Every decade or so, the business world invents another term for how it extracts managerial and decision-making value from computerized data. In the 1970s the favored term was decision support system, accurately reflecting the importance of a decision-centered approach to data analysis. In the early ’80s, executive information systems were the preferred nomenclature, which addressed the use of these systems by senior managers. Later in that decade, emphasis shifted to a more technical-sounding online analytical processing (OLAP). The ’90s saw the rise of business intelligence as a descriptor.
It appears, however, that another shift is taking place in the label for how we take advantage of data to make better decisions and manage organizations. The new label is analytics, which began to come into favor in the middle of this century’s first decade at least for the more statistical and mathematical form of data analysis.
Types of Data Analytics
o Descriptive Analytics (What is happening in the business?)
o Diagnostic Analytics (Why did it happen?)
o Predictive Analytics (What will happen?)
o Prescriptive Analytics (What should we do?)
Descriptive analytics is the interpretation of historical data to better comprehend changes that have arisen in a business. It uses data aggregation and data mining to furnish insight into the past and answer: “what has happened?” It is comprehensive, precise, live data and effective visualization. Common examples are company reports that simply deliver a historic review of a company’s operations, sales, financials, inventory, customers, and stakeholders.
Diagnostic analytics is less attentive on what has occurred but rather focused on why something happened. It is categorized by techniques such as drill-down, data discovery, data mining and correlations to dive deeper at the data to understand the root causes of events and behaviours; ability to segregate all perplexing information. An example is how a company can drill the inventory down to aging days categories to find out why these aging inventories are not delivered to customers.
Predictive analytics is about analysing what the user has done historically, in order to make sound decisions about what they will want next. It uses the verdicts of descriptive and diagnostic analytics incorporate with statistical models and forecasting techniques to provide the company with actionable insights based on data as well as to predict future trends. An example is the demand forecasting trend. It is essential to note that forecasting is just an estimate and its accuracy depend highly on the data quality and constancy of the situation. Thus, the company must set an acceptable accuracy bandwidth.
Prescriptive analytics is the last phase of business analytics, which also comprises descriptive and predictive analytics. It uses optimization (example business rules) and simulation algorithms to advocate what actions to take to eradicate a future problem or take full advantage of a promising trend. An example is optimized production planning and inventory in the supply chain to make sure the company can deliver the right products at the right time and optimizing the customer experience.
Cost Management in Cold Chain Analytics
Cost management is the process of effectively planning and controlling the costs involved in a business. It is considered one of the more challenging tasks in business management. Real-time information helps to establish benchmarks, optimize processes to lower costs.
The data collected can allow cold chain a focal point to help make educated, well informed decisions, which can lead to a more cost-effective supply chain. Cost management can be breakdown into three parts: Inventory cost, Operating cost and Management cost.
Inventory cost are cost associated for keeping and ordering of goods. Operating cost is the expenses of a business such as device, equipment, manpower and facility. Management cost is to organize and coordinate to achieve a business objective. With data analytics, it allows the management to make real time decision and understand current business situation to better business operation and assumption. Example: Data analytics in management cost allow a more realistic prediction into future costing and budgeting.
Resource Management in Cold Chain Analytics
It is the process of using company’s resources in the most efficient way to produce the highest quality outcome. It consists of three types of resources: Manpower, Machine and Material. Overproducing or producing at the wrong time (when consumer demand is not there) will impact the profitability of a company.
With data analytics, it helps the company to understand what is the current business demand, identify demand trend and assumption to allow better resource deployment and solution. Before a product makes it to the supplier, it moves along a line of suppliers that specialize in transportation, third-party logistics, packaging, etc.
Data analytics empower company with real-time information and assess into supply delays, wrong deliveries, and other interruptions so that resources can be redeployed effectively as require by the business demand.
Process and Quality Management
It is used to measure, monitor and control business activities that ensure it meets operational, financial and legal goals. It consists of three types of processes: warehousing, delivery and administrative process. Data analytics is part of the continuous process of real time tracking of orders and shipments. It provides the exact location of packages for incoming supplies to outgoing orders which is crucial to supply scheduling and logistic service to customer.
As today’s technology allow consumers to know the precise location of a package, customers expect cold chain management to be able to provide that information when required. Data Application can manage transactions of high volume, velocity and variety to provide automated reporting, pattern identification to point out quality risks and areas for improvement, and avenues to cut-down cost.
