The supply chain is emerging as the next home for chatbot deployment. As organizations explore chatbots, they must ensure their various data management processes can keep up with the technology.
Chatbots have become a hot topic in IT circles. Pundits who are excited about the technology envision a world in which artificial intelligence software capable of voice recognition and auditory response can hold simple conversations with users. With this ability in play, a chatbot can automatically search databases, adjust backend data and interact with users in an intuitive way. Digital assistants such as Apple's Siri and Microsoft's Cortana are prime examples of chatbots. While these technologies have gained a growing role in the consumer, finance and accounting sectors, they are beginning to extend their reach.
Research from Eye For Transport found that just 21 percent of organizations in the supply chain sector were engaging with chatbots in some way. By the second quarter of 2017, that figure rose to 51 percent. Chatbots are gaining momentum quickly as supply chain organizations embrace the technology as a way to more easily deliver information to users and allow for intuitive data transmission within complex work environments. All of this innovation comes as part of a large move to digitization in the supply chain, and organizations that want to maximize value potential need to make sure the backend processes that inform chatbots can provide the data quality needed to make the automated systems work.
In its simplest form, a chatbot is a software program capable of responding to user queries in a pre-ordained way. The technology has recently experienced a new wave of popularity largely because of the increased accessibility of artificial intelligence systems. The connection is clear. Think of it this way: If a chatbot is programmed to answer user questions, it must be manually coded to recognize queries, find the right data and communicate the answer. That presents a huge project burden and cost for IT teams. Furthermore, the need for incredible precision as end users make requests limits the technology's value and makes for a very niche solution.
With artificial intelligence technologies becoming more popular, chatbots can now be set up with AI minds and machine learning algorithms that allow them to respond to natural speech, provide answers to questions that are worded in unusual ways and offer a more human-like response. Organizations need only put some basic rules in place and establish the AI, and the rest of the work is completed through a machine learning setup that uses existing data to understand the types of information users will need. The technical burden behind the chatbot is reduced and the end-user experience is greatly simplified because the software can respond to normal speech.
A Medium report described this advance by pointing out that chatbots are now increasingly able to function as information acquisition tools. In the past, many information gathering processes involved a person-to-person interaction in which two individuals had to collaborate to update one another on a situation.
For example, a forklift operator may reach out to a warehouse manager via wireless headset if a routing problem comes up. At that point, the forklift would idle and the manager would assess the problem, look at other scheduled work and suggest an alternate route.
This type of human-to-human interaction is inherently inefficient, and it becomes extremely problematic when logistics organizations rely on paper-based records. With chatbot software in place, the forklift operator could simply speak a question into a headset and have the chatbot analyze data from across the warehouse in real time to notify the user of the best action. A process that could take minutes and depend on human availability is now completed in moments.
These efficiency gains can happen across the entire supply chain. However, the rise of chatbots brings up a simple question - how are organizations going to ensure that bots actually have the data they need to respond to user queries? This is a major concern, and it is relevant to businesses whether they are considering chatbots or not. Chatbots are emblematic of the increasingly digital nature of the supply chain management sector, and organizations that want to keep up with new operational demands must seriously evaluate their data acquisition and management practices.
Data is becoming the lifeblood of the supply chain. Organizations that can track their products over the entire supply cycle can reduce risk and simplify regulatory compliance. Furthermore, companies can use this data to more effectively align their production schedules with supply availability, leading to cascading efficiency gains as each line of the business begins to run on a coordinated schedule based on real-world data, not projected resource availability. Chatbots can take this kind of coordinated environment to another level by allowing for more machine-to-machine communication and by allowing for more intuitive and productive human-to-machine interactions.
While a digital supply chain support by emerging technologies such as chatbots sounds exciting, it is only as valuable as the data collection and distribution systems that reside under the surface.
Automated data collection tools that work across mobile device ecosystems are increasingly critical in the supply chain sector. Whether an organization is using smartphones and tablets to let users log shipping details at the point of entry, equipping workers with mobile barcode scanners so they can update orders to remote facilities or using voice picking headsets to improve data management during picking processes, being able to automatically log data updates on the go is critical.
Any delay in a supply update can mean derailed production further down the line. With businesses becoming more dependent on a coordinated data system, companies have an opportunity to hold less inventory, improve efficiency and maximize their capital assets. However, this also means there is less margin for error. A worker forgetting to log a broken part, or simply a delay in updating the system, can cause planned processes to break down.
Ramping up data collection functionality within the supply chain keeps all systems informed of circumstances in near real time. This allows the vision for a connected, digital supply chain to become a reality.
Of course, all the data collection tools in the world won't help with digitization if the various systems within your supply chain can't talk to one another. A chatbot won't be able to alert a manager to a potential inventory problem if a voice picking system is updating data exclusively within a siloed warehouse management system and that data doesn't make it to the enterprise resource planning platform.
ERP integration turns an ERP into a more effective hub for data management, providing for almost immediate updates to data and ensuring everybody can stay informed at all times.
Chatbots are a prime example of how the digitized supply chain is driving innovation and changing what is possible in the sector. However, improving the underlying data collection and management systems is a critical first step in this path toward a fully modern logistics operation. Companies that want to get off on the right foot need automated data collection and ERP integration tools to set digitization in motion. RFgen offers the full suite of technologies companies need to update how they collect, manage and use data within the supply chain.
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