Tag Archives: Procurement Analytics

Introduction

Transportation Analytics in a supply chain refers to the movement of products from one point to another. It starts at the beginning of the supply chain when supplies arrive at the warehouse and goes to the end-user when the customer’s order is delivered right to their door. Because of its importance, warehouse managers should investigate transportation in their supply chains. In the end, this is the only method to cut total expenses in a scenario where transportation can account for up to 60% of total operational costs or a significant amount of a company’s supply chain costs. Few activities in the supply chain have as much of an impact on business as transportation selection. Delivery techniques ensure that deliveries to and from the business go smoothly and reach their destinations on time. Because transportation is crucial to the company’s performance, it is critical to incorporate it into the supply chain management strategy. Transportation is regarded as one of the three essential components of supply chain management because of its importance.

Transportation analytics rapidly power mobility information and insights, altering transportation planning by making vital data collection and understanding more accessible, faster, cheaper, and safer. Cities, transit agencies, transportation departments, and other entities increasingly turn to transportation analytics to solve challenges, prioritize investments, and gain stakeholder support.

The transportation analytics market was worth USD 15.65 billion in 2021 and is predicted to grow to USD 77.33 billion by 2029, with a CAGR of 22.10 percent from 2022 to 2029. Because of its ability to simplify commercial and personal transportation, Predictive Analytics accounts for the most prominent type of segment in the corresponding industry.

Usage of Transportation Analytics

Big data is heavily used in supply chain management to evaluate operational hazards, improve communication, secure proprietary data, and improve supply chain accessibility. This data is used by industries in a variety of ways, including predictive analytics and the creation of more efficient cloud-based platforms.

Usage of Transportation Analytics

Usage of Transportation Analytics

Predictive Analytics

Data mining, statistics, and machine learning are used in predictive analytics to assess future supply demands, inventory, and customer behavior. Companies use predictive analytics and machine learning to forecast future physical hazards in the supply chain and financial, customer, and other operational risks.

Cloud-Based Platforms

Cloud technology will be critical in the future of transportation and supply chain management. It can help lower costs by reducing the influence of physical/geographic barriers, merging, and replacing various in-person processes, mitigating some of the consequences of market swings, and consolidating and replacing various in-person processes. Optimized data, on the other hand, is critical to the success of cloud-based platforms. Data must be effectively recorded, transmitted, and used to profit from cloud technology fully.

Cloud storage has its own set of security concerns. As more businesses and industries migrate to the cloud, fraudsters will find the technology increasingly appealing. In addition to the protections provided by cloud providers, businesses should always examine what security measures are needed. Larger companies also often use many cloud providers across their operations. Companies must have solid policies for preferred vendors, best practices, and the involvement of internal IT teams in this situation.

Role of Transportation Analytics Professionals

The growth of e-commerce has led to higher expectations on speed, agility, and visibility. Manufacturers, merchants, and consumers have pushed transportation and warehousing companies to develop quickly to meet ever-increasing service demands. Transportation management is evolving thanks to supply chain technology fueled by data and analytics—these practical tools aid businesses in being more educated, efficient, and long-lasting.

Role of Transportation Analytics Professionals

Role of Transportation Analytics Professionals

Monitoring

Technology has catapulted the business beyond simple track-and-trace data into a new world of supply chain visibility in just a few years. Customers can now not only follow their items as they travel, but they may also receive text or email notifications when delivery vehicles are stationary for an extended period. The same information can show whether delivery is within a mile of its destination, allowing receiving facility managers to plan and avoid surprises. This increased awareness has ramifications that go beyond on-time delivery. Companies will be able to carry less inventory due to this data because they can precisely pinpoint their products’ locations and when they are needed. Over time, this could result in significant cost reductions. Data is also allowing for more personalization and control in the transportation industry. Internet of Things (IoT) sensors in trailers now allow drivers and dispatchers to watch and report on temperature, humidity, movement, and other vital elements in real-time, allowing them to intervene before a problem arises.

