How data improve decision-making in business? (Warehousing guide)
Decision-making in business takes a primary role in every step of the supply chain: from business planning and supplying raw materials to distributing goods to the final customer. And throughout this chain, there are numerous questions which the management has to give answers to, in order to ensure successful performance and retention of customer value:
Which suppliers to choose? Are we going to operate for mass production or job-shop? How do we organize our manufacturing process? Do we plan to have a safety inventory? What activities are we going to outsource? How will we handle orders? What shipping method will we use? And many, many others.
As the warehousing processes are in the center of every supply chain, just between the industry and the customers, it is essential to make well-thought-through decisions and give meaningful answers to all the operational questions regarding this sector. Fortunately, data integration is a key tool that can facilitate these tasks and provide management with valuable insights to ease decision-making.
Companies operating in the warehousing sector can benefit from data analytics and the adoption of emerging technologies to identify new opportunities, boost their performance quality, and improve their decision-making process.
Real-time track and trace
Goods pass through a number of different stages when they enter the warehouse: Receiving, handling, picking, packing, and distributing. Being able to identify where particular product batch or specific unit is located is crucial for the quality of planning, risk assessment, and decision-making processes. This way, the businesses ensure a transparent flow of activities and operations, improved visibility of orders, and availability of accurate information regarding each product unit.
Knowing where our products are, enables us to monitor, measure, and analyze our inventory levels, and make better tactical decisions accordingly. In other words, we can prevent any obsolete inventory, mitigate occurred demand and supply risks, decrease excessive safety stock, improve demand planning, and avoid any stock-outs. What is more, we can store such real-time data to create a detailed, auditable database of all products, resources, and processes, which to share with operational partners.
GS 1 has established official data sharing standards, which enhance the process of distributing supply chain details with partner networks. The adoption of such a database and integration of common standards facilitate communication between organizations and enhance partners’ relationship building. In addition, this ensures that the right product reaches the right customer, at the right time, without any extra cost.
Another aspect of tracking and tracing product units is the ability to receive accurate inbound and outbound information, as well as to monitor when goods enter and exit the warehouse. This way, the company can measure the average time that products stay as inventory assets and improve demand-planning to boost the inventory turnover rate.
One specific technology which is a must for real-time data tracking is RFID. It has become a key tool for companies who want to thrive in operational planning because of its complex characteristics:
- Provide accurate real-time data
- Gives information about the serial number, date of manufacturing, date of expiration, batch number, toxicity, material, special requirements about storage (such as temperature), and others
- Can be integrated into IoT network
- It can be wirelessly changed or updated even if the unit is at a different location
Such tracking technology allows warehousing organizations to track individual product units, assembly parts, goods batches, and containers, by providing automated real-time data collection from each product that is integrated into the system. This way, the companies have constant access to the condition and location of their products and can act immediately if any problem occurs.
Machine capital depreciation
One other aspect regarding the impact of data on the decision-making process in warehousing is machine depreciation. This is an important point because automated appliances are used in every process, from receiving to delivering.
As real-time data integration provides constant, up-to-date information about the condition, location, and state of the physical assets, it facilitates the employees to identify real-time problems with the machine capital. This way, the management can quickly intervene and make a relevant decision whether to reorganize operational sequence, forgo certain functions, repair machines, purchase a new one, etc.
In the long run, this has a positive impact on the depreciation pace of the machine capital, as the appliances’ life is prolonged, which generates lower costs and shrinkage of contingency expenses.
Taking into account the fact that warehousing includes plenty of tasks, from receiving to the distribution of goods, it can be concluded that optimization of those processes is critical for the efficient performance of the business entities. But in order to optimize them effectively, we need strategic, evidence-based, and data-driven decision-making.
This is the time when high-quality data analytics comes into help. By integrating all the data generated in the warehousing processes, the organization has a transparent overview of the operational flow. As a result, it has the opportunity to recognize strengths and areas of improvement, identify changes, limitations, trends, and tendencies in demand and other essential operational factors, such as productivity, and inventory, and employee turnover.
This way, the business entity can adequately assess the severity and probability of risk factors, prepare strategic planning, and set KPIs based on relevant data analytics to adjust supply and demand forecast. What is more, it has the opportunity to lower costs and order processing time, reduce operational inconsistencies, optimize employee functions, and improve their safety. And by achieving better labor allocation, the employees become an intellectual asset, assisted by the machine workforce. Besides, the use of machine learning enhances the quality of the automated workflow, decreasing the time for order processing.
The implementation of Warehousing Management System (WMS) enables the companies to gain all the above-mentioned insights, based on integrated data from machines, IoT networks, AI devices, RFID, barcodes, etc. Those valuable and meaningful insights help the management to create and build complex customized solutions in accordance with the business needs. What is more, warehousing decision-making can be aligned with the corporate objectives, by integrating Enterprise Resource Planning (ERP) with the WMS, in order to ensure efficient development and profit growth.
Warehousing is continuously becoming an extremely complex sector, where business needs and client demands meet. With the emerging technologies that become available together with the vast amounts of data that are being generated, it is impossible to ensure high- quality decision making without using data analytics as a primary managerial tool for strategic planning.