Supply chain leaders must be ready to implement big data in order to continuously improve.

This guest post comes to us from Adam Robinson, director of marketing for Cerasis, a top freight logistics company and truckload freight broker.

Supply chain leaders are enthralled with the idea of using big data, but they tend to fail to understand how to disseminate big data in their organization properly. True, they may know how to roll out big data in a single warehouse, or they may have heard their competitors used branded systems for implementing this new technology. However, the fundamental problem remains.

Supply chain leaders must be ready to implement big data and leverage it to improve their companies without any delay or inhibition. This may sound impossible and frightening, but they must only understand how big data always goes back to these two, simple principles of measurement: review and action.

Ask traditional questions, and let big data provide answers.

The most common complaint of newer companies using big data analytics capabilities tends to revolve around traditional questions of business strategy. Consider the following elements explains John Richardson of Inbound Logistics, that impact business strategy.

• Increasing order efficiency.
• Demand forecasts.
• The quantity of each product.
• Inventory location and management.
• Raw material suppliers and logistics.
• Transportation modes used in procurement and shipping.
• Distribution of goods prior to purchase.
• Demand fluctuations.

Each of these elements more traditionally handles by outsourcing analysis of processes to supply chain consultant. This is actually where the concept of third-party logistics providers involved. However, rapid growth and diversification of products are making shippers, manufacturers, and suppliers rethink their business strategy. In other words, consumers can get practically anything they want at a moment’s notice, and more consumers are expressing a willingness to wait for a product a few days if free shipping is a possibility. So, this need to adapt operations reflects the common concerns of traditional customers and supply chain entities. However, there is a distinction.

Previously, these entities only needed to focus on their local demographic for ensuring continued stability. But the rise of the internet has given consumers and everyone else the ability to access any product from any seller and any place on the globe. This is a traditional business strategy, and it is essential that the modern supply chain is willing to use big data all operations to create a more positive result than consumers, stakeholders or government organization ever needed before.

Performance measurement and management.

As explained in a previous blog post, continuous improvement in an organization can be achieved through the use of performance measurement tools via big data. Mostly, this reflects the skills and actual working capacity of employees. Since employees represent one of the largest expenses an organization may face.

Having the best staff members available can mean the difference between success or failure in a company. Furthermore, big data can help employees understand what they do and do not need to do in order to improve their performance scores. This will also help to prevent oversight from managers and keep all employees on track to complete their responsibilities as assigned.

Performance measurement does not have to be limited to the performance of employees either. It can be expanded to identify poorly performing machines, or it can be used to isolate inefficiencies in collaboration with suppliers or vendors. Ultimately, performance measurement is a metaphor for tracking any metric in the course of the supply chain, but it’s key to being effective is found in transferring the insights gleaned from big data into actionable results.

For example the operational efficiency of a given loading is directly related to how quickly pickers are able to fulfill orders and move them onto the dock. Obviously trucks can only be loaded so fast; what is the number of pickers appropriate for the current workload, or which route through the factory is adding an extra 20 minutes to each worker’s duties?

These questions illustrate that the most insignificant details can be driving forces of inefficiency in the supply chain. But they represent opportunities for continuous improvement. Changes in the design or layout of the warehouse or alterations to the truck schedule may require changing the duties of a certain worker at a moment’s notice. Essentially, the worker must be able to access continuous data measurements on all factors affecting his or her responsibilities.

Supply chain leaders need to focus on continuous improvement.

Continuous improvement is a complex notion. It is based on hundreds, if not hundreds of thousands, of individual metrics from various collection points and analyzed in real time. All of this reflects a very large volume of data. It can be digested and broken down into usable bits, much like the biological processes of the stomach making it essential to surviving the coming season.

This comparison is much more than a metaphor;  it is the real issue being faced by supply chain entities and their leaders. Supply chain leaders must use big data to gather insight and create quantifiable measures of performance and functionality across their enterprises on a recurring, frequent and immediate basis.

Related posts:

• Driverless Trucks Filling the Gap of the Driver Shortage
• How Big Companies Use Big Data
• The Case Against Big Data for Some Supply Chain Companies