Plan, Visualize, Manage Promotion and Incentive Program Budget

Plan, Visualize, Manage Promotion and Incentive Program Budgets

In the modern digital world customers with information at finger tips are making more informed decisions in buying or leasing products.

Manufacturers can now connect with customers directly, transform the traditional selling practices by offering customized incentive programs. Using advanced analytics manufacturers can get deeper insights into customer sentiments and influencing factors for closing a deal with the customer.

With dynamically changing ownership models and competitive incentives landscapes, manufactures and retailers need more tools to manage incentive programs and budgets to swiftly modify programs by gaining insights into customer’s preferences to win deals.

TekAnalytics offers a flexible and easy to customize solution for Trade Promotion and Incentive Programs Management. The end-to-end Incentive Planning and Trade Promotion management solutions covers process steps for a series of activities aimed at successful product sales. Sales promotions and program activities, retailer incentives, promotional campaigns and many other tasks can be accurately planned, budgeted, executed and audited using TEK-TPIM®.

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Optimized inventory with Improved Product Flow and Turn

About the Client: Major US Manufacturer and Distributor

The Client is a manufacturer and distributor of food packaging and foodservice products, supplying packers, processors, supermarkets, restaurants, institutions, and foodservice outlets across North America.

Business Challenge:

  • The current setup has no Integrated supply chain consolidated reports
  • For manufacturers, not only is it difficult to predict the maintenance needs of equipment but also to determine the necessary inventory of spare parts for potential repairs.
  • Increase availability of spare parts for servicing and repairing of machinery and manufacturing assets and reduce the cost of maintaining inventory

The Solution:

  • Gather data from supply chain history for drilling, rigging, pipeline materials, other inputs, part failure, and part usage history, lead time history (for reorders), history of disruptions to supply chain, planned and the actual history of ramping of capacity.
  • Establish an optimal freight cost model that can ensure on-time delivery.
  • Characterize statistical properties (distributions and correlations) in parts demand and lead times.
  • Understand and account for historical disruptions by leveraging data from SAP BW and flat files to leverage SAP Analytics Cloud (Analytics, Planning, and Predictive ).
  • Link data sources with an analytics engine.
  • Using the current state of inventory and chosen economic scenario, automatically set optimal inventory levels for all parts.   

Results and Benefits:

  • Develop a flexible framework for using inputs of historical data, economic and manufacturing capacity scenarios, and predictive models to set optimal inventory levels by using SAC predictive tools
  • Optimized inventory level-setting with fully quantified operating characteristics of expected cost and probability of stocking out

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Maximize Auto Parts fill rate with SAC Predictive reporting

About the Client: Fortune 100 Automotive Customer

The Fortune 100 Automotive Customer is one of the world’s largest and most important car manufacturers with 6 million market capture. It has 50 production locations across the five continents with 200000 employees across the globe. To be a leading, profitable volume manufacturer as well as playing a leading role in the new world of the automobility industry in the long term.

Business Challenge:

Maximizing the Dealer orders Fill percent or Fill Rate is always a key focus in the automotive after-sales stream. Increasing fill rate without piling excessive inventory at a plant requires not only a deep dive into fill rate and supply chain metrics across multiple dimensions but also deep learnings into data patterns and identify influencers impacting the fill rates.

The Solution:

A complete cloud-based reporting solution for tracking fill rate from Plant to the part level. Our consultants in partnership with the business developed a regression-based predictive model by taking the last 3 years’ dealer order history and fill rates into the account. The Model predicts future fill rates for the dealer odder demand data in the planning system.

  • Gather data from supply chain history for drilling, rigging, pipeline materials, other inputs, part failure, and part usage history, lead time history (for reorders), history of disruptions to supply chain, planned and the actual history of ramping of capacity.
  • Unified Historical and planning data along with master data within BW/4HANA
  • Reporting Model which enables multiple drill-down levels and secured at region leve
  • An easy-to-use yet very effective SAC-based visualization to track Fill rates historically and also drill down to material level with multiple end-user level capabilities including Ranking, Ad-hoc exploring.
  • Considering 3 years dealer order history, created a regression model which predicts fill rate and quantities at a part level for demand planning data

Results and Benefits:

  • The SAC story completely replaced excel based fill rate tracking. Users no longer need to perform any manual excel calculations to derive KPI’s which tremendously save time in analyzing.
  • A very user friendly yet robust UI design
  • A deeper insights into the fill rate data across multiple dimensions and
  • 95% confident regression model trained from 3 years historical data which predicts the future fill rates, this will help users to maintain optimal inventory

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