 

Fleet Management Systems

The use of fleet management technologies is also helping to improve transportation efficiency. Vehicles communicate with systems regularly, getting information such as how long they have been on the road, where they are going, and which route would be the most efficient. These solutions cut down on idle time for drivers, improve fuel efficiency, increase safety, and cut down on paperwork. This continuous connectivity between trucks and warehouses or manufacturing facilities also allows for increased flexibility and real-time responses to unanticipated incidents. By increasing transparency in the transportation business, digital freight platforms enable enterprises to think beyond today’s shipment. Thanks to technology, shippers may see regional trends, individual lane cost information, and driver preferences, while carriers can get specifics like loading/unloading durations and lane history data. All this information can aid in lowering operating costs without compromising service.

Vehicle-to-Vehicle Communication

Finally, data will play a part in one of the most intriguing breakthroughs in transportation: platooning, in conjunction with other technologies. Platooning is a method of transporting three or four trucks through the lengthy segments of the highways. The lead vehicle requires a driver, while the other tracks follow a digital tether a short distance apart. All vehicles respond with near-zero reaction time because the lead vehicle controls its speed, direction, and braking. When the platoon is within range of a destination, it pulls over to a designated parking lot, where each truck is greeted by a driver who will guide it to its delivery location. Because only one driver will be needed for every three or four trucks on the road, this application will save money on driver labor. It has the potential to improve traffic safety by reducing human error and accelerating reaction times. The technique also reduces vehicle distance, boosting the road network’s ability. Platooning is also good for the environment. Vehicles that travel at a constant, controlled speed emit less CO2 and consume less fuel. Tests have already shown that this technology can save a three-truck platoon up to 11% on gasoline expenditures.

Magistral’s services on Transportation Analytics

Magistral’s services support a strong customer focus and guarantee that goods are delivered on time to customers, regardless of location. They also optimize routes and safeguard profit margins without losing delivery timeliness. They also understand and negotiate a more complex logistics landscape, with more options than ever. Other services include:

Carrier Profiles: It includes pricing, suitability, specialization, and other important parameters while deciding on the type of transportation.

Dashboards and Visualization: KPIs development and tracking help in measuring the performance of the overall business.

Logistics Management: This step includes fleet optimization, last-mile delivery, and process management. All these services help in the acquirement and storage of the goods.

Data Science: This is done to identify areas of improvement for delivery and quality while reducing costs.

Contract Management: This helps in the preparation of contracts, bid management, vendor shortlisting, and negotiations.

About Magistral Consulting

Magistral Consulting has helped multiple companies to reduce operations costs through its offerings in Procurement and Supply Chain.

About the Author

The article is Authored by the Marketing Department of Magistral Consulting. For any business inquiries, you could reach out to prabhash.choudhary@magistralconsulting.com

 

Introduction

The practice of employing quantitative methodologies to obtain actionable insights and outcomes from data is known as procurement analytics. It entails gathering and analyzing data to enable fact-based decision-making and competitive advantage. It usually reports on what has happened in the past and makes estimations based on historical data using predictive analytics to predict what will happen in the future.

Procurement analytics uses quantitative methodologies to obtain meaningful insights and outcomes from data to provide companies and firms with better visualization of their procurement budget. Predictive analytics software gathers data from internal and external sources and organizes it in procurement dashboards. They enable businesses and organizations to use procurement data to make informed decisions and obtain strategic, competitive benefits.

Procurement analytics is critical for enhancing the efficiency of a company’s overall business operations and providing helpful market knowledge to aid strategic business choices. Without it, organizations often miss cost-cutting opportunities, do not meet KPIs, encounter supply chain disruptions, and pay higher costs.

Importance of Procurement Analytics

Analytics is often recognized as one of procurement’s most valuable resources and disruptive forces. Most Chief Procurement Officers (CPOs) consider analytics the most crucial technology in their organizations.

Procurement Analytics Importance

Importance of Procurement Analytics

Over the next decade, analytics has also been identified as the most disruptive force in procurement. It is a prevalent misperception that procurement analytics is limited to spend analytics. In truth, analytics affects all aspects of a company’s operations, from strategic sourcing to category management and procure-to-pay. Here are the reasons why analytics are so vital in procurement.

