How Big Data Analytics Can Transform Supply Chain Management

Faster delivery speed. More packages delivered in a day. Maintain low delivery costs. Improve demand forecast and delivery accuracy. Make minimal waste. These are the goals that we all want to achieve in the supply chain system, whereas achieving one goal often means trading off another goal performance:

How can we achieve faster delivery speed while maximizing the number of packages delivered and maintaining low delivery cost? How can we lower delivery costs and improve efficiency, while maintaining speed and quality?

Here comes technology plays a role in big data analytics to offer smart solutions!

Source: jyler.com

The first thing required in implementing big data analytics in the supply chain system is to track and record every movement in an information system. From the point where we order or manufacture a product, make an inbound delivery to the warehouse, sort and store the product, and up until we make the last-mile delivery to the customers, every information should be recorded in a database. Once we have built a supply chain database that stores the big data, then it’s time to cast the magic to turn the data into solutions! What magic can we do?

Procurement & Manufacturing

• Improve demand forecast. With the development of predictive analysis techniques, such as machine learning implementation, can help to increase forecast accuracy and even predict trends. An accurate demand forecast can bring a significant impact on reducing waste, such as high inventory costs due to overstocks, yet avoiding out-of-stock, which can lead to lost sales as well. Moreover, it enables us to build a more sticky relationship with vendors. We can place purchase orders in advance with the forecasted quantity, have vendors prioritize our orders, thus we receive the orders on time and with the right amount.

Fulfillment & Inventory Control

Source: multichannelmerchant.com

• Capacity planning. By implementing the demand forecast, we can then predict how much storage capacity that we need for each product, and predict how much manpower that we need to handle the picking up, storing, packing, and shipping processes. This is where machine learning plays a significant role in predictive analytics. To be more advanced, we can also perform a more in-depth analysis of customer basket sizes, such as finding the correlation between products or doing clustering analysis. Products that tend to be purchased together can be grouped in the same location to make the handling process more efficient. A more efficient and lean system can be achieved by minimizing waste on space, manpower, and travel distance.
• Determine potential expansion. As the business grows and expands, we need to increase our inventory capacity as well. Then this leads to another question, where should we expand to achieve an effective distribution system? By collecting and analyzing our customers’ data, such as customer profile and purchase behavior, we can categorize them by area and identify unique behavior per area. We can also detect whether if there is an untapped market. For example, a customer segment in a distant area may have purchased several times in large basket size but has a long period between each purchase.

Logistics & Distribution

• Route optimization. In achieving an efficient delivery cost and speed, route choices matter the most. Here’s where operations research can be implemented to determine the shortest path with maximum delivery points that can be reached. Moreover, with an integrated and real-time database, big data analytics can enable route recommendations to avoid congestions during deliveries.
• Just-in-time (JIT). By enabling an accurate demand forecast, allocating optimum resources for handling and delivering packages, determining the most efficient routes, this integration leads us to timely delivery for customers with minimum waste. We manage to cut costs, time, and distance.

Sales & Marketing

• Product recommendation. Aside from determining potential expansion, analyzing customer purchase behavior can also shed light on what type of product they prefer to buy. For perishable goods, we can give customers a reminder when they need to stock up again. For experience products, we can provide product recommendations with similar characteristics, such as a movie with a similar genre. We can also drive cross-selling by offering accessories that can support the product they previously purchased, such as luggage cover or phone case. By giving product recommendations, we contribute to decreasing customer fatigue to make a purchase decision and offer a value-added service as well.
• Product improvement. Big data analytics can not only be applied for quantitative or structured data, but it can also be utilized for unstructured data, such as texts. We can collect customer reviews and perform analysis on a semantic level, such as text mining or NLP, to gain insights from customer feedback about how our product and supply chain system can still be improved. We can also analyze sentiment on customers’ voice on social media, and even watch over our competitor movement as well.

Source: visionedgemarketing.com

Returns

• Enhance service recovery. Through a good service recovery process, we may ask customers about what are the concerns that drive them to make returns. This information is valuable for us to analyze what improvements can be made for our products and services, and also to avoid customer churns. Moreover, after all, returns are inevitable, so it is wiser for us to mitigate by providing allowances for resource capacity to handle returns. Previous return data can be a useful source of information to make predictions, plan, and reserve capacity as needed.

Lastly, in implementing big data analytics, maintaining relationships with stakeholders is essential. Not only with end consumers, but also with delivery couriers, vendors, and other outsourced parties. To have a ‘big’ database system, we need to share our data with related stakeholders as well. Imagine if we have forecasted and arranged schedules neatly for a month ahead, but it turns out that the couriers are out-of-capacity, or the vendors are out-of-stock, then our schedule is meaningless. Or imagine if every time a customer makes a purchase, it is counted as a unique user because we don’t keep the customer records, then we can’t analyze their repeat order behavior.

In conclusion, enabling a data-driven supply chain management system requires collaboration within many parties, to minimize waste and maximize productivity in the long-run.