in an offshore location to update and key in the data. Fourth, they could pay a third‐party aggregator for the milestones who often utilizes a combination of cheap labor and EDI connections. Lastly, they could train or hire someone to train an RPA bot that could go out and do the work of their local or offshore staff.
As previously mentioned, the key is to have a highly repeatable task. The task is mental and independent and does not have huge volumes happening at the same time if used by a small‐ to medium‐sized company. Typically, the forwarder and carriers are not updating their system's interfaces frequently, so training the bot to navigate to different fields on a carrier's website and the forwarder's ERP system is feasible. Managers need to consider whether or not their systems will be changing soon in terms of the interface or the architecture as that will help to also decide whether to use RPA (Penttinen et al. 2018). If, for example, your company just rolled out the logistics platform CargoWise and there are no updates to your ERP coming soon, then it might make sense to start an RPA pilot. A difficulty here would be in the formatting of identifications used by different carriers and modes of shipping; however, this task is surmountable. The key is figuring out which trigger would initiate the bots tracking of events and dictating how often it would need to go out to find those events.
RPA Adoption at DHL
RPA adoption in logistics has shown many of the same positive results that have been seen in other industries. Notably, DHL, one of the largest global logistics providers, were early adopters of the technology and have seen massive returns through time saved by RPA bots. DHL Supply Chain in North America has offset over 5000 hours of manual work with RPA, and globally, there were over 30 000 hours saved per year during the pilot programs that they rolled out (Selko 2019). “Before RPA many basic, day‐to‐day tasks would need to wait to be completed until capacity allowed. Now they are done quickly and with a high level of accuracy, freeing our employees to spend less time doing busy work and more time doing the strategic value‐added work they enjoy,” said Jim Monkmeyer, president, transportation, DHL Supply Chain, North America (Selko 2019). DHL's RPA ambitions did not end with these savings though as they look to double the number of hours saved by the end of 2020. DHL's bots have been deployed primarily for assembling documents to create pay on delivery (POD) as well as scheduling deliveries and tracking milestones. In the future, they plan to use RPA combined with AI to automate more complicated tasks.
Navigating Your RPA Journey
Who Should Own This RPA Journey?
If you decided to go ahead and start the process of using RPA in your company, the first task would be to assign individuals to lead the project. A chief information officer (CIO) should have the knowledge and internal leverage to be able to coordinate between RPA developers and business teams. The CIO should be able to facilitate the cohabitation of bot and worker