Manufacturing Planning Analytics

Production scheduling and Inventory Optimisation

Trusted Data collaborated with a medium-sized food manufacturer to improve operational responsiveness & inventory management, in order to efficiently supply a growing customer base.

How our client realised in excess of £0.9mn annualised net value

A fast-growing food manufacturer had previously struggled to align production, supply/demand planning and distribution during a fast period of growth during the previous 2 years.

There were several key pressures on the business during this period; the requirement to meet strict service levels with key retail accounts, capacity constraints at the 2 main production facilities and inventory tying up working capital.

Trusted Data were required to develop an integrated application that could handle the constraints and requirements across several functions, in order to align schedules from material /demand planning through to warehousing.

AI-driven Planning and Scheduling for Manufacturing


Forecasting had been driven by the commercial team, which were providing higher level forecasts at the SKU / account level by quarter / year.

The supply chain function, including production, found it hard to operationalise this at a weekly / monthly level.

The problem was compounded during peak trading / promotional periods when there was a requirement for rapid order turnaround, particularly with key accounts.

The Client had 3 main hurdles to overcome:

  • Excess finished goods inventory was tying up capital, due to speculative, over-ambitious forecasting
  • Limited warehousing capacity at 2 key sites causing warehouse space allocation issues
  • Production struggled to convert sorders during 2 main trading quarters resulting in delayed customer fulfilment and lost sales.



The root cause of the Clients’ problem was delayering LT demand forecasts into appropriate supply and production plans.

Trusted Data developed a planning solution in 2 key phases:

  • build a robust predictive demand forecast at the relevant time horizon.
  • integrate the demand forecast with material planning and production scheduling.

The planning application needed to consider a range of key dimensions:

(1) Many SKU’s shared common raw materials (though in various consumption quantities)

(2) Material consumption by SKU, real-time material inventory and material replenishment lead time, all needed to be modelled correctly and aligned with careful selection of the right demand forecasting horizon.

(3) Production yield and line output varied by SKU and this needed to be considered as part of optimising material and production scheduling.

(4) Finally, to truly enhance the production planning process, our application had to consider for SKU production prioritisation, line changedown times and SKU/material commonalities.

The end result was a JIT operations function that had the correct volume of raw materials inventory at any time, a production plan that minimised extensive changedowns and ensured that the right volume of SKU’s were produced for warehousing / distribution at the right time.

By combining the effect this has on production output, man-hours, inventory availability and service level increase, it was agreed that the net P&L impact was just shy of £1 million per annum.


Forecast error reduced below


Finished Goods Inventory reduction


Retailer service level increase