As an example, a prawn farm can use RFID and IoT sensors to detect up and down-stream issues regarding health of prawns and variant temperature changes, all of which affected the overall quality of prawn. This allow quality assurance to customer.
Responsive management is to respond and react quickly to any changing conditions and customer interactions as occurred. Just as both predictive and prescriptive analytics can address the pain points described above within a production workflow, they can also address similar challenges within the context of a supply chain more broadly. This helps cold chain understand what information is needed and response effectively.
For instance, by using the same advanced analytics processes described above, it can improve the functioning of transport logistics processes. Not only can prescriptive analytics analyse the existing freight network usage to find potential areas for improvement but also improved demand predictions that helps to plan the transports in advance with added certainty. Contrast to this with the current reality in transport management, which is defined by almost opaque levels of complexity, in which planners don’t always know how best to plan out their networks or how to efficiently reroute disrupted shipments. Data analytics provide the answer to it.
Customer satisfaction management (CSM)
It is a measurement of how products and services supplied by a company meet or surpass customer expectation. CSM being the key to retain existing customers, help improve customer support and satisfaction, to enable better customer experience for cold chain products and services.
Data analytics can enhance customer satisfaction dramatically, as it allows cold chain operation to pick the most ideal shipping methods, utilize the best carriers, reduce the potential for damage and halt delays – all leading to improved service. By providing customers with access to data – real-time tracking – companies and customers alike can quickly see what is in transit, helping with inventory management efforts and mitigate the gap for customer’s satisfaction.
An example is the use of customer satisfaction data to improve product, quality and service. By using this data, it helps business to understand what are the products, quality and services (ratings) for cold chain to improve and exceed customer expectation.
Data analytics help companies manage respondent supply chains as they can better comprehend customers and market trends that predict and proactively strategize supply chain-related activities. It also helps to identify all relevant parameters to capture product condition, data transaction across the cold chain processes specifically beneficial for multiple players involved such as food processing companies, logistics service providers, ports, wholesalers and retailers to understand how data can be effectively used for better decision-making in cold chain and to invest the specific technologies which will suit the purpose.
One good example is with IoT and predictive analytics, smart parts and sensors can detect when a temperature is about to fail so that the manufacturer can embark on better quality control, proactively reroute them to a retailer or distribution centre in the shortest timeframe. This enables manufacturers to reduce excess inventory and costs by minimizing wastage, avoid the cost and disruption of unscheduled downtime, and ultimately maximize customer satisfaction. Data analytics is the essence of the next cold chain era, one who apprehend a good set of analysed data will take the leadership in dominating the distribution market as compare to her competitors.
Alex Bekker. (2019). “4 types of Data Analytics to Improve Decision-Making”. Retrieved from https://www.scnsoft.com/blog/4-types-of-data-analytics, accessed 05/09/2019.
Cornerstone. (2019). “Diagnostic Analytics”. Retrieved from https://www.cornerstoneondemand.com/glossary/diagnostic-analytics, accessed 06/09/2019.
Jake Frankenfield. (2019). “Descriptive Analytics”. Retrieved from https://www.investopedia.com/terms/d/descriptive-analytics.asp, accessed 06/09/2019.
Lim Hun Meng, GDSCM. (2019). “Data Analytics for Optimizing Inventory”. Retrieved from SIPMM: https://sipmm.edu.sg/data-analytics-optimising-inventory, accessed 05/09/2019.
Manu Jeevan. (2018). “Predictive vs Descriptive vs Diagnostics Analytics”. Retrieved from https://www.edvancer.in/predictive-vs-descriptive-vs-diagnostic-analytics, accessed 07/09/2019.
Pat Research. (2018). “Predictive Analytics”. Retrieved from https://www.predictiveanalyticstoday.com/what-is-predictive-analytics, accessed 07/09/2019.
Quora. (2010-present). “How is Data Analytics used in Supply Chain Management”. Retrieved from https://www.quora.com/How-is-Data-Analysis-used-in-Supply-Chain-Management, accessed 05/09/2019.
Wong Kok Fu, DLSM. (2018). “Data Warehousing for Integrated Logistics”. Retrieved from SIPMM: https://sipmm.edu.sg/data-warehousing-integrated-logistics, accessed 06/09/2019.