What is Traceability?
In this blog, we will focus on the traceability and its challenges in industry. Traceability is the capability to follow all the stages in the life of a product, from its manufacture to its destruction. Traceability therefore allows, for a given product, the identification of:
- The origin of its raw materials
- The places where the product and its factors were stored
- All the stages of the product in the product inflow,
- All controls carried out on the product and its factors,
- Furthermore, all outfit used during product,
- All clients who consumed the product.
In addition, the following section defines traceability at different situations:
- Upstream traceability, which refers to the identification of suppliers and raw materials relating to this product;
- Internal traceability, which refers to the tracking of the product in the product inflow, between the event of raw materials and the finished product;
- Downstream traceability, which refers to the identification of consumers.
Traceability and Its Challenges in Industry
In the ensuing sections, we ’ll cover the major complications of product tracing perpetration and give potential guidelines to address the issues.
Manual Operations Increase Errors, Delays, and Costs
Traceability and its challenges in industry also includes manual operations that increases errors, operational costs, and delays. Numerous procedures enabling supply chain traceability in manufacturing are performed manually, adding the burden to data trustability. For example, track- and- trace operations involve surveying thousands of products or parcels with handheld devices, while tab data is manually entered into commercial systems. This system is labor-intensive, time- consuming, and prone to human error. Furthermore, similar issues can in fact be the cause of multiple inaccuracies and delays performing in lost or returned products and unreliable predictions or analytics which ultimately escalates costs.
To streamline product traceability and its challenges in industry where manual efforts are needed, a company should automate as numerous processes as possible and label all the physical particulars involved. Applying barcodes, QR codes, and computer vision to both goods and pallets used for transportation. To make code surveying more accessible and reduce the costs, distributors might replace professional handheld devices with regular mobile apps. fresh tools and devices for traceability within factories or storages may cover pick/ pack operations to insure each carton contains the right goods in the correct quantum. Microsoft explains that we can find analogous mechanisms in conveyor belts with integrated detectors and computer-vision features. Other companies conclude for picking wagons with built-in scales.
The Huge Quantum of Connected Data
When enforced, however, traceability software, barcode compendiums, and IoT detectors induce massive aqueducts of real- time data coming from factories, storages, vehicles, etc. The problems with data operation launch from the very morning — collection — and escalate as the system operates. First, supply chain data can be veritably miscellaneous in terms of type (material volumes, payload dates, etc.) and source (IoT detectors, ERP systems, etc.). Second, manufacturers may deal with both structured and unshaped data (varying from dates in a delivery schedule to satellite imagery for route optimization). Third, seasonal/ vacation spikes in demand contribute to peak data loads that can put a strain on logistics software. Fourth, the correlation between goods/ products and spare corridor materials coming from multiple suppliers can be extremely complex.
Thus, comprehensive data operation should be part of your supply chain strategy — covering data quality, metadata operation, data warehousing, etc.
A Fragmented Picture
Indeed after collecting sufficient telemetry and assuring data trustability, companies may still have limited supply chain visibility due to information silos. This happens when data is in insulated systems, creating a fragmented view about product movement.
Why does this occur? A typical manufacturing software ecosystem encompasses a multitude of operations that collect and process data covering specific areas of the supply chain. CRM software stores client data, while ERP systems handle commercial information regarding most business functions or reporting. Manufacturing execution systems (MES), on the other hand, take care of monitoring manufacturing processes, similar as the transformation of materials into goods.
A standalone supply chain traceability system for manufacturing makes the workflow indeed more complicated. As a result, Bain & Company’s 2021 check revealed that 56 percent of directors consider ensuring interoperability the main product traceability challenge. All this calls for data integration — very frequently, in real time.
Turning Data into Precious Perceptivity and Predictions
As the WEF and Bain & Company reported, value doesn’t come from data per se, but from analytics and predictive modeling. This means processing data from IoT detectors, logistics software or supplier systems, and turning it into practicable perceptivity. For illustration, data analytics software may examine historical information to identify the reasons behind a product bottleneck, for example, delayed delivery of crucial factors. As a result, this result could give recommendations on alternative routes or suppliers.
It’s also possible to read forthcoming product demand spikes grounded on seasonal trends, helping associations to address the challenges timely. McKinsey estimates (2022) that independent supply chain planning grounded on intelligent predictions and analytics can affect in 4 percent profit growth and a 10 percent cost reduction. Predictive maintenance is another step toward making value out of data. By covering asset conditions (e.g., vehicles or detectors), one can detect anomalies and carry out preventative repairs.
However, data analytics for decision-making introduces its own conditions, according to Hitachi. First, analytical results should be fuelled with applicable data. Second, this data should be duly cleansed and prepared most probably, unified or converted. Third, it may have limited integrability/ interoperability, as we wrote over. As a result, logical software can be relatively precious to make and maintain, not to mention the conditions for proper proficiency (in BI, ETL, OLAP, data science, etc.).
Else, a data model created with ML to imitate scripts. Including demand or payload appearance time, will be unstable, leading to incorrect forecasts. Still, ML requires a huge computing power and time. ML algorithms go through a complex training process, involving large data sets, and building a predictive model can take months.
Ever More Effective Technologies
Technologies for traceability, as shown in the above illustration, are constantly evolving. Today, we regularly hear about artificial intelligence, augmented reality or RFID. The traceability results now make it possible to go very far in the type of information for tracing. Yet, companies are looking to apply new services that complement their offer. This is why it’s always intriguing to find out about new technologies for optimal industrial traceability.
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