Category Management

When applied correctly, analytics provide category managers with superpowers. Category managers can use procurement analytics to find cost-cutting possibilities, segment and prioritize suppliers, address supply risk, and foster innovation.

Strategic Sourcing

Data informs the most effective company strategy. Analytics aids strategic sourcing by finding the ideal dates and locations for sourcing events and proposal requests. It can decide which suppliers to include in sourcing projects and provide detailed information on their quality and risk levels.

Contract Management

Analytics is beneficial throughout the contract lifecycle management process. It can send notifications when contracts need to be renegotiated and provide information for supplier discussions. Furthermore, analytics can find maverick spending to increase contract coverage and compliance.

Procure-to-Pay

The transactional part of procurement can benefit significantly from procurement analytics. Purchase order cycles may be tracked, and analytics can enhance payment terms. Payment accuracy, rebate opportunities, and mistaken payments can be checked while eliminating fraud.

Sustainability and CSR

Companies increasingly recognize the benefits of analytics in assessing sustainability, corporate social responsibility, and associated risk in the supply chain and procurement. Procurement decisions can have an environmental or social impact, and analytics can reveal opportunities for more sustainable options.

Steps of Procurement Analytics

During the projected period, the procurement analytics market is expected to increase from USD 2.6 billion in 2021 to USD 8.0 billion in 2026, with a Compound Annual Growth Rate (CAGR) of 25.3 percent.

Procurement Analytics Process

Procurement Analytics Process

The use of procurement analytics technologies and services is projected to be driven by several factors, including increased expenditure on marketing and advertising by businesses, a changing landscape of consumer intelligence to drive the market, and the expansion of customer channels.

Procurement analytics provides insight into spending, supplier performance, and prospects for cost savings. However, even if spending data is already stored in systems, making sense of it is typically tricky. Before insights can be discovered, three data processing procedures are needed.

Data Extraction

It begins with data extraction from all sources and consolidation into a single central database. Data is ready to be enriched and sanitized once it has been extracted. Data extraction is converting obsolete and jumbled data sources into a clear, unified format that is easy to understand and analyze.

Data Cleansing and Categorization

The data must then be classified into distinct and well-defined groups. A precise data classification is needed for practical expenditure analysis, making the heterogeneous spend data easier to oversee and manage across the company. This procedure unifies all purchase transactions into a single taxonomy, allowing customers to see their total spending in one place. This step can also enrich data by using automatic translations or consolidating suppliers.

Reporting and Analytics

The data is now ready to be analyzed after it has been categorized. Expenditure analysis gives the spend visibility that helps deliver intelligent analysis for faster opportunity identification, better sourcing decisions, and complete spending management. Access to dependable spend analytics is essential for significant cost savings and the realization of potential opportunities.

Advanced Procurement Analytics

Advanced analytics approaches employ computers to find patterns in large data sets, allowing procurement analysts to query their data, find statistically significant pricing drivers, and cluster the data based on those drivers. The clusters show a group of purchases with no notable cost driver changes, revealing the variances in vendor performance. One significant advantage is that, unlike individuals, advanced analytics algorithms do not make conclusions based on gut instinct or place disproportionate emphasis on data outliers. The tools also make it possible to evaluate thousands of permutations fast to see which statistical clusters best suit the data.

Negotiation

Preparing a fact base with information on prior transactions is the first step in effective negotiations. By inputting a description of the prospective transaction, advanced analytics allows the manufacturer to find a cluster of providers at once. The average price of similar purchases is highlighted in a summary of cluster data and a list of accessible vendors and their prices. The manufacturer can come to the bargaining table with prices based on historical data and information on vendors who work in this market armed with a solid, quantitative fact basis.

Vendor Management

Vendor segmentation and management are all about building relationships. As a result, it is more susceptible to the various biases that affect human interaction. While the personal element of the relationship should be respected, decisions about vendor performance should be based on facts rather than emotions. Advanced analytics can help reduce biases from the evaluation because it is especially beneficial in isolating vendor performance within a cluster.

