Alexander Rinke — June 1, 2018 From humble beginnings as an online bookseller, Amazon has become a household name and the most influential force in retail. The company has scaled rapidly and shifted consumer expectations toward an ‘on-demand’ economy. Now, with a few clicks, consumers want to know all the options available to them when shopping for a product, and have the ability to track an item from the time they place an order until it arrives at their doorstep. These heightened consumer expectations, as a result, are transforming how businesses analyze their logistical networks to understand how to speed internal cycle times and ensure the correct quantities of stock. In an Amazon era, it’s critical for a business to have complete visibility into its supply chain to meet consumers’ needs.
The importance of an efficient supply chain
Amazon has set standards in operational excellence and customer experience with its rapid delivery times and high levels of automation. Over the past 20 years, the company has made impressive strides towards the perfection of its supply chain and become a global retail leader. Comparatively,research from GEODIS shows that just 6% of supply chain managers globally have complete visibility across their supply chain, and 77% have no visibility or restricted visibility. Parallel to that, IOTA research shows thatin 2017 more data was generated than in the past 5000 years combined, and it will continue to increase tenfold in the next decade. The digital age is continuing to advance and businesses are collecting copious amounts of data, but they are finding it impossible to effectively utilize the data without the right technology in place.
Emerging analytics solutions have enabled organizations to make more informed decisions by sifting through the large amounts of data being collected to uncover hidden patterns, correlations and customer preferences. However, there is a restricting factor of traditional analytics solutions:they require a business to first put forward a hypothesis about where they want to look and what areas they want to focus on for further investigation. In reaction to this, new categories of big data analytics, such as process mining, are emerging to assist organizations in pinpointing inefficiencies within their core business processes, for instance along the supply chain.
Using digital traces to visualize the supply chain
Machine learning and artificial intelligence power process mining technology, which uses the digital traces left behind by every IT-driven operation in a company and then provides complete transparency into how processes are operating in real life. This enables supply chain professionals access to a visual reconstruction of the entire organization’s business processes. Once this insight is obtained, they can analyze how well their logistics and supply chain is operating from order entry to the delivery at a customer’s doorstep. This allows process owners to see how efficient (or inefficient) their distribution network is, and identify any causes of delays. Process mining can pinpoint larger systemic weaknesses in the ordering process and highlight granular details like vendor data and invoice tracking.
Satisfying customer demand
Having a stable supply chain is an essential factor in providing complete, fast, on-time deliveries to customers. If companies aren’t able to deliver on their guarantees, customers grow frustrated and will often make their complaints public on social media, tarnishing the brand’s reputation in the process. For example, the recent Kentucky Fried Chicken (KFC) distribution issues in the UK caused hundreds of stores to run out of chicken and temporarily close, which garnered a large amount of negative social and traditional media attention, impacting consumer trust in the brand. What customers don’t see is that there are many potential root causes along the supply chain resulting in longer wait times.
If a company is running a global supply chain with a substantial number of product variations and local market requirements, identifying the exact issue and analyzing the causes for late delivery can be a massive undertaking. The root of the issue could fall under logistics, production, or the order handling process. Internal and external factors like quality issues could cause a lot of rework, a logistics provider could deliver too late, or an order could be stuck in an internal approval process. A company shouldn’t wait to identify issues in the supply chain until customer satisfaction is impacted and churn has increased. The problem is that process owners don’t always have an idea of the scope and impact of specific bottlenecks in the supply chain unless they were first aware that they should be looking for an issue in the first place. The good news is that businesses now have the option to turn to process mining to reduce inefficiencies and monitor compliance, production, and supplier performance to cut throughput times, and ensure delivery times that will result in higher customer satisfaction.
Amazon has become a dominating force in the on-demand economy, using its supply chain efficiency as a key to success. It’s a wake-up call for many businesses trying to remain competitive in this digital age. New analytics technology like process mining is providing the answer for many organizations by uncovering and repairing hidden inefficiencies and providing visibility into business processes so that companies to stay ahead of customer demands.
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