Manufacturing businesses have been completely dependent on big data over the last few years. Information replaced raw data, turning decision-making into a job where regular accuracy is normal. But it is wrong to assume that ample data always leads to accurate strategic planning.
Most big manufacturing firms are gradually realizing that to remain in competition, just planning and acting is not enough inside a manufacturing unit. Some manufacturers offer better responsiveness and flexibility to customers. For higher efficiency and minimum overheads, manufacturers have to analyze data for insights. But the inability of rigorous decision-making intensifies on the ladder of competitiveness.
Modern analytics applications are designed to match the depth and sophistication of manufacturing operations. Web-based SCM on a service-based architecture is capable of deep analysis of data. The cost of using such technology has reduced and remarkably benefited the users.
Why supply-chain analytics improves performance:
- Surveys clearly indicate that manufacturing units using supply-chain analytics understand upcoming challenges better. Using supply-chain analytics enables clear understanding and visibility about market functionalities and operations. They can thus proactively act upon these challenges to increase profits and efficiency.
- The economic condition in the global market since 2007 (recession) has experienced almost half the companies declining in gross profits. Only one-third of the companies saw increase in gross margins. The remaining either didn’t respond or expressed flat sales. Those confident in data had used it for strategic decisions and experienced increased revenue. The not-so-confident ones hardly announced any revenue growth.
- Companies confident in analytics provide quality decisions and are observed to do so more efficiently. They are making their key functions better, have higher asset value, and are in a better position to keep their inventories at higher levels. Needless to say, the businesses that did not feel the significance of data are struggling for better efficiency and cannot accurately forecast their inventories.
- Using predictive analytics, manufacturers can use data in their systems and bring out real-time information to use them and further optimize verdicts. Questions about increasing prices of products in high demand—will it improve sales—and so on—can be easily evaluated through prescriptive analytics.
When a company has data inputs analyzed by an SCM application, the company can control overall costs more effectively, and protect profits as well.
- Analytics is capable of directly facing challenges concerned with shrinking of profit margins as per-unit costs increase. But manufacturers benefit from day-to-day improvements due to deeper IT implementation. Some respondents categorically state better productivity, reduced overall cost, and higher customer satisfaction, especially after using web-based SCM.
- SOA for planning and demand forecasting can widen product distribution and profit margins. One combines data from all replenishments and demand-planning processes to generate forecasts about production levels, sales goals (to be set), and SCM distribution plans. The target is to improve the outcome of each and every process in a supply chain company.
Supply chain leaders expect future designs to generate deeper strategic decisions, control costs, enhance customer service, and provide better demand forecasts. The new generation supply-chain, embedded with abilities of advanced analytics, will enable optimization depending upon constraints, business modeling and simulation, advanced forecasting, planning a scenario, and what-if analyses.
Author Bio:-
Amy Jackson is a freelancer and has experience of over 7 years in supply chain optimization solutions, Supply chain planning Solutions, and Analytics. She has been writing on supply chain planning, predictive analytics solutions, Procurement Service Provider solutions and other solutions. Follow her on Google+ and get to know about the latest business trends.