At MOS, we provide business process outsourcing services for various industry verticals such as healthcare, legal, IT, insurance, real estate, retail, telecommunications, eCommerce, and more. We serve both large and small organizations and can handle projects of any size.

In this podcast, Jessica Schwartz, Solutions Manager at Managed Outsource Solutions (MOS), discusses why accurate data is important for supply chain and logistics management.

Podcast Highlights

01:08 Role of Data Analytics in Supply Chain Management

04:58 Role of Data Entry Outsourcing

Read Transcript

My name is Jessica. I’m a Solutions Manager at Managed Outsource Solutions (MOS) and I’m here to talk to you about why accurate data is important for supply chain and logistics management.

Supply chain management involves managing the flow of raw materials and ensuring that the finished product meets all requirements and reaches the consumer. Logistics is the part of the supply chain that plans the movement, storage, and flow of goods, services and information inside and outside the organization. Logistics comprises transportation, warehousing, packaging and more. Supply chain and logistics performance depends on the availability of accurate information based on which managers can make strategic decisions. With data analytics, managers can gain insight and extract value from the large amounts of data and drive efficiency in operations. Back office outsourcing is a practical and reliable way to ensure data accuracy for supply chain analytics.

01:08 Role of Data Analytics in Supply Chain Management

While supply chain management covers all the policies and actions for producing finished goods from raw materials and getting them to the customer, logistics focuses on timely delivery of the right products at the right place. Supply chains generate huge volumes of data. Efficient data analytics and management are crucial to take advantage of massive amounts of critical, time-sensitive information generated by supply chain processes.

Data analytics improves decision making for all activities across the supply chain, according to top-tier consulting firms like Mckinsey and IBM:

  • Sales, Inventory and Operations Planning: Analyzing point of sale (POS) data, inventory data, and production volumes in real time can identify inconsistencies between supply and demand. This information can be used to manage price changes, set the timing of promotions or introducing new lines.
  • Logistics capacity needs: By increasing forecast accuracy, data analytics provides a better view of the company’s logistics capacity needs. This information can be used to reduce obsolescence, inventory levels, and stock outs.
  • Sourcing: Analyzing supply processes in real time can identify deviation from normal delivery patterns. By mapping its supply chains and using data about strikes, fires, or bankruptcies, a firm can track disruptions in transportation or anywhere across the supply chain and take decisive actions.
  • Demand forecasting: Data analytics can enable accurately prediction of demand for different product categories based on factors that influence consumer preferences.
  • Planning: Planning is the most data-driven process in the supply chain. Today, new internal and external data sources are driving real-time demand and supply shaping.
  • Manufacturing: In manufacturing, advanced analytics is being used to look into historical process data, identify bottlenecks and reveal underperforming components and processes, and spot patterns and relationships among stages and inputs. This information can be used to optimize the factors that have the most impact on production.
  • Production of customized products: Innovative tools allow manufacturers to collect, analyze and visualize customer feedback in near-real-time. By making it possible to identify changes in consumer behavior and demand, big data analytics can help manufacturers produce customized goods as effectively as other items.
  • Transportation and warehousing: New technologies, data sources, and analytics can identify additional waste in the warehouse process so that it can be addressed. For instance, new technologies increase warehouse visibility by providing real-time data on location, equipment and inventory.
  • Point of sale: Data analytics is used shelf-space optimization and mark-down pricing, and even to help retailers decide which products to place in high value locations.

04:58 Role of Data Entry Outsourcing

Organizations collect huge volumes of data and use data entry services and analytics to transform this data into accurate usable information. Partnering with the right business process outsourcing company is crucial to ensure accurate data to optimize supply chain performance, boost operational efficiency, gain profits and build robust relationships with customers, suppliers and partners.

Thank you for listening and we hope you have a good day!