Improving operations can save money for food, beverage manufacturers

Reducing operating expenses for food and beverage manufacturing applications does not mean compromising product quality and human safety.

By Niranjan Kulkarni February 18, 2020

Like any resource-constrained industry facing uncertain demands and external competition, the food industry is under constant pressure to reduce operating expenses, shorten lead times, improve flexibility and increase throughput. Designing new facilities or expanding existing ones is an expensive undertaking and is often ranked low on a list of options for companies.

The mantra these days is to “do more with less” (or at least do more with the same). In other words: Get more throughput from a facility with fewer resources and reduced operating expenses. Oftentimes, this means reducing investment in R&D, technology and marketing, but that shouldn’t be the first option.

Reducing operating expenses by addressing inventory, lead times, headcount, spatial needs and/or improving resource-sharing capabilities, etc. is the rational line of thought. Reducing operating expenses does not mean compromising product quality and human safety. Savings can be achieved by improving operational inefficiencies and eliminating non-value added activities in the value streams. Small investments in operations improvement, continuous improvement and teams of industrial engineers may be necessary. The benefits achieved by these teams pay for much more than the investments made in them.

When considering some of the tools and techniques to improve operational efficiencies and quantify/prioritize improvement options, it is important to understand the operational nature of the food industry. This industry can be characterized as a high-mix high-volume industry.

The high mix in upstream operations, such as raw material kitting/cleaning, blending, processing/cooking and filling, can be attributed to the variety of ingredients and flavors, unique recipes and different levels of concentrations needed to make products. In the downstream operations (primary and/or secondary packaging), the high mix can be ascribed to several aspects, such as different container or package selections (bottles, cans, packs, blisters, pouches, etc.), container or package shapes and sizes, labeling requirements and pallet size configurations, just to name a few.

It is well known that a high-mix environment can create several inefficiencies: inconsistency in product routing, need for dedicated/specialized equipment and an increased number of setups and changeovers, etc. It also increases variability in processing times and may result in longer lead times. On the other hand, the high-volume manufacturing environment may necessitate the need for increased pieces of equipment, which leads to larger space requirements, increased needs for material handling, additional personnel and overall infrastructure improvements.

Additionally, depending on the type of product manufactured/packaged and the investment capability of the facility, the processes can be manual or automated. Other commonly seen attributes that can increase lead times include unplanned equipment downtime, yield issues, material stock outs, etc. All the aforementioned factors, exacerbated by the uncertain and highly variable demands, impact the overall efficiency and operating expenses and subsequently, profitability for the company.

Many food and nutraceutical companies are now realizing that they can leverage and benefit from the cost-saving techniques and models used by the automotive and semiconductor industries. These industries have leveraged Industrial Engineering tools and have pioneered lean techniques and Six Sigma methodologies, having used them extensively to eliminate non-value added activities and variability within their operations.

The application of specific tools and techniques will be demonstrated with two real-life case studies below.

Improving packaging line efficiencies

The site under consideration, which belonged to a multinational company, produced both liquid and powder food products. There were several stock keeping units (SKUs) produced and packaged at this site. As part of an internal improvement initiative and plan to make the site more attractive for internal investments, the site leaders wanted to improve operations and reduce expenses. The primary focus of this effort was on studying the packaging operations labor (direct and indirect) and identifying all improvement opportunities.

There was a lot of variability due to number of SKUs, reliance on operators for cleaning and changeovers, equipment attendance, unplanned equipment outages, etc. The team had several improvement recommendations but did not have a good understanding of their implications on upstream or downstream efficiencies. To address these issues, the team decided to develop and study the discrete-event simulation (DES) models.

DES models are very effective in characterizing inherent process/operational variability and interactions. These models work on the queueing theory principles and help us understand the impact of policies and changes at a holistic level. The ability to model stochastic events, such as equipment failures, unavailability of resources, unexpected changes in demand, etc., allows DES models to mimic the real- world operations. They are often employed to study “what-if” scenarios, quantify impact of change and optimize operations.

The methodology adopted for this study started with the identification of primary functional areas within the facility and followed with data collection, an exercise that was divided into two phases: The first phase focused on performing shadowing activities and personnel interviews whereas the second phase focused on collecting relevant input data elements via time studies or extraction from data systems that were required to build and study a DES model. After having developed the baseline model, it was validated against key metrics: throughput, downtime and scheduled starts. Based on the hypothesis testing performed, it was concluded that the model statistically replicated the actual operations.

To minimize the frequency of cleaning and changeover, product grouping was performed by applying the formal concept analysis technique. Lean techniques were employed to reduce non-value added time, namely waiting, transportation and motion. The preventive maintenance program was also evaluated to improve the reliability of packaging equipment.

On-site shadowing activities and brainstorming with area owners helped generate over 75 improvement opportunities. Simulations helped quantify opportunities that had the biggest impact on operating expenses. These options were ranked based on their return on investment capability. Cycle time for cleaning/changeover operations was reduced by 50 percent, waste generation on the packaging lines was reduced by 20 percent and increased throughputs were obtained without increasing staff. The overall savings realized for this project were more than $3.8 million annually. This exercise helped management justify changes and propose a business case to secure internal funding to implement these recommendations for the site.

Designing operations and facility to accommodate growth

The study focused on a site that manufactured ready-to-eat foods, flavors and additives and manufactured and packaged hundreds of SKUs that relied on manual operations. This site had experienced significant increase in demands and wanted to identify means to satisfy them. Though a site expansion was unavoidable forever, the site leaders wanted to understand the best strategy and timeline for expansion. They also wanted to reduce the overall footprint of the planned expansion.

Detailed value stream maps (VSMs) were developed for select processes. The value stream mapping technique is a popular lean technique that helps identify waste in the process. A VSM is richer than a flow chart. Using unique symbols, it shows information and material flow, captures cycle time to complete activities and shows operator involvement while differentiating between value-added and non-value added activities. This technique is used to depict the current state and to create a vision of the future state processes.

The maps helped identify and eliminate certain redundant activities during regular operations and cleaning while challenging the team to reduce cycle times for specific bottlenecks. Overall equipment efficiencies quantified by the maps highlighted a need to change the operating schedules to improve equipment utilization.

Takt times were calculated as a function of demand. Cleaning times and procedures were standardized. Alongside the VSMs, DES models were created to develop a strategy to meet these projected demands. The DES model also helped create a “pit-stop strategy” for operators, meaning operators on the line recognized the tasks they were responsible for and the sequence in which these tasks needed to be completed.

The following changes were implemented to reach the projected demands: installing a flexible process line, ensuring the cleaning times did not exceed the set target cleaning times, repurposing the headcount from main production lines to the new flexible process line, optimizing the production schedule and creating a seven-day work week.


Food manufacturing and packaging companies have to guarantee their products are less expensive than their competitors while still satisfying compliance, quality and safety constraints. Reducing operating expenses by addressing operational inefficiencies can help reduce the cost of goods. Tools and techniques from the industrial engineering discipline and lean Six Sigma domain can prove valuable when improving operations.

Though small investments in such teams is necessary, the benefits achieved pay for much more than the investments made. It is unfortunate that these departments are often not given due importance and are immediately downsized under the cost-cutting premise. Such a move can be detrimental in the long-run.

This article originally appeared on CRB’s websiteCRB is a CFE Media content partner.

Original content can be found at

Author Bio: Niranjan Kulkarni, PhD, director of operations improvement, CRB