An important step to answering this question is to establish a comprehensive view of all data sources and how they both integrate with, and interact with supply chain processes to drive decision-making. By taking some simple steps, you can avoid hidden, yet critical bottlenecks and barriers, such as poorly synthesized data, redundant technologies, decentralized processes and /or data and task latency, which can prevent your organization from leveraging the true value of your data.
Below, we look at five ways to increase the quality of the data influencing your supply chain decisions:
Integrate data at the detailed level of operations: Tying your data systems at the operational level to expand and expedite data flow can provide faster and better insights as you’re making supply chain decisions. By grabbing data at an operational level, you’re working with a stronger data set that is not altered when it is pre-filtered or synthesized through another system. An example would be to identify critical modules and functions within the ERP that can be tapped directly to provide information in real or near-real time outside of batch processes. One company, which manufactured high-tech items with short lead times, tapped directly into real-time materials consumption and scrap/rework data to allow buyers to adjust daily orders to prevent shortages and late penalties.
Another area to evaluate is bypassing modules and repositories that may employ filtering and rounding rules as they receive or process data for export; or removing the rules deployed within the modules that can impact the data. These “rules”, especially in supply chains that operate in high-volume or time-sensitive environments, can sometimes create inaccurate data in calculating a variety of KPIs, such as supplier performance measurement, pricing variance, inventory safety stock levels and more. The impact may seem insignificant when viewed daily, however, small adjustments in data interpretation can product a large impact over time.
Remove functional and technological redundancies: By identifying areas where multiple systems or resources are performing the same processes, you can avoid decision-making based on multiple interpretations of the same data. Each time your data stops for processing, whether that be by a system or a resource, it is altered in one way or another. Ultimately, this approach only bogs the system down and increases the chance of interpretive errors as the data is continuously synthesized the further it travels. A simplistic example would be the game of telephone. No matter the level of diligence the players exert, it is a rare occasion that the initial message reaches its destination with zero alteration.
This can often mean reducing the level of contribution of a legacy system to a newer system that can more aptly perform the required data movement and transformation/processing. Here’s an example that is closer to home. Let’s say you have an ERP, an MRP, and separate systems for purchasing collaboration and for invoicing. In this scenario, your data is going through three additional systems with different rules and update cycles. Often times, newer purchasing collaboration systems can do much of the same work performed by the MRP and AP systems—including real-time forecast adjustments and triple matching of invoices.
Develop a centralized, functional data network: Similar to removing technological redundancies, developing a centralized data network increases your ability to look at your data strategically. Creating a holistic view that incorporates all systems of record across the enterprise allows for insights into KPIs at the macro and micro level while minimizing the need for time- and labor-intensive data processing and analysis. The current trend is to utilize a centralized supply chain collaboration system to do much of this work. By tying the enterprise’s systems of record into a single network, KPI and other data can be made more readily available throughout all levels of supply chain operations. As a result, the supply chain can be evaluated as a whole or in individual parts more rapidly and with a higher level of confidence in the accuracy and timeliness of the data. For example, supplier performance can be evaluated across the enterprise, division and location levels. Price variance and spend management can be monitored in near-real time, allowing for out-of-range results to be more quickly addressed.
Minimize/remove data and task latency: As alluded to above, latency also impacts supply chain decision-making. In fact, in a recent survey, slow decision-making ranked consistently among the top challenges for supply chain decision makers. Start by performing a data age analysis. How old is the information used by your systems and employees? For some tasks, multiple-hour delays can be too long and lead to additional hard and soft dollar costs. Are you absorbing inefficiencies and errors in your supply chain operations because the data for more time-sensitive functions is not as current as it could be? Often times, taking a pulse approach where relevant data is pushed individually based on functional need, rather than a batch/release process for all systems and resources, can have a significant impact. Synchronizing task schedules and system updates with functional dependencies can dramatically improve data flow, enabling work and decisions to be made with the most current information possible.
Leverage additional automation: In the same survey mentioned above, automation also ranked consistently among the top challenges for supply chain decision makers. The key is identifying where automation can provide a tangible impact. Begin with an evaluation of your KPIs and dependent data. Is the information the result of duplicate data entry to get the data from one system to another? Does compiling it for analysis require multiple manual processes? If so, these areas are good candidates for automation to reduce errors and increase KPI accuracy. An example of a functional area would be to evaluate the level of integration and automation between planning/forecast, procurement and accounts payable systems. If your current solution is not capable of going from supplier RFQ commitment to purchase order to invoice without re-keying the information, there are inefficiencies and errors that can be eliminated through automation.
Implementing these five simple steps will allow you to begin overcoming the barriers to creating a more complete picture from your organization’s data. Recognizing and remedying these areas for improvement will lead to the refining of your raw materials – the data in your supply chain – that power both tactical and strategic decision-making within your organization and allow you to realize the full potential and value of the available data.
Source: TAKE Solutions
Keywords: supply chain decision-making, data flow, supply chains, supplier performance measurement, data age analysis, RFQ commitment, planning/forecast, procurement, accounts payable systems, Big Data