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We are on the verge of a major upheaval in the way inventory is managed. This revolution is a result of the availability of the huge amounts of real-time data that are now routinely generated on the internet and through the interconnected world of enterprise software systems and smart products. In order to make effective use of this new data and to stay competitive, managers will need to redesign their supply-chain processes.
Advanced machine learning and optimization algorithms can look for and exploit observed patterns, correlations, and relationships among data elements and supply chain decisions – e.g., when to order a widget, how many widgets to order, where to put them, and so on. Such algorithms can be trained and tested using past data. They then can be implemented and evaluated for performance robustness based on actual realizations of customer demands. For example, does use of these data-driven tools lower cost and/or enhance customer service?
The traditional paradigm for supply-chain management is to develop sophisticated tools to generate forecasts that accurately predict the value and the level of uncertainty of future demand. These forecasts are then used as an input to an optimization problem that evaluates trade-offs and respects constraints in order to come up with decisions about managing materials. This two-step process, which is embodied in all current material-management planning and control systems, can be replaced by a single-step process that looks for the best relationship among all of the data and the decisions. Based on learning from the past, a “best” relationship can be identified, which will generate decisions, as future uncertainty is resolved, that are better than the decisions derived from the traditional two-step approach of first forecast and then optimize.
This approach is not restricted by any a priori assumptions about the nature of the market and the behaviors that lead to customer demands or about the trade-0ffs and constraints that have to be considered in order to evaluate material-management decisions. Instead, the power of computer learning, supplemented by management input based on context-specific knowledge, is used to find the best relationship between all possible decisions and full range of the data. Use of this relationship can lead to better operational performance. It will lead to better outcomes because it utilizes all of the data available to current methods along with extensive additional data that currently is ignored and which may be relevant.
Real-Time Delivery Tracking
Big Data’s management systems include real-time analytics solutions that can be used to strengthen fulfillment. These systems include both Big Data hardware/software for warehousing and processing and inputs from bar-codes, radio frequency identification (RFID) tags, global positioning systems (GPS) devices, among others. Such systems can capture traffic sensor data, road network data, and vehicle data, in real-time to allow logistics managers the capacity to optimize delivery scheduling. They can address unforeseen events (such as accidents and inclement weather) effectively; track packages and vehicles in real-time no matter where they are; automate notices sent to customers in the event of a delay; and provide customers with real-time delivery status updates. Firms can also aggregate and filter relevant unstructured data from sources, such as social networking sites for insights on the delivery process, and respond to issues in real-time.
Further, vehicle sensor information can be used for predictive maintenance –maximizing the life of business equipment (in this case, vehicles and transportation-related equipment such as forklifts) by scheduling preventive maintenance based on current and historical data.
Transportation data, when integrated into a commercial or in-house implementation of a distributed file system, such as Hadoop, a network-based one like Gluster, or other similar system, can be leveraged by other strategic business units. For example, a firm can configure its transportation business intelligence system to route notification of delivery delays to customer service centers automatically; customer service representatives can then anticipate, and respond to, customer complaints appropriately.
Optimized Supplier Management
To maximize profits, firms want to sell the most products at the lowest costs. Cost determinations become increasingly complex the more raw materials used to produce a product, the greater the variability in the price of those inputs, the more products the firm offers, and the larger the geographical distribution area. The supplier relationship management process – which once, for many firms, had more to do with drinks, golf games, and other shared social experiences – these days, must incorporate more quantitative measures to determine whether the firm is receiving the most bang for its buck.
Big Data allows firms to develop complex mathematical models that forecast margins if different mixes of suppliers are chosen. These models can take into account a wide range of variables, such as the additional costs due to variations in the speed with which different suppliers can deliver their goods; one-time switching costs, such as long-term contract cancellations; and even estimates of supplier reliability, which firms can use to generate performance predictions of various supplier mixes. Managers can then select those with the highest return on the lowest investment to maximize profits.
Optimized Pricing
Similar to supplier selection, Big Data has many benefits for pricing. Firms can use consumer data, from both internal and external sources, to develop pricing models that maximize profit margins, and use predictive analytics tools to forecast demand for a particular product at different price points. Firms can then test these price points with soft launches, and incorporate consumer behavior and feedback – both quantitative and qualitative – into their pricing strategies. Further, firms can develop models to determine which combinations of related products consumers are likely to buy together, and use this information to develop and refine upselling strategies.
Another application of Big Data management and analysis to pricing involves sales forecasting. Firms can use predictive analytics to make real-time predictions about the firm’s sales performance overall, in a region, or even a specific location; they can adjust pricing to ensure that they meet those projections when necessary. Dynamic pricing can also be used to maximize revenue during times of increased market demand and/or supply shortages. Common in ground and air transportation during the holidays, dynamic pricing allows operators to increase prices for empty bus, plane, and train tickets when empty seats are scarce. However, industries ranging from hotels to sports entertainment to retail employ dynamic pricing to increase revenue.
Automatic Product Sourcing
In late 2013, Amazon filed a patent in the U.S. for the process of predictive shipping – a distribution method wherein a firm uses predictive analytics to forecast future sales based on historical data; they then source and ship products to local and/or regional distribution centers in advance of those orders. It remains to be seen how successful this method may be, yet given Amazon’s pioneering success in the online retail space, driven in no small part by its embrace of Big Data management tools, techniques and technologies, it would be tough to bet against them.
Twelve years earlier, the firm filed a patent for automated product sourcing– a process and its related technologies that played no small part in Amazon’s success; it has since been replicated by many other online retailers to varying degrees of success. Automated process sourcing refers to a firm’s ability to, upon receipt of a customer order, analyze inventory at multiple fulfillment centers, estimate delivery times, and return multiple delivery options (at different price points) to the customer in real-time. This enhances value for the customer, and allows Amazon to optimize distribution, as well as inventory management. Many other firms, from Best Buy to eBay, have either developed their own automated product sourcing systems or purchased software and process management solutions from vendors.