Yearly Planning

Advanced analytics can be handy in assessing purchasing data to support a comprehensive sourcing strategy. Inventory-carrying decisions can also be influenced by modeling. Based on the data, the procurement team may decide whether to pay the carrying cost for more inventory or pay a premium for spot purchases.

Magistral’s Services on Procurement Analytics

Procurement is recognized as a crucial business contributor by many firms. Procurement expenses account for 40 to 70 percent of all costs and are a variable source of competitive advantage. Effective organizations use data to manage supplier relationships, grow their businesses, and even bring innovative ideas to life. In the last two years, more data has been created than in humankind’s history, posing unfamiliar problems for procurement analytics. Developing analytical technologies speeds up the data-to-insights process and opens new possibilities. Procurement analytics can boost operational efficiency throughout the sourcing and supplier management process. The following are the most common services offered by Magistral for procurement analytics:

-Spend Analytics

-Low-Cost Country Sourcing

-Sourcing Strategy

-Vendor Rationalization

-Bid Management

-RFP Management

About Magistral Consulting

Magistral Consulting has helped multiple companies to reduce operations costs through its offerings in Procurement and Supply Chain offerings

About the Author

The article is Authored by the Marketing Department of Magistral Consulting. For any business inquiries, you could reach out to prabhash.choudhary@magistralconsulting.com

Introduction

Because it is impossible to foresee the outcome of an uncertain occurrence, commodity intelligence entails lowering uncertainty by regulating risk variables. To effectively manage commodities risks, however, a clear perspective of the status of the business and the risks associated with it is needed, as well as a suitable risk management framework with the right people and the necessary tools.

Pricing, supply, and demand instability in commodity markets directly and significantly affect the company’s procurement budget, ability to save money, and overall profitability. The problem is that many commodity markets are incredibly volatile. Monitoring commodity price predictions and trends are integral to procurement teams’ and organizations’ strategic plans. It enables them to make data-driven planning and choices, foresee pricing-related risks, and manage suppliers proactively while avoiding supply chain disruption caused by price fluctuation.

Commodity price risk refers to the likelihood that price variations in commodities can result in financial losses for commodity purchasers or producers. Buyers are exposed to the possibility of higher-than-expected commodities prices. Commodity producers face the danger of lower commodity prices. Commodity producers and consumers can both use commodities markets to mitigate risk. Commodity price risk is a severe concern for businesses and consumers, not just commodity dealers, as the purchase and processing of diverse commodities, ranging from metals and energy to agricultural and food goods, is needed for everything from raw materials to finished products.

Methods of Measuring Commodity Risks

Producers most vulnerable to price drops earn less money for the commodities they create. Commodity consumers most vulnerable to rising prices increase the cost of the commodities they produce. The time lag between placing an order and receiving goods and exchange rate variations pose a risk to exporters and importers. Such risks should be effectively controlled for a firm to focus on its core operations without being exposed to unnecessary hazards. The methods used for measuring commodity intelligence include:

Methods of measuring commodity risks

Methods to measure commodity risks

Sensitivity analysis

Sensitivity Analysis is performed by selecting arbitrary commodity price movements or basing commodity price movements on historical data. The risk is estimated by adding the currency and commodity price changes if the commodities are priced in a foreign currency.

Portfolio Approach

The company analyses commodities risk and a more extensive examination of the potential impact on financial and operational activities using a portfolio approach. The risk is calculated using stress testing for each variable and a combination of variables in a portfolio approach.

Value at Risk

When doing a sensitivity study known as “Value at Risk,” some businesses, particularly financial institutions, adopt a probability method. In addition to the sensitivity analysis of pricing changes outlined previously, the corporations assess the likelihood of the event occurring. As a result, sensitivity analysis is used to simulate the potential impact of commodity price movements on its exposures by analyzing historical price history and applying it to current exposures.

 

Commodity Intelligence for Profitability

Even though the costs of raw materials, services, and other commodities fluctuate so often in today’s dynamic market environment, it is astonishing to see that the end product’s price is virtually always consistent. Procurement managers continuously look for the most cost-effective products but may have to buy even if the price is high to meet the production schedule. On the other hand, Procurement managers can boost the company’s profitability by monitoring commodity price volatility and altering sourcing strategy. Adjusting the sourcing strategy does not imply buying in quantity when prices are low, as this could result in waste.

Profitability by Commodity Intelligence

How to attain profitability using commodity intelligence?

Futures Procurement Contract

Signing a formal agreement to buy a specific commodity at a predetermined price at a specific period in the future is one of the best strategies to limit risks associated with commodity price volatility. The oil and gas industries and other commodities such as industrial metals, precious metals, seeds, cattle, and grains use futures contracts extensively. Such signed agreements allow the organization to manage better the risks associated with shifting commodity prices while increasing income predictability.

Price and Technology Trends

Companies may not always have the option of passing on higher commodity prices to their customers. Based on past data and projected patterns, significant commodity prices can be watched and predicted. Observing current market patterns and the global economy and employing standard forecasting tools can be a good signal for predicting commodity prices.

Bundling Services

Procurement managers who cannot limit risk due to variable commodity prices may use product and service bundling with a dependable supplier. Bundling products or services together hold the end product’s price by stabilizing the commodity’s ultimate price.

Price Forecasting Models

With the introduction of big data, purchase managers now have access to enormous data and information. An exact prediction of future commodity prices can be produced with proper prediction and study of elements influencing commodity prices. Purchase managers might use this data to make bulk purchases or postpone the procedure to increase overall profitability.

Future of Commodity Intelligence

At various periods, commodity markets have shown high price volatility, with unanticipated changes in demand or supply causing significant price fluctuations. It is not always easy for a commodity trader to keep track of every tiny change in a commodity price or other factors that affect that price. With commodity volatility and unpredictability increasing, and more data sources available to support decision-making, one thing is sure: AI will play a significant part in commodity intelligence in the future. It is possible to supply commodity intelligence unlike any other using artificial intelligence (AI).

Natural language processing (NLP) and machine learning (ML) are commonly used in commodity forecasting to automatically break down organized and unstructured data and construct models that predict commodity prices with minimal human interaction. Things that would usually be invisible to the naked eye can be brought to light, allowing manufacturers to foresee production, traders to forecast pricing, and buyers to plan more strategic procurement. NLP employs rendered algorithms to analyze written material, allowing techniques such as sentiment analysis to extract information from news articles, emails, and social media postings. Traders often use it to analyze current events and forecast market developments. On the other hand, machine learning (ML) involves algorithms that can be trained to act and think like people over time to improve predictions. A supervised learning approach means that as these algorithms are exposed to more data, experts who train the models can ensure that they keep improving.

Magistral’s services on Commodity Intelligence

Magistral’s services help companies to get an exact picture of their market position by accessing the correct forecasts and analytical reports, cutting through the market’s noise, and figuring out which risk indicators threaten their category and overall procurement strategy. Continuous insight programs that allow them to reach their full potential as strategic advisors to the rest of the company are also created. The services provided by Magistral are as below:

Predictive Price Analytics: All the services like Predictive Price Modeling, Price Tracking, Should-Cost Modeling, and Data Analytics are included under this head.

Expert Interviews: Niche Area Reports and Interviews of Specific Commodity Experts help in understanding the prices and other factors related to that commodity.

Risk Management Support: In this, Risk Intelligence reports and Custom reports are made to analyze the risk and reduce it further.

Price Tracking and Visualizations: Various MIS, Dashboards, and Data Analytics with layouts are prepared.

Business Impact Analysis: All those factors which affect the business are identified like supply disruptions, price changes, and volatility. Proper reports are made to explain how and what impacts it can cause to the business.

About Magistral Consulting

Magistral Consulting has helped multiple companies to reduce operations costs through its offerings in Procurement and Supply Chain offerings.

About the Author

The article is Authored by the Marketing Department of Magistral Consulting. For any business inquiries, you could reach out to prabhash.choudhary@magistralconsulting.com