By the time you read this sentence, over four thousand parcels would have been shipped.
In 2020, global parcel volume reached 131.2 billion, according to the Pitney Bowes Parcel Shipping Index. This figure equates to 4,160 parcels shipped per second, an increase of 27 per cent year-over-year. The study featured data from 13 major markets around the world.
With the explosion of eCommerce post-pandemic, warehouses are busier than ever, reinforcing the need for intelligent supervision. At the same time, challenges too have increased manifold. According to a study by IHL Group, overstocks and out-of-stocks cost retailers $1.1 trillion globally in lost revenue. Changing customer expectations, like same day delivery, have resulted in the supply chain, logistics and fulfilment teams running on a shorter lead time. Warehouse business bottom lines are influenced by omnichannel sales, seasonal demand fluctuation, overstocking, out-of-stock situations, backorders, order returns and socio-political forces at play.
There are more players entering the market. Today, warehouse management involves optimising distribution centres, brick-and-mortar stores as a fulfilment centre and localised eCommerce fulfilment centres. There is also an increased opportunity for growth. Supply chain leaders who broaden their distribution networks stand to meet the rising demand and grow their business.
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In the ’90s, supply chain partners began to use Electronic Data Interchange (EDI) and Enterprise Resource Planning (ERP) systems to exchange information more smoothly. In the following decade, they moved on to business intelligence and predictive analytics software solutions to optimise how they used their networks. The challenge today is not in collecting the data but how best to put that data to work. Approximately 20 per cent of all supply chain data is structured and can be easily analysed, which means that 80 per cent of supply chain data is unstructured or dark data. Artificial Intelligence is a gamechanger here because it can be used to process both structured and unstructured data — and provide summaries and analyses of that information in real time.
There are several solution providers for warehouse management systems such as Veridian, Cyzerg, Solum, NetSuite WMS, Fishbowl Inventory, 3PL Warehouse Manager, Softeon, Infor SCM, HighJump and more.
There’s a ton of data that warehouses generate related to procurement, processing and distribution of goods. Master data refers to information about suppliers, customers and inventory or operations, transaction data looks into the different exchanges, and business data considers policies and customer behaviour and expectations. Data collected from connected devices, IoT sensors, telematics devices, and GPS feeds from vehicles are part of smart warehousing and a prerequisite to get the most from your analytics.
Modern Warehouse management systems (WMS) are built to collect and analyse all data. Analytics from these systems can help supply chain leaders understand how to layout inventory across all shipping channels to optimise performance or cut costs, test platforms for security vulnerabilities, track information to improve operational efficiency, plan for future trends or to mitigate risks.
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Analytics can fall into four categories — descriptive, predictive, prescriptive and cognitive, depending on the action or insight one hopes to gain from the data. Descriptive analytics lays out the state of operations based on historical data, traditional business intelligence tools will present this data by identifying patterns. Predictive analytics, an offshoot of advanced analytics, uses data mining, artificial intelligence, machine learning and data modelling to make predictions based on historical and current patterns. This helps prepare for the probability of future events while simultaneously assessing risks associated with it. Prescriptive analytics is typically used to find solutions to existing business problems. The models will use graph analysis, simulation, complex event processing, neural networks and recommendation engines and leverage historical and industry data to present insights that ultimately help make business decisions. Finally, cognitive analytics applies human-like intelligence to understanding data which may include open-source data available on the internet. It can be used to answer questions. It’s an intelligent solution that can apply context and work with large amounts of information using semantics and deep learning techniques.
Early adopters to the use of WMS, equipped with intelligence, include Alibaba, Flipkart and Amazon. According to 3PL’s annual logistics study, 80 per cent of respondents point to big data as a core competency to having an optimum and high-performing supply chain. Besides forecasting, big data helps leaders gain real-time visibility of the supply chain — knowing when items are due to arrive and their intended destination along with a view of delays and repercussions on costs and prices. For large firms that enlist the help of third-party warehousing or logistics fleets, this means gaining an omnipresent view of vehicle locations, traffic situations, etc. This can help take quick action in response to delays, stock supply or demand fluctuations. Smarter order management means lower wastage, reduced costs, and fewer reasons for customers to be left in the dark. A bird’s eye view of inventory is especially important for retail companies that offer in-store and online purchases. Apart from the visibility of their stocks, companies can analyse competitor data to update prices in a way that takes advantage of market opportunities.
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An intangible aspect that’s often overlooked when considering how these technologies affect business outcomes is supplier relationship management. By creating a streamlined approach, you improve efficiency for both your business and your suppliers. Using technology to determine decision-making helps develop a mutually beneficial relationship with suppliers, especially those considered strategic partnerships.
There is more to come. More than half of shippers (52 per cent) and third-party logistics partners (63 per cent) said 5G technology is either moderately or critically important. The 5th generation of mobile networks is designed to provide comprehensive connectivity among machines, objects and devices. It promises to enhance supply chain operations, performance and real-time communications, ultimately driving the digital supply chain